← BlogPerfect World

Visual project report · DES304 · Emerging Technologies · Lane 2 · 2026

PerfectWorld

Helpful AI and the life we shape

Abstract / Perfect World

I began by believing technology could make life better.

But as AI became capable of doing more for us, I began questioning whether better outcomes always meant better experiences.

If technology can shape our lives, what do we still want to shape ourselves?

Project overview

A speculative image of a Quest wearer talking with another person, connected by a thin red line.
AI-generated concept image from my exhibition poster; designer-directed with OpenAI.

From Designing Better Systems to Questioning What Makes a Better Life

This project investigates how increasingly capable artificial intelligence might reshape human experiences, decisions and relationships in Aotearoa, through a series of speculative design explorations.

Responding to the Emerging Technologies brief, I began with Tickers, a speculative system in which human effort becomes visible and measurable.

Through research, prototyping, critique and reflection, the investigation expanded into AI Senior, Civic Score, LifeRPG and Future University. Each direction tested a different question about assistance, authority, development and learning, and each left a question the next one had to answer.

The investigation eventually developed into Perfect World, a speculative future in which AI could genuinely support human wellbeing, relationships and personal development.

At the exhibition, a two-minute promotional film from that future plays on a loop. Visitors can put on a Meta Quest headset and meet another person while an AI guide, Ticker, offers help during their conversation. Rather than warning people about AI, Perfect World lets them experience help they might genuinely want, then asks how much of a good life they would still want to shape themselves. Its value is a way to discuss AI guidance through lived experience rather than abstract argument.

This report documents how research, world-building, iteration and critique transformed my questions. The motivation stayed the same; what changed was my understanding of what a better life might mean.

DES304 Capstone · 26 October 2026. Images tagged Spec are AI-generated, designer-directed studies; Rec marks real project records.

Perfect World

Contents

  1. Act I · Why we began
  2. The Design Brief
  3. Positionality
  4. The Personal Contradiction
  5. Design Methodology
  6. One belief, an evolving inquiry
  7. Two courses, five worlds
  8. Five worlds, one changing question
  9. What carried forward, and what advanced
  10. World 01 · Tickers
  11. Tickers: the signal
  12. When Support Becomes Surveillance
  13. Brainstorming
  14. Imagined systems of judgement
  15. Aotearoa, 2031
  16. Making the future tangible
  17. Making: Learning from existing systems
  18. Making: How Tickers works
  19. Making: Visual storyboard
  20. Making: User flow, phone to desktop
  21. Making: Wireframes and visual identity
  22. Making: Mid-fidelity to code, mobile
  23. Making: Why a desktop app, and the pipeline
  24. Testing: What I wanted to test
  25. Testing: What the evidence showed
  26. Testing: When the system got it wrong
  27. Reflecting: Redesigning the judgement, and what carried forward
  28. Act II · Imagining the future
  29. World 02 · AI Senior
  30. Signal
  31. When Help Becomes Authority
  32. Brainstorming
  33. Aotearoa, 2037
  34. Making the future tangible
  35. The idea: Project idea and positioning
  36. The idea: Visual storyboard
  37. The idea: How the AI senior works
  38. Two surfaces: User flow, manager workspace
  39. Prototyping: Precedents
  40. Prototyping: Brand and UI components
  41. Prototyping: UI development, workspace to desktop
  42. Prototyping: Core design elements, workspace
  43. Prototyping: Core design elements, desktop senior
  44. R&D: Ticker Cube
  45. R&D: Why a living AI senior, and its architecture
  46. R&D: A stateful senior, and ethics as architecture
  47. Closing: System map
  48. The turn: what Crit 2 changed, and what carried forward
  49. World 03 · Civic Score
  50. Emerging signals of algorithmic governance
  51. Research: Societies already imagined
  52. What if AI could allocate opportunity?
  53. The first Civic Score proposal
  54. When the proposal was challenged
  55. Design response: A more desirable civic future
  56. The imagined society: What kind of society is this?
  57. The system: Who has power in Civic?
  58. How it becomes normal
  59. Living with it: Living under Civic Score
  60. Living with it: Two residents
  61. Brand: Civic brand and brand in use
  62. Exhibition: Making the future experienceable
  63. Exhibition: From scoring to changing the values
  64. Technical check: What could be demonstrated?
  65. What Civic Score changed

Perfect World / continued

  1. World 04 · LifeRPG / UNI
  2. AI helps practice and can harm the exam
  3. Imagining learning beyond scores
  4. LifeRPG: the first direction
  5. When personal progress becomes another score
  6. Research: What learning still needs
  7. Research: What future would need a new university?
  8. From an app to an institution
  9. UNI in one minute, and positioning
  10. The imagined society: Building UNI’s society
  11. How it becomes normal
  12. How the institution operates
  13. A learner’s route: enrol to credential
  14. Living and learning in UNI
  15. Exhibition: How could visitors experience UNI?
  16. Prototyping: Brand, and what was actually designed
  17. Evaluation: When the idea was challenged
  18. What UNI changed
  19. Life after necessity
  20. World 05 · Perfect World
  21. Help is turning personal
  22. What makes a life worth living?
  23. Opening up possible futures
  24. Futures we have already imagined
  25. How could we arrive here?
  26. Choosing Perfect World
  27. Act III · Building Perfect World
  28. Learning from future cities
  29. The site
  30. Planning the city
  31. One cluster, sun for every home
  32. What makes it possible
  33. When it fails, and how ready it is
  34. How nobody has to work
  35. Who runs Perfect World
  36. How it becomes ordinary
  37. A world that understands you
  38. Guide, not steer
  39. From world plan to 3D city
  40. Revealing Perfect World
  41. Act IV · Living in Perfect World
  42. Places within the city
  43. Six lives, and Mina’s day
  44. Act V · Experiencing and reflecting
  45. Bringing Perfect World into the present
  46. Exploring the experience
  47. The exhibition, who it is for, and the visitor journey
  48. Branding a public institution
  49. The mark, colour and type
  50. The brand in use
  51. Film storyboards: directions 01 and 02
  52. Film storyboards: directions 03 and 04
  53. Film storyboards: directions 05 and 06
  54. Film storyboards: reference plans A and B
  55. Film storyboards: reference plans C and D
  56. Film storyboards: reference plans E and F
  57. Film storyboards: reference plans G and H
  58. The final film: let’s see the world
  59. What should the guide actually do?
  60. How Ticker works
  61. Visual storyboard: the encounter
  62. UX and conversational guidance
  63. UI and visual design development
  64. Building the AI system
  65. Making the system inspectable
  66. Redesigning the guide
  67. Communicating the exhibition: references
  68. Communicating the exhibition: the world poster
  69. Communicating the exhibition: the encounter poster
  70. Designing the exhibition space
  71. Bringing everything together
  72. Testing, critique and iteration
  73. My position within the future I designed
  74. What the exhibition could change
  75. What Perfect World taught me
  76. A life of your own
  77. Your Turn
  78. References
  79. Credits

The design brief · Emerging Technologies & Speculative Design · DES304 · University of Auckland

Wero — The Challenge

How might we use emerging technologies and speculative design to reimagine how people in Aotearoa connect, collaborate, and create?

The DES304 Emerging Technologies stream challenges designers to investigate how emerging technologies might reshape human relationships, collaboration and creative practices within plausible futures in Aotearoa New Zealand.

Rather than developing technological solutions alone, the brief encourages speculative artefacts, systems and experiences that make future possibilities tangible and invite critical discussion.

My Interpretation

I understood the brief as an opportunity to explore not only what emerging technologies could enable, but also how they might change people's everyday experiences and relationships.

Artificial intelligence became my primary interest because of its expanding ability to assist with work, creativity and decision-making.

I wanted to investigate the possibilities and tensions created when increasingly intelligent assistance becomes part of ordinary life.

The challenge considers three themes

Connect
How emerging technologies could transform relationships between people, places, communities and identities.
Collaborate
How technology might enable new forms of collaboration, decision-making and participation.
Create
How emerging technologies might reshape creative practices and the ways people make things.
A person seen from behind in silhouette at a dark window at night, looking across a harbour and city lights, a thin red edge of light along their shoulder.

Understanding the designer behind the investigation.

SpecAI-generated study, 25 October 2026 (Higgsfield, designer-directed).

Positionality · 01 / 02

Positionality

I was born in Korea and moved to New Zealand during primary school. Moving between different environments made me more aware of relationships, belonging and the experiences that connect people.

My faith and experiences of volunteering have also influenced how I understand the purpose of design. I believe technology can improve people’s lives, but wellbeing extends beyond convenience, productivity and material conditions. Relationships, care and opportunities to contribute to others matter as much.

Studying design and computer science lets me move between imagining possibilities and building working systems. I enjoy understanding how things work and turning ideas into tangible outcomes.

That background has a bias. It encourages me to prioritise functionality, efficiency and measurable outcomes, and as someone who builds systems, I tend to answer uncertainty with infrastructure: more signals, more structure, more rules.

As tauiwi, I also design with responsibilities under Te Tiriti o Waitangi. A project that imagines systems which read people, hold their data or shape their choices has to ask whose values those systems serve, and who has a say in them.

The challenge is not simply to make life easier, but to question what makes a life better in the first place.

A person’s hands hovering just above a laptop keyboard in a dark room, beside a half-finished pencil diagram, a thin red light on the hands.

When achieving more doesn’t always feel like gaining more.

SpecAI-generated study, 25 October 2026 (Higgsfield, designer-directed).

The personal contradiction · 02 / 02

The Personal Contradiction

My growing use of artificial intelligence made this tension increasingly personal.

I remember spending hours working through programming problems, experimenting with different solutions and gradually developing an understanding of how something worked. Although the process could be frustrating, the satisfaction of solving the problem often came from the experience of working through it.

As generative AI became more capable, my approach began to change.

Instead of investigating every problem myself, I could ask AI to analyse a codebase, generate possible solutions or develop an initial implementation. Tasks that previously required considerable time could be completed much faster.

This was genuinely useful. It enabled me to explore more ambitious ideas and accomplish things I might otherwise have struggled to achieve.

However, I also noticed that my relationship with the work was changing.

I was increasingly reviewing, directing and refining outcomes rather than participating directly in every stage of producing them.

Sometimes I achieved a better result while feeling less connected to the process itself.

This made me question an assumption I had rarely challenged:

If technology can help us achieve what we want more effectively, does that necessarily mean we gain a better experience?

This personal contradiction became an important motivation for the investigation.

Speculative design as an iterative inquiry.

This investigation draws on speculative design, critical design, provotyping and practice-led design research.

Speculative design provides an approach for imagining possible futures and exploring their social implications rather than predicting a single technological outcome (Dunne & Raby, 2013).

Critical design supports the examination of assumptions and values embedded within technological systems. Provotyping uses provocative artefacts to expose tensions and encourage discussion, rather than simply demonstrating that a solution works (Boer & Donovan, 2012).

Practice-led design research recognises that creating and examining design artefacts can itself contribute to understanding a research question (Frayling, 1993).

Together, these approaches informed an iterative process.

An evolving methodology

These stages did not occur as a fixed linear sequence.

Earlier directions often began with ideas for intelligent systems and investigated their wider implications through design.

As the investigation developed, I increasingly questioned this product-first approach and began placing greater emphasis on constructing future conditions before determining the most appropriate design intervention.

This methodological shift became an important part of the investigation itself.

Design methodology

Design Methodology

  1. 01Explore · Vision and Opportunity

    Investigate emerging technologies, identify opportunities, gather inspiration and develop initial speculative questions.

  2. 02Imagine · Future World-Building

    Construct possible future conditions and explore how technological, social and cultural developments might reshape everyday life.

  3. 03Ideate · Speculative Interventions

    Generate concepts and consider how a product, service, system or experience might exist within the imagined future.

  4. 04Develop · Making and Iteration

    Translate ideas into tangible artefacts through sketches, branding, interface development, system diagrams, prototypes and technical experimentation.

  5. 05Examine · Evidence and Evaluation

    Use relevant research, critique, formative feedback and testing where available to identify what the design communicates, what it demonstrates and what remains unresolved.

  6. 06Reflect · Reframing the Question

    Examine the assumptions and limitations revealed by each exploration. Consider whether to refine the current direction, change its scope or investigate a different possibility.

One belief, an evolving inquiry

What remained constant

I want technology to help people live better lives. Better includes relationships, learning, meaningful participation and the ability to shape a life of one’s own.

What developed

Across the projects, I became less satisfied with judging improvement through completed tasks, accurate decisions or recognised achievement alone.

What the final exhibition asks

How much of a good life would you still want to shape yourself?

  1. A laptop in a dark room showing the Tickers explanation panel.

    01Effort

    Tickers
  2. A laptop in a dark room showing the AI Senior's listening state.

    02Work

    AI Senior
  3. A printed Civic opportunity-decision notice on a dark desk beside a glowing band.

    03Civic life

    Civic Score
  4. A learner at a desk wearing smart glasses with a LifeRPG progress overlay.Future University's website on a laptop and phone.

    04Learning

    LifeRPG → UNI
  5. A mixed-reality view in a busy atrium suggesting what two visitors could talk about.

    05A whole life

    Perfect World

The spine

How the question was worded

How the question was worded, brief to refined

  1. The course brief

    How might we use emerging technologies and speculative design to reimagine how people in Aotearoa connect, collaborate, and create?

  2. v1 · Tickers · DES303

    How might a speculative future productivity system make the tension between support, surveillance, and accountability experienceable in student collaboration in Aotearoa?

    Changed because In the four-person session, reading and research were read as distraction: activity alone could not explain intention.Evidence p. 

  3. v2 · AI Senior · DES303

    How might a speculative AI senior make the shift from AI support to AI authority experienceable in Aotearoa’s future workplaces?

    Changed because At Crit 2 the senior was read as another workplace monitoring system: more context gave the assistant more authority.Evidence p. 

  4. v3 · Civic Score · the capstone’s original HMW, A1, July

    How might we use speculative design to help people experience and question whether the benefits of an autonomous civic system justify its authority to judge citizens?

    Changed because In the July critique people rejected a scoring state on sight, so coercion made the question too easy to answer.Evidence p. 

  5. v4 · LifeRPG → Future University

    How might emerging AI technologies reshape how people in Aotearoa learn, demonstrate capability, and progress toward goals they choose for themselves?

    Changed because Critique and research showed learning needs people and places beyond an assistant, while jobs and credentials still supplied the reason to take part.Evidence p. 

  6. v5 · Perfect World · refined, October

    How might we immerse people in a desirable AI-supported future that invites them to reconsider what freedom and personal choice mean?

Methodology / timelineDES303 · Weeks 1–10

Two courses, five worlds

Tickers and AI Senior were built and tested in DES303. The capstone began with Civic Score in July, moved through LifeRPG and Future University, then made Perfect World real.

W1W2W3W4W5W6W7W8W9W10
  1. Earlier worldsBrief, research and tech demo

    W1–4
  2. Tickers + Focus Integrity

    TestFlightW7 formative
  3. AI Senior + Ticker Cube

    Crit 2
  4. Civic Score

  5. LifeRPG / UNI

  6. Perfect WorldPerfect World: world + film

  7. Posters + encounter studies

  8. Making it workQuest guide build + Research Lab

  9. Report, installation, exhibition

Methodology / timelineDES304 Capstone

Dated in journals, commits or files

Approximate span, reconstructed

Critique, test or submission

AugSepOct
  1. Future Work deck
  2. Critique redraw
  3. World reportUNI model v1.1
  4. Tabletop plan
  5. Studies
  6. Commits 3–22 OctTechnical run
  7. Test + A3 PDF

The June–July break between DES303 and DES304 falls in the gutter; each page has its own scale. Branches overlapped. Original plan: Miro Frame 1.

Five worlds, one changing question

Each project took a present signal, imagined a future and brought an artefact into the room. Each established something, exposed a limit, and handed the next project something to extend.

Project

Signal

What if

  1. A laptop in a dark room showing the Tickers explanation panel.

    01TickersDES303 · Weeks 4–7Implemented prototype

    AI makes polished output cheap. A finished submission no longer shows the effort behind it.

    What if effort had to be proved? A culture where activity records verify contribution.

  2. A laptop in a dark room showing the AI Senior's listening state.

    02AI SeniorDES303 · Weeks 8–10Implemented desktop prototype

    AI agents now plan and carry out multi-step work, not just single tasks.

    What if AI became your senior at work, planning, allocating and reviewing it?

  3. A printed Civic opportunity-decision notice on a dark desk beside a glowing band.

    03Civic ScoreDES304 · H03 civic workFictional scenario

    Algorithms already inform public-service decisions in Aotearoa.

    What if survival were guaranteed, but scarce opportunities went by Civic Value?

  4. A learner at a desk wearing smart glasses with a LifeRPG progress overlay.

    04LifeRPG / UNIDES304 · UNI model 16 SeptConcept (LifeRPG)

    AI can complete the work, so a result no longer shows that someone learned.

    What if growth became a trusted public record, with a university built around it?

  5. A mixed-reality view in a busy atrium suggesting what two visitors could talk about.

    05Perfect WorldDES304 · Sept–OctFictional world

    Automation could secure essentials, and assistants increasingly suggest before we ask.

    What if essentials were secure without compulsory work, and an AI guide truly understood what matters to you?

How the inquiry advanced

Five worlds, one changing question

Each limit became the next project’s starting point.

Established

Unresolved

Extended next

  1. I could collect activity evidence and make its interpretation inspectable.

    Reading, thinking and research could look like inactivity or distraction.Evidence p. 

    → 02 AI SeniorAI Senior added task context, clarification and correctable memory.

  2. Interpretations could be inspected, explained and corrected by the person they describe.

    Better context did not settle who authorised the assistant to judge.Evidence p. 

    → 03 Civic ScoreCivic Score examined that authority at the scale of public decisions.

  3. Reasons on request, context you can add, a person who reviews, and essentials that never depend on a score.

    Even a fairer judgement still defined people through an institution's values.Evidence p. 

    → 04 LifeRPG / UNILifeRPG and UNI supported a capability the person chose, and asked what learning needs.

  4. Meaningful practice needs people, places, opportunity and participation, not only an assistant.

    Jobs and credentials still supplied much of the reason to develop.Evidence p. 

    → 05 Perfect WorldPerfect World granted secure essentials and useful help, then asked what people would still choose to develop.

  5. A desirable future made tangible: a world, a film and a working guided encounter.

    Whether visitors notice, and can name, the guide's influence. That is what the evaluation asks.Evidence p. 

    → youThe visitor's own answer.

Each project is a speculation, not a forecast. “Unresolved” names the limit that research, critique or making exposed; the next project kept what worked and extended it.

What carried forward, and what advanced

Each project made support more capable while exposing a question its improvements could not settle. Two things accumulated: principles from research and critique, and capabilities I built.

Principles, from research and critique

  1. Tickers mark
    01 Tickers
    AI Senior mark
    02 AI Senior

    01Context before judgement

    Reading and research looked like distraction until the task was known. Ask what the person is doing before reading them.

    In Perfect World. The guide asks when its context is low, instead of acting.

  2. AI Senior mark
    02 AI Senior
    Civic Score mark
    03 Civic Score

    02Explanation and correction

    A reading the person cannot see or change becomes an authority. Show the reason; let the person correct it.

    In Perfect World. Each memory note names who said it and can be corrected.

  3. Civic Score mark
    03 Civic Score

    03Protected choice and human oversight

    Essentials never depend on a score, and a person reviews. Help that is a condition stops being help.

    In Perfect World. Decline or stop at any time; a film-and-discussion route without the headset.

  4. LifeRPG / UNI mark
    04 LifeRPG / UNI

    04Development beyond output

    Having something done is not learning it. Practice needs people, places and opportunity.

    In Perfect World. Suggestions leave the doing, and the conversation, to the visitors.

What carried forward

What advanced

What I built, and how it changed

Four capabilities, each shown as I first built it and as it works in Perfect World. The label says how much actually carried over.

  1. 01Task and context

    Carried-forward principle

    Then. A task title gave the tracker nothing to judge activity against.

    Now. The system asks about the work first, so activity is read against an agreed task.See p. 

    Tickers: a session is a name and a duration
    Then Tickers: a session is a name and a duration
    AI Senior: the brief is clarified before work starts
    Now AI Senior: the brief is clarified before work starts
  2. 02Inspectable decisions

    Carried-forward principle · new implementation

    Then. The evidence was visible, but not why the system acted on it.

    Now. Every chance to speak shows its readings and the reason the guide spoke or held back.See p. 

    Tickers: a focus score with its evidence rows
    Then Tickers: a focus score with its evidence rows
    Perfect World Lab: each opportunity's scores and authored thresholds
    Now Perfect World Lab: each opportunity's scores and authored thresholds
  3. 03Memory and correction

    New implementation

    Then. Built for one person, kept over time. In a conversation two people speak.

    Now. Each note names who said it, a correction lands on the right person, and memory clears at handover.See p. 

    AI Senior: one worker's pattern, edit or forget
    Then AI Senior: one worker's pattern, edit or forget
    Perfect World: attributed, session-only memory
    Now Perfect World: attributed, session-only memory
  4. 04Conversation

    New implementation

    Then. A question to one worker, on a screen.

    Now. The guide listens to two people and speaks into their conversation; a speaker label sets display only, never identity.See p. 

    AI Senior: uncertain, so it asks the user
    Then AI Senior: uncertain, so it asks the user
    Perfect World: a spoken session between two speakers
    Now Perfect World: a spoken session between two speakers

Labels are deliberate. The exhibition has its own runtime, database and privacy boundary; similar screens show continuity of ideas, not copied code. Civic and UNI contributed principles and constraints, not code.

A person’s hands on a laptop keyboard in darkness, lit only by the screen and a thin red edge light.

The current signal

The changing relationship between work and effort

Emerging signals of an AI-supported future

SpecAI-generated study, 25 October 2026 (Higgsfield, designer-directed).

World 01 / 05 · Tickers

Tickers

What I designed

A task-commitment app for friends and a desktop Focus Integrity tracker that reads activity during a declared session.

Implemented prototype · Tested: TestFlight, about ten people; one four-person session

Why

AI makes polished output cheap, so a finished submission no longer shows who did the work, and activity records can misread meaningful work.

Who

Student collaborators who stake effort on a session, and the peers whose judgements about contribution depend on the record.

How

Declare a task → Record permitted activity → Interpret it → Review or contest the reading.

The question this version examined

Should a record of my activity decide whether my work counts?

TickersAI SeniorCivic ScoreLifeRPG / UNIPerfect World

Signal · World 01 of 5

1. Students use AI faster than institutions can respond

Generative AI moved from novelty to habit in two years. In the HEPI survey series, the share of UK undergraduates using it on assessed work rose from 53% to 94% (fig. 1). Institutions were slower: when UNESCO asked in 2023, fewer than one in ten had formal guidance (fig. 4). That is the condition Tickers starts from. If the final artefact no longer shows who did the work, someone will look for evidence somewhere else.

When output is easy to produce, the final artefact stops showing who did the work.

Key finding · author’s reading of the evidence

Figure 1: UK undergraduates using generative AI, 2024 to 2026 (HEPI Student Generative AI Surveys)

40608010020242025202666%92%95%53%88%94%
  • Any generative AI use
  • To help with assessed work

Also in the evidence

  1. 01
    Tools already carry thinking

    People use devices and their surroundings to carry part of their thinking (Hutchins, 1995; Risko & Gilbert, 2016). The open question is how that part is made visible.

  2. 02
    Visibility changes a promise

    Task apps keep a commitment private; once friends can see it, a commitment becomes something to prove (my DES303 precedent analysis).

  3. 03
    A record is not proof

    A credential can show who issued a claim and its status without proving that the claim is true (W3C, 2025).

  4. 04
    Agents are measured in bounded tasks

    METR measures bounded software-task horizons, not autonomous occupations or households (METR, 2026). The scenario extends beyond that evidence.

The question this raisesSo how do we know who actually worked?

Measurement answers whether work was done. It cannot say what the work meant to the person, or what help they used. That gap is where Tickers began: could a record of effort be trusted at all, and by whom?

Data: (Freeman, 2025; Mucha, 2021; Stephenson & Armstrong, 2026; UNESCO, 2023). The monitoring figures are from a vendor survey and are dated by the article.

TickersAI SeniorCivic ScoreLifeRPG / UNIPerfect World

Signal · World 01 of 5 · The evidence

1.1Use is near-universal, guidance is rare, and measurement has become the answer to the trust problem that follows

In the UK, 95% of undergraduates used generative AI in 2026, up from 66% in 2024, and 94% used it to help with assessed work (Freeman, 2025; Stephenson & Armstrong, 2026). When UNESCO surveyed slightly over 450 schools and universities in 2023, fewer than 10% had formal guidance, and universities (13%) were ahead of schools (7%) (UNESCO, 2023).

Employers had already turned to measurement. A vendor survey of 1,250 US employers found that 60% of those with remote staff used monitoring software, mostly to understand how time is spent (Mucha, 2021). This is the logic Tickers extends: when attention moves from what was produced to how it was produced, effort becomes something to be recorded, scored and contested.

Figure 2: Three numbers that frame the signal (HEPI 2026; UNESCO 2023; vendor survey 2021)

95%

UK undergraduates using generative AI, 2026

HEPI
60%

US employers with remote staff using monitoring software

Vendor survey, 2021
<10%

Schools and universities with formal guidance

UNESCO, 2023
94%

used generative AI to help with assessed work, 2026

66%

used generative AI in 2024

13%

of universities had formal guidance in 2023

7%

of schools had formal guidance in 2023

1,250

US employers in the monitoring survey

450+

schools and universities in the UNESCO survey

Figure 3: Why US employers use monitoring software (vendor survey of 1,250 employers, 2021)

To understand how employees use their time79%
To confirm people work a full day65%
To prevent personal use of work equipment50%
Considering adopting monitoring software17%

Figure 4: Institutions with formal guidance on generative AI (UNESCO survey, May 2023)

Universities

13%87%

Schools

7%93%
  • Formal guidance
  • No guidance reported

When Support Becomes Surveillance

Personal observation

DES303 · the first question

As a design and computer science student, I was interested in how technology could help people follow through on their intentions.

I recognised the appeal of systems that could organise tasks, encourage commitment and make progress visible. These tools could be especially useful when working collaboratively, where people rely on others to fulfil their responsibilities.

However, the research encouraged me to question what happens when support becomes increasingly dependent on monitoring.

A completed outcome does not necessarily reveal how it was produced. Yet recording someone’s behaviour does not automatically explain the effort, thought or intention behind it either.

I began wondering whether a system could make collaborative accountability more transparent without reducing meaningful work to observable activity.

Rather than treating this simply as a technical problem to solve, I saw an opportunity to explore the social implications of a future in which digital evidence increasingly influences how people trust and evaluate one another.

A glass-walled study hall glowing orange at dusk, students silhouetted at laptops, a security camera on a pole against a deep blue sky.

When Support Becomes Surveillance

Initial vision

A future where AI helps people remain accountable to their chosen commitments, while making the tension between support and surveillance visible.

How might we · v1 · DES303

How might a speculative future productivity system make the tension between support, surveillance, and accountability experienceable in student collaboration in Aotearoa?

It was the question my DES303 prototype set out to test.

The next step

Next, I imagined how a society might adopt such a system, what people would experience while using it, and how a product could make those implications tangible.

SpecAI-generated study, 25 October 2026 (Higgsfield, designer-directed).

Early research
What if?
Three clusters
Surveillance framed as support
Simulated companionship and synthetic care
Deskilling and dependence
Behaviour shaping through optimisation
Connection and accountability
Surveillance and control
Invisible normalisation
Public systems and intelligent support
Connection becoming accountability
Support becoming control
Technology becoming normal by disappearing

02 — Ideation

Constructing the future and translating it into a speculative design concept.

Key question

What could this future look and feel like, and how could I make it tangible?

Accompany robot / live agent
3D campus navigation
Tickers / Focus Integrity
Suggests the next action in real time
Raises autonomy and dependency questions
Outsources judgement
Efficient, personalised movement
May increase dependence on guided infrastructure
May reduce self-navigation
Scores focus and verifies attention
Frames monitoring as support and accountability
Most direct link to the research
Quick to test as a low-fidelity prototype

Brainstorming and idea generation

I began with a “What if?” brainstorm, sorted the questions into four themes, then collapsed them into three broader future conditions. From those I drafted three concept directions and took Tickers forward, because it connected most directly to my research on human contribution, could be built and tested quickly, and exposed the tension between helpful support and intrusive judgement. I chose verified effort first because it turned the trust problem into an interaction I could design, test and question.

Filled: the direction taken forward

Retrospective comparison · added October 2026, not part of the DES303 research

Objects: AI-generated renders in the Tickers icon style (Higgsfield, designer-directed)

Imagined Systems of Judgement

Reading Tickers against fiction that has already imagined work and effort being watched. Each makes a different part of verified effort visible.

Fifteen Million Merits

Black Mirror, series 1, 2011

People pedal exercise bikes all day to earn merits, and the merits buy everything from food to a chance at escape.

What happens when effort itself becomes the currency?

Severance

Apple TV+, 2022

A procedure splits workers' memories between office and home, so the company keeps a version of them that knows nothing else.

How far can a workplace reshape autonomy once people consent to it?

The Circle

Dave Eggers, novel, 2013

A tech company makes total transparency a virtue: small cameras everywhere, participation ranked, sharing expected.

What happens when being seen working becomes proof of being good?

What Tickers Shares with Them

MeritsSeveranceThe CircleTickers
Who judgesThe system and the audienceThe employerColleagues and followersFriends and a desktop agent
What is scoredEffort, as meritsThe work selfParticipation and sharingDeclared effort
Can the person answer backOnly by performingNoBarelyYes: explain and challenge
Why people accept itIt pays for lifeA clean work-life splitBelonging and statusTrust and motivation

Design consequence

To the people inside them, none of these systems feels like control. Each is accepted because it pays, protects or belongs.

So Tickers had to be attractive and genuinely useful, with its judgement arriving quietly, through help. That is what made it worth testing.

Tickers · Prototyping · 21

Tickers · Prototyping · 21

Retrospective comparison · added October 2026, not part of the DES303 research

Objects: AI-generated renders in the Tickers icon style (Higgsfield, designer-directed)

Imagined Systems of Judgement

Reading Tickers against fiction that has already imagined work and effort being watched. Each makes a different part of verified effort visible.

Fifteen Million Merits

Black Mirror, series 1, 2011

People pedal exercise bikes all day to earn merits, and the merits buy everything from food to a chance at escape.

What happens when effort itself becomes the currency?

Severance

Apple TV+, 2022

A procedure splits workers' memories between office and home, so the company keeps a version of them that knows nothing else.

How far can a workplace reshape autonomy once people consent to it?

The Circle

Dave Eggers, novel, 2013

A tech company makes total transparency a virtue: small cameras everywhere, participation ranked, sharing expected.

What happens when being seen working becomes proof of being good?

What Tickers Shares with Them

MeritsSeveranceThe CircleTickers
Who judgesThe system and the audienceThe employerColleagues and followersFriends and a desktop agent
What is scoredEffort, as meritsThe work selfParticipation and sharingDeclared effort
Can the person answer backOnly by performingNoBarelyYes: explain and challenge
Why people accept itIt pays for lifeA clean work-life splitBelonging and statusTrust and motivation

Design consequence

To the people inside them, none of these systems feels like control. Each is accepted because it pays, protects or belongs.

So Tickers had to be attractive and genuinely useful, with its judgement arriving quietly, through help. That is what made it worth testing.

Tickers · Prototyping · 21

Tickers · Prototyping · 21

This future

Tickers · Ideation

01The promise

What does it promise?

More transparent collaboration, recognised contribution and stronger commitments.

In this imagined 2031 Aotearoa, AI-assisted work has become ordinary. As finished outcomes get easier to produce, students and collaborators look for new ways to understand the human contribution behind them.

Today’s signal

Finished work is easy to produce: in the UK, 94% of undergraduates used generative AI to help with assessed work in 2026. (Stephenson & Armstrong, 2026)

02The records

What gets recorded?

People declare intentions, record activity and share evidence of progress.

Digital accountability systems offer an answer. People declare intentions, record working sessions and share evidence of participation. AI offers provisional interpretations, and collaborators can question or review the result.

Today’s signal

Measurement already exists: 60% of US employers with remote staff used monitoring software, mostly to understand how time is spent. (Mucha, 2021)

03The institutions

Who recognises it?

Universities and organisations may begin recognising verification records.

These systems appeal because they promise structure, transparency and fairness. Over time, universities and collaborative groups may begin to recognise the records as evidence of contribution.

Today’s signal

The rules are not yet written: fewer than 10% of schools and universities surveyed had formal guidance on generative AI in 2023. (UNESCO, 2023)

04The social norm

What becomes ordinary?

People increasingly expect proof, not simply a completed outcome.

But expectations shift too. A student who carried a group project through hours of reading and planning has little on record, while a teammate’s tidy session log looks like more work. Verified effort offers that student a way to be seen, and asks them to keep proving it.

Today’s signal

A record can show who issued a claim and its status without proving that the claim is true or adequate. (W3C, 2025)

05The tension

Where does it rub?

When contribution becomes measurable, does everything meaningful become visible?

The future I wanted to explore is not one where technology obviously controls people, but one where useful assistance gradually changes what society considers trustworthy.

The chain from work to trust

Intention → Activity → Evidence → Judgement

Aotearoa, 2031

All institutional arrangements are designer-authored conditions of a fictional 2031 future, not observed policies or predictions. Today’s signals are real, bounded findings from the sources cited.

02 — Ideation · Concepts

Making the future tangible

I explored four ways to communicate this future: a digital Proof Space, a physical Ticker Cube, a Verified Effort Receipt and an Explain Panel.

Each explores a different part of the future: the everyday interaction, monitoring in the room, institutional authority, and the right to reply. I chose the Proof Space because it could carry both the usefulness of shared accountability and the discomfort of having my work interpreted by a system.

Proof Space✓Taken forward

Digital · on screen

Proof Space

An interactive interface for declaring and verifying work

How it works
A person declares a task, records a working session and shares the evidence with collaborators.
The tension
Feedback that helps someone focus can also feel like being watched and judged.
Ticker Cube02

Physical · on the desk

Ticker Cube

A physical object bringing monitoring into everyday space (built later, in AI Senior)

How it works
Monitoring leaves the screen and takes a place in the room, signalling the state of a session by light.
The tension
Being observed becomes part of the furniture, easy to stop noticing.

Four ways to make it tangible

Effort Receipt03

Institutional · on record

Effort Receipt

An institutional record showing what counts as verified contribution

How it works
A printed or filed summary listing which effort was verified and which was not.
The tension
Who decides what counts, and what happens to work that leaves no trace?
Explain Panel04

Digital · a right to reply

Explain Panel

A response space for adding context or contesting a judgement

How it works
Someone can add context, correct an AI reading or contest a result before it is accepted as evidence.
The tension
A right to explain helps, but only once the record has already spoken.

I then developed initial interface sketches exploring how Focus Integrity feedback could feel supportive while introducing a sense of surveillance and judgement.

SpecAI-generated studies, 25 October 2026 (Higgsfield, designer-directed). Speculative concepts, not records.

Tickers · what already exists

Learning from Existing Systems

Before designing anything I looked at how products already ask for commitment and reward it. Task tools reduce friction but keep nothing at stake; focus apps reward a streak that answers only to you; CRED, a payments app, rewards a verified behaviour with a sense of occasion.

CRED · Rewards a verified behaviour, paying on time, with a sense of occasion: one loud action, dark surfaces, 3D objects.

Todoist · Fast capture and priorities in colour. Nothing is at stake when a task slips.

TickTick · Tasks, a timeline and a Pomodoro timer. It records time, but only for you.

Opal · Blocks apps and turns focus into a score, a streak and gemstones that answer only to you.

Taken forward. Hierarchy, a reward tone and trust through finish, applied to effort instead of payments. Left behind. A record that only the person reads.

In-app screens via Mobbin and CRED's NeoPOP, for precedent analysis

Tickers · the speculative proposition

Positioning

The proposition. Tickers asks people to prove effort to the friends they made a commitment with: friends stake time and gems on a session, a desktop agent reads the work, and anyone in the room can challenge a proof that looks like a bluff.

I placed nearby products on two questions: is effort self-declared or backed by system-supported evidence, and is a commitment kept alone or staked with others? Tickers aimed for the empty corner. The prototype later showed what that corner really holds: evidence a system can collect, not effort it can verify.

Staked with others

Kept alone

Self-declared

System-supported evidence

Tickers

To be

Todoist

Task capture

TickTick

Tasks and a focus timer

Todomate

Friends see your list

Forest

Locks the phone

Opal

Blocks apps, scores focus

Focusmate

Co-working on camera

Workplace monitoring

Imposed, not chosen

A conceptual comparison from the DES303 precedent analysis, not a measured ranking

Tickers · Prototyping · 07

Tickers · Prototyping · 07

Tickers · what already exists

Learning from Existing Systems

Before designing anything I looked at how products already ask for commitment and reward it. Task tools reduce friction but keep nothing at stake; focus apps reward a streak that answers only to you; CRED, a payments app, rewards a verified behaviour with a sense of occasion.

CRED · Rewards a verified behaviour, paying on time, with a sense of occasion: one loud action, dark surfaces, 3D objects.

Todoist · Fast capture and priorities in colour. Nothing is at stake when a task slips.

TickTick · Tasks, a timeline and a Pomodoro timer. It records time, but only for you.

Opal · Blocks apps and turns focus into a score, a streak and gemstones that answer only to you.

Taken forward. Hierarchy, a reward tone and trust through finish, applied to effort instead of payments. Left behind. A record that only the person reads.

In-app screens via Mobbin and CRED's NeoPOP, for precedent analysis

Tickers · the speculative proposition

Positioning

The proposition. Tickers asks people to prove effort to the friends they made a commitment with: friends stake time and gems on a session, a desktop agent reads the work, and anyone in the room can challenge a proof that looks like a bluff.

I placed nearby products on two questions: is effort self-declared or backed by system-supported evidence, and is a commitment kept alone or staked with others? Tickers aimed for the empty corner. The prototype later showed what that corner really holds: evidence a system can collect, not effort it can verify.

Staked with others

Kept alone

Self-declared

System-supported evidence

Tickers

To be

Todoist

Task capture

TickTick

Tasks and a focus timer

Todomate

Friends see your list

Forest

Locks the phone

Opal

Blocks apps, scores focus

Focusmate

Co-working on camera

Workplace monitoring

Imposed, not chosen

A conceptual comparison from the DES303 precedent analysis, not a measured ranking

Tickers · Prototyping · 07

Tickers · Prototyping · 07

Focus Integrity
Arena
Proof
Explain
Bluff challenge
Gems and pot

How Tickers works

Tickers. One record of effort,
staked, checked and answered.

Six parts sit around one record of verified effort: the arena makes a commitment social, Focus Integrity reads the work, proof and the bluff challenge settle what happened, the explain path lets the person answer, and the pot makes it count.

Service model

What each part gives,
and what it asks back.

Each part offers the person something, and each asks for something in return: time, signals or an account of oneself.

Arena→ OffersFriends stake time and gems on a session← Asks backProof, challenges and a share of the pot
Focus Integrity→ OffersA live focus reading on the desktop← Asks backWork signals, kept on the device
Explain→ OffersThe reason behind a judgement← Asks backContext, corrections and appeals

Where it rubs. Every "asks back" is something the person gives up: time, signals or an account of themselves. The project keeps those costs in view instead of hiding them in the background.

Tickers · Prototyping · 04

Tickers · Prototyping · 04

Focus Integrity
Arena
Proof
Explain
Bluff challenge
Gems and pot

How Tickers works

Tickers. One record of effort,
staked, checked and answered.

Six parts sit around one record of verified effort: the arena makes a commitment social, Focus Integrity reads the work, proof and the bluff challenge settle what happened, the explain path lets the person answer, and the pot makes it count.

Service model

What each part gives,
and what it asks back.

Each part offers the person something, and each asks for something in return: time, signals or an account of oneself.

Arena→ OffersFriends stake time and gems on a session← Asks backProof, challenges and a share of the pot
Focus Integrity→ OffersA live focus reading on the desktop← Asks backWork signals, kept on the device
Explain→ OffersThe reason behind a judgement← Asks backContext, corrections and appeals

Where it rubs. Every "asks back" is something the person gives up: time, signals or an account of themselves. The project keeps those costs in view instead of hiding them in the background.

Tickers · Prototyping · 04

Tickers · Prototyping · 04

Visual Storyboard

Mapping the product experience: one student, one group project and one session, from declaring the work to the record it leaves.

Panels 04 to 06 happen on the desktop, the rest on the phone. Panels 05 and 06 carry the question the rest of the chapter tests: the score drops while the work is real, and the person has to answer back.

01Effort becomes invisible

In a group project every part arrives finished. Nobody can tell who did the work, or how.

02Declare the work

A task goes in with a name and a time. Declaring it is the first commitment.

03Stake it with friends

Friends host a room and put time and gems behind the session.

04Focus on the desktop

The tracker reads the work as it happens and keeps its score in the open.

05The score drops

Reading a long article looks like idling. The score falls, though the work is real.

06Answer back

Instead of failing the session, Tickers asks. The person labels the moment as aligned.

07Proof and challenge

At the end, proof goes to the room, and a friend can call a bluff.

08A verified record

Survivors share the pot. The record keeps the effort, and the correction.

Illustrations generated with Higgsfield (GPT Image), designer-directed; interface details simplified

Tickers · Prototyping · 03

Tickers · Prototyping · 03

Visual Storyboard

Mapping the product experience: one student, one group project and one session, from declaring the work to the record it leaves.

Panels 04 to 06 happen on the desktop, the rest on the phone. Panels 05 and 06 carry the question the rest of the chapter tests: the score drops while the work is real, and the person has to answer back.

01Effort becomes invisible

In a group project every part arrives finished. Nobody can tell who did the work, or how.

02Declare the work

A task goes in with a name and a time. Declaring it is the first commitment.

03Stake it with friends

Friends host a room and put time and gems behind the session.

04Focus on the desktop

The tracker reads the work as it happens and keeps its score in the open.

05The score drops

Reading a long article looks like idling. The score falls, though the work is real.

06Answer back

Instead of failing the session, Tickers asks. The person labels the moment as aligned.

07Proof and challenge

At the end, proof goes to the room, and a friend can call a bluff.

08A verified record

Survivors share the pot. The record keeps the effort, and the correction.

Illustrations generated with Higgsfield (GPT Image), designer-directed; interface details simplified

Tickers · Prototyping · 03

Tickers · Prototyping · 03

macOS tracker
Start a session
Declare the task
Link an arena seat
Screen recording
Accessibility
Camera (optional)
Start the tracker
Live overlay
Collapsed HUD
Expanded HUD
Focus score
Integrity · Engagement
Score graph
Impact line
Signal rows
Grace countdown
Display check
Quick label
Pause · End · Cam

↳ Needs

The score felt okay, but I wanted to know why it dropped.

Research tab
Sessions table
Session detail
Timeline validation
Model runs
Model comparison
Agreement vs labels
Per-model aggregate
Model detail
Segment review
Segment timeline
Segment drill-down
Raw tick rows
Camera summary
Capsule and verdict
Model predictions
Human label

↳ Needs

Reading looks inactive, so the system needs a pause or reading mode.

Tools
Labelling
Camera diagnostics
Research debug
Export and verify
Log viewer

↳ Needs

Screen evidence feels strong, but I would not want raw screenshots shared.

After the session
Integrity to backend
Score on the seat
Session summary
Proof against evidence
Bluff resolution

↳ Needs

YouTube can be work, depending on the task.

User Flow · Desktop

The macOS build mapped from its code: the floating overlay that sits beside the work, and the Research tab where a session is replayed, compared and labelled. A session declared on the phone starts the tracker here; its score and summary go back to the phone and the arena seat.

Highlighted pills trace the research loop. Declare a task, watch the expanded HUD, label the moment, then open the session, drill into a segment and compare the model with your own label.

Needs paraphrased from the Week 7 friend test (four participants)

Mapped from the Flutter macOS build: FloatingOverlayWindow and the research screens

Tickers · Prototyping · 06

Tickers · Prototyping · 06

macOS tracker
Start a session
Declare the task
Link an arena seat
Screen recording
Accessibility
Camera (optional)
Start the tracker
Live overlay
Collapsed HUD
Expanded HUD
Focus score
Integrity · Engagement
Score graph
Impact line
Signal rows
Grace countdown
Display check
Quick label
Pause · End · Cam

↳ Needs

The score felt okay, but I wanted to know why it dropped.

Research tab
Sessions table
Session detail
Timeline validation
Model runs
Model comparison
Agreement vs labels
Per-model aggregate
Model detail
Segment review
Segment timeline
Segment drill-down
Raw tick rows
Camera summary
Capsule and verdict
Model predictions
Human label

↳ Needs

Reading looks inactive, so the system needs a pause or reading mode.

Tools
Labelling
Camera diagnostics
Research debug
Export and verify
Log viewer

↳ Needs

Screen evidence feels strong, but I would not want raw screenshots shared.

After the session
Integrity to backend
Score on the seat
Session summary
Proof against evidence
Bluff resolution

↳ Needs

YouTube can be work, depending on the task.

User Flow · Desktop

The macOS build mapped from its code: the floating overlay that sits beside the work, and the Research tab where a session is replayed, compared and labelled. A session declared on the phone starts the tracker here; its score and summary go back to the phone and the arena seat.

Highlighted pills trace the research loop. Declare a task, watch the expanded HUD, label the moment, then open the session, drill into a segment and compare the model with your own label.

Needs paraphrased from the Week 7 friend test (four participants)

Mapped from the Flutter macOS build: FloatingOverlayWindow and the research screens

Tickers · Prototyping · 06

Tickers · Prototyping · 06

From sketch to prototype · 01 wireframes, 02 visual identity

First Wireframe Flow

Before any styling, I drew the arena as one loop: find a room, wait in the lobby, set the mission, work in the live room, submit proof, call a bluff, see the outcome and read the summary. Drawing the whole loop first showed where the stakes are decided: at proof, and at the challenge.

01 Room list

02 Lobby

03 Mission setup

04 Live room

05 Submit proof

06 Call a bluff

07 Outcome

08 Summary

First wireframes, April 2026 (DES303)

Brand

The Tickers cover: an isometric clock, clipboard and calendar above the app icon and wordmark

Logo

Tickers

A neon-red ring around a green check on black, with the name set in JetBrains Mono beside it, never inside.

Key elements

Clock · the session

Clipboard · the task

Calendar · the streak

Isometric, white with neon-green sides and a heavy black line. Generated with Gemini and placed on the cover by me.

Colour and type · Inter for words, JetBrains Mono for numbers

Action
#E5FE40

Alive
#00FF88

Red
#FF3366

Gold
#FFD700

Cyan
#00F0FF

Ground
#050505

Tickers · Prototyping · 09

Tickers · Prototyping · 09

From sketch to prototype · 01 wireframes, 02 visual identity

First Wireframe Flow

Before any styling, I drew the arena as one loop: find a room, wait in the lobby, set the mission, work in the live room, submit proof, call a bluff, see the outcome and read the summary. Drawing the whole loop first showed where the stakes are decided: at proof, and at the challenge.

01 Room list

02 Lobby

03 Mission setup

04 Live room

05 Submit proof

06 Call a bluff

07 Outcome

08 Summary

First wireframes, April 2026 (DES303)

Brand

The Tickers cover: an isometric clock, clipboard and calendar above the app icon and wordmark

Logo

Tickers

A neon-red ring around a green check on black, with the name set in JetBrains Mono beside it, never inside.

Key elements

Clock · the session

Clipboard · the task

Calendar · the streak

Isometric, white with neon-green sides and a heavy black line. Generated with Gemini and placed on the cover by me.

Colour and type · Inter for words, JetBrains Mono for numbers

Action
#E5FE40

Alive
#00FF88

Red
#FF3366

Gold
#FFD700

Cyan
#00F0FF

Ground
#050505

Tickers · Prototyping · 09

Tickers · Prototyping · 09

From sketch to prototype · 03 mobile

Mid-fi

Code

To-do

Mid-fi

Code

Arena rooms

Mid-fi

Code

Arena room

Mid-fidelity to Code

A first mid-fidelity pass covered the central screens before the same screens were coded in Flutter. The reward tone carried the win screen well, but the to-do and in-arena screens felt over-styled for the work they carried, so I simplified the hierarchy.

Labelled to-do rows became a list of days; plain room names became cards with the buy-in, pot, players and time left; the winner-takes-the-pot screen became the arena room.

The arena room

04

The arena room

Inside a room the countdown, the pot and the players read at a glance. Progress, the leaderboard and each person's declared tasks sit below, all in the open.

Focus setup

05

Focus setup

Before a session, one screen explains which apps are blocked and how Screen Time and the Pomodoro timer behave, so the person agrees to the rules knowing what they are.

Statistics

06

Statistics

Today's and total focus, the streak, the best day and the completion rate, by day, week or month. The record is the person's own before it is anyone else's.

Tickers · Prototyping · 11

Tickers · Prototyping · 11

From sketch to prototype · 03 mobile

Mid-fi

Code

To-do

Mid-fi

Code

Arena rooms

Mid-fi

Code

Arena room

Mid-fidelity to Code

A first mid-fidelity pass covered the central screens before the same screens were coded in Flutter. The reward tone carried the win screen well, but the to-do and in-arena screens felt over-styled for the work they carried, so I simplified the hierarchy.

Labelled to-do rows became a list of days; plain room names became cards with the buy-in, pot, players and time left; the winner-takes-the-pot screen became the arena room.

The arena room

04

The arena room

Inside a room the countdown, the pot and the players read at a glance. Progress, the leaderboard and each person's declared tasks sit below, all in the open.

Focus setup

05

Focus setup

Before a session, one screen explains which apps are blocked and how Screen Time and the Pomodoro timer behave, so the person agrees to the rules knowing what they are.

Statistics

06

Statistics

Today's and total focus, the streak, the best day and the completion rate, by day, week or month. The record is the person's own before it is anyone else's.

Tickers · Prototyping · 11

Tickers · Prototyping · 11

From sketch to prototype · 04 desktop

Why a Desktop App

The phone could only ask people to report that they focused. The desktop app tries to show it: not as a product feature, but as a research instrument for the question Tickers asks, whether effort can be evidenced and whether the verdict is fair.

BEFORE · EARLY PROTOTYPEUserPhoneLocal storagetap startsaveOne device, one timer, no verification: the user's word was the whole system.AFTER · RUNNING SYSTEMUserPhoneBackendDesktopOther playersdeclarestart focus (declared task)seat, betscore, violationslive stateloop · every few secondswindow · input · face · screenIntent on the phone, verification on the desktop, shared truth on the backend.

After DES303 Week 5, Figure 5: one focus session, before and after the desktop

Focus Integrity Pipeline

Most ticks never reach a model: the certainty gate decides clear cases locally, and the escalation to text and then vision is cost-aware by design. The score is a confidence estimate, not the truth.

1CollectorsEvery few seconds: active window, browser tab, Chrome activity, input, screen diff, camera presence and system state.

↓

2Feature extractorA rolling buffer turns ticks into switching rate, dwell time, idle seconds, typing bursts, face stability and task-keyword match.

↓

3aClear → decided locallyOn-task, off-task or away. No model is called.
3bAmbiguous → AI judgeText model returns on_task, confidence, need_vision, reason; vision only if the screen changed.

↓

4TriScoreIntegrity, Engagement and Confidence, 0–100, with a timeline and violations.

↓

5State machine → interventionFocused, drift risk, distracted or recovery; the overlay warns, counts down, and the result reaches the arena.

Judge rules. The declared task is authoritative. Banned apps (video, games) are off-task. Idle with no face is away.

Passive tasks allow low input. Neutral apps with no task-keyword match are ambiguous, which is where vision is used.

Tickers · Prototyping · 15

Tickers · Prototyping · 15

From sketch to prototype · 04 desktop

Why a Desktop App

The phone could only ask people to report that they focused. The desktop app tries to show it: not as a product feature, but as a research instrument for the question Tickers asks, whether effort can be evidenced and whether the verdict is fair.

BEFORE · EARLY PROTOTYPEUserPhoneLocal storagetap startsaveOne device, one timer, no verification: the user's word was the whole system.AFTER · RUNNING SYSTEMUserPhoneBackendDesktopOther playersdeclarestart focus (declared task)seat, betscore, violationslive stateloop · every few secondswindow · input · face · screenIntent on the phone, verification on the desktop, shared truth on the backend.

After DES303 Week 5, Figure 5: one focus session, before and after the desktop

Focus Integrity Pipeline

Most ticks never reach a model: the certainty gate decides clear cases locally, and the escalation to text and then vision is cost-aware by design. The score is a confidence estimate, not the truth.

1CollectorsEvery few seconds: active window, browser tab, Chrome activity, input, screen diff, camera presence and system state.

↓

2Feature extractorA rolling buffer turns ticks into switching rate, dwell time, idle seconds, typing bursts, face stability and task-keyword match.

↓

3aClear → decided locallyOn-task, off-task or away. No model is called.
3bAmbiguous → AI judgeText model returns on_task, confidence, need_vision, reason; vision only if the screen changed.

↓

4TriScoreIntegrity, Engagement and Confidence, 0–100, with a timeline and violations.

↓

5State machine → interventionFocused, drift risk, distracted or recovery; the overlay warns, counts down, and the result reaches the arena.

Judge rules. The declared task is authoritative. Banned apps (video, games) are off-task. Idle with no face is away.

Passive tasks allow low input. Neutral apps with no task-keyword match are ambiguous, which is where vision is used.

Tickers · Prototyping · 15

Tickers · Prototyping · 15

Testing · formative test

What I Wanted to Test

The build could collect evidence. Before reading a single score I set out what I was testing, with whom, and what I expected: the central question had moved from whether the system worked to whether its judgement could be trusted.

Participants4 friends and classmates

Sessions10-minute focus sessions

TasksCoding, essay writing, lecture watching, research browsing

EvidenceApp and window context, input activity, screen change, optional camera presence, model score

My labelsEach segment labelled aligned, partial, distracted or unclear

AfterwardsShort questions about fairness, trust and privacy

Week 7 test session with the tracker running, face blurred

What I Assumed

Five assumptions from my Week 6 notes. Each one became something to check against the sessions, not a claim the test could prove.

The live tracker each participant ran

  1. 1More evidence will make the score feel fairer.
  2. 2Low typing or low input means drift.
  3. 3Browser or YouTube use is usually distraction.
  4. 4Camera presence makes the score stronger.
  5. 5The model can make a final judgement.

What I asked after each session

Did the score feel fair?1–5

Did the system explain itself clearly?1–5

Did any evidence feel too invasive?1–5

Would you trust this score if money or reputation was involved?1–5

What should happen when the system is uncertain?Short answer

From the DES303 Week 7 record · a small formative test, not a validation study

Tickers · Prototyping · 22

Tickers · Prototyping · 22

Testing · formative test

What I Wanted to Test

The build could collect evidence. Before reading a single score I set out what I was testing, with whom, and what I expected: the central question had moved from whether the system worked to whether its judgement could be trusted.

Participants4 friends and classmates

Sessions10-minute focus sessions

TasksCoding, essay writing, lecture watching, research browsing

EvidenceApp and window context, input activity, screen change, optional camera presence, model score

My labelsEach segment labelled aligned, partial, distracted or unclear

AfterwardsShort questions about fairness, trust and privacy

Week 7 test session with the tracker running, face blurred

What I Assumed

Five assumptions from my Week 6 notes. Each one became something to check against the sessions, not a claim the test could prove.

The live tracker each participant ran

  1. 1More evidence will make the score feel fairer.
  2. 2Low typing or low input means drift.
  3. 3Browser or YouTube use is usually distraction.
  4. 4Camera presence makes the score stronger.
  5. 5The model can make a final judgement.

What I asked after each session

Did the score feel fair?1–5

Did the system explain itself clearly?1–5

Did any evidence feel too invasive?1–5

Would you trust this score if money or reputation was involved?1–5

What should happen when the system is uncertain?Short answer

From the DES303 Week 7 record · a small formative test, not a validation study

Tickers · Prototyping · 22

Tickers · Prototyping · 22

Testing · results

What the Evidence Showed

One replayed session (92 ticks, 4 min 32 s, ten 30-second segments) went through seven evidence models and was compared with my own labels. These are exploratory outputs against my labels, not validated accuracy: the aim was the trade-off between evidence, privacy and acceptance.

ModelEvidenceReadsPrivacy costScoreLabels matched
M0Timer onlySession timeLowest1005/10
MAMetadataApp, window, inputLow935/10
MSScreenScreen changeMedium866/10
MBMetadata + screenApp, input, screenMedium905/10
MAIMetadata + screen + AIPlus AI judge, 5 callsMedium935/10
CPRESCamera presenceFace presentHigh855/10
MPRESMetadata + screen + presenceAll of the aboveHighest885/10

Score check, agreement with my labels · privacy cost is my own reading

Reading it carefully. More evidence did not raise agreement in step: the screen-only model matched my labels most often, and the richest model was not the fairest. Ten segments from one session can show a pattern to question, not a ranking.

What the four participants said

TaskModel readMy labelFairPrivacyTrust
P1CodingMostly alignedAligned4/54/53/5
P2Essay writingPartialAligned / unclear3/54/52/5
P3Lecture watchingDistractedAligned2/53/51/5
P4Research browsingPartialPartial4/52/52/5

“The score felt okay, but I wanted to know why it dropped.” P1

“Reading looks inactive, so the system needs a pause or reading mode.” P2

“YouTube can be work depending on the task.” P3

“Screen evidence feels strong, but I would not want raw screenshots shared.” P4

Fairness against trust if money or reputation were involved

P1
P2
P3
P4

■ Fairness, average 3.25/5   ■ Trust, average 2.0/5. A score could feel fair and still not be trusted with anything at stake.

Peer test findings

What does the score mean?Some read it as an attention score. It should not sound like mind-reading.

Which evidence is reasonable?Active app and window title: they connect to the declared task.

Which evidence is invasive?Camera presence, more than app or window data.

If the score is wrong?Correction, appeal or clarification: contestable, not final.

Would you trust it in an arena?Only if the reason behind the score is shown.

Tickers · Prototyping · 17

Tickers · Prototyping · 17

Testing · results

What the Evidence Showed

One replayed session (92 ticks, 4 min 32 s, ten 30-second segments) went through seven evidence models and was compared with my own labels. These are exploratory outputs against my labels, not validated accuracy: the aim was the trade-off between evidence, privacy and acceptance.

ModelEvidenceReadsPrivacy costScoreLabels matched
M0Timer onlySession timeLowest1005/10
MAMetadataApp, window, inputLow935/10
MSScreenScreen changeMedium866/10
MBMetadata + screenApp, input, screenMedium905/10
MAIMetadata + screen + AIPlus AI judge, 5 callsMedium935/10
CPRESCamera presenceFace presentHigh855/10
MPRESMetadata + screen + presenceAll of the aboveHighest885/10

Score check, agreement with my labels · privacy cost is my own reading

Reading it carefully. More evidence did not raise agreement in step: the screen-only model matched my labels most often, and the richest model was not the fairest. Ten segments from one session can show a pattern to question, not a ranking.

What the four participants said

TaskModel readMy labelFairPrivacyTrust
P1CodingMostly alignedAligned4/54/53/5
P2Essay writingPartialAligned / unclear3/54/52/5
P3Lecture watchingDistractedAligned2/53/51/5
P4Research browsingPartialPartial4/52/52/5

“The score felt okay, but I wanted to know why it dropped.” P1

“Reading looks inactive, so the system needs a pause or reading mode.” P2

“YouTube can be work depending on the task.” P3

“Screen evidence feels strong, but I would not want raw screenshots shared.” P4

Fairness against trust if money or reputation were involved

P1
P2
P3
P4

■ Fairness, average 3.25/5   ■ Trust, average 2.0/5. A score could feel fair and still not be trusted with anything at stake.

Peer test findings

What does the score mean?Some read it as an attention score. It should not sound like mind-reading.

Which evidence is reasonable?Active app and window title: they connect to the declared task.

Which evidence is invasive?Camera presence, more than app or window data.

If the score is wrong?Correction, appeal or clarification: contestable, not final.

Would you trust it in an arena?Only if the reason behind the score is shown.

Tickers · Prototyping · 17

Tickers · Prototyping · 17

Testing · the ambiguous case

When the System Got It Wrong

One moment from my own session shows the gap between what the system could see and what I was doing.

1Declared taskwrite DES303 Week 7 blog
2Observed behaviourGoogle Chrome · “DES303 Week 7 - Canvas” · low typing, 38 s idle · screen static (4% change) · no face in frame
3Model readingdrift_risk · confidence 0.61
4My explanationI was reading course material for the blog.
5DisagreementMy label: aligned / partial. The same signals mean opposite things depending on the task.

The segment drill-down I labelled sessions in; values above are from tick 34 of my own session

Same Signal, Opposite Meanings

SegmentSystem sawModelMy labelWhat it was
03/10Chrome active, low typing, static screenDrift riskAligned / partialI was reading course material for the blog
04/10YouTube open, no typingDistractedPartialIt could be a tutorial; the system lacked task context
05/10Camera absentOff-taskUnclearI may have been writing notes on paper
The signalWhat the system assumedWhat it could also mean
Chrome is activeBrowsing instead of workingResearching or checking references
YouTube is openDistractedA lecture, tutorial or design reference
Screen is staticStopped workingReading, thinking or analysing
Low typingIdlePlanning, reading or reviewing
No face on cameraLeft the sessionWriting on paper or using another device

“Reading looks inactive, so the system needs a pause or reading mode.”

P2 · essay writing

“YouTube can be work depending on the task.”

P3 · lecture watching

Segments and readings from the DES303 Week 7 record

Tickers · Prototyping · 23

Tickers · Prototyping · 23

Testing · the ambiguous case

When the System Got It Wrong

One moment from my own session shows the gap between what the system could see and what I was doing.

1Declared taskwrite DES303 Week 7 blog
2Observed behaviourGoogle Chrome · “DES303 Week 7 - Canvas” · low typing, 38 s idle · screen static (4% change) · no face in frame
3Model readingdrift_risk · confidence 0.61
4My explanationI was reading course material for the blog.
5DisagreementMy label: aligned / partial. The same signals mean opposite things depending on the task.

The segment drill-down I labelled sessions in; values above are from tick 34 of my own session

Same Signal, Opposite Meanings

SegmentSystem sawModelMy labelWhat it was
03/10Chrome active, low typing, static screenDrift riskAligned / partialI was reading course material for the blog
04/10YouTube open, no typingDistractedPartialIt could be a tutorial; the system lacked task context
05/10Camera absentOff-taskUnclearI may have been writing notes on paper
The signalWhat the system assumedWhat it could also mean
Chrome is activeBrowsing instead of workingResearching or checking references
YouTube is openDistractedA lecture, tutorial or design reference
Screen is staticStopped workingReading, thinking or analysing
Low typingIdlePlanning, reading or reviewing
No face on cameraLeft the sessionWriting on paper or using another device

“Reading looks inactive, so the system needs a pause or reading mode.”

P2 · essay writing

“YouTube can be work depending on the task.”

P3 · lecture watching

Segments and readings from the DES303 Week 7 record

Tickers · Prototyping · 23

Tickers · Prototyping · 23

Reflecting · evidence and response

Redesigning the Judgement

Each change answers something the four-person session or my own session showed. The first five changed the tracker in Week 7; the last could not be solved inside it.

What the test showedSourceWhat I changed
“The score felt okay, but I wanted to know why it dropped.”P1 · four-person sessionShow the reason beside the number
“Reading looks inactive, so the system needs a pause or reading mode.”P2 · four-person sessionA pause or reading state before any warning
“YouTube can be work depending on the task.”P3 · four-person sessionJudge evidence against the declared task, not app categories
Camera presence read as the most invasive evidence, and did not prove focusSession debrief · P4Make the camera optional and separate from scoring
All four wanted correction, appeal or an uncertain state before a score countedSession debriefSegment labels, explanation and contestable results
Reading course material for my blog was scored drift_risk, 0.61My own session · tick 34Clarify the task with AI before the session starts

The responses in the build: quick label · segment drill-down · timeline with my labels

Reflecting · what carried forward

What Tickers Established

I was making the system better at watching, not at understanding.

Established

I could collect activity evidence and make its interpretation inspectable and contestable.

Unresolved

Reading, thinking and research could look like inactivity or distraction. Activity alone could not explain intention.

Retained

Evidence collection, an uncertain state, and the person’s ability to challenge a reading.

Extended next

AI Senior added task context, clarification before work, and a memory the person can correct.

Week 8 · the extension: a brief the AI asks questions about before work starts

What if AI understood the work first?

Next · World 02 · AI Senior →

Tickers · Prototyping · 24

Tickers · Prototyping · 24

Reflecting · evidence and response

Redesigning the Judgement

Each change answers something the four-person session or my own session showed. The first five changed the tracker in Week 7; the last could not be solved inside it.

What the test showedSourceWhat I changed
“The score felt okay, but I wanted to know why it dropped.”P1 · four-person sessionShow the reason beside the number
“Reading looks inactive, so the system needs a pause or reading mode.”P2 · four-person sessionA pause or reading state before any warning
“YouTube can be work depending on the task.”P3 · four-person sessionJudge evidence against the declared task, not app categories
Camera presence read as the most invasive evidence, and did not prove focusSession debrief · P4Make the camera optional and separate from scoring
All four wanted correction, appeal or an uncertain state before a score countedSession debriefSegment labels, explanation and contestable results
Reading course material for my blog was scored drift_risk, 0.61My own session · tick 34Clarify the task with AI before the session starts

The responses in the build: quick label · segment drill-down · timeline with my labels

Reflecting · what carried forward

What Tickers Established

I was making the system better at watching, not at understanding.

Established

I could collect activity evidence and make its interpretation inspectable and contestable.

Unresolved

Reading, thinking and research could look like inactivity or distraction. Activity alone could not explain intention.

Retained

Evidence collection, an uncertain state, and the person’s ability to challenge a reading.

Extended next

AI Senior added task context, clarification before work, and a memory the person can correct.

Week 8 · the extension: a brief the AI asks questions about before work starts

What if AI understood the work first?

Next · World 02 · AI Senior →

Tickers · Prototyping · 24

Tickers · Prototyping · 24

A small glowing cube device on a dark desk beside a hand, with a shadowy figure leaning back behind it.

The current signal

The changing relationship between work and authority

Emerging signals of an AI-managed future

SpecAI-generated study, 25 October 2026 (Higgsfield, designer-directed).

World 02 / 05 · AI Senior

AI Senior

What I designed

A conversational work partner that agrees a task contract with the worker, asks when unsure and keeps a memory the worker can correct.

Implemented desktop prototype · Planning interface and Ticker Cube · Critiqued at Crit 2

Why

Activity cannot be read well without knowing the work. But once an assistant interprets the work, it starts to hold authority over it.

Who

The worker first. Managers and colleagues see the same work with different interests.

How

Clarify the task → Approve its contract → Work → Answer when it is unsure → Correct its memory.

The question this version examined

Who authorised the assistant to set the standard it judges me by?

TickersAI SeniorCivic ScoreLifeRPG / UNIPerfect World

Signal · World 02 of 5

1. Agents take on longer tasks as exposure grows

Tickers asked who verifies effort. AI Senior asked what happens when the assistant plans the work as well as doing it. Agents now run software tasks that take a person an hour or more, and the first estimates of how much work this touches are in (figs. 1 and 2). The project treats these as present signals, not forecasts. Exposure describes tasks, not predicted job losses.

Exposure is not job loss. It means the tasks inside a job are changing.

Key finding · author’s reading of the evidence

Figure 1: What a seven-month doubling would mean for task length (METR; illustrative arithmetic, not a forecast)

×1NowAbout 50 minutes: a leading model in 2025
×2+7 months
×4+14 months
×8+21 months
×16+28 months

Also in the evidence

  1. 01
    Automation leaves the hard cases

    Automation may leave people with difficult exceptions (Bainbridge, 1983), which is a different job from the one they had before.

  2. 02
    Use is not one thing

    Appropriate use differs from misuse or disuse (Parasuraman & Riley, 1997), so a high adoption figure does not say whether the help is good.

  3. 03
    Planning becomes judging

    Once assistance helps define the task, it also helps define what counts as successful work.

  4. 04
    Agents coordinate steps

    An agent can coordinate steps towards a goal, not only answer a question, so the work it supports is a sequence.

The question this raisesWho decides what counts as done?

Capability measures say what an agent can finish, not who decides what counts as finished. As assistance moves from doing steps to planning and reviewing them, it takes part in that decision. That authority question is what AI Senior puts on the table. I carried it into Civic Score, where the same judgement could reach someone’s opportunities.

Data: (Gmyrek et al., 2025; Kwa et al., 2025; METR, 2026; Organisation for Economic Co-operation and Development, 2026). Exposure estimates describe tasks, not predicted job losses. The OECD figures are as reported by the EU Digital Skills and Jobs Platform, because the OECD page could not be read.

TickersAI SeniorCivic ScoreLifeRPG / UNIPerfect World

Signal · World 02 of 5 · The evidence

1.1The length of work AI agents can finish keeps doubling, and one in four jobs is exposed

Frontier AI agents’ task time horizons have doubled roughly every seven months since 2019, and a leading model could complete software tasks that take a human expert about 50 minutes half of the time (Kwa et al., 2025). METR cautions that its horizons describe bounded software tasks, not occupations (METR, 2026).

The ILO estimates that about one in four workers worldwide is in an occupation with some exposure to generative AI, and 34% of employment in high-income countries (Gmyrek et al., 2025). Firms are following: across the OECD, AI use rose from 8.7% of firms in 2023 to 20.2% in 2025, and large firms (52%) are far ahead of small ones (17.4%) (Organisation for Economic Co-operation and Development, 2026).

Figure 2: Exposure to generative AI at work (ILO, 2025)

1 in 4

Workers worldwide in an occupation with some exposure

ILO, 2025
34%

Employment exposed in high-income countries

ILO, 2025
3.3%

Global employment in the highest-exposure category

ILO, 2025
4.7%

of women’s employment worldwide is in the highest-exposure category

2.4%

of men’s employment worldwide is in that category

9.6%

of women’s employment in high-income countries

3.5%

of men’s employment in high-income countries

8.7%

of OECD firms used AI in 2023

20.2%

of OECD firms used AI in 2025

Figure 3: Share of employment in the highest-exposure category (ILO, 2025)

Women, high-income countries9.6%
Men, high-income countries3.5%
Women, worldwide4.7%
Men, worldwide2.4%

Figure 4: OECD firms using AI (OECD, January 2026)

All firms, 2023

8.7%91.3%

All firms, 2024

14.2%85.8%

All firms, 2025

20.2%79.8%

Large firms, 2025

52%48%

Small firms, 2025

17.4%82.6%
  • Uses AI
  • Does not use AI

When Help Becomes Authority

Personal observation

From a tracker that watched to a senior that asks

I noticed that whenever my system could not be trusted, my instinct was to build more of it.

When people questioned whether Focus Integrity was fair, I added collectors, compared seven models and built a replay system. The tracker became better at watching, but not better at understanding what someone was trying to do.

That pattern is mine. As someone who builds systems, I answer uncertainty with infrastructure: more signals, more structure, more rules.

The fix I reached for was to give the system the task. Let it read the brief, ask questions and write the plan before it judged anything. It worked, and it made me uneasy, because the system that now helped define my work was the same one that decided whether I had done it.

I decided not to perfect that feature but to explore it: what it feels like when the most helpful presence at work is also the one setting the standard.

An office at dusk where one worker sits at a desk while a small glowing device on the desk faces them, the rest of the floor empty.

When Help Becomes Authority

Initial vision

An AI senior that understands the task, asks before it judges and lets the worker answer back, while making visible how much authority that help carries.

How might we · v2 · DES303

How might a speculative AI senior make the shift from AI support to AI authority experienceable in Aotearoa's future workplaces?

It turned the prototype from a tracker into a senior.

The next step

From here I imagined a workplace that had accepted an AI senior, what a working day inside it would feel like, and how a system could make that authority tangible.

SpecAI-generated study, 25 October 2026 (Higgsfield, designer-directed).

Week 7 finding
Task context
Capstone formats
Watching is not understanding
Chrome can be research
A still screen can be reading
Labels after the fact come too late
Clarify the brief
Plan and delegate
Write the procedure
Agree a focus contract
Manager dashboard
Workplace simulation
Ticker Cube in the room

02 — Ideation

Opening the problem again after Week 7, and again after Crit 2.

Key question

What would make an AI that helps with work also feel like it has authority over it?

AI project manager
Monitoring platform
Conversational AI senior
Overlaps with Motion
Plans and assigns the work
The worker loses freedom
Overlaps with ActivTrak
Measures activity, not the task
Invasive by default
Imports task context
Asks before it judges
Learns from correction
Staged as a workplace

Brainstorming and idea generation

After Week 7 I asked what the system had to understand before it had the right to judge. That opened task context, B2B planning and several capstone formats. Crit 2 then pushed back on overlap with planning and monitoring tools, so I opened a second round and narrowed it to task-aligned verification through a conversational AI senior.

Filled: the direction taken forward

This future

AI Senior · Ideation

01The promise

What does it promise?

Clearer work, fairer judgement and a senior who is always available.

In this imagined 2037 Aotearoa, AI agents plan as well as do. An AI senior reads the brief, asks what is unclear and turns it into a plan before anyone starts.

Today’s signal

The length of tasks AI agents can finish has doubled roughly every seven months since 2019. (Kwa et al., 2025)

02The records

What gets recorded?

Contracts, evidence and every correction a worker makes.

A focus contract states the output, the tools and what counts as drift. Evidence is checked against it, and a worker’s answers become memory the system uses next time.

Today’s signal

One in four workers worldwide is in an occupation with some exposure to generative AI. (Gmyrek et al., 2025)

03The institutions

Who adopts it?

Employers make an AI senior standard practice.

Firms adopt AI seniors because they are consistent and never tired. Managers approve plans rather than write them, and verified alignment becomes part of how work is reviewed.

Today’s signal

AI use across OECD firms rose from 8.7% in 2023 to 20.2% in 2025. (Organisation for Economic Co-operation and Development, 2026)

04The social norm

What becomes ordinary?

Being asked to explain your work by an AI.

It becomes normal to be questioned mid-task by something polite and patient. Some prefer it to a human manager, because it feels neutral.

Today’s signal

In an experiment, workers managed by AI accepted lower pay without becoming less motivated. (Dong et al., 2025)

05The tension

Where does it rub?

If the assistant helps set the task, whose standard is the work judged by?

Not a robot boss: a helpful senior whose authority grows because it is useful, polite and always there.

The chain from task to authority

Brief → Contract → Evidence → Judgement

Aotearoa, 2037

All workplace arrangements are designer-authored conditions of a fictional 2037 future, not observed policies or predictions. Today’s signals are real, bounded findings from the sources cited.

02 — Ideation · Concepts

Making the future tangible

Four forms could carry this future: a conversational AI senior on the desktop, a staged workplace simulation, Ticker Cube in the room and a manager’s planning dashboard.

I chose the conversational AI senior, staged inside a workplace simulation, because it let people feel both the help and the authority in one moment: being asked to explain.

AI Senior desktop✓Taken forward

Digital · on the desktop

AI Senior desktop

A conversational senior beside the work

How it works
It turns a brief into a focus contract, notices drift and asks when it is unsure.
The tension
Being asked is kinder than being judged, and makes being judged easier to accept.
Workplace simulation02

Spatial · in the exhibition

Workplace simulation

A staged office where the audience works under AI

How it works
A participant receives a task, works for five minutes and watches a public dashboard escalate towards a fictional “You are fired”.
The tension
It makes authority felt, but risks reading as only dystopian.

Four ways to make it tangible

Ticker Cube03

Physical · on the desk

Ticker Cube

A physical senior for work away from the screen

How it works
A Raspberry Pi cube with a screen, camera and voice for sketching, making and studio work.
The tension
A body makes the senior present, and its watching harder to ignore.
Planning dashboard04

Institutional · for managers

Planning dashboard

The manager’s side of the AI senior

How it works
A brief becomes phases, tasks, assignees and procedures, approved by a manager.
The tension
Close to Motion, and the worker disappears behind the plan.

I then developed the desktop AI senior, from the B2B planning prototype to a living assistant that asks, remembers and shows its evidence.

SpecAI-generated studies, 25 October 2026 (Higgsfield, designer-directed). Speculative concepts, not records.

AI Senior · The idea

Project Idea

What makes the AI senior different? Workplace software either plans the work or watches it. The AI senior does both, and asks when it is not sure.

It is set in a speculative Aotearoa, 2037, where AI assistants sit inside every team. Before a focus block, the senior reads the brief from the tools people already use and agrees a focus contract with the worker. During the work it checks whether behaviour still matches that contract, and when the evidence is unclear it does not warn. It asks: is this still part of your task?

Taskcontext+Aseniorthatasks

Together they make the judgement fairer, and that is also the risk: a senior that helped write the standard is easy to trust when it applies it. So the idea keeps a third part in view, correction memory, where the worker's answers become patterns they can see, edit or forget.

What I wanted to test

Does asking before judging make an AI senior fairer, or only make its authority easier to accept?

Positioning

I placed the precedents on two questions: does a system watch activity or understand the task, and does it report to a manager or talk with the worker? Planners understand the task but answer to managers; monitoring tools watch and report. The AI senior aims for the corner where it understands the task and talks back, which is also where its authority is easiest to accept.

Talks with the worker

Reports to the manager

Watches activity

Understands the task

AI Senior

To be

ActivTrak

Activity analytics for managers

Hubstaff

Time and screenshots

Motion

AI plans and assigns

Linear

Tasks, owners, status

Slack

Where work is discussed

ChatGPT

Talks, holds no contract

Focus Integrity

Week 7 · watched, then labelled

My positioning from the DES303 Week 8–9 competitor analysis, not a measured comparison

AI Senior · Prototyping · 01

AI Senior · Prototyping · 01

AI Senior · The idea

Project Idea

What makes the AI senior different? Workplace software either plans the work or watches it. The AI senior does both, and asks when it is not sure.

It is set in a speculative Aotearoa, 2037, where AI assistants sit inside every team. Before a focus block, the senior reads the brief from the tools people already use and agrees a focus contract with the worker. During the work it checks whether behaviour still matches that contract, and when the evidence is unclear it does not warn. It asks: is this still part of your task?

Taskcontext+Aseniorthatasks

Together they make the judgement fairer, and that is also the risk: a senior that helped write the standard is easy to trust when it applies it. So the idea keeps a third part in view, correction memory, where the worker's answers become patterns they can see, edit or forget.

What I wanted to test

Does asking before judging make an AI senior fairer, or only make its authority easier to accept?

Positioning

I placed the precedents on two questions: does a system watch activity or understand the task, and does it report to a manager or talk with the worker? Planners understand the task but answer to managers; monitoring tools watch and report. The AI senior aims for the corner where it understands the task and talks back, which is also where its authority is easiest to accept.

Talks with the worker

Reports to the manager

Watches activity

Understands the task

AI Senior

To be

ActivTrak

Activity analytics for managers

Hubstaff

Time and screenshots

Motion

AI plans and assigns

Linear

Tasks, owners, status

Slack

Where work is discussed

ChatGPT

Talks, holds no contract

Focus Integrity

Week 7 · watched, then labelled

My positioning from the DES303 Week 8–9 competitor analysis, not a measured comparison

AI Senior · Prototyping · 01

AI Senior · Prototyping · 01

Visual Storyboard

Mapping the experience: one junior designer, one vague brief and one working day under an AI senior, from the first question to the record it leaves.

Panels 01 to 03 happen before the work, between the manager workspace and the focus contract. Panels 04 to 06 happen on the desktop. Panels 07 and 08 show where the record goes.

01A vague brief

A manager sends one line: make a landing page for the launch. Nothing says what counts as the work.

02The senior asks

Before planning anything, the senior asks who it is for, what proof it should show and what must exist by 5 pm.

03A focus contract

The answers become a contract: two hero options, Figma and competitor sites, social media as drift. The worker approves it.

04Research looks like drift

Browsing competitor sites looks like idling. The task match drops to unclear, 54% uncertain.

05Asked, not warned

Instead of a warning, the orb asks: is this still part of your task? The worker explains.

06The answer is kept

Chrome for competitor research is stored as a learned pattern, which the worker can edit or forget.

07On the wall

In the studio a team dashboard shows everyone's alignment. Help has become something others can see.

08A verified summary

The day ends as confirmed, uncertain and corrected time, with a cube on the desk. Useful, and a little uneasy.

Illustrations generated with Higgsfield (GPT Image), designer-directed; interface details simplified

AI Senior · Prototyping · 03

AI Senior · Prototyping · 03

Visual Storyboard

Mapping the experience: one junior designer, one vague brief and one working day under an AI senior, from the first question to the record it leaves.

Panels 01 to 03 happen before the work, between the manager workspace and the focus contract. Panels 04 to 06 happen on the desktop. Panels 07 and 08 show where the record goes.

01A vague brief

A manager sends one line: make a landing page for the launch. Nothing says what counts as the work.

02The senior asks

Before planning anything, the senior asks who it is for, what proof it should show and what must exist by 5 pm.

03A focus contract

The answers become a contract: two hero options, Figma and competitor sites, social media as drift. The worker approves it.

04Research looks like drift

Browsing competitor sites looks like idling. The task match drops to unclear, 54% uncertain.

05Asked, not warned

Instead of a warning, the orb asks: is this still part of your task? The worker explains.

06The answer is kept

Chrome for competitor research is stored as a learned pattern, which the worker can edit or forget.

07On the wall

In the studio a team dashboard shows everyone's alignment. Help has become something others can see.

08A verified summary

The day ends as confirmed, uncertain and corrected time, with a cube on the desk. Useful, and a little uneasy.

Illustrations generated with Higgsfield (GPT Image), designer-directed; interface details simplified

AI Senior · Prototyping · 03

AI Senior · Prototyping · 03

Context import
Focus contract
Focus Integrity
Ask
Correction memory
Privacy

How the AI senior works

AI Senior. One task contract,
agreed, checked and answered.

Six parts sit around one task contract. Context comes in from the tools people already use, the contract says what the work is, Focus Integrity checks behaviour against it, the senior asks when unsure, the answer becomes memory, and privacy limits what any of it may keep.

Service model

What each part gives,
and what it asks back.

Each part offers the worker something, and each asks for something in return: agreement, an explanation or a little more of how they work.

Focus contract→ OffersA clear brief and an agreed standard before work starts← Asks backApproval of what counts as the work, and as drift
The senior asks→ OffersA question instead of a silent warning← Asks backAn explanation, in words or by voice
Correction memory→ OffersFewer repeated wrong judgements← Asks backPatterns about how you work, kept over time

Where it rubs. Every "asks back" teaches the senior more about the worker. The better it understands, the more its judgement is worth, and the harder it becomes to refuse.

AI Senior · Prototyping · 04

AI Senior · Prototyping · 04

Context import
Focus contract
Focus Integrity
Ask
Correction memory
Privacy

How the AI senior works

AI Senior. One task contract,
agreed, checked and answered.

Six parts sit around one task contract. Context comes in from the tools people already use, the contract says what the work is, Focus Integrity checks behaviour against it, the senior asks when unsure, the answer becomes memory, and privacy limits what any of it may keep.

Service model

What each part gives,
and what it asks back.

Each part offers the worker something, and each asks for something in return: agreement, an explanation or a little more of how they work.

Focus contract→ OffersA clear brief and an agreed standard before work starts← Asks backApproval of what counts as the work, and as drift
The senior asks→ OffersA question instead of a silent warning← Asks backAn explanation, in words or by voice
Correction memory→ OffersFewer repeated wrong judgements← Asks backPatterns about how you work, kept over time

Where it rubs. Every "asks back" teaches the senior more about the worker. The better it understands, the more its judgement is worth, and the harder it becomes to refuse.

AI Senior · Prototyping · 04

AI Senior · Prototyping · 04

Workspace
Sign in
Sign in
Sign up
Organisation
Invite the team
Projects
Project list
New project
Write the brief
AI questions
Answer and refine
Plan streaming
Phases
Tasks
Review and edit

↳ Needs

Bring my tasks in from Motion; don't make me move.

People
People list
Add people
Role and skills
Workload
Availability

↳ Needs

If AI picks who does what, who can say no?

Delegation
Auto-delegation
Assignment reasons
Gantt timeline
Team view
SOP generation
Final approval

↳ Needs

If it plans and assigns, isn't it just Motion?

Handoff
Worker handoff
Focus contract
Focus Integrity
Risk view
Verified summary

↳ Needs

Would workers actually want this?

User Flow · Manager Workspace

The manager workspace mapped from the Week 8 build: how a vague brief becomes a project, a plan, assignments and procedures, and finally a focus contract handed to the worker. Each branch carries a need raised at Crit 2.

Highlighted pills trace the briefing loop. A brief is questioned, planned, delegated, written up as a procedure, approved and handed to a worker as a focus contract.

This side answers to the manager. It was the clearest demo at Crit 2, and the closest to Motion. The next spread maps the side that talks to the worker.

Needs paraphrased from Crit 2 feedback (Week 9)

Mapped from the Week 8 B2B build: projects, people, phases, Gantt and risk

AI Senior · Prototyping · 05

AI Senior · Prototyping · 05

Workspace
Sign in
Sign in
Sign up
Organisation
Invite the team
Projects
Project list
New project
Write the brief
AI questions
Answer and refine
Plan streaming
Phases
Tasks
Review and edit

↳ Needs

Bring my tasks in from Motion; don't make me move.

People
People list
Add people
Role and skills
Workload
Availability

↳ Needs

If AI picks who does what, who can say no?

Delegation
Auto-delegation
Assignment reasons
Gantt timeline
Team view
SOP generation
Final approval

↳ Needs

If it plans and assigns, isn't it just Motion?

Handoff
Worker handoff
Focus contract
Focus Integrity
Risk view
Verified summary

↳ Needs

Would workers actually want this?

User Flow · Manager Workspace

The manager workspace mapped from the Week 8 build: how a vague brief becomes a project, a plan, assignments and procedures, and finally a focus contract handed to the worker. Each branch carries a need raised at Crit 2.

Highlighted pills trace the briefing loop. A brief is questioned, planned, delegated, written up as a procedure, approved and handed to a worker as a focus contract.

This side answers to the manager. It was the clearest demo at Crit 2, and the closest to Motion. The next spread maps the side that talks to the worker.

Needs paraphrased from Crit 2 feedback (Week 9)

Mapped from the Week 8 B2B build: projects, people, phases, Gantt and risk

AI Senior · Prototyping · 05

AI Senior · Prototyping · 05

Precedents

To begin the visual work I looked at the products the AI senior sits between. Motion plans and schedules work with AI. Vanta and 7shifts watch and score staff, standing in for ActivTrak, which Mobbin does not hold. Linear holds the tasks and Slack the talk around them. Each knows part of the work; none asks the worker what the work is.

Auto-scheduled task

“What are you working on?”

All tasks on time

Motion. An AI project manager that schedules tasks, predicts deadlines and balances capacity. Calm and clear, but the worker appears only as capacity.

Taken forward. Calm, dense layouts and mono numbers from planning tools, and from monitoring tools a rule of what not to do: never score without saying why.

Vanta
An agent on every computer, a tick or a cross per device, reported upward. Monitoring as compliance.

7shifts
Each employee's lates, no-shows and dropped shifts, scored for the manager. The record speaks; the worker does not.

Linear
Issues, owners and status on a fast board. The task context the senior would import.

Slack
Where the work is actually discussed. Context the senior could read, and the reason connectors need limits.

In-app screens of Motion, Vanta, 7shifts, Linear and Slack via Mobbin, for precedent analysis

AI Senior · Prototyping · 07

AI Senior · Prototyping · 07

Precedents

To begin the visual work I looked at the products the AI senior sits between. Motion plans and schedules work with AI. Vanta and 7shifts watch and score staff, standing in for ActivTrak, which Mobbin does not hold. Linear holds the tasks and Slack the talk around them. Each knows part of the work; none asks the worker what the work is.

Auto-scheduled task

“What are you working on?”

All tasks on time

Motion. An AI project manager that schedules tasks, predicts deadlines and balances capacity. Calm and clear, but the worker appears only as capacity.

Taken forward. Calm, dense layouts and mono numbers from planning tools, and from monitoring tools a rule of what not to do: never score without saying why.

Vanta
An agent on every computer, a tick or a cross per device, reported upward. Monitoring as compliance.

7shifts
Each employee's lates, no-shows and dropped shifts, scored for the manager. The record speaks; the worker does not.

Linear
Issues, owners and status on a fast board. The task context the senior would import.

Slack
Where the work is actually discussed. Context the senior could read, and the reason connectors need limits.

In-app screens of Motion, Vanta, 7shifts, Linear and Slack via Mobbin, for precedent analysis

AI Senior · Prototyping · 07

AI Senior · Prototyping · 07

Brand · AI Senior

The voice orb: a glowing yellow core inside a glass sphere, ringed in red and yellow

Mark · the voice orb

Idle
Understanding
Speaking

Three states, one colour each. Red for idle stays: a senior at rest still looks as if it is watching.

Key element · the Ticker Cube

The Ticker Cube render

The same orb, given a body for work away from the screen: a glass cube that sits on the desk and shows when it is listening.

Colour and type · JetBrains Mono for evidence, Inter for sentences

Action
#E5FE40

Focus
#00FF88

Timer
#00F0FF

Danger
#FF3366

Ground
#050505

Orb states: Week 10 build captures. Orb and Cube renders: generated concept studies (Higgsfield), designer-directed.

AI Senior UI Components

Technical Evidence

Task match

unclear

Uncertainty

54%

Cache

hit

Deterministic

unclear

Relation memory

none

Semantic judge

not called

Action

ask_user

Focus contract

Create two B2B landing page hero options

OutputTwo desktop hero layouts with headline, CTA and proof block

AllowedCompetitor pages, visual references, Figma

AmbiguousYouTube, long static screen, app switching

Ask whenAmbiguity lasts more than a few minutes

APPROVEEDIT

Ask sheet

Is this still part of your task?

Chrome · competitor sites · 6 min

RELATEDNOT RELATEDPAUSE

States

LOCAL SUMMARIES ONLYASK_USERCOOLDOWNWARN

Learned pattern

Chrome · competitor research

related: landing page

EDITFORGET

Permission block

Screen summaryON · LOCAL

ReadsChanged or static, app category

NeverRaw screenshots leave the device

Listening overlay · Week 10 build

The listening overlay asking what task to start focus for

Say the wake word and the senior asks what to focus on; the answer starts the session and becomes the focus contract.

AI Senior · Prototyping · 08

AI Senior · Prototyping · 08

Brand · AI Senior

The voice orb: a glowing yellow core inside a glass sphere, ringed in red and yellow

Mark · the voice orb

Idle
Understanding
Speaking

Three states, one colour each. Red for idle stays: a senior at rest still looks as if it is watching.

Key element · the Ticker Cube

The Ticker Cube render

The same orb, given a body for work away from the screen: a glass cube that sits on the desk and shows when it is listening.

Colour and type · JetBrains Mono for evidence, Inter for sentences

Action
#E5FE40

Focus
#00FF88

Timer
#00F0FF

Danger
#FF3366

Ground
#050505

Orb states: Week 10 build captures. Orb and Cube renders: generated concept studies (Higgsfield), designer-directed.

AI Senior UI Components

Technical Evidence

Task match

unclear

Uncertainty

54%

Cache

hit

Deterministic

unclear

Relation memory

none

Semantic judge

not called

Action

ask_user

Focus contract

Create two B2B landing page hero options

OutputTwo desktop hero layouts with headline, CTA and proof block

AllowedCompetitor pages, visual references, Figma

AmbiguousYouTube, long static screen, app switching

Ask whenAmbiguity lasts more than a few minutes

APPROVEEDIT

Ask sheet

Is this still part of your task?

Chrome · competitor sites · 6 min

RELATEDNOT RELATEDPAUSE

States

LOCAL SUMMARIES ONLYASK_USERCOOLDOWNWARN

Learned pattern

Chrome · competitor research

related: landing page

EDITFORGET

Permission block

Screen summaryON · LOCAL

ReadsChanged or static, app category

NeverRaw screenshots leave the device

Listening overlay · Week 10 build

The listening overlay asking what task to start focus for

Say the wake word and the senior asks what to focus on; the answer starts the session and becomes the focus contract.

AI Senior · Prototyping · 08

AI Senior · Prototyping · 08

Project list

New project

Phases

Tasks

Adding people

People

Delegation, Gantt timeline and team, one long workspace page

Workspace Build

In Week 8 the briefing loop became a manager-facing product: a project list, a brief, generated phases and tasks, people with roles and workload, and a delegation timeline. It was the clearest thing I had built, and it showed the senior deciding the work before anyone started it.

Focus

Timeline

Feedback

Privacy

Settings

Learned patterns

Desktop Rebuild

After Crit 2 I stopped growing the planner and rebuilt the desktop around the worker. Each page exposes one part of the senior: Focus starts from a task, Timeline keeps what happened, Feedback shows what was learned, Privacy shows what is kept.

The Week 7 tracker hid its reasoning behind a score. Here every page is something the worker can open, read and argue with.

AI Senior · Prototyping · 10

AI Senior · Prototyping · 10

Project list

New project

Phases

Tasks

Adding people

People

Delegation, Gantt timeline and team, one long workspace page

Workspace Build

In Week 8 the briefing loop became a manager-facing product: a project list, a brief, generated phases and tasks, people with roles and workload, and a delegation timeline. It was the clearest thing I had built, and it showed the senior deciding the work before anyone started it.

Focus

Timeline

Feedback

Privacy

Settings

Learned patterns

Desktop Rebuild

After Crit 2 I stopped growing the planner and rebuilt the desktop around the worker. Each page exposes one part of the senior: Focus starts from a task, Timeline keeps what happened, Feedback shows what was learned, Privacy shows what is kept.

The Week 7 tracker hid its reasoning behind a score. Here every page is something the worker can open, read and argue with.

AI Senior · Prototyping · 10

AI Senior · Prototyping · 10

New project

01

Write the brief

A project starts as a brief in the manager's own words. Before planning anything, the senior asks what the brief leaves open.

Phase planning

02

Phases

The answers stream into phases, each with an outcome and a date, which the manager edits before anything is assigned.

Task detail

03

Tasks

Each phase breaks into tasks with expected tools, an expected output and the behaviour that would count as drift.

Adding people

04

People

People are added with role, skills, workload and availability: the data the senior uses to suggest who does what.

Delegation and Gantt

05

Delegation

Assignments and their reasons sit on a Gantt timeline with the team beside it, so the plan, the people and the dates read together.

Projects

06

Project list

Every project shows its phase, people and risk. The manager's view, and the one Crit 2 said looked most like Motion.

AI Senior · Prototyping · 11

AI Senior · Prototyping · 11

New project

01

Write the brief

A project starts as a brief in the manager's own words. Before planning anything, the senior asks what the brief leaves open.

Phase planning

02

Phases

The answers stream into phases, each with an outcome and a date, which the manager edits before anything is assigned.

Task detail

03

Tasks

Each phase breaks into tasks with expected tools, an expected output and the behaviour that would count as drift.

Adding people

04

People

People are added with role, skills, workload and availability: the data the senior uses to suggest who does what.

Delegation and Gantt

05

Delegation

Assignments and their reasons sit on a Gantt timeline with the team beside it, so the plan, the people and the dates read together.

Projects

06

Project list

Every project shows its phase, people and risk. The manager's view, and the one Crit 2 said looked most like Motion.

AI Senior · Prototyping · 11

AI Senior · Prototyping · 11

Focus

01

Task first

The Focus page will not start without a task. The worker declares one or picks one imported from Canvas or GitHub, and it becomes the contract.

Listening overlay

02

Say it

Say the wake word and the senior asks what to focus on. The spoken answer starts the session.

Technical Evidence

03

Show the working

Every decision shows its working: task match, uncertainty, cache, rules, memory, judge and the action chosen. Here: unclear, 54%, ask_user.

Learned patterns

04

Learned patterns

Each correction becomes an app or domain pattern, held in context rather than as a fixed rule, and the worker can change it.

Privacy

05

Permission blocks

Every sensing layer is its own block: microphone, desktop metadata, input aggregate, screen summary and camera labels.

Voice orb

06

A voice without a face

Red while idle, yellow while it listens, green while it speaks. A calmer voice raised its own question: could reassurance hide the authority of the request?

AI Senior · Prototyping · 12

AI Senior · Prototyping · 12

Focus

01

Task first

The Focus page will not start without a task. The worker declares one or picks one imported from Canvas or GitHub, and it becomes the contract.

Listening overlay

02

Say it

Say the wake word and the senior asks what to focus on. The spoken answer starts the session.

Technical Evidence

03

Show the working

Every decision shows its working: task match, uncertainty, cache, rules, memory, judge and the action chosen. Here: unclear, 54%, ask_user.

Learned patterns

04

Learned patterns

Each correction becomes an app or domain pattern, held in context rather than as a fixed rule, and the worker can change it.

Privacy

05

Permission blocks

Every sensing layer is its own block: microphone, desktop metadata, input aggregate, screen summary and camera labels.

Voice orb

06

A voice without a face

Red while idle, yellow while it listens, green while it speaks. A calmer voice raised its own question: could reassurance hide the authority of the request?

AI Senior · Prototyping · 12

AI Senior · Prototyping · 12

Ticker Cube

The desktop senior can read a screen, but design work also happens with paper, a soldering iron or a model, and to the desktop that looks like nothing. Ticker Cube gives the senior a body in the room: a Raspberry Pi 5 with a touch display, a CCTV-style lens, a microphone, a speaker and a visible privacy light.

Camera module and C-mount lens

Pi with touch display

Concept renders

The face is a question. A cube that smiles when you focus makes the watching friendlier, and harder to notice.

Crit 2 asked what the Cube adds if the desktop already watches. The answer is a different kind of work, not more watching, and the Cube waits until the desktop senior works. A body also makes the senior's presence harder to ignore.

Technical Validation

Week 10 chose the hardware and wrote down what each part has to prove. Only the first two are done; sensing and interpreting studio work remain a proposal, and local processing alone would not settle consent or authority.

AreaQuestionStatus
ComputeCan it run the interface, store evidence, sync with the PC and process locally?Raspberry Pi 5 chosen over ESP32
DisplayCan the worker see the task, timer, warning and correction quickly?Touch display mounted
CameraCan it read room-based work without feeling like hidden spying?HQ camera, C-mount lens; untested
ControlsCan the worker dismiss, pause or correct without the screen?Planned
PrivacyCan the worker see and control when sensing is on?Indicator or shutter planned
SyncCan the Cube send task-state summaries back to the desktop?Planned
OfflineDoes basic task state work without Wi-Fi?Planned

Proof modes

Desktop senior · screen work

· Apps, windows and input

· Screen changed or static

· Writing, coding, research

Ticker Cube · room work

· Presence and a camera label

· Voice, away from the keyboard

· Sketching, making, studio work

Hardware photographs, Week 10; renders and the desk scene are generated concept studies

AI Senior · Prototyping · 15

AI Senior · Prototyping · 15

Ticker Cube

The desktop senior can read a screen, but design work also happens with paper, a soldering iron or a model, and to the desktop that looks like nothing. Ticker Cube gives the senior a body in the room: a Raspberry Pi 5 with a touch display, a CCTV-style lens, a microphone, a speaker and a visible privacy light.

Camera module and C-mount lens

Pi with touch display

Concept renders

The face is a question. A cube that smiles when you focus makes the watching friendlier, and harder to notice.

Crit 2 asked what the Cube adds if the desktop already watches. The answer is a different kind of work, not more watching, and the Cube waits until the desktop senior works. A body also makes the senior's presence harder to ignore.

Technical Validation

Week 10 chose the hardware and wrote down what each part has to prove. Only the first two are done; sensing and interpreting studio work remain a proposal, and local processing alone would not settle consent or authority.

AreaQuestionStatus
ComputeCan it run the interface, store evidence, sync with the PC and process locally?Raspberry Pi 5 chosen over ESP32
DisplayCan the worker see the task, timer, warning and correction quickly?Touch display mounted
CameraCan it read room-based work without feeling like hidden spying?HQ camera, C-mount lens; untested
ControlsCan the worker dismiss, pause or correct without the screen?Planned
PrivacyCan the worker see and control when sensing is on?Indicator or shutter planned
SyncCan the Cube send task-state summaries back to the desktop?Planned
OfflineDoes basic task state work without Wi-Fi?Planned

Proof modes

Desktop senior · screen work

· Apps, windows and input

· Screen changed or static

· Writing, coding, research

Ticker Cube · room work

· Presence and a camera label

· Voice, away from the keyboard

· Sketching, making, studio work

Hardware photographs, Week 10; renders and the desk scene are generated concept studies

AI Senior · Prototyping · 15

AI Senior · Prototyping · 15

Why a Living AI Senior

The tracker watched, guessed and warned. A senior has to understand the context, estimate how unsure it is, ask, accept a correction and adapt: alive in the sense of present and responsive, not conscious. It took three models to get there.

WEEK 7 · THE TRACKERWatchesGuessesWarnsWEEK 10 · THE LIVING SENIORUnderstands contextEstimates uncertaintyAsksWorker correctsAdaptsUncertainty is a reason to ask, not a reason to warn. Each answer changes the next judgement.

Week 7

Tick model

↳ More data did not create more understanding.

Week 9

Layered local LLM

↳ It felt smarter, but still guessed from too little context.

Week 10

Stateful AI senior

↳ Intelligence comes from the architecture, not one smarter model.

After DES303 Week 10, Figure 2 and the model-evolution table

Architecture

Four layers that each do what the others cannot: existing tools hold the work, the workspace turns a brief into a contract, the desktop checks the work against it, and the Cube carries that check into the room. Only summaries cross between them.

Context sourcesCanvas · GitHub · LinearRead-only imports with minimal scopesExternal write-back stays disabledManager workspaceNext.js · AI SDK · ZodBrief → questions → phases → tasks → peoplePlans returned as schema-checked structured outputEvery generated step can be edited or approvedDesktop seniorTauri 2 · React · RustCollectors, Focus Integrity core and memory cratesWake word, speech recognition and voice outputLocal model route through Ollama (Qwen)Permission blocks; local summaries only by defaultTicker CubeRaspberry Pi 5 · plannedTouch display, CCTV-style lens, microphone, speakerSends task-state summaries to the desktoptask contextfocus contracttask-level summariesroom summariestask state

Stacks from the Week 8 workspace and Week 10 desktop repositories; the Cube is planned

AI Senior · Prototyping · 16

AI Senior · Prototyping · 16

Why a Living AI Senior

The tracker watched, guessed and warned. A senior has to understand the context, estimate how unsure it is, ask, accept a correction and adapt: alive in the sense of present and responsive, not conscious. It took three models to get there.

WEEK 7 · THE TRACKERWatchesGuessesWarnsWEEK 10 · THE LIVING SENIORUnderstands contextEstimates uncertaintyAsksWorker correctsAdaptsUncertainty is a reason to ask, not a reason to warn. Each answer changes the next judgement.

Week 7

Tick model

↳ More data did not create more understanding.

Week 9

Layered local LLM

↳ It felt smarter, but still guessed from too little context.

Week 10

Stateful AI senior

↳ Intelligence comes from the architecture, not one smarter model.

After DES303 Week 10, Figure 2 and the model-evolution table

Architecture

Four layers that each do what the others cannot: existing tools hold the work, the workspace turns a brief into a contract, the desktop checks the work against it, and the Cube carries that check into the room. Only summaries cross between them.

Context sourcesCanvas · GitHub · LinearRead-only imports with minimal scopesExternal write-back stays disabledManager workspaceNext.js · AI SDK · ZodBrief → questions → phases → tasks → peoplePlans returned as schema-checked structured outputEvery generated step can be edited or approvedDesktop seniorTauri 2 · React · RustCollectors, Focus Integrity core and memory cratesWake word, speech recognition and voice outputLocal model route through Ollama (Qwen)Permission blocks; local summaries only by defaultTicker CubeRaspberry Pi 5 · plannedTouch display, CCTV-style lens, microphone, speakerSends task-state summaries to the desktoptask contextfocus contracttask-level summariesroom summariestask state

Stacks from the Week 8 workspace and Week 10 desktop repositories; the Cube is planned

AI Senior · Prototyping · 16

AI Senior · Prototyping · 16

A Stateful Senior

The senior is not one large prompt. It holds the current task, the session, recent corrections and what it is allowed to do, and most decisions never reach a model. Asking is an action it chooses, like warning or staying silent.

1ContextThe current task, its contract and any imported work items: what the work is meant to be.

↓

2SignalsDesktop metadata, an input aggregate and a screen summary, each behind its own permission block.

↓

3aRules and memory decideClear cases are settled by deterministic rules and relation memory, often from cache.
3bUnclear → askA semantic judge can be called, or the worker asked. In the captured case: ask_user.

↓

4Action authoritySilent, ask, warn, cooldown or auto-dismiss, from timing, context, uncertainty and past corrections.

↓

5Correction → memoryThe answer becomes a relation or pattern; a decision trace records why it asked.

The captured case. Task match unclear, 54% uncertain, no memory to lean on: the senior chose ask_user.

Week 10 build · Technical Evidence

Ethics as Architecture

Crit 2 made ethics the project rather than a closing section. In Aotearoa the Privacy Act 2020 governs information about staff, so each risk had to be answered in the build. If the system watches the worker, the worker must be able to see, question and correct it; that makes its authority visible, not fair.

RiskAnswered in the build by
Hidden collectionEvery sensing layer is a permission block that says what it reads and what it never keeps.
Raw evidence exposedLocal summaries only by default; raw screen, audio and camera stay on the device.
MisjudgementAsk when uncertain; Technical Evidence shows why the senior asked.
Power imbalanceWorker-facing first. Managers see limited task-level summaries and cannot switch monitoring on silently.
Learning in the darkLearned patterns can be read, edited or forgotten.
Connector creepRead-only imports, minimal scopes, external write-back disabled.

What Week 10 validates, and what it does not

Shown in the prototype

· Sessions start from a task

· Activity kept across sessions

· Corrections become patterns

· The senior shows why it asks

· Sensing is split and visible

· Voice states read at a glance

· Canvas and GitHub seed context

Not claimed

· That it detects focus accurately

· That 54% is a calibrated probability

· That the ethics are solved

· That workers have tested it

· That the Cube senses anything yet

· That every connector is live

· That the local model is reliable

Employment New Zealand (2024); Office of the Privacy Commissioner; DES303 Week 10 claims table

AI Senior · Prototyping · 17

AI Senior · Prototyping · 17

A Stateful Senior

The senior is not one large prompt. It holds the current task, the session, recent corrections and what it is allowed to do, and most decisions never reach a model. Asking is an action it chooses, like warning or staying silent.

1ContextThe current task, its contract and any imported work items: what the work is meant to be.

↓

2SignalsDesktop metadata, an input aggregate and a screen summary, each behind its own permission block.

↓

3aRules and memory decideClear cases are settled by deterministic rules and relation memory, often from cache.
3bUnclear → askA semantic judge can be called, or the worker asked. In the captured case: ask_user.

↓

4Action authoritySilent, ask, warn, cooldown or auto-dismiss, from timing, context, uncertainty and past corrections.

↓

5Correction → memoryThe answer becomes a relation or pattern; a decision trace records why it asked.

The captured case. Task match unclear, 54% uncertain, no memory to lean on: the senior chose ask_user.

Week 10 build · Technical Evidence

Ethics as Architecture

Crit 2 made ethics the project rather than a closing section. In Aotearoa the Privacy Act 2020 governs information about staff, so each risk had to be answered in the build. If the system watches the worker, the worker must be able to see, question and correct it; that makes its authority visible, not fair.

RiskAnswered in the build by
Hidden collectionEvery sensing layer is a permission block that says what it reads and what it never keeps.
Raw evidence exposedLocal summaries only by default; raw screen, audio and camera stay on the device.
MisjudgementAsk when uncertain; Technical Evidence shows why the senior asked.
Power imbalanceWorker-facing first. Managers see limited task-level summaries and cannot switch monitoring on silently.
Learning in the darkLearned patterns can be read, edited or forgotten.
Connector creepRead-only imports, minimal scopes, external write-back disabled.

What Week 10 validates, and what it does not

Shown in the prototype

· Sessions start from a task

· Activity kept across sessions

· Corrections become patterns

· The senior shows why it asks

· Sensing is split and visible

· Voice states read at a glance

· Canvas and GitHub seed context

Not claimed

· That it detects focus accurately

· That 54% is a calibrated probability

· That the ethics are solved

· That workers have tested it

· That the Cube senses anything yet

· That every connector is live

· That the local model is reliable

Employment New Zealand (2024); Office of the Privacy Commissioner; DES303 Week 10 claims table

AI Senior · Prototyping · 17

AI Senior · Prototyping · 17

Unclear casesTask stateSummaries onlyTask contextWork itemsModel callsBrief, approvalsFocus contractTask-level alignmentWorkerManagerDesktop seniorTicker CubeLocal model (Ollama)Workspace backendManager workspaceCanvasGitHub and LinearCloud modelPublic dashboardRaw screen, audio and camera stay on these devices
Physical space flow
Information flow
Resource flow
Digital space flow

System Map

AI Senior · Prototyping · 19

AI Senior · Prototyping · 19

Unclear casesTask stateSummaries onlyTask contextWork itemsModel callsBrief, approvalsFocus contractTask-level alignmentWorkerManagerDesktop seniorTicker CubeLocal model (Ollama)Workspace backendManager workspaceCanvasGitHub and LinearCloud modelPublic dashboardRaw screen, audio and camera stay on these devices
Physical space flow
Information flow
Resource flow
Digital space flow

System Map

AI Senior · Prototyping · 19

AI Senior · Prototyping · 19

World 02 · AI Senior · The turn · 05 Examine

What I Tested, What I Heard

I treated the Crit 2 deck in Week 9 as a test object: if people could explain the AI senior back to me, the direction was clear; if they read it as another productivity tool, the gap was still weak. Afterwards I compared ten routes, rebuilt the desktop senior in Week 10 and checked the build against its own claims.

Crit 2 deck, Week 9 · six of the surviving slides

Crit 2 askedWhat changedIn the build

“What makes this different from Motion?”

The brief is read as context. Planning tools seed the task instead of being replaced.

Workspace brief

“Is this another monitoring system?”

Activity is judged against the task, and the senior shows why it is unsure before acting.

Technical evidence · ask_user

“Would workers want this?”

Worker-facing first: local summaries only by default; managers see task-level summaries.

Privacy mode

“Where is human freedom?”

What the senior learns is shown as a pattern the worker can edit or forget.

Learned pattern

Questions from my Crit 2 notes · captures from the Week 8 workspace and Week 10 desktop build

06 Reflect

What AI Senior Established

After the crit I laid out ten routes before choosing one, and kept the parts that put the worker's answer before the system's judgement.

Task-aligned verificationKeep
Conversational AI seniorKeep
Workplace simulationKeep
Worker challenge and correctionKeep
AI project managerReject as main
Surveillance dashboardReject as main
Ticker CubeLater
Privacy-as-stake modeLater
AI performance reviewLater
Office leaderboardLater

SELECTEDTask-aligned verification + conversational AI senior + workplace simulation

Ten routes after critique, redrawn from the Week 9 decision board

Established

Interpretations the worker can inspect, question and correct: a task contract, an ask when unsure, an editable memory.

Unresolved

Better context did not settle who authorised the senior to judge. The build does not show accurate focus detection, a calibrated 54%, or worker testing.

Retained

Ask before judging; show the reason; let the person correct what was learned.

Extended next

Civic Score took that authority from one workplace to public decisions about opportunity.

The next question→ World 03 · Civic Score

Does a useful judgement give a system the right to govern?

AI Senior · Prototyping · 09

AI Senior · Prototyping · 09

World 02 · AI Senior · The turn · 05 Examine

What I Tested, What I Heard

I treated the Crit 2 deck in Week 9 as a test object: if people could explain the AI senior back to me, the direction was clear; if they read it as another productivity tool, the gap was still weak. Afterwards I compared ten routes, rebuilt the desktop senior in Week 10 and checked the build against its own claims.

Crit 2 deck, Week 9 · six of the surviving slides

Crit 2 askedWhat changedIn the build

“What makes this different from Motion?”

The brief is read as context. Planning tools seed the task instead of being replaced.

Workspace brief

“Is this another monitoring system?”

Activity is judged against the task, and the senior shows why it is unsure before acting.

Technical evidence · ask_user

“Would workers want this?”

Worker-facing first: local summaries only by default; managers see task-level summaries.

Privacy mode

“Where is human freedom?”

What the senior learns is shown as a pattern the worker can edit or forget.

Learned pattern

Questions from my Crit 2 notes · captures from the Week 8 workspace and Week 10 desktop build

06 Reflect

What AI Senior Established

After the crit I laid out ten routes before choosing one, and kept the parts that put the worker's answer before the system's judgement.

Task-aligned verificationKeep
Conversational AI seniorKeep
Workplace simulationKeep
Worker challenge and correctionKeep
AI project managerReject as main
Surveillance dashboardReject as main
Ticker CubeLater
Privacy-as-stake modeLater
AI performance reviewLater
Office leaderboardLater

SELECTEDTask-aligned verification + conversational AI senior + workplace simulation

Ten routes after critique, redrawn from the Week 9 decision board

Established

Interpretations the worker can inspect, question and correct: a task contract, an ask when unsure, an editable memory.

Unresolved

Better context did not settle who authorised the senior to judge. The build does not show accurate focus detection, a calibrated 54%, or worker testing.

Retained

Ask before judging; show the reason; let the person correct what was learned.

Extended next

Civic Score took that authority from one workplace to public decisions about opportunity.

The next question→ World 03 · Civic Score

Does a useful judgement give a system the right to govern?

AI Senior · Prototyping · 09

AI Senior · Prototyping · 09

A young woman in profile in grey haze, chin resting on her hand, a white wrist band glowing light purple on her wrist.

The question AI Senior left

When assistance becomes social authority

From the workplace to the public service

SpecAI-generated study, 25 October 2026 (Higgsfield, designer-directed).

World 03 / 05 · Civic Score

Civic Score

What I designed

A fictional civic service, explored through a decision notice, a resident's day and interface and values-console prototypes.

Fictional scenario · Interface and values-console prototypes · July critique; October demonstrator

Why

A useful decision does not settle who may judge people or allocate opportunity.

Who

Residents seeking opportunities, and the decision-makers and reviewers who act on the score. Māori rights and authority need separate consideration.

How

Read the evidence → Apply the values → Explain the decision → Add context or request review.

The question this version examined

Would you accept being judged if basic survival were secure?

TickersAI SeniorCivic ScoreLifeRPG / UNIPerfect World

Signal · World 03 of 5

1. AI is arriving in public services without structure

In public services, a judgement can reach someone’s opportunities. Today’s evidence is modest and local: a survey of public and community service workers, and a voluntary charter (figs. 1 and 2). It shows use arriving before structure. Whether the workers surveyed are typical of the sector is not stated.

Use is arriving before training, consultation or structure.

Key finding · author’s reading of the evidence

Figure 1: From AI in the workplace to personal use (PSA survey, April 2025; sample size not stated)

All respondents100%
AI is used in my workplace55%
I use AI personally45%

Also in the evidence

  1. 01
    A charter, not a rule

    Aotearoa’s Algorithm Charter commits signatories to explanation, human oversight and challenge routes (Stats NZ, 2020).

  2. 02
    Data carries authority

    Te Mana Raraunga foregrounds authority, context, collective benefit and guardianship in data (Te Mana Raraunga, 2018).

  3. 03
    Questions of partnership

    The health-sector Te Tiriti framework adds questions of authority, equity, options and partnership (Ministry of Health – Manatū Hauora, n.d.).

  4. 04
    Benefits and concerns together

    The PSA reports both workplace benefits and concerns about surveillance and worker involvement among its respondents (Public Service Association Te Pūkenga Here Tikanga Mahi, 2025).

The question this raisesWho answers for the judgement?

These are present-day commitments, read here as secondary sources. They do not describe the integrated authority Civic Score imagines, and they are not Māori consultation or co-design. The fiction is mine; the questions about authority are real. The scenario makes that authority integrated and fictional so the question can be argued without describing real policy.

Data: (Public Service Association Te Pūkenga Here Tikanga Mahi, 2025; Taylor Fry, 2021). The PSA release describes the survey as covering public and community service workers; it does not give a sample size.

TickersAI SeniorCivic ScoreLifeRPG / UNIPerfect World

Signal · World 03 of 5 · The evidence

1.1Public services in Aotearoa already use AI, mostly self-started and mostly untrained

In a 2025 survey by the Public Service Association, 55% of respondents said AI was already used in their workplace and 45% used it themselves. 94% of AI users said their use was self-initiated, and only 12% of AI users said they had received training. An overwhelming majority were not satisfied with the current levels of consultation (Public Service Association Te Pūkenga Here Tikanga Mahi, 2025). The release does not state how many people responded.

Government has set rules for the algorithms it uses. The Algorithm Charter for Aotearoa New Zealand is voluntary, and a year after launch it had 27 signatories, covering more than half of government agencies (Taylor Fry, 2021). Neither a survey nor a charter says what happens when a system’s judgement reaches a person’s opportunities.

Figure 2: What respondents said about AI at work (PSA, April 2025)

55%

Said AI was already used in their workplace

PSA, 2025
45%

Use AI personally

PSA, 2025
12%

AI users who had training

PSA, 2025
94%

of AI users said their use was self-initiated

81%

said AI helped them do parts of their job more effectively

77%

said AI helped them save time

54%

agreed AI could streamline processes for better public outcomes

27

agencies had signed the voluntary Algorithm Charter within a year

50%+

of government agencies are covered by those signatories

Figure 3: What respondents said AI did for them (PSA, April 2025)

Helped me do parts of my job more effectively81%
Helped me save time77%
Could streamline processes for better public outcomes54%
AI users who said they had received training12%

Figure 4: How AI users came to use it (PSA, April 2025)

AI users, how use began

Self-initiated 94%6%

AI users, training

12%Did not report training 88%

Civic Score · Research and precedents

Societies Already Imagined

Civic is fiction, but each of its parts already exists somewhere. These four were adopted because they offered something: care, reward, convenience, calm. I compared why people accept them and what authority each one holds.

Singapore · 2020

TraceTogether Token

A free contact-tracing wearable issued to every resident. Bluetooth proximity only, no GPS, records deleted after 25 days. A petition against it gathered over 36,000 signatures.

↳ Band Day: a state wearable, handed out free, framed as care.

Rongcheng, China · 2014–

Social credit pilot

Residents started on 1,000 points, graded A to D. High scorers got deposit-free bikes and heating discounts; top names were posted at city hall.

↳ Rewards, not punishment. But Civic hides the number: no public board.

Estonia · 2000s–

e-Estonia and X-Road

Agencies share data over one exchange layer, so most public services run online and tax returns arrive pre-filled in minutes.

↳ Convenience is the hook. One tap, no forms, no queues.

Oura · 2015–

Readiness score

A ring reads overnight heart rate, temperature and sleep, and the app opens on one calm number with cards beneath. No streaks, no badges.

↳ Rhythm 77%: one soft number that feels like self-care.

What Civic combines. No single precedent makes Civic. It joins personal sensing, digital public services, algorithmic judgement and promised fairness into one society. The cases show separate mechanisms, not proof of that future.

When Judgement Goes Wrong

The failures show what happens when the process behind a decision is hard to understand, contest or correct. Harm usually arrived through a fair-sounding process, not a cruel one.

Australia · 2016–2020

Robodebt

Debts raised automatically by averaging tax income. Around 470,000 wrongful debts; a royal commission in 2023 called it crude, cruel, neither fair nor legal.

↳ Why “How we decided” must show the chain, not just the verdict.

Netherlands · 2020

SyRI ruling

A court halted a welfare-fraud risk system: residents could not see its risk indicators or how the model worked, which breached the right to privacy.

↳ Visible values. The booth shows the weights a court could not see.

Aotearoa · 2015

MSD risk modelling

A study to score every newborn for risk of abuse was withdrawn by the minister: “These are children, not lab rats.”

↳ Here, the line was drawn once already. Civic asks where it sits now.

In fiction · how scoring is made to feel normal

Black Mirror · 2016

Nosedive

Everyone rates everyone, one to five stars, and the average decides where you can live and fly.

↳ Civic drops peer rating: a calm institution judges, not neighbours.

Anime series · 2012

Psycho-Pass

A city system reads each citizen's state of mind as a number, and a high reading marks a threat before anything is done.

↳ Civic never reads a mind, but it infers intent: marked est., the weak link.

Dunne & Raby · 2013

United Micro Kingdoms

A speculative England split into four counties; the Digitarians live by market-run tracking and data.

↳ A world built to be argued with, not predicted.

AI-generated illustrations (Higgsfield, designer-directed). They picture the idea of each case; none is a photograph of it

Desk research, October 2026: Harvard Berkman Klein; Foreign Policy; e-Estonia; Oura;

Royal Commission into Robodebt; The Hague District Court; MSD and ODT

Civic Score · Prototyping · 07

Civic Score · Prototyping · 07

Civic Score · Research and precedents

Societies Already Imagined

Civic is fiction, but each of its parts already exists somewhere. These four were adopted because they offered something: care, reward, convenience, calm. I compared why people accept them and what authority each one holds.

Singapore · 2020

TraceTogether Token

A free contact-tracing wearable issued to every resident. Bluetooth proximity only, no GPS, records deleted after 25 days. A petition against it gathered over 36,000 signatures.

↳ Band Day: a state wearable, handed out free, framed as care.

Rongcheng, China · 2014–

Social credit pilot

Residents started on 1,000 points, graded A to D. High scorers got deposit-free bikes and heating discounts; top names were posted at city hall.

↳ Rewards, not punishment. But Civic hides the number: no public board.

Estonia · 2000s–

e-Estonia and X-Road

Agencies share data over one exchange layer, so most public services run online and tax returns arrive pre-filled in minutes.

↳ Convenience is the hook. One tap, no forms, no queues.

Oura · 2015–

Readiness score

A ring reads overnight heart rate, temperature and sleep, and the app opens on one calm number with cards beneath. No streaks, no badges.

↳ Rhythm 77%: one soft number that feels like self-care.

What Civic combines. No single precedent makes Civic. It joins personal sensing, digital public services, algorithmic judgement and promised fairness into one society. The cases show separate mechanisms, not proof of that future.

When Judgement Goes Wrong

The failures show what happens when the process behind a decision is hard to understand, contest or correct. Harm usually arrived through a fair-sounding process, not a cruel one.

Australia · 2016–2020

Robodebt

Debts raised automatically by averaging tax income. Around 470,000 wrongful debts; a royal commission in 2023 called it crude, cruel, neither fair nor legal.

↳ Why “How we decided” must show the chain, not just the verdict.

Netherlands · 2020

SyRI ruling

A court halted a welfare-fraud risk system: residents could not see its risk indicators or how the model worked, which breached the right to privacy.

↳ Visible values. The booth shows the weights a court could not see.

Aotearoa · 2015

MSD risk modelling

A study to score every newborn for risk of abuse was withdrawn by the minister: “These are children, not lab rats.”

↳ Here, the line was drawn once already. Civic asks where it sits now.

In fiction · how scoring is made to feel normal

Black Mirror · 2016

Nosedive

Everyone rates everyone, one to five stars, and the average decides where you can live and fly.

↳ Civic drops peer rating: a calm institution judges, not neighbours.

Anime series · 2012

Psycho-Pass

A city system reads each citizen's state of mind as a number, and a high reading marks a threat before anything is done.

↳ Civic never reads a mind, but it infers intent: marked est., the weak link.

Dunne & Raby · 2013

United Micro Kingdoms

A speculative England split into four counties; the Digitarians live by market-run tracking and data.

↳ A world built to be argued with, not predicted.

AI-generated illustrations (Higgsfield, designer-directed). They picture the idea of each case; none is a photograph of it

Desk research, October 2026: Harvard Berkman Klein; Foreign Policy; e-Estonia; Oura;

Royal Commission into Robodebt; The Hague District Court; MSD and ODT

Civic Score · Prototyping · 07

Civic Score · Prototyping · 07

Why now
What if?
Three tensions
Less human labour needed
Wearables becoming everyday
Automated judgement already exists
AI in public services, untrained
Survival were guaranteed
The system were often right
Your Civic Value were public
The values were visible
Better for most, unjust for one
Success becoming authority
Measuring a life, missing its reasons

02 — Ideation

Constructing the future and translating it into a speculative design concept.

Key question

What would make people accept a system that judges them?

Public scoreboard
Decision notice
A public service you wear
Civic Value shown to everyone
Coercion is easy to see
People reject it on sight
One letter, one consequence
Readable and inspectable
Meets the system only at the end
Band and app speak wellness
The score is five taps deep
Judgement felt as care
Values you can move at the booth

What if AI could allocate opportunity?

I started from the three signals and asked a harder version of the question: what would have to be true for people to welcome a system that decides who gets what is scarce? The “what if” questions narrowed to three tensions (fairness against privacy, recognition against judgement, a better society against the individual) and then to three ways the system could reach people. I took the wearable public service forward, because it made the judgement personal and everyday while keeping it calm enough to be welcomed.

Filled: the direction taken forward

The first Civic Score proposal

Initial proposal

A civic system that judged contribution

I took the question of authority from one workplace to a whole society. My first proposal was a civic system that read people’s signals, judged what each person contributed, and used that judgement to share out opportunity.

The proposal read behavioural, contextual and physiological signals (voice, video, location and the body) and inferred stress, attention, intent and social impact. A civic value weighting turned those inferences into access to work, support and opportunity in a future Aotearoa.

I did not want an obviously cruel state. Public services in Aotearoa already use algorithms, mostly to help, and the commitments people ask for are explanation, oversight and a way to challenge. So I granted the system what it promises: survival is guaranteed, services just work, and opportunity no longer depends on who you know.

That left the question I could not answer for myself: if a system makes society better for most people, does that success give it the authority to judge the few it cannot understand?

Presenting it changed the design more than any reading did. The next pages show what the critique challenged, and how I responded.

A civic service hall glowing softly in blue-grey dawn fog, a lamp post with a small sensor dome and camera in the foreground, its tiny light purple.

The first Civic Score proposal

Initial vision

A future where a civic system shares out what is scarce fairly and kindly, while making visible that every judgement it makes depends on values someone chose.

How might we · the original question · A1 presentation, July 2026

How might we use speculative design to help people experience and question whether the benefits of an autonomous civic system justify its authority to judge citizens?

Main debate: just because a system can make society better, should it have the right to judge people?

The next step

Next: what the critique challenged, and the design response that made Civic more desirable and harder to dismiss.

SpecAI-generated study, 25 October 2026 (Higgsfield, designer-directed).

Civic Score · Initial proposal

What I Presented

The first Civic Score read people's signals and judged what each person contributed. This is what I presented, before any redesign.

Sense

Voice, video, location and body signals

›

Infer

Inferred stress, attention, intent and social impact

›

Judge

Civic value weighting

›

Act

Access, work and support

My July plan · not built

W1–2Frame the debate; A1 pitch
W3–4Sensor spikes: EDA, EEG, camera and voice
W5–6Data pipeline; choose the sensing stack
W8Participant test 1 (3–5 students)
W10Participant test 2 (5–8 students)

What it was never trying to do

Detect morality scientifically

Prove that EEG or EDA reveal intention

Build a real social-credit system

Predict the exact future of work

The critique redirected the project before any of the sensing sprints began.

A1 presentation, slides and weekly plan from my Miro board, July 2026

When the Proposal Was Challenged

The critique did not ask for a better sensor. It asked what the system cost the people inside it.

01

Initial proposal

A civic system reads behavioural, contextual and physiological signals to judge contribution and steer opportunity.

›

02

Critique

Privacy and security. Individual harm. Relationships between citizens. Human agency.

›

03

Design response

Guarantee essentials. Remove public ranking. Add appeals. Make the values visible.

›

04

Remaining problem

Even if the process is accountable, why should a system hold the power to evaluate people and allocate opportunity?

“How would it impact the agency of the user?”

“As a human, what’s the cost?”

v1 · what I presentedv2 · what changed
Body signals (EEG, EDA, camera) to infer intentA band reads rhythm, place and presence; intent is marked as an estimate, the weak link
Civic Value decides access, work and supportSurvival is never scored (the Floor); only scarce openings use Civic Value
A score people could seeA score five taps deep, never on a public board
Values built into the systemValues published by a citizens’ assembly, and movable at a booth
A booth as a proposalA working values console: same evidence, different verdict (built for this report)

Critique transcript summary on Miro, July 2026; speakers not identified. v2 was built for this report in October 2026

Civic Score · Initial proposal

Civic Score · Initial proposal

Civic Score · Initial proposal

What I Presented

The first Civic Score read people's signals and judged what each person contributed. This is what I presented, before any redesign.

Sense

Voice, video, location and body signals

›

Infer

Inferred stress, attention, intent and social impact

›

Judge

Civic value weighting

›

Act

Access, work and support

My July plan · not built

W1–2Frame the debate; A1 pitch
W3–4Sensor spikes: EDA, EEG, camera and voice
W5–6Data pipeline; choose the sensing stack
W8Participant test 1 (3–5 students)
W10Participant test 2 (5–8 students)

What it was never trying to do

Detect morality scientifically

Prove that EEG or EDA reveal intention

Build a real social-credit system

Predict the exact future of work

The critique redirected the project before any of the sensing sprints began.

A1 presentation, slides and weekly plan from my Miro board, July 2026

When the Proposal Was Challenged

The critique did not ask for a better sensor. It asked what the system cost the people inside it.

01

Initial proposal

A civic system reads behavioural, contextual and physiological signals to judge contribution and steer opportunity.

›

02

Critique

Privacy and security. Individual harm. Relationships between citizens. Human agency.

›

03

Design response

Guarantee essentials. Remove public ranking. Add appeals. Make the values visible.

›

04

Remaining problem

Even if the process is accountable, why should a system hold the power to evaluate people and allocate opportunity?

“How would it impact the agency of the user?”

“As a human, what’s the cost?”

v1 · what I presentedv2 · what changed
Body signals (EEG, EDA, camera) to infer intentA band reads rhythm, place and presence; intent is marked as an estimate, the weak link
Civic Value decides access, work and supportSurvival is never scored (the Floor); only scarce openings use Civic Value
A score people could seeA score five taps deep, never on a public board
Values built into the systemValues published by a citizens’ assembly, and movable at a booth
A booth as a proposalA working values console: same evidence, different verdict (built for this report)

Critique transcript summary on Miro, July 2026; speakers not identified. v2 was built for this report in October 2026

Civic Score · Initial proposal

Civic Score · Initial proposal

Civic Score · Design response

A More Desirable Civic Future

Why make it desirable? The first Civic read as a dystopia, and a system built to be rejected teaches little. So I redesigned it to be welcomed: it guarantees survival, makes services effortless and shares out what is scarce more fairly than who-you-know ever did. The revision was not meant to remove every ethical problem. It made the question of authority harder, inside a system that genuinely seems good.

It is set in a speculative Aotearoa, 2040, where AI does much of the paid work and a wage no longer shows what a person gives. Every resident wears a Civic Band from 16. It opens doors, carries the Floor and quietly reads rhythm, place and presence, so Civic can notice who should be offered a studio, a course or a project role.

Guaranteedfloor+Noticedcontribution

Together they make a society that works, and that is the risk: the same reading that makes the system fair is the reading that judges you. So the idea has a third part, a visible judgement. Every decision shows how it was made, and at the booth the visitor can move the values themselves.

Ōtautahi, 2040. AI-generated speculative study

Positioning

I placed present-day systems on two questions: is the judgement imposed or welcomed, and are its values hidden or visible? Most sit in the imposed corner, which makes them easy to oppose. Civic aims for the welcomed, visible corner: the system people would choose, which is where the question of authority is hardest.

Imposed

Welcomed

Values hidden

Values visible

Civic

To be

Social credit pilots

Imposed, rules unclear

AI hiring screens

Ranks people unseen

Gig-platform ratings

Assigns work, scores workers

Benefit algorithms

Decides access, little say

Algorithm Charter

Explanation and challenge

Health wearables

Chosen, wellness-framed

Author's positioning from the Civic deck research, not a measured comparison

Civic Score · Prototyping · 01

Civic Score · Prototyping · 01

Civic Score · Design response

A More Desirable Civic Future

Why make it desirable? The first Civic read as a dystopia, and a system built to be rejected teaches little. So I redesigned it to be welcomed: it guarantees survival, makes services effortless and shares out what is scarce more fairly than who-you-know ever did. The revision was not meant to remove every ethical problem. It made the question of authority harder, inside a system that genuinely seems good.

It is set in a speculative Aotearoa, 2040, where AI does much of the paid work and a wage no longer shows what a person gives. Every resident wears a Civic Band from 16. It opens doors, carries the Floor and quietly reads rhythm, place and presence, so Civic can notice who should be offered a studio, a course or a project role.

Guaranteedfloor+Noticedcontribution

Together they make a society that works, and that is the risk: the same reading that makes the system fair is the reading that judges you. So the idea has a third part, a visible judgement. Every decision shows how it was made, and at the booth the visitor can move the values themselves.

Ōtautahi, 2040. AI-generated speculative study

Positioning

I placed present-day systems on two questions: is the judgement imposed or welcomed, and are its values hidden or visible? Most sit in the imposed corner, which makes them easy to oppose. Civic aims for the welcomed, visible corner: the system people would choose, which is where the question of authority is hardest.

Imposed

Welcomed

Values hidden

Values visible

Civic

To be

Social credit pilots

Imposed, rules unclear

AI hiring screens

Ranks people unseen

Gig-platform ratings

Assigns work, scores workers

Benefit algorithms

Decides access, little say

Algorithm Charter

Explanation and challenge

Health wearables

Chosen, wellness-framed

Author's positioning from the Civic deck research, not a measured comparison

Civic Score · Prototyping · 01

Civic Score · Prototyping · 01

Civic Score · The imagined society

What Kind of Society Is This?

Aotearoa, 2040: a speculative scenario, not a prediction. Before the band or the score, I set the conditions of the world, and gave each one a reason.

01

Everyday life

AI coordinates transport, care rosters and paperwork, and does much of the paid work.

Why I made it

So a wage stops showing what someone gives: the gap Civic claims to fill.

02

The Floor

Home, food, care and transit, guaranteed for life and never scored.

Why I made it

It separates survival from evaluation. A world where people earn their food is easy to reject; this one is not.

03

Scarce opportunity

Studios, limited courses, harbour housing and project roles stay scarce.

Why I made it

Scarcity gives allocation a believable reason to exist, and to stay contested.

All conditions are designer-authored for a fictional 2040. Images: AI-generated speculative studies (Higgsfield)

04

The Civic Band

From 16, every resident wears a band that opens doors and reads rhythm, place and presence.

Why I made it

The sensing arrives through convenience: people sign up for doors that open, not to be measured.

05

Public institutions

A Values Assembly publishes the weights each year; a Commons Office hears appeals.

Why I made it

So the AI never seems to invent society's values. Setting them in public still does not make each decision fair.

06

Social expectations

Being read becomes part of being a resident, and a polite “not this season” feels reasonable.

Why I made it

People accept evaluation because it seems helpful and usually right: the condition this chapter tests.

Life above the Floor. With essentials guaranteed, people garden, make, care, play and learn. Civic notices some of this as contribution and misses the rest.

Can someone live outside Civic? On the Floor, yes: survival never needs the band's readings. But openings go only to residents whose week Civic can read, so opting out keeps survival and gives up opportunity.

Civic Score · Prototyping · 20

Civic Score · Prototyping · 20

Civic Score · The imagined society

What Kind of Society Is This?

Aotearoa, 2040: a speculative scenario, not a prediction. Before the band or the score, I set the conditions of the world, and gave each one a reason.

01

Everyday life

AI coordinates transport, care rosters and paperwork, and does much of the paid work.

Why I made it

So a wage stops showing what someone gives: the gap Civic claims to fill.

02

The Floor

Home, food, care and transit, guaranteed for life and never scored.

Why I made it

It separates survival from evaluation. A world where people earn their food is easy to reject; this one is not.

03

Scarce opportunity

Studios, limited courses, harbour housing and project roles stay scarce.

Why I made it

Scarcity gives allocation a believable reason to exist, and to stay contested.

All conditions are designer-authored for a fictional 2040. Images: AI-generated speculative studies (Higgsfield)

04

The Civic Band

From 16, every resident wears a band that opens doors and reads rhythm, place and presence.

Why I made it

The sensing arrives through convenience: people sign up for doors that open, not to be measured.

05

Public institutions

A Values Assembly publishes the weights each year; a Commons Office hears appeals.

Why I made it

So the AI never seems to invent society's values. Setting them in public still does not make each decision fair.

06

Social expectations

Being read becomes part of being a resident, and a polite “not this season” feels reasonable.

Why I made it

People accept evaluation because it seems helpful and usually right: the condition this chapter tests.

Life above the Floor. With essentials guaranteed, people garden, make, care, play and learn. Civic notices some of this as contribution and misses the rest.

Can someone live outside Civic? On the Floor, yes: survival never needs the band's readings. But openings go only to residents whose week Civic can read, so opting out keeps survival and gives up opportunity.

Civic Score · Prototyping · 20

Civic Score · Prototyping · 20

Civic Score · The system

How the Civic System Works

Civic shares out what is scarce in four moves, each a bigger claim than the last. The resident never sees the chain; the booth and the app's “How we decided” screen make it visible.

01

Measure

Collects

Heart rhythm, sleep, movement, location, proximity to other bands, voice (optional)

›

02

Infer

Estimates

Stress, attention, intent, social contribution, project momentum

est. · the weak link

›

03

Judge

Weighs

Safety, happiness, growth, privacy, freedom, set by the Values Assembly

›

04

Act

Decides

Civic Value, then access: studios, courses, roles, housing

The FloorHome · food · care · transit. Guaranteed, never scored.

Each step makes a bigger claim than the last. A recording says this happened; an inference says this is why; a judgement says this is what matters; an action says this is what you get.

What the body can and cannot say

Heart rhythm, sleep and movement can give context about a day. That they reveal intention, morality or social contribution is this fiction’s premise, not a finding; the technical check later shows why.

The Commons Office, where a person reviews the decision. AI-generated speculative study

Who Has Power in Civic?

Who decides what counts as contribution, and what happens to the people who disagree?

PartWho controls itThe question it raises
1Civic BandThe resident and the operator, under set permissionsWhat must someone surrender to take part?
2AI interpretationAn algorithmic service running agreed modelsCan ambiguous behaviour be read fairly?
3Values AssemblyA fictional citizens' bodyWho takes part, and whose values count?
4Commons OfficeHuman reviewersCan people meaningfully challenge a decision?
5AllocationCivic institutions applying the weightsWho benefits when one person is put ahead of another?
6The FloorGuaranteed public provisionWhat stays unconditional, whatever Civic Value says?

Fictional institutions of the 2040 world, separated from the technical chain

Civic Score · Prototyping · 21

Civic Score · Prototyping · 21

Civic Score · The system

How the Civic System Works

Civic shares out what is scarce in four moves, each a bigger claim than the last. The resident never sees the chain; the booth and the app's “How we decided” screen make it visible.

01

Measure

Collects

Heart rhythm, sleep, movement, location, proximity to other bands, voice (optional)

›

02

Infer

Estimates

Stress, attention, intent, social contribution, project momentum

est. · the weak link

›

03

Judge

Weighs

Safety, happiness, growth, privacy, freedom, set by the Values Assembly

›

04

Act

Decides

Civic Value, then access: studios, courses, roles, housing

The FloorHome · food · care · transit. Guaranteed, never scored.

Each step makes a bigger claim than the last. A recording says this happened; an inference says this is why; a judgement says this is what matters; an action says this is what you get.

What the body can and cannot say

Heart rhythm, sleep and movement can give context about a day. That they reveal intention, morality or social contribution is this fiction’s premise, not a finding; the technical check later shows why.

The Commons Office, where a person reviews the decision. AI-generated speculative study

Who Has Power in Civic?

Who decides what counts as contribution, and what happens to the people who disagree?

PartWho controls itThe question it raises
1Civic BandThe resident and the operator, under set permissionsWhat must someone surrender to take part?
2AI interpretationAn algorithmic service running agreed modelsCan ambiguous behaviour be read fairly?
3Values AssemblyA fictional citizens' bodyWho takes part, and whose values count?
4Commons OfficeHuman reviewersCan people meaningfully challenge a decision?
5AllocationCivic institutions applying the weightsWho benefits when one person is put ahead of another?
6The FloorGuaranteed public provisionWhat stays unconditional, whatever Civic Value says?

Fictional institutions of the 2040 world, separated from the technical chain

Civic Score · Prototyping · 21

Civic Score · Prototyping · 21

This future

Civic Score · Ideation

01The promise

What does it promise?

Survival guaranteed, services that simply work, and opportunity that does not depend on who you know.

In this imagined 2040 Aotearoa, AI does much of the paid work. Wages stop showing what a person contributes, so the essentials become unconditional: the Floor.

Today’s signal

There is no widespread displacement yet, but the AI employment gap for young workers widened to 19% in 2026. (Stanford Digital Economy Lab, 2026)

02The records

What gets recorded?

Heart rhythm, place, presence and time with others, read continuously by a band and the city.

Above the Floor, some things stay scarce. To share them fairly, Civic needs to know how people live, so every resident wears a band that reads rhythm, place and presence.

Today’s signal

Measuring people at work is already common: 60% of US employers with remote staff used monitoring software. (Mucha, 2021)

03The institutions

Who decides what counts?

A Values Assembly sets the weights each year; a Commons Office reviews appeals.

The model infers stress, intent and social impact, then weighs them against values a citizens’ assembly publishes. A human office hears appeals.

Today’s signal

The rules are still voluntary: the Algorithm Charter had 27 signatories after one year, covering more than half of agencies. (Taylor Fry, 2021)

04The social norm

What becomes ordinary?

Wearing the band, being noticed, and accepting the season’s decision.

Because it is fair and usually right, people stop noticing it. Being read becomes part of being a resident, and a polite “not this season” feels reasonable.

Today’s signal

People can accept machine management readily: in an experiment, AI-managed workers tolerated lower pay without losing motivation. (Dong et al., 2025)

05The tension

Where does it rub?

When a system is usually right, does that give it the right to judge?

No state obviously controls anyone here. A fair, helpful system quietly decides what each person deserves.

The chain from life to access

Measure → Infer → Judge → Act

Aotearoa, 2040

All institutional arrangements are designer-authored conditions of a fictional 2040 future, not observed policies or predictions. Today’s signals are real, bounded findings from the sources cited.

Civic Score · Living with it

Living Under Civic Score

One resident, one season. The story starts with everything Civic gives, so that the decision near the end lands as reasonable, and the last panel hands the judgement to the visitor.

01

Band Day

At 16, a resident is fitted with her Civic Band at the hall. Her family is proud.

02

The Floor

The band opens her door. Her food crate is waiting. Survival is never in question.

03

Mum first

Most evenings she cares for her mother. The band reads it as staying home, stressed.

04

An opening

A studio place opens for the season. She applies, hopeful, sketches on the wall.

05

Not this season

A polite notice arrives. Her rhythm suggests now isn't the right moment.

06

How we decided

Measure, infer, judge, act: Civic Value 60. The data never saw her mother.

07

Review

She explains to two reviewers. They agree with the system. It is usually right.

08

The booth

A visitor moves one value. The same evidence becomes a studio place.

Why the story is gentle. An earlier Civic deck made the score public, and people rejected the world on sight. Here nobody is punished: the Floor holds, the staff are kind, the review is real. The only loss is a studio place, which is exactly why it is hard to argue with.

Storyboard panels: AI-generated pencil studies, 25 October 2026 (Higgsfield, designer-directed)

Civic Score · Prototyping · 03

Civic Score · Prototyping · 03

Civic Score · Living with it

Living Under Civic Score

One resident, one season. The story starts with everything Civic gives, so that the decision near the end lands as reasonable, and the last panel hands the judgement to the visitor.

01

Band Day

At 16, a resident is fitted with her Civic Band at the hall. Her family is proud.

02

The Floor

The band opens her door. Her food crate is waiting. Survival is never in question.

03

Mum first

Most evenings she cares for her mother. The band reads it as staying home, stressed.

04

An opening

A studio place opens for the season. She applies, hopeful, sketches on the wall.

05

Not this season

A polite notice arrives. Her rhythm suggests now isn't the right moment.

06

How we decided

Measure, infer, judge, act: Civic Value 60. The data never saw her mother.

07

Review

She explains to two reviewers. They agree with the system. It is usually right.

08

The booth

A visitor moves one value. The same evidence becomes a studio place.

Why the story is gentle. An earlier Civic deck made the score public, and people rejected the world on sight. Here nobody is punished: the Floor holds, the staff are kind, the review is real. The only loss is a studio place, which is exactly why it is hard to argue with.

Storyboard panels: AI-generated pencil studies, 25 October 2026 (Higgsfield, designer-directed)

Civic Score · Prototyping · 03

Civic Score · Prototyping · 03

Civic Score · Living with it

When It Works

Resident A · the system works

A neighbour paints murals for her street and runs a free Saturday class. No employer would ever list it. Civic reads the hours, the people who keep coming back and the community events, and in spring offers her a studio place at a studio where she knows no one.

The promise

Fairness, visibility and access without connections. If Civic never worked like this, nobody would have adopted it.

Proposed fictional scenarios. Illustrations AI-generated in the Civic style (Higgsfield, designer-directed)

When It Misunderstands

Resident B · the storyboard's resident

She cares for her mother most evenings. The band reads staying home, a raised heart rate and few events: low contribution, high stress. The same studio place goes to someone else, and the review agrees with the system.

The tension

One system's reading cannot hold the meaning of a life. Both live under the same values; only one of them is seen.

Civic Score · Prototyping · 22

Civic Score · Prototyping · 22

Civic Score · Living with it

When It Works

Resident A · the system works

A neighbour paints murals for her street and runs a free Saturday class. No employer would ever list it. Civic reads the hours, the people who keep coming back and the community events, and in spring offers her a studio place at a studio where she knows no one.

The promise

Fairness, visibility and access without connections. If Civic never worked like this, nobody would have adopted it.

Proposed fictional scenarios. Illustrations AI-generated in the Civic style (Higgsfield, designer-directed)

When It Misunderstands

Resident B · the storyboard's resident

She cares for her mother most evenings. The band reads staying home, a raised heart rate and few events: low contribution, high stress. The same studio place goes to someone else, and the review agrees with the system.

The tension

One system's reading cannot hold the meaning of a life. Both live under the same values; only one of them is seen.

Civic Score · Prototyping · 22

Civic Score · Prototyping · 22

Civic Score · Brand

Civic Brand

Civic had to look like a public service people would welcome: calm, plural, polite. Each part is borrowed from hospitals, transit and good civic signage, so the second reading of the eye arrives on its own.

Brand line

We see you.

To residents it means recognised. To the audience it means watched. The line never changes; the reader does.

Colour · purple is data, never the mark

Haze

#D9DADB

ground

Panel

#EEEEEE

pages

Card

#F6F6F6

cards

Ink

#25282C

text

Soft

#8B8E93

labels

Lilac

#B9A6F2

data

Violet

#8C72E8

bars

Pale

#DCD2FA

rest

Outfit. A soft geometric sans in light weights, with big calm numbers and small uppercase labels marked ///. It reads like a timetable, not a scoreboard.

The mark is never red or purple. Red would make it an alarm; purple belongs to the data. Ink on grey or white, white on ink.

The voice is warm and second person. It says rhythm and noticed, never score or penalty.

Brand in Use

Mock-ups: AI-generated with Higgsfield, 26 October 2026, from my real Civic screens, mark and band render

Civic Score · Prototyping · 10

Civic Score · Prototyping · 10

Civic Score · Brand

Civic Brand

Civic had to look like a public service people would welcome: calm, plural, polite. Each part is borrowed from hospitals, transit and good civic signage, so the second reading of the eye arrives on its own.

Brand line

We see you.

To residents it means recognised. To the audience it means watched. The line never changes; the reader does.

Colour · purple is data, never the mark

Haze

#D9DADB

ground

Panel

#EEEEEE

pages

Card

#F6F6F6

cards

Ink

#25282C

text

Soft

#8B8E93

labels

Lilac

#B9A6F2

data

Violet

#8C72E8

bars

Pale

#DCD2FA

rest

Outfit. A soft geometric sans in light weights, with big calm numbers and small uppercase labels marked ///. It reads like a timetable, not a scoreboard.

The mark is never red or purple. Red would make it an alarm; purple belongs to the data. Ink on grey or white, white on ink.

The voice is warm and second person. It says rhythm and noticed, never score or penalty.

Brand in Use

Mock-ups: AI-generated with Higgsfield, 26 October 2026, from my real Civic screens, mark and band render

Civic Score · Prototyping · 10

Civic Score · Prototyping · 10

Civic Score · First exhibition concept

Making the Future Experienceable

My first plan made Civic a live social experiment in the Engineering atrium: a public score board, wearable sensing, and a shared task scored as it happened. The aim was to make institutional judgement visible through a live social interaction.

The A1 deck's live board, with the screen redesigned in Civic's interface on 26 October. AI-assisted composite; names invented

01

Live Civic Score board

Students publicly judged

A large screen in the Engineering Atrium shows each participant's Civic Score, changing as the system reads them.

02

Wearable sensing

Heart rate, EDA, behaviour

Real or simulated readings. An EEG headband for attention, fatigue and compliance appeared in the visuals, but was never part of the interaction plan.

03

Shared activity

Two or three people, one decision

Participants negotiate a decision while the AI scores interruptions, disagreement, support and physiological reactions.

The visitor journey

1

Enter

A post-scarcity future: needs are guaranteed, desirable opportunities are not.

2

Get a score

A citizen profile, possibly linked to a wearable.

3

Do the task

Negotiate a shared decision while the AI watches.

4

Watch it move

The board shows behaviour read as contribution or harm.

5

Apply

For creative resources: one person approved, another rejected.

6

Question it

How was behaviour read, who set the values, why did it decide access?

Original proposal from my planning and the A1 deck (July 2026); planned, not built. Journey pictures AI-generated

Experience Being Scored

The live board as the A1 deck imagined it

What it communicated

The pressure of being measured and compared with others, in public, while it happens. Every visitor would feel the score move.

What challenged it

What Civic most needed to read, intent, is the least readable. A live public board needs consent from everyone on it. And an overt scoreboard makes the system too easy to reject.

The question this version asked

What does it feel like to be scored?

Civic Score · Prototyping · 16

Civic Score · Prototyping · 16

Civic Score · First exhibition concept

Making the Future Experienceable

My first plan made Civic a live social experiment in the Engineering atrium: a public score board, wearable sensing, and a shared task scored as it happened. The aim was to make institutional judgement visible through a live social interaction.

The A1 deck's live board, with the screen redesigned in Civic's interface on 26 October. AI-assisted composite; names invented

01

Live Civic Score board

Students publicly judged

A large screen in the Engineering Atrium shows each participant's Civic Score, changing as the system reads them.

02

Wearable sensing

Heart rate, EDA, behaviour

Real or simulated readings. An EEG headband for attention, fatigue and compliance appeared in the visuals, but was never part of the interaction plan.

03

Shared activity

Two or three people, one decision

Participants negotiate a decision while the AI scores interruptions, disagreement, support and physiological reactions.

The visitor journey

1

Enter

A post-scarcity future: needs are guaranteed, desirable opportunities are not.

2

Get a score

A citizen profile, possibly linked to a wearable.

3

Do the task

Negotiate a shared decision while the AI watches.

4

Watch it move

The board shows behaviour read as contribution or harm.

5

Apply

For creative resources: one person approved, another rejected.

6

Question it

How was behaviour read, who set the values, why did it decide access?

Original proposal from my planning and the A1 deck (July 2026); planned, not built. Journey pictures AI-generated

Experience Being Scored

The live board as the A1 deck imagined it

What it communicated

The pressure of being measured and compared with others, in public, while it happens. Every visitor would feel the score move.

What challenged it

What Civic most needed to read, intent, is the least readable. A live public board needs consent from everyone on it. And an overt scoreboard makes the system too easy to reject.

The question this version asked

What does it feel like to be scored?

Civic Score · Prototyping · 16

Civic Score · Prototyping · 16

Civic Score · Revised exhibition

From Scoring to Changing the Values

The revision turns the visitor from a subject of the system into someone who can examine and alter its values. The first concept asked what it feels like to be scored. This one asks who decides what the score means.

Installation study: an AI-generated composite, not a built exhibit

01

Encounter

Meet a resident and the decision Civic made.

02

Understand

Inspect the evidence and the criteria behind it.

03

Intervene

Move the values the system applies.

04

Compare

See the same resident get a different outcome.

05

Question

Ask who should have the authority to set them.

First concept

What does it feel like to be scored?

Revised

Who gets to decide what the score means?

The Values Console

Same evidence, one value moved. Raising freedom from 10 to 45 lifts Civic Value from 60 to 67, and the studio place is offered. The decision was never only about her; it followed whichever values came first.

Real

The console and its arithmetic, built in October

Composited

The installation and the deck's 12 to 96 sequence

Not done

Testing with visitors

Console captures from the Civic build, October 2026

Civic Score · Prototyping · 23

Civic Score · Prototyping · 23

Civic Score · Revised exhibition

From Scoring to Changing the Values

The revision turns the visitor from a subject of the system into someone who can examine and alter its values. The first concept asked what it feels like to be scored. This one asks who decides what the score means.

Installation study: an AI-generated composite, not a built exhibit

01

Encounter

Meet a resident and the decision Civic made.

02

Understand

Inspect the evidence and the criteria behind it.

03

Intervene

Move the values the system applies.

04

Compare

See the same resident get a different outcome.

05

Question

Ask who should have the authority to set them.

First concept

What does it feel like to be scored?

Revised

Who gets to decide what the score means?

The Values Console

Same evidence, one value moved. Raising freedom from 10 to 45 lifts Civic Value from 60 to 67, and the studio place is offered. The decision was never only about her; it followed whichever values came first.

Real

The console and its arithmetic, built in October

Composited

The installation and the deck's 12 to 96 sequence

Not done

Testing with visitors

Console captures from the Civic build, October 2026

Civic Score · Prototyping · 23

Civic Score · Prototyping · 23

Civic Score · Technical check

What Could Be Demonstrated?

I checked each part of the first exhibition against published evidence, and sorted what I could show into what is real, what would have to be simulated and what stays speculative.

July planTo showWhat the evidence saysVerdict
Heart rate (wrist)Stress and calm on the boardSome watches stay under 5% error on a treadmill; the Empatica E4 was over 15%. A pulse shows arousal, not contribution.Real
Real, but it proves little.
EDA (wrist)StressWESAD: about 80% across three states, 15 people, in a lab. Wrist EDA samples at 4 Hz.Simulate
Simulate.
Behaviour (camera and mic)Interruptions, supportThe best diarisation models still miss about 11% of speech, mostly where people talk over each other: the interruptions themselves. Nothing reliably detects “support”.Simulate
Simulate, or score by hand behind the screen.
EEG headband (explored)Attention, complianceA Muse records usable EEG, but attention results come from samples as small as five people. NZ's 2025 biometrics code allows attention tracking only for safety.Fiction
Fiction only.
Live public boardStudents publicly judgedEach participant must be told what is collected, and why, before or as it is collected.Real
Real, with consent and invented names.

Real

· The Civic app prototype, six screens

· The values console: same evidence, 60 to 67

· A wrist heart rate, which shows arousal, not contribution

Simulated

· EDA stress

· Interruptions and support in a shared task

· The live board's invented names

Speculative

· EEG attention and compliance

· Intent, morality and social contribution

· The 2040 institutions

What the check showed. What Civic most needed to read, intent and attention, is the least readable, and in New Zealand the most restricted.

Check made in October 2026: Edinburgh Napier wrist-PPG study; Schmidt et al. 2018 (WESAD); ETH Zurich diarisation benchmark 2025;

Bird et al. 2018 (Muse); Privacy Commissioner, Biometric Processing Privacy Code 2025

What I Built

Connect

Your Week

Civic Rhythm

Openings

Not this season

How we decided

Captures from the Civic Band board, October 2026

Civic Score · Prototyping · 24

Civic Score · Prototyping · 24

Civic Score · Technical check

What Could Be Demonstrated?

I checked each part of the first exhibition against published evidence, and sorted what I could show into what is real, what would have to be simulated and what stays speculative.

July planTo showWhat the evidence saysVerdict
Heart rate (wrist)Stress and calm on the boardSome watches stay under 5% error on a treadmill; the Empatica E4 was over 15%. A pulse shows arousal, not contribution.Real
Real, but it proves little.
EDA (wrist)StressWESAD: about 80% across three states, 15 people, in a lab. Wrist EDA samples at 4 Hz.Simulate
Simulate.
Behaviour (camera and mic)Interruptions, supportThe best diarisation models still miss about 11% of speech, mostly where people talk over each other: the interruptions themselves. Nothing reliably detects “support”.Simulate
Simulate, or score by hand behind the screen.
EEG headband (explored)Attention, complianceA Muse records usable EEG, but attention results come from samples as small as five people. NZ's 2025 biometrics code allows attention tracking only for safety.Fiction
Fiction only.
Live public boardStudents publicly judgedEach participant must be told what is collected, and why, before or as it is collected.Real
Real, with consent and invented names.

Real

· The Civic app prototype, six screens

· The values console: same evidence, 60 to 67

· A wrist heart rate, which shows arousal, not contribution

Simulated

· EDA stress

· Interruptions and support in a shared task

· The live board's invented names

Speculative

· EEG attention and compliance

· Intent, morality and social contribution

· The 2040 institutions

What the check showed. What Civic most needed to read, intent and attention, is the least readable, and in New Zealand the most restricted.

Check made in October 2026: Edinburgh Napier wrist-PPG study; Schmidt et al. 2018 (WESAD); ETH Zurich diarisation benchmark 2025;

Bird et al. 2018 (Muse); Privacy Commissioner, Biometric Processing Privacy Code 2025

What I Built

Connect

Your Week

Civic Rhythm

Openings

Not this season

How we decided

Captures from the Civic Band board, October 2026

Civic Score · Prototyping · 24

Civic Score · Prototyping · 24

Civic Score · what I learnt

I tried to make an intelligent society fairer. Then I began questioning why people had to be judged at all.

01

Making a system fairer did not resolve its authority

I protected essentials, removed public scores, added review and made the values inspectable. Meaningful improvements, but none answered whether a system should allocate opportunity by its own reading of contribution.

02

A good outcome for society can still harm a person

A system that improves access overall can still be wrong about one life. An aggregate improvement cannot justify every decision made about a particular person.

03

I was still defining people by what a system could recognise

Even the fairer Civic evaluated people and decided what they deserved. Rather than make that judgement more sophisticated, I wanted to ask what AI could do if it did not start from a measure of worth.

Retrospective reflection, October 2026.

Civic Score · the turning point

Civic · judging contribution

LifeRPG / UNI · developing capability

Established

Reasons on request, context a resident can add, a person who reviews, and essentials that never depend on a score.

Unresolved

Even a fairer judgement still defined people through an institution’s values and its reading of contribution.

Retained

Explanation, review by a person, and help that is never a condition of survival.

Extended next

LifeRPG and UNI supported a capability the person chose, and asked what learning needs.

What if AI could help people discover and build their capabilities, rather than deciding what they deserved?

Next · World 04 · LifeRPG / UNI →

Civic Score · Prototyping · 25

Civic Score · Prototyping · 25

Civic Score · what I learnt

I tried to make an intelligent society fairer. Then I began questioning why people had to be judged at all.

01

Making a system fairer did not resolve its authority

I protected essentials, removed public scores, added review and made the values inspectable. Meaningful improvements, but none answered whether a system should allocate opportunity by its own reading of contribution.

02

A good outcome for society can still harm a person

A system that improves access overall can still be wrong about one life. An aggregate improvement cannot justify every decision made about a particular person.

03

I was still defining people by what a system could recognise

Even the fairer Civic evaluated people and decided what they deserved. Rather than make that judgement more sophisticated, I wanted to ask what AI could do if it did not start from a measure of worth.

Retrospective reflection, October 2026.

Civic Score · the turning point

Civic · judging contribution

LifeRPG / UNI · developing capability

Established

Reasons on request, context a resident can add, a person who reviews, and essentials that never depend on a score.

Unresolved

Even a fairer judgement still defined people through an institution’s values and its reading of contribution.

Retained

Explanation, review by a person, and help that is never a condition of survival.

Extended next

LifeRPG and UNI supported a capability the person chose, and asked what learning needs.

What if AI could help people discover and build their capabilities, rather than deciding what they deserved?

Next · World 04 · LifeRPG / UNI →

Civic Score · Prototyping · 25

Civic Score · Prototyping · 25

The UNI sigil, four woven graphite loops with cobalt light along their seams, floating above a dark grid floor.

The question Civic Score left

From judging people to helping them grow

From evaluation to development

SpecAI-generated render from the UNI sigil, 25 October 2026 (Higgsfield, designer-directed).

World 04 / 05 · LifeRPG / UNI

LifeRPG / UNI

What I designed

LifeRPG, a progression concept, developed into Future University: a speculative institution with an identity, public website and learner portal.

Concept (LifeRPG) · Speculative institution · Website and portal designs · Critique

Why

Assisted production does not show that someone learned, and an assistant cannot supply every condition for practice.

Who

Learners, the educators and practice supervisors who guide them, and the people who rely on credentials.

How

Choose a goal → Practise with support → Select the evidence → A person assesses it.

The question this version examined

Who defines mastery, and can I opt out?

TickersAI SeniorCivic ScoreLifeRPG / UNIPerfect World

Signal · World 04 of 5

1. AI helps practice and can harm the exam

Civic Score asked who judges. LifeRPG and UNI turn to something a person might choose: becoming capable. The evidence that makes this hard is consistent. Help that raises performance today can lower what someone can do alone tomorrow, unless it is designed to give hints rather than answers (fig. 1). At university the same tension is now an institutional question (figs. 2 to 4).

A good result no longer shows that someone learned.

Key finding · author’s reading of the evidence

Figure 1: Change in score compared with students with no AI (Bastani et al., 2025)

+48%Plain chatPractice, with AI
+127%Tutoring versionPractice, with AI
−17%Plain chatExam, AI removed
about 0%Tutoring versionExam, AI removed

Also in the evidence

  1. 01
    Performing is not learning

    Immediate performance and lasting learning differ (Soderstrom & Bjork, 2015).

  2. 02
    Goals can be shared

    Shared goals make participation relational (Tomasello et al., 2005), which a score on its own does not show.

  3. 03
    Two models, two kinds of effort

    LifeRPG’s help modes and UNI’s separation of private practice from assessed evidence made different kinds of participation visible.

  4. 04
    A local student study

    UoA’s Cogniti research used student surveys and focus groups (Engineering Education Research Centre, University of Auckland, 2026). Like the other evidence here, it does not represent everyone.

The question this raisesWhat did the person actually learn?

The mathematics result is bounded: one high-school experiment, not a verdict on AI in learning. What it does show is that finishing something and learning from it can come apart. Producing more while questioning what I could explain myself made that distinction personal. LifeRPG and UNI are my attempts to make that difference visible.

Data: (Bastani et al., 2025; Freeman, 2025; Stephenson & Armstrong, 2026). The experiment took place in high-school mathematics, not higher education; it is a bounded finding. The 2026 “did not” share is the remainder of the reported 94%.

TickersAI SeniorCivic ScoreLifeRPG / UNIPerfect World

Signal · World 04 of 5 · The evidence

1.1AI lifted practice scores by up to 127%, but the unguarded group scored 17% lower once it was removed

In a field experiment with nearly a thousand high-school mathematics students, access to GPT-4 raised practice scores by 48% for a plain chat interface and by 127% for a tutoring version designed to give hints. When access was removed, the plain-chat group scored 17% lower than students who never had AI, while the tutoring group scored about the same as the control (Bastani et al., 2025). All changes are relative to a control group.

At university the same question is now an institutional one. In HEPI’s 2026 survey, 94% of UK undergraduates used generative AI to help with assessed work (Stephenson & Armstrong, 2026). A year earlier, 76% said their institution would spot AI use in assessments and 80% said its policy was clear (Freeman, 2025). Policy exists; whether a result shows learning is a different question.

Figure 2: UK undergraduates and generative AI (HEPI 2025, 2026)

94%

Used AI for assessed work, 2026

HEPI 2026
95%

Used AI in any form, 2026

HEPI 2026
76%

Said institution would spot AI use, 2025

HEPI 2025
53%

used AI for assessments in 2024

88%

used AI for assessments in 2025

66%

used AI in any form in 2024

80%

said their institution’s AI policy was clear (2025)

48%

practice gain, plain chat (field experiment)

127%

practice gain, tutoring version

Figure 3: What students said about their institution, and what they did (HEPI 2025, 2026)

Used AI for assessed work (2026)94%
Said their institution’s AI policy was clear (2025)80%
Said their institution would spot AI use in assessments (2025)76%

Figure 4: Used generative AI for assessments, 2024 to 2026 (HEPI)

2024

53%47%

2025

88%12%

2026

94%6%
  • Used AI for assessments
  • Did not
Senior pipeline
What if?
LifeRPG, round one
AI absorbs junior work
Less practice, less exposure
Outputs look better faster
Fewer future seniors
Online learning
Internship
Apprenticeship
Qualification
Goal, gap, quest, evidence, review
Learn, Coach, Work, Verify
Levels employers could trust

02 — Ideation

Round one: from judging people to helping them grow.

Key question

Who takes responsibility for becoming capable when AI does the work that used to teach it?

Capability passport
Senior review network
Future University
A portable verified profile
Becomes a score of people
Hard to opt out of
Industry reviewers sign off
Who pays the reviewers?
No place to practise
A teacher, a place, a judgement
Practice guaranteed, not suggested
AI teaches, people decide
Records never merged into a score

Imagining learning beyond scores

I started from the senior pipeline and asked what could replace the junior job as a place to learn, without turning growth into another ranking. The first round led to LifeRPG: a personal AI that turns real experiences into chosen challenges, feedback and progression. The pages that follow show what LifeRPG proposed, what its critique exposed, and why a second round moved the idea from an app to an institution.

Filled: the direction taken forward

LifeRPG · Round one · Concept

LifeRPG: the first direction

A personal AI that turns real experiences into chosen challenges, feedback and progression. Six moves made the loop; none of them asked the system to rank a person against anyone else.

Discover

01Discover

Name an interest, including “not sure”, and see possible directions.

Choose

02Choose

Accept one substantial real-world quest, not a checklist of micro-tasks.

Attempt

03Attempt

Practise and act, with AI help set to the task.

Reflect

04Reflect

Look back at what happened and what was learnt.

Develop

05Develop

Build capability through further, harder experiences.

Demonstrate

06Demonstrate

Choose evidence for one specific capability claim.

Objects: AI-generated renders in the UNI glass style (Higgsfield, designer-directed)

LifeRPG · Round one · Development

What each idea opened, and what it broke

The companion study from the LifeRPG presentation: an AI-generated scene

Idea exploredWhy I explored itThe problem it introduced
Personal progressionDevelopment without a civic rankingGrowth could still become an implicit score
AI-generated questsMake development accessibleA quest for every step turns life into AI micromanagement
Shared projectsConnect learning to collaborationThe last person to commit is not always the main contributor
Portable capability recordHelp people show what they knowInstitutions might read the record as the measure of a person
AI companion, glassesMake help contextual and presentThe medium was unjustified, and inference felt too authoritative

Ideas and problems from my LifeRPG development record, rounds 4–6 and 13

Future University · Process

Future University · Process

LifeRPG · Round one · Concept

LifeRPG: the first direction

A personal AI that turns real experiences into chosen challenges, feedback and progression. Six moves made the loop; none of them asked the system to rank a person against anyone else.

Discover

01Discover

Name an interest, including “not sure”, and see possible directions.

Choose

02Choose

Accept one substantial real-world quest, not a checklist of micro-tasks.

Attempt

03Attempt

Practise and act, with AI help set to the task.

Reflect

04Reflect

Look back at what happened and what was learnt.

Develop

05Develop

Build capability through further, harder experiences.

Demonstrate

06Demonstrate

Choose evidence for one specific capability claim.

Objects: AI-generated renders in the UNI glass style (Higgsfield, designer-directed)

LifeRPG · Round one · Development

What each idea opened, and what it broke

The companion study from the LifeRPG presentation: an AI-generated scene

Idea exploredWhy I explored itThe problem it introduced
Personal progressionDevelopment without a civic rankingGrowth could still become an implicit score
AI-generated questsMake development accessibleA quest for every step turns life into AI micromanagement
Shared projectsConnect learning to collaborationThe last person to commit is not always the main contributor
Portable capability recordHelp people show what they knowInstitutions might read the record as the measure of a person
AI companion, glassesMake help contextual and presentThe medium was unjustified, and inference felt too authoritative

Ideas and problems from my LifeRPG development record, rounds 4–6 and 13

Future University · Process

Future University · Process

LifeRPG · What it exposed

When personal progress becomes another score

01 · what the concept exposed

A level is still a score

A single progression number collapsed different kinds of experience and invited claims about worth: Civic Score again, as levels and badges.

02 · what the concept exposed

A record becomes authority

A portable capability record could be read by employers and institutions as the truth about a person.

03 · what the concept exposed

A guide becomes a manager

Generating a quest for every small step would make the AI a micromanager of someone’s life.

LifeRPG · The turning point

Who makes learning real?

An AI can guide your learning. But who makes that learning real?

A personal AI can provideIt cannot supply by itself
Explanations at any hourA teacher accountable for the standard
Practice exercises and hintsA real place with real consequences
Feedback and suggestionsA supervisor beside the beginner
A record of activityAn assessment others will recognise
RecommendationsTime, money and an available place

Why the project grew

The project had to become bigger than the app, without rebuilding Civic Score as levels and badges.

Future University · Process

Future University · Process

LifeRPG · What it exposed

When personal progress becomes another score

01 · what the concept exposed

A level is still a score

A single progression number collapsed different kinds of experience and invited claims about worth: Civic Score again, as levels and badges.

02 · what the concept exposed

A record becomes authority

A portable capability record could be read by employers and institutions as the truth about a person.

03 · what the concept exposed

A guide becomes a manager

Generating a quest for every small step would make the AI a micromanager of someone’s life.

LifeRPG · The turning point

Who makes learning real?

An AI can guide your learning. But who makes that learning real?

A personal AI can provideIt cannot supply by itself
Explanations at any hourA teacher accountable for the standard
Practice exercises and hintsA real place with real consequences
Feedback and suggestionsA supervisor beside the beginner
A record of activityAn assessment others will recognise
RecommendationsTime, money and an available place

Why the project grew

The project had to become bigger than the app, without rebuilding Civic Score as levels and badges.

Future University · Process

Future University · Process

Research · How learning institutions work

What learning still needs

Six models each solve one part of the problem. The question was not which looks most futuristic, but what AI can provide and what still needs places, people and responsibility.

ModelTeachingPracticeJudgementWhat UNI takes
Typical universityLecturers and tutorsOptional internships, often self-foundStaff grade exams and assignmentsThe baseline
Co-op degreeAcademic terms on campusPaid work terms alternate with studyUniversity grades study; employers review work termsPractice built into the degree
Degree apprenticeship (UK)University modules beside a jobThe apprentice’s own job, employer-paidEnd-point assessment of competenceA host that pays and supervises
42 schoolNo teachers: peers and projectsProjects in the school’s labsPeer evaluation and automated testsProjects over lectures, free tuition
Minerva UniversityLive online seminarsStudents live in rotating citiesFaculty assess across seminarsPractice placed in real cities
SkillsFuture (Singapore)Approved training providersMostly outside the schemeProvider assessmentsPublic credit for adults at any age
UNI, 2040Studios, with your AI insideA guaranteed, funded place at a hostFour decisions, people onlyAll of the above, publicly

From each programme’s public description, simplified · bottom: AI-generated 2040 studies

Research · Synthesis

What AI provides, and what it doesn’t

Personalised explanations→Educational standards and qualified teaching oversight
Simulated exercises→Practical experience with real consequences
Feedback and suggestions→Accountable evaluation of capability
Learning recommendations→Available places, time, money and supervision
Documentation of activity→Recognition by educational and professional bodies
Access to information→Community, equipment, peers and facilities

When instruction becomes abundant, practice, recognition and opportunity may remain scarce.

A scenario-driven design insight, not a prediction about all education

Future University · Process

Future University · Process

Research · How learning institutions work

What learning still needs

Six models each solve one part of the problem. The question was not which looks most futuristic, but what AI can provide and what still needs places, people and responsibility.

ModelTeachingPracticeJudgementWhat UNI takes
Typical universityLecturers and tutorsOptional internships, often self-foundStaff grade exams and assignmentsThe baseline
Co-op degreeAcademic terms on campusPaid work terms alternate with studyUniversity grades study; employers review work termsPractice built into the degree
Degree apprenticeship (UK)University modules beside a jobThe apprentice’s own job, employer-paidEnd-point assessment of competenceA host that pays and supervises
42 schoolNo teachers: peers and projectsProjects in the school’s labsPeer evaluation and automated testsProjects over lectures, free tuition
Minerva UniversityLive online seminarsStudents live in rotating citiesFaculty assess across seminarsPractice placed in real cities
SkillsFuture (Singapore)Approved training providersMostly outside the schemeProvider assessmentsPublic credit for adults at any age
UNI, 2040Studios, with your AI insideA guaranteed, funded place at a hostFour decisions, people onlyAll of the above, publicly

From each programme’s public description, simplified · bottom: AI-generated 2040 studies

Research · Synthesis

What AI provides, and what it doesn’t

Personalised explanations→Educational standards and qualified teaching oversight
Simulated exercises→Practical experience with real consequences
Feedback and suggestions→Accountable evaluation of capability
Learning recommendations→Available places, time, money and supervision
Documentation of activity→Recognition by educational and professional bodies
Access to information→Community, equipment, peers and facilities

When instruction becomes abundant, practice, recognition and opportunity may remain scarce.

A scenario-driven design insight, not a prediction about all education

Future University · Process

Future University · Process

Research · Four futures

What future would need a new university?

UNI cannot exist just because AI gets better. Someone has to fund teaching, pay supervisors and keep places open. I varied two things, institutional support and paid opportunity, to get four futures.

Broad support for learning and securityMore paid work ←

Supported adaptationA

Supported adaptation

Institutions fund retraining, supervision and paid learning time; enough opportunities exist to use new understanding.

Everyday · Students keep cohorts; workers return to modules, labs or mentored work.

Risk · Growth becomes an expectation rather than a choice.

UNI’s working setting

Restricted, unequal support

Abundant tools, fragmented accessB

Abundant tools, fragmented access

AI is everywhere but provision is disconnected; credentials multiply faster than agreement on what they mean.

Everyday · An impressive portfolio has to be explained again for every employer; a good course may be unaffordable.

Risk · A dashboard hides that no pathway is feasible.

Broad support for learning and security→ Less paid work

Productive automation, shared securityC

Productive automation, shared security

Fewer paid hours are needed, and part of the gain becomes support, services and time.

Everyday · Adults pursue care, research, making and civic work not tied to a promotion.

Risk · Support stays unequal; status rankings creep back.

The question Perfect World takes up

Restricted, unequal support

The credential treadmillD

The credential treadmill

Entry roles stay scarce, support is conditional, and standardised records screen people.

Everyday · Short credentials pile up without the supervised work needed for more.

Risk · A navigation tool becomes a way to blame people for structural scarcity.

UNI explores Future A: public support and supervised practice. Future C, where automation buys time rather than more work, is the question I carried out of this chapter.

Scenario tools from my LifeRPG research record, not predictions · photos: AI-generated 2040 studies (Higgsfield)

Future University · Process

Future University · Process

Research · Four futures

What future would need a new university?

UNI cannot exist just because AI gets better. Someone has to fund teaching, pay supervisors and keep places open. I varied two things, institutional support and paid opportunity, to get four futures.

Broad support for learning and securityMore paid work ←

Supported adaptationA

Supported adaptation

Institutions fund retraining, supervision and paid learning time; enough opportunities exist to use new understanding.

Everyday · Students keep cohorts; workers return to modules, labs or mentored work.

Risk · Growth becomes an expectation rather than a choice.

UNI’s working setting

Restricted, unequal support

Abundant tools, fragmented accessB

Abundant tools, fragmented access

AI is everywhere but provision is disconnected; credentials multiply faster than agreement on what they mean.

Everyday · An impressive portfolio has to be explained again for every employer; a good course may be unaffordable.

Risk · A dashboard hides that no pathway is feasible.

Broad support for learning and security→ Less paid work

Productive automation, shared securityC

Productive automation, shared security

Fewer paid hours are needed, and part of the gain becomes support, services and time.

Everyday · Adults pursue care, research, making and civic work not tied to a promotion.

Risk · Support stays unequal; status rankings creep back.

The question Perfect World takes up

Restricted, unequal support

The credential treadmillD

The credential treadmill

Entry roles stay scarce, support is conditional, and standardised records screen people.

Everyday · Short credentials pile up without the supervised work needed for more.

Risk · A navigation tool becomes a way to blame people for structural scarcity.

UNI explores Future A: public support and supervised practice. Future C, where automation buys time rather than more work, is the question I carried out of this chapter.

Scenario tools from my LifeRPG research record, not predictions · photos: AI-generated 2040 studies (Higgsfield)

Future University · Process

Future University · Process

From an App to an Institution

Design response

Why a personal AI was not enough

An AI can guide someone’s learning. It cannot, by itself, give them a teacher, a place to practise or a judgement other people will trust.

LifeRPG could explain, suggest and record. But the critique and my own research kept returning to what it could not supply: someone accountable for the standard, a real environment with real consequences, a supervisor beside the beginner, and an assessment that a profession would recognise.

Across Tickers and AI Senior I had seen the same gap at my own desk: my outputs improved faster than what I could explain without the assistant. If AI absorbs the work juniors used to learn through, the question is not which app tracks growth best. It is who takes responsibility for people becoming capable.

So the second round stopped designing a product and started designing the conditions: teachers, funded places, people who judge, and a way to say no. That is an institution, not an app, and it had to sit inside a society that would pay for it.

A graduate alone at night in a dark open-plan office, lit by a laptop showing a finished report beside an AI chat, rows of empty junior desks behind.

From an app to an institution

Initial vision

A future where AI scales teaching and practice, while people remain accountable for judging what someone has actually learned.

How might we · v4

How might emerging AI technologies reshape how people in Aotearoa learn, demonstrate capability, and progress toward goals they choose for themselves?

LifeRPG answered it first as a personal app; Future University answered it again as a public institution.

The next step

Next: the society that would make such an institution necessary, and what it would promise.

SpecAI-generated study, 25 October 2026 (Higgsfield, designer-directed).

UNI · The idea

UNI in one minute

Aotearoa, 2040. After capability apps lost employers’ trust, universities, polytechnics and practice hosts joined into one public learning network: Future University, UNI.

01The problem: AI breaks the pipeline

University

Teaches, grades the output

AI makes outputs cheap, so a grade stops proving learning.

→

Junior job

Practice, mistakes, mentoring

AI absorbs junior work, so the training ground disappears.

→

Senior

Judgement, leads, mentors

So who trains the next senior when AI does the junior work?

02The bet

When AI can produce the output, a university’s job is to guarantee what AI can’t.

01

A teacher. People design the learning; your AI explains inside it.

02

A place to practise. A funded, supervised place at a real host.

03

A judgement. A named person decides, gives reasons and can be challenged.

03How it works

Your AI, in a declared mode

Learn, Coach, Work or Verify. The task sets the mode, and everyone can see it.

Practice at a funded host

Employers, iwi, councils and labs are paid to supervise, never to award credit.

Four records, never merged

Private practice, chosen evidence, an assessed claim, a credential. No score of a person.

People decide

Host, team, assessor and Board each decide one thing. AI never awards credit.

04 · What it asks visitors

Instruction is abundant. Practice, judgement and recognition are scarce.

Does guaranteeing practice free people, or build a better gatekeeper?

UNI · The idea

Positioning

I placed existing models on two questions: is a place to practise guaranteed or left to you, and is learning open to anyone at any age, or tied to an enrolment or a job? LifeRPG, my first round, sat near the AI tutors. UNI aims for the empty corner, public and with a place guaranteed, which is also where it depends most on hosts and public money.

Open to anyone, at any age

Tied to an enrolment or a job

Practice left to you

A supervised place guaranteed

UNITo be
AI tutorsTeach anyone, no place
SkillsFuturePublic credit, any age
LifeRPGMy round one: an app
42 schoolFree projects, no host
Typical universityInternships self-found
MinervaCities, not hosts
Co-op degreeWork terms built in
Degree apprenticeshipA job comes first

My positioning from the precedent study, not a measured comparison

Future University · Process

Future University · Process

UNI · The idea

UNI in one minute

Aotearoa, 2040. After capability apps lost employers’ trust, universities, polytechnics and practice hosts joined into one public learning network: Future University, UNI.

01The problem: AI breaks the pipeline

University

Teaches, grades the output

AI makes outputs cheap, so a grade stops proving learning.

→

Junior job

Practice, mistakes, mentoring

AI absorbs junior work, so the training ground disappears.

→

Senior

Judgement, leads, mentors

So who trains the next senior when AI does the junior work?

02The bet

When AI can produce the output, a university’s job is to guarantee what AI can’t.

01

A teacher. People design the learning; your AI explains inside it.

02

A place to practise. A funded, supervised place at a real host.

03

A judgement. A named person decides, gives reasons and can be challenged.

03How it works

Your AI, in a declared mode

Learn, Coach, Work or Verify. The task sets the mode, and everyone can see it.

Practice at a funded host

Employers, iwi, councils and labs are paid to supervise, never to award credit.

Four records, never merged

Private practice, chosen evidence, an assessed claim, a credential. No score of a person.

People decide

Host, team, assessor and Board each decide one thing. AI never awards credit.

04 · What it asks visitors

Instruction is abundant. Practice, judgement and recognition are scarce.

Does guaranteeing practice free people, or build a better gatekeeper?

UNI · The idea

Positioning

I placed existing models on two questions: is a place to practise guaranteed or left to you, and is learning open to anyone at any age, or tied to an enrolment or a job? LifeRPG, my first round, sat near the AI tutors. UNI aims for the empty corner, public and with a place guaranteed, which is also where it depends most on hosts and public money.

Open to anyone, at any age

Tied to an enrolment or a job

Practice left to you

A supervised place guaranteed

UNITo be
AI tutorsTeach anyone, no place
SkillsFuturePublic credit, any age
LifeRPGMy round one: an app
42 schoolFree projects, no host
Typical universityInternships self-found
MinervaCities, not hosts
Co-op degreeWork terms built in
Degree apprenticeshipA job comes first

My positioning from the precedent study, not a measured comparison

Future University · Process

Future University · Process

UNI · The imagined society · Aotearoa, 2040

Building UNI’s society

AI explains anything and does much of the work beginners once learnt through, so society funds places to learn, practise and show capability for life. UNI is a public network that connects existing universities, polytechnics and hosts; it does not replace them.

People

01People

Learners of every age return, change direction and join shared projects.

↳ Lifelong learning only matters if leaving and coming back is ordinary.

Learning

02Learning

AI explains and sets practice; educators set the conditions and supervise.

↳ So help stays inside a frame someone is accountable for.

Physical places

03Physical places

Studios, labs, community organisations and hosts give real environments.

↳ Practice needs consequences a simulation cannot give.

Funding

04Funding

Public money and host organisations pay for places and supervision, within limits.

↳ Someone has to pay for the senior beside the beginner; the costs stay visible.

Assessment

05Assessment

People judge evidence against standards; AI help is declared.

↳ A judgement others trust needs a person who can be questioned.

Opportunity

06Opportunity

Records help people seek recognised roles; organisations still decide.

↳ UNI can open doors. It cannot create jobs.

Culture

07Culture

Studying one studio a term at forty is as ordinary as starting at eighteen.

↳ The institution only works if learning is not a stage of life.

Open tensions

08Open tensions

Who gets a funded place? Who judges competence? Must everyone keep proving progress?

↳ These stay open on purpose; the chapter tests them.

The point of the world

A world people live in, not a technology brand: its promises still depend on places, people and money.

Fictional scenario. Network, places, funding and credential authority are world-building assumptions; images AI-generated

Future University · Process

Future University · Process

UNI · The imagined society · Aotearoa, 2040

Building UNI’s society

AI explains anything and does much of the work beginners once learnt through, so society funds places to learn, practise and show capability for life. UNI is a public network that connects existing universities, polytechnics and hosts; it does not replace them.

People

01People

Learners of every age return, change direction and join shared projects.

↳ Lifelong learning only matters if leaving and coming back is ordinary.

Learning

02Learning

AI explains and sets practice; educators set the conditions and supervise.

↳ So help stays inside a frame someone is accountable for.

Physical places

03Physical places

Studios, labs, community organisations and hosts give real environments.

↳ Practice needs consequences a simulation cannot give.

Funding

04Funding

Public money and host organisations pay for places and supervision, within limits.

↳ Someone has to pay for the senior beside the beginner; the costs stay visible.

Assessment

05Assessment

People judge evidence against standards; AI help is declared.

↳ A judgement others trust needs a person who can be questioned.

Opportunity

06Opportunity

Records help people seek recognised roles; organisations still decide.

↳ UNI can open doors. It cannot create jobs.

Culture

07Culture

Studying one studio a term at forty is as ordinary as starting at eighteen.

↳ The institution only works if learning is not a stage of life.

Open tensions

08Open tensions

Who gets a funded place? Who judges competence? Must everyone keep proving progress?

↳ These stay open on purpose; the chapter tests them.

The point of the world

A world people live in, not a technology brand: its promises still depend on places, people and money.

Fictional scenario. Network, places, funding and credential authority are world-building assumptions; images AI-generated

Future University · Process

Future University · Process

This future

Future University · Ideation

01The promise

What does it promise?

A teacher, a place to practise and a judgement a person is accountable for.

In this imagined 2040 Aotearoa, AI explains anything at any hour. Future University, UNI, promises what an app cannot: human teaching, a supervised place and a fair judgement.

Today’s signal

AI help can raise practice scores and still lower what people can do once it is removed. (Bastani et al., 2025)

02The records

What gets recorded?

Four stages, never merged: practice, evidence, claim, credential.

Private practice stays with the learner. Chosen evidence declares what helped. An assessor judges a claim, and only the Assessment Board issues credit.

Today’s signal

A verifiable credential proves who issued a claim, not that the claim is true. (W3C, 2025)

03The institutions

Who recognises it?

A public network of universities and practice hosts.

After capability apps lost employers’ trust, a public Practice Guarantee funded supervised places, and UNI formed in 2038 to coordinate teaching, practice and credit.

Today’s signal

Dropping degree requirements in announcements has not reliably changed who gets hired. (The Burning Glass Institute & Harvard Business School, 2024)

04The social norm

What becomes ordinary?

Learning at any age, around work and care.

It becomes normal to take one studio a term on protected hours, to declare the mode your AI worked in, and to explain your reasoning to a person.

Today’s signal

Hiring of the youngest workers has fallen most in AI-exposed roles. (Stanford Digital Economy Lab, 2026)

05The tension

Where does it rub?

Who gets a place, and does guaranteeing practice free people or build a better gatekeeper?

The future I wanted to explore is not a robot tutor, but a fair public institution whose promises still depend on places, people and money.

The chain from learning to credit

Practice → Evidence → Claim → Credential

Aotearoa, 2040

All institutional arrangements are designer-authored conditions of a fictional 2040 future, not observed policies or predictions. Today’s signals are real, bounded findings from the sources cited.

UNI · How the institution operates · 01

What makes UNI possible?

The UNI sigil in graphite and blue
StudiosTeach and inquire
Practice NetworkA funded place
Assessment BoardPeople judge
The RecordFour stages you own
StewardshipAccess and appeals
Your AIHelp, mode by mode

UNI · How the institution operates · 02

How is it sustained?

Five flows hold UNI together. Read each line left to right: who gives what, to whom.

01

Money

Public funding
pays for teaching and supervision
Studios and hosts

The public pays for teaching and for supervised places, through the Practice Guarantee of 2036.

02

A place

Practice host
offers a supervised place
Learner

A host gives a real problem, a named supervisor and funded hours. Hosts never award credit.

03

Credit

Learner
evidence, explained
Assessment Board
credit
The Record

Only the Assessment Board can turn evidence into credit, and it gives its reasons.

04

Recognition

The Record
recognised, or not
Employers

Employers and professional bodies decide for themselves whether to recognise it.

05

Access

Stewardship
plans and appeals
Learner

A steward plans around money, time and care, and handles challenges to a decision.

AI teaches. People decide.

UNI’s network, funding and credential authority are invented structures of the 2040 world, not partnerships

Future University · Process

Future University · Process

UNI · How the institution operates · 01

What makes UNI possible?

The UNI sigil in graphite and blue
StudiosTeach and inquire
Practice NetworkA funded place
Assessment BoardPeople judge
The RecordFour stages you own
StewardshipAccess and appeals
Your AIHelp, mode by mode

UNI · How the institution operates · 02

How is it sustained?

Five flows hold UNI together. Read each line left to right: who gives what, to whom.

01

Money

Public funding
pays for teaching and supervision
Studios and hosts

The public pays for teaching and for supervised places, through the Practice Guarantee of 2036.

02

A place

Practice host
offers a supervised place
Learner

A host gives a real problem, a named supervisor and funded hours. Hosts never award credit.

03

Credit

Learner
evidence, explained
Assessment Board
credit
The Record

Only the Assessment Board can turn evidence into credit, and it gives its reasons.

04

Recognition

The Record
recognised, or not
Employers

Employers and professional bodies decide for themselves whether to recognise it.

05

Access

Stewardship
plans and appeals
Learner

A steward plans around money, time and care, and handles challenges to a decision.

AI teaches. People decide.

UNI’s network, funding and credential authority are invented structures of the 2040 world, not partnerships

Future University · Process

Future University · Process

UNI · How the institution operates · 03

A learner’s route: enrol to credential

01Enrol

Cost comes before the course.

Ari talks to a steward, picks a route (degree, studio, practice or inquiry) and plans around money, time and care.

Who decides

Ari, with a steward

Your AI

—

02Get a place

A guaranteed, funded place at a host.

A place offer letter names a supervisor and funded hours. Declining it carries no penalty.

Who decides

The host offers, Ari accepts

Your AI

—

03Learn

Help is declared, not hidden.

Foundations in the studio, practice at the host, then a team prototype. The teacher sets how far the AI may help.

Who decides

A teacher sets the help mode

Your AI

LEARNCOACHWORK

UNI · How it works · Ari, Systems Design, LES301

Who decides at each step

04Show it

Ari chooses what moves from practice to evidence.

Ari picks the evidence, declares what helped, and explains it again after a changed condition.

Who decides

Ari chooses what to show

Your AI

VERIFY

05Judgement

A person, not a model, decides.

An assessor decides and gives reasons. Ari can challenge the decision by a set date.

Who decides

An assessor

Your AI

—

06Credential

Only the Board issues credit.

The Record keeps four stages, never merged into a score. Employers recognise the credit, or not.

Who decides

The Assessment Board

Your AI

—

Icons: AI-generated studies, 26 Oct 2026 (Higgsfield, designer-directed)

Future University · Process

Future University · Process

UNI · How the institution operates · 03

A learner’s route: enrol to credential

01Enrol

Cost comes before the course.

Ari talks to a steward, picks a route (degree, studio, practice or inquiry) and plans around money, time and care.

Who decides

Ari, with a steward

Your AI

—

02Get a place

A guaranteed, funded place at a host.

A place offer letter names a supervisor and funded hours. Declining it carries no penalty.

Who decides

The host offers, Ari accepts

Your AI

—

03Learn

Help is declared, not hidden.

Foundations in the studio, practice at the host, then a team prototype. The teacher sets how far the AI may help.

Who decides

A teacher sets the help mode

Your AI

LEARNCOACHWORK

UNI · How it works · Ari, Systems Design, LES301

Who decides at each step

04Show it

Ari chooses what moves from practice to evidence.

Ari picks the evidence, declares what helped, and explains it again after a changed condition.

Who decides

Ari chooses what to show

Your AI

VERIFY

05Judgement

A person, not a model, decides.

An assessor decides and gives reasons. Ari can challenge the decision by a set date.

Who decides

An assessor

Your AI

—

06Credential

Only the Board issues credit.

The Record keeps four stages, never merged into a score. Employers recognise the credit, or not.

Who decides

The Assessment Board

Your AI

—

Icons: AI-generated studies, 26 Oct 2026 (Higgsfield, designer-directed)

Future University · Process

Future University · Process

UNI · Living with it · Storyboard

Living and learning in UNI

A world is easier to judge from the inside. One ordinary week shows where AI helps, where a person decides, and what is never measured. Each scene names the rule of the institution that makes it possible.

01Mon · Plan with a steward
Plan with a steward

Ari maps the semester around a part-time job before anything is recorded.

↳ Stewards plan around money, time and care.

02Mon · Foundations in the studioLEARN
Foundations in the studio

A teacher frames the question; the AI explains inside that frame.

↳ Educators set how far the AI may help.

03Tue · The place is real
The place is real

A letter names the depot, the problem and a senior who will supervise.

↳ UNI funds supervised places at real hosts.

04Tue · Practice at the depotCOACH
Practice at the depot

Hands on the shuttle; the AI gives hints only after Ari tries.

↳ Hosts are paid to supervise, never to grade.

05Wed · Inquiry across ages
Inquiry across ages

Moana, 67, and a first-year map the harbour for no grade at all.

↳ Open inquiry is never assessed, by design.

06Thu · Choose the evidenceWORK
Choose the evidence

On the train, Ari picks what moves from private practice to evidence.

↳ Practice stays private unless Ari moves it.

07Fri · Explain it to a personVERIFY
Explain it to a person

Named tools only. An assessor asks why, then decides and gives reasons.

↳ Only people award credit, with reasons.

08Sat · Not tracked
Not tracked

Sam, a carer in her forties, studies one studio a term at the kitchen table.

↳ Returning around work and care is ordinary.

Scenes: AI-generated studies, 25 Oct 2026 (Higgsfield). Fictional people; not observed learning

Future University · Process

Future University · Process

UNI · Living with it · Storyboard

Living and learning in UNI

A world is easier to judge from the inside. One ordinary week shows where AI helps, where a person decides, and what is never measured. Each scene names the rule of the institution that makes it possible.

01Mon · Plan with a steward
Plan with a steward

Ari maps the semester around a part-time job before anything is recorded.

↳ Stewards plan around money, time and care.

02Mon · Foundations in the studioLEARN
Foundations in the studio

A teacher frames the question; the AI explains inside that frame.

↳ Educators set how far the AI may help.

03Tue · The place is real
The place is real

A letter names the depot, the problem and a senior who will supervise.

↳ UNI funds supervised places at real hosts.

04Tue · Practice at the depotCOACH
Practice at the depot

Hands on the shuttle; the AI gives hints only after Ari tries.

↳ Hosts are paid to supervise, never to grade.

05Wed · Inquiry across ages
Inquiry across ages

Moana, 67, and a first-year map the harbour for no grade at all.

↳ Open inquiry is never assessed, by design.

06Thu · Choose the evidenceWORK
Choose the evidence

On the train, Ari picks what moves from private practice to evidence.

↳ Practice stays private unless Ari moves it.

07Fri · Explain it to a personVERIFY
Explain it to a person

Named tools only. An assessor asks why, then decides and gives reasons.

↳ Only people award credit, with reasons.

08Sat · Not tracked
Not tracked

Sam, a carer in her forties, studies one studio a term at the kitchen table.

↳ Returning around work and care is ordinary.

Scenes: AI-generated studies, 25 Oct 2026 (Higgsfield). Fictional people; not observed learning

Future University · Process

Future University · Process

Exhibition · Alternatives

How could visitors experience UNI?

LifeRPG companion

LifeRPG companionStudied

An embodied AI offering quests and contextual help

↳ The benefits, and the dependence, of personal guidance

Capability record

Capability recordConcept

Inspect a record of work, help and claimed learning

↳ How evidence becomes institutional authority

Future University portal

Future University portalBuilt

Move through one learner’s courses, places and decisions

↳ How a future institution might actually work

UNI open-day booth

UNI open-day boothRendered

Meet the university through its brand, site and a place offer

↳ Why people might want it, and how close that is to promotion

Learning Transition Desk

Learning Transition DeskProposed

Respond to a changed problem with AI help, then inspect what was learnt

↳ The gap between a good result and real capability

Status as of October 2026 · images: AI-generated studies and renders, except the portal screenshot

Exhibition · Documented proposal

The Learning Transition Desk

0–1 minChoose a role, a new student or a returning worker, and a curiosity, including “not sure”.
1–3The System presents a shared challenge and two possible contributions, and says why.
3–5Inspect an AI-supported proposal; make or revise one decision.
5–7A change: connectivity fails and some users cannot use a smartphone. Respond with permitted help.
7–9Inspect the evidence record; correct an attribution or reject an unsupported claim.
9–11Compare the next routes under two labelled institutional policies.
11–12Debrief and reset. No credential is awarded.

A booth makes UNI credible but risks promotion. The desk makes the gap visible: getting an answer from AI versus explaining or adapting it.

From my research record’s exhibition plan; planning times, not built or tested · AI-generated study

Future University · Process

Future University · Process

Exhibition · Alternatives

How could visitors experience UNI?

LifeRPG companion

LifeRPG companionStudied

An embodied AI offering quests and contextual help

↳ The benefits, and the dependence, of personal guidance

Capability record

Capability recordConcept

Inspect a record of work, help and claimed learning

↳ How evidence becomes institutional authority

Future University portal

Future University portalBuilt

Move through one learner’s courses, places and decisions

↳ How a future institution might actually work

UNI open-day booth

UNI open-day boothRendered

Meet the university through its brand, site and a place offer

↳ Why people might want it, and how close that is to promotion

Learning Transition Desk

Learning Transition DeskProposed

Respond to a changed problem with AI help, then inspect what was learnt

↳ The gap between a good result and real capability

Status as of October 2026 · images: AI-generated studies and renders, except the portal screenshot

Exhibition · Documented proposal

The Learning Transition Desk

0–1 minChoose a role, a new student or a returning worker, and a curiosity, including “not sure”.
1–3The System presents a shared challenge and two possible contributions, and says why.
3–5Inspect an AI-supported proposal; make or revise one decision.
5–7A change: connectivity fails and some users cannot use a smartphone. Respond with permitted help.
7–9Inspect the evidence record; correct an attribution or reject an unsupported claim.
9–11Compare the next routes under two labelled institutional policies.
11–12Debrief and reset. No credential is awarded.

A booth makes UNI credible but risks promotion. The desk makes the gap visible: getting an answer from AI versus explaining or adapting it.

From my research record’s exhibition plan; planning times, not built or tested · AI-generated study

Future University · Process

Future University · Process

UNI · Prototyping · Identity

Brand: an institution, not an app

UNI had to look like public infrastructure: charcoal, hairline grids, tall type (League Gothic, Hanken Grotesk) and one blue. Red is kept for a single job, the square beside “decided by”.

UNI
The woven mark · four stages
Charcoal
UNI
Signal Blue
UNI
Concrete
UNI

Signal Blue

#1F4DFF

Primary

Blue Light

#6684FF

Glow, numbers

Charcoal

#151619

Ground

Ink

#0E0F11

Deep sections

Concrete

#C7C9CD

Light sections

Mist

#E8E9EB

Text on dark

Decision Red

#FF2B1C

One use only

Website screenshot, 25 Oct 2026 · sign, letter, notice and poster: AI-generated mockups

UNI · Prototyping · Three stages

What was actually designed

01World-building

Concept

The fictional university: its network, places, funding, assessment and governance, written up as a world bible.

Designed artefacts
02Made

Designed artefacts

Identity, public website and learner portal (working), learner scenarios, route diagrams, booth renders.

Proposed interactions
03Not built

Proposed interactions

The Learning Transition Desk, an AI conversation, an optional spatial interface, evidence review. Unity: an editor baseline only.

Working website and portal, Next.js · screenshots October 2026 · fictional learner data · Unity: editor baseline

Future University · Process

Future University · Process

UNI · Prototyping · Identity

Brand: an institution, not an app

UNI had to look like public infrastructure: charcoal, hairline grids, tall type (League Gothic, Hanken Grotesk) and one blue. Red is kept for a single job, the square beside “decided by”.

UNI
The woven mark · four stages
Charcoal
UNI
Signal Blue
UNI
Concrete
UNI

Signal Blue

#1F4DFF

Primary

Blue Light

#6684FF

Glow, numbers

Charcoal

#151619

Ground

Ink

#0E0F11

Deep sections

Concrete

#C7C9CD

Light sections

Mist

#E8E9EB

Text on dark

Decision Red

#FF2B1C

One use only

Website screenshot, 25 Oct 2026 · sign, letter, notice and poster: AI-generated mockups

UNI · Prototyping · Three stages

What was actually designed

01World-building

Concept

The fictional university: its network, places, funding, assessment and governance, written up as a world bible.

Designed artefacts
02Made

Designed artefacts

Identity, public website and learner portal (working), learner scenarios, route diagrams, booth renders.

Proposed interactions
03Not built

Proposed interactions

The Learning Transition Desk, an AI conversation, an optional spatial interface, evidence review. Unity: an editor baseline only.

Working website and portal, Next.js · screenshots October 2026 · fictional learner data · Unity: editor baseline

Future University · Process

Future University · Process

Evaluation · Critique to decision

When the idea was challenged

ChallengeWhat it exposedDesign response
“There’s no evidence. These are all statements.”The proposal claimed more than its evidence supported.Separate research findings, fictional assumptions and untested outcomes.
“Why smart glasses? Why not just track computer use?”The medium was not justified.The learning interaction comes first; a spatial interface is optional.
“What is the provocation?”It read as a better learning service, not a question about society.Make the tension between assisted output, learning and institutional judgement explicit.
Personal capability scoresProgression could recreate social ranking.Separate practice, chosen evidence, assessment and credentials.
No places to practiseAI tutoring alone could not replace supervised experience.A funded practice network and human institutional roles.
Employment-focused pathwaysThe system could still push everyone towards economic productivity.Examine learning, exploration and participation beyond employment.

Rows 1–3: the critique transcript, speakers not identified · rows 4–6: tensions from my development research

Evaluation · Honest limits

What was tested, and what was not

Done

· A presentation critique

· Research comparisons of six institutional models

· Concept development across two rounds

· A working website and portal

· Self-evaluation against the critique

Not done

· A participant study

· Any measure of learning outcomes

· Evidence that qualifications become fairer

· Visitor testing of the desk

A future formative test · not done

Show a short fictional learner scenario and a piece of AI-assisted work. Ask what the work proves, what more evidence the participant would need, and who should decide. Then show UNI’s review process and ask whether it changes their view. It would test whether the tensions are understandable, not whether UNI improves learning.

Future University · Process

Future University · Process

Evaluation · Critique to decision

When the idea was challenged

ChallengeWhat it exposedDesign response
“There’s no evidence. These are all statements.”The proposal claimed more than its evidence supported.Separate research findings, fictional assumptions and untested outcomes.
“Why smart glasses? Why not just track computer use?”The medium was not justified.The learning interaction comes first; a spatial interface is optional.
“What is the provocation?”It read as a better learning service, not a question about society.Make the tension between assisted output, learning and institutional judgement explicit.
Personal capability scoresProgression could recreate social ranking.Separate practice, chosen evidence, assessment and credentials.
No places to practiseAI tutoring alone could not replace supervised experience.A funded practice network and human institutional roles.
Employment-focused pathwaysThe system could still push everyone towards economic productivity.Examine learning, exploration and participation beyond employment.

Rows 1–3: the critique transcript, speakers not identified · rows 4–6: tensions from my development research

Evaluation · Honest limits

What was tested, and what was not

Done

· A presentation critique

· Research comparisons of six institutional models

· Concept development across two rounds

· A working website and portal

· Self-evaluation against the critique

Not done

· A participant study

· Any measure of learning outcomes

· Evidence that qualifications become fairer

· Visitor testing of the desk

A future formative test · not done

Show a short fictional learner scenario and a piece of AI-assisted work. Ask what the work proves, what more evidence the participant would need, and who should decide. Then show UNI’s review process and ask whether it changes their view. It would test whether the tensions are understandable, not whether UNI improves learning.

Future University · Process

Future University · Process

LifeRPG and Future University · what I learnt

I tried replacing judgement with development. Designing the conditions for development made me ask why people had to become productive at all.

01

A completed result is not the same as learning

AI could improve someone’s output without showing what they understood or could do alone.

→ Separate AI-supported production, practice, evidence and assessment.

02

Technology alone cannot create the conditions for development

Meaningful practice needs people, equipment, funding, time, places and real opportunities.

→ Expand LifeRPG from an assistant into a speculative institution.

03

Helping someone develop is not defining what they should become

Personal pathways support exploration, but can push narrow optimisation towards qualifications or jobs.

→ Protect choice, changing interests, human advising and learning that is never assessed.

04

I was still designing around being economically useful

Even reimagined, employment and recognised capability stayed the reason the institution existed.

→ Ask what learning would mean if working to survive were no longer necessary.

Retrospective reflection, October 2026.

From becoming capable to choosing what matters

UNI · becoming capable

Future C · time, not more work

Established

Meaningful practice needs people, places, opportunity and participation, not only an assistant.

Unresolved

I was still designing a society where people developed capability largely because they needed to work.

Retained

Declared help, and the difference between learning something and having it done.

Extended next

Perfect World granted secure essentials and useful help, then asked what people would still choose to develop.

What remains meaningful when we no longer have to achieve anything to deserve a good life?

Next · Life after necessity →

Future University · Process

Future University · Process

LifeRPG and Future University · what I learnt

I tried replacing judgement with development. Designing the conditions for development made me ask why people had to become productive at all.

01

A completed result is not the same as learning

AI could improve someone’s output without showing what they understood or could do alone.

→ Separate AI-supported production, practice, evidence and assessment.

02

Technology alone cannot create the conditions for development

Meaningful practice needs people, equipment, funding, time, places and real opportunities.

→ Expand LifeRPG from an assistant into a speculative institution.

03

Helping someone develop is not defining what they should become

Personal pathways support exploration, but can push narrow optimisation towards qualifications or jobs.

→ Protect choice, changing interests, human advising and learning that is never assessed.

04

I was still designing around being economically useful

Even reimagined, employment and recognised capability stayed the reason the institution existed.

→ Ask what learning would mean if working to survive were no longer necessary.

Retrospective reflection, October 2026.

From becoming capable to choosing what matters

UNI · becoming capable

Future C · time, not more work

Established

Meaningful practice needs people, places, opportunity and participation, not only an assistant.

Unresolved

I was still designing a society where people developed capability largely because they needed to work.

Retained

Declared help, and the difference between learning something and having it done.

Extended next

Perfect World granted secure essentials and useful help, then asked what people would still choose to develop.

What remains meaningful when we no longer have to achieve anything to deserve a good life?

Next · Life after necessity →

Future University · Process

Future University · Process

Life after necessity · the question every world left

What if people no longer needed to work to live well?

Four worlds carried the same three assumptions. Before designing the last one, I re-asked each of them.

People need to prove their contribution.

→

What if basic security and belonging are unconditional?

AI should help people become more capable.

→

What if capability is pursued for curiosity, enjoyment or relationships, not employment?

Improving life means helping people achieve goals.

→

What if making achievement easier changes the value of the experience itself?

The research that follows looks at post-work futures, Deep Utopia and The Culture, and the economic and physical conditions automation would need, before Perfect World is set out.

The inquiry so far

01

Tickers · Could AI make effort trustworthy?

Observing was not understanding.

02

AI Senior · Could AI understand and guide work?

Assistance introduced authority.

03

Civic Score · What if that authority reached society?

Fairer judgement was not justified judgement.

04

LifeRPG · Could AI help people develop instead of ranking them?

Personal progression could become another score.

04

Future University · What institutions does meaningful learning need?

Learning still served work and achievement.

05

Life after necessity · What would people do if work stopped being necessary?

Meaning might come from chosen activity, relationships and experience.

Perfect World imagines a society where AI supports that life, and asks who shapes our choices when help becomes extraordinarily capable.

Next · World 05 · Perfect World →

Life after necessity

Life after necessity

Life after necessity · the question every world left

What if people no longer needed to work to live well?

Four worlds carried the same three assumptions. Before designing the last one, I re-asked each of them.

People need to prove their contribution.

→

What if basic security and belonging are unconditional?

AI should help people become more capable.

→

What if capability is pursued for curiosity, enjoyment or relationships, not employment?

Improving life means helping people achieve goals.

→

What if making achievement easier changes the value of the experience itself?

The research that follows looks at post-work futures, Deep Utopia and The Culture, and the economic and physical conditions automation would need, before Perfect World is set out.

The inquiry so far

01

Tickers · Could AI make effort trustworthy?

Observing was not understanding.

02

AI Senior · Could AI understand and guide work?

Assistance introduced authority.

03

Civic Score · What if that authority reached society?

Fairer judgement was not justified judgement.

04

LifeRPG · Could AI help people develop instead of ranking them?

Personal progression could become another score.

04

Future University · What institutions does meaningful learning need?

Learning still served work and achievement.

05

Life after necessity · What would people do if work stopped being necessary?

Meaning might come from chosen activity, relationships and experience.

Perfect World imagines a society where AI supports that life, and asks who shapes our choices when help becomes extraordinarily capable.

Next · World 05 · Perfect World →

Life after necessity

Life after necessity

Two people in profile leaning in towards each other, a thin red line of light between them.

The question every world left

Beyond work and necessity

From preparing people for work to a life that does not depend on it

SpecAI-generated study, 25 October 2026 (Higgsfield, designer-directed).

World 05 / 05 · Perfect World

Perfect World

What I designed

A desirable future communicated through a city film and posters, then a guided mixed-reality conversation between two visitors.

Fictional world · City film and two posters · Implemented Quest encounter · Visitor evaluation in progress

Why

To investigate choice when AI help is useful and welcome, so its influence is harder to dismiss or to notice.

Who

Pairs of visitors who each consent; visitors who watch the film and join the discussion; the operator and bystanders.

How

Encounter the future → Choose a route → Consent → Talk, with optional guidance → Reflect.

The question this version examined

How much of a good life would you still want to shape yourself?

TickersAI SeniorCivic ScoreLifeRPG / UNIPerfect World

Signal · World 05 of 5

1. Assistants are everyday, and personal use is young

Life After Necessity granted secure essentials. Perfect World grants something harder: help that works. Today’s assistants are everyday tools, used in Aotearoa and across the OECD, and the youngest adults are the most likely to turn to them for personal support (figs. 1 and 2). Most of that help is still asked for. The project imagines what changes when it is offered first.

Most of this help is still asked for. The next step is an assistant that suggests first.

Key finding · author’s reading of the evidence

Figure 1: Share of US adults who get emotional support from chatbots, by age (Pew Research Center, February 2026)

20%Aged 18–29
13%Aged 30–49
4%Aged 50–64
2%Aged 65+

Also in the evidence

  1. 01
    Connection matters, with limits

    Social connection and support matter, but mortality associations do not establish this guide’s health effect (Coan & Sbarra, 2015; Holt-Lunstad et al., 2015).

  2. 02
    Preferences are constructed

    Preferences can be built through context and choice (Lichtenstein & Slovic, 2006).

  3. 03
    Choosing without knowing

    Transformative experiences complicate choosing with complete advance knowledge (L. A. Paul, 2014).

  4. 04
    Meaning goes beyond pleasure

    Meaning and mattering extend beyond pleasant feeling (Costin & Vignoles, 2020; Martela & Steger, 2016).

The question this raisesWhat if it suggested before we ask?

Help that is asked for is easy to judge. Help that arrives first can shape what someone goes on to want, and a change in interests alone cannot show either manipulation or benefit. The exhibition leaves room to decline for that reason. In the exhibition, the other visitor has their own invitation and a practical choice to decline.

Data: (InternetNZ, 2025; Organisation for Economic Co-operation and Development, 2026; Pew Research Center, 2026). The InternetNZ sample excludes people who are offline; the “less often” and “not in the past year” shares are arithmetic from the reported 58% and 79%. The OECD figures are as reported by the EU Digital Skills and Jobs Platform, because the OECD page could not be read.

TickersAI SeniorCivic ScoreLifeRPG / UNIPerfect World

Signal · World 05 of 5 · The evidence

1.1Generative AI has become an everyday assistant, and the youngest users already turn to it for advice and support

Across the OECD, more than a third of people used generative AI tools in 2025, including about three quarters of students and 41% of employed people (Organisation for Economic Co-operation and Development, 2026). In Aotearoa, 58% of online adults surveyed by InternetNZ used AI at least weekly, and 79% had used it in the past year (InternetNZ, 2025).

Use is turning personal. In a 2026 Pew survey, 20% of US adults aged 18 to 29 said they get emotional support from chatbots, against 13% of those aged 30 to 49, 4% of those aged 50 to 64 and 2% of those aged 65 and over (Pew Research Center, 2026). Perfect World imagines the next step: an assistant that understands what matters to us and suggests before we ask.

Figure 2: Generative AI use in Aotearoa and the OECD (InternetNZ 2025; OECD 2025)

58%

Online NZ adults using AI at least weekly

InternetNZ, 2025
79%

Online NZ adults using AI in the past year

InternetNZ, 2025
about 75%

OECD students aged 16+ using generative AI

OECD, 2025
41.1%

of employed people in the OECD used generative AI

36.7%

of unemployed people

12.5%

of people outside the labour market

20.2%

of OECD firms used AI in 2025

20%

of US adults aged 18–29 get emotional support from chatbots

2%

of US adults aged 65+

Figure 3: Who uses generative AI in OECD countries, 2025 (OECD)

Students aged 16 and overabout 75%
Employed people41.1%
Unemployed people36.7%
Outside the labour market12.5%

Figure 4: How often online New Zealand adults use AI (InternetNZ, Nov to Dec 2025)

Online adults, 18+

58%21%21%

OECD firms, 2025

20.2%79.8%
  • More use
  • Some use
  • No use

What Makes a Life Worth Living?

If nobody has to work to live, what should help be for? Before imagining the city I read four areas of research on what makes a life go well, and noted what each would ask of an AI guide.

Meaningful effort

01

Meaningful effort

Why can difficult things be rewarding?

People value outcomes more when effort went into them, even though effort is costly (Inzlicht, Shenhav and Olivola, 2018).

The guide should not remove every challenge.

Motivation and autonomy

02

Motivation and autonomy

Why does choosing an activity matter?

Activities felt as chosen sustain motivation and wellbeing; support that respects autonomy works better than pressure (Deci and Ryan, 2012; Slemp et al., 2024).

Help has to tell chosen effort from imposed obligation.

Happiness and wellbeing

03

Happiness and wellbeing

Is feeling good enough for a good life?

Happiness partly adapts after major life changes (Lucas, 2007), and a good life can be rich in experience, not only happy or meaningful (Oishi and Westgate, 2022).

No single happiness score to optimise.

Relationships

04

Relationships

How do other people shape us?

Loneliness and social isolation raise mortality risk (Holt-Lunstad et al., 2015); kindness to others is linked to wellbeing (Hui et al., 2020).

Guidance may change who people meet, and who they become.

Design synthesis

A good future cannot be defined only by making every task easier.

Photographs: film keyframes, AI-generated (Spec) · findings from the sources cited

Perfect World · World · 05

Perfect World · World · 05

What Makes a Life Worth Living?

If nobody has to work to live, what should help be for? Before imagining the city I read four areas of research on what makes a life go well, and noted what each would ask of an AI guide.

Meaningful effort

01

Meaningful effort

Why can difficult things be rewarding?

People value outcomes more when effort went into them, even though effort is costly (Inzlicht, Shenhav and Olivola, 2018).

The guide should not remove every challenge.

Motivation and autonomy

02

Motivation and autonomy

Why does choosing an activity matter?

Activities felt as chosen sustain motivation and wellbeing; support that respects autonomy works better than pressure (Deci and Ryan, 2012; Slemp et al., 2024).

Help has to tell chosen effort from imposed obligation.

Happiness and wellbeing

03

Happiness and wellbeing

Is feeling good enough for a good life?

Happiness partly adapts after major life changes (Lucas, 2007), and a good life can be rich in experience, not only happy or meaningful (Oishi and Westgate, 2022).

No single happiness score to optimise.

Relationships

04

Relationships

How do other people shape us?

Loneliness and social isolation raise mortality risk (Holt-Lunstad et al., 2015); kindness to others is linked to wellbeing (Hui et al., 2020).

Guidance may change who people meet, and who they become.

Design synthesis

A good future cannot be defined only by making every task easier.

Photographs: film keyframes, AI-generated (Spec) · findings from the sources cited

Perfect World · World · 05

Perfect World · World · 05

UNI’s turn
What if the help works?
Three tensions
Necessity is taken care of
Why still learn?
Why still be judged?
Why think at all?
Essentials are secure
Help is everywhere
A guide that understands
Every option is good
Comfort or growth
Recommendation or authorship
Delegation or choice

02 — Ideation

Five responses to a life beyond necessary work.

Key question

If the future works, where does the question still live, and how could a visitor feel it?

Tabletop choice
AI-selected challenge
Film + encounter
Two objects, one recommended
An object is not a life
Became exhibition v2
The guide picks a hard thing
Even growth can be authored
Became film direction 05
Film: believe the future
Quest: feel personal help
Reflection: question it
Fits a short visit

Opening up possible futures

I started from one “What if?”: what if people no longer had to work, and the help simply worked? I mapped five directions: almost unlimited leisure; AI doing everything while people rarely act; a society of voluntary care, creativity and challenge; intelligent systems that actively guide people towards fulfilling experiences; and material security combined with human choice and unusually good guidance. I took the last forward, because freedom from work alone was not the question. The question was what happens when AI also helps people decide how to use that freedom.

Filled: the direction taken forward

Futures We Have Already Imagined

Fiction had already imagined societies where technology meets every need. Each shows what such a world gives, and what people lose in it. Red marks were part of my research at the time; grey ones were added while writing this report.

01My research
Psycho-Pass

Key visual, Psycho-Pass 3 (Production I.G, 2019)

Psycho-Pass

An AI reads every mind and decides who is a danger.

I take

Help that never sleeps

I avoid

A system that scores people

Is a calm city worth being read?

02Found later
Brave New World

First edition cover (Huxley, 1932)

Brave New World

Happiness engineered from birth, kept by a pill.

I take

Security for everyone

I avoid

Contentment as control

Would I notice being kept happy?

03Found later

The Culture

Iain M. Banks’ novels, 1987–2012 · no cover reproduced

The Culture

Machines run a post-scarcity civilisation; people do as they please.

I take

Work as a choice

I avoid

Minds that decide for you

What is left for people to do?

04Found later
Cyberpunk: Edgerunners

Key art (Studio Trigger & CD Projekt, 2022)

Cyberpunk: Edgerunners

Astonishing technology, owned by a few, sold to the many.

I take

Nothing

I avoid

Capability without security

Who owns the machines?

05My research
Den-noh Coil

DVD vol. 1 cover (Madhouse, 2007)

Den-noh Coil

Children live in a city layered with augmented glasses.

I take

A layer over the ordinary

I avoid

Glasses that replace the world

Where does the real street end?

06My research
Deep Utopia

Book cover (Bostrom, 2024)

Deep Utopia

A thought experiment: what is life for once effort is unnecessary? (Bostrom, 2024)

I take

The question itself

I avoid

Answering it for people

Why do anything?

Perfect World · World · 01

Perfect World · World · 01

Futures We Have Already Imagined

Fiction had already imagined societies where technology meets every need. Each shows what such a world gives, and what people lose in it. Red marks were part of my research at the time; grey ones were added while writing this report.

01My research
Psycho-Pass

Key visual, Psycho-Pass 3 (Production I.G, 2019)

Psycho-Pass

An AI reads every mind and decides who is a danger.

I take

Help that never sleeps

I avoid

A system that scores people

Is a calm city worth being read?

02Found later
Brave New World

First edition cover (Huxley, 1932)

Brave New World

Happiness engineered from birth, kept by a pill.

I take

Security for everyone

I avoid

Contentment as control

Would I notice being kept happy?

03Found later

The Culture

Iain M. Banks’ novels, 1987–2012 · no cover reproduced

The Culture

Machines run a post-scarcity civilisation; people do as they please.

I take

Work as a choice

I avoid

Minds that decide for you

What is left for people to do?

04Found later
Cyberpunk: Edgerunners

Key art (Studio Trigger & CD Projekt, 2022)

Cyberpunk: Edgerunners

Astonishing technology, owned by a few, sold to the many.

I take

Nothing

I avoid

Capability without security

Who owns the machines?

05My research
Den-noh Coil

DVD vol. 1 cover (Madhouse, 2007)

Den-noh Coil

Children live in a city layered with augmented glasses.

I take

A layer over the ordinary

I avoid

Glasses that replace the world

Where does the real street end?

06My research
Deep Utopia

Book cover (Bostrom, 2024)

Deep Utopia

A thought experiment: what is life for once effort is unnecessary? (Bostrom, 2024)

I take

The question itself

I avoid

Answering it for people

Why do anything?

Perfect World · World · 01

Perfect World · World · 01

How Could We Arrive Here?

An illustrative sequence, not a forecast. Technology runs above the line; the decisions a society would have to make run below it. Better AI alone does not create shared security: every step below the line is a choice.

2026

Help is everyday, and mostly asked for

58% of online NZ adults use AI weekly (InternetNZ, 2025)

2030

AI’s power bill: data centres compete with homes

~945 TWh by 2030 (International Energy Agency, 2025)

2031

Clean power becomes the cheap default

85.5% of NZ electricity renewable, 2024 (MBIE, 2025)

2034

Machines leave the factory: transit and field robots

5 million industrial robots in use (IFR, 2026)

↑ Technology

↓ What society decides

2036

An Algorithm Act: systems that decide must explain

NZ’s Algorithm Charter is voluntary (Stats NZ, 2020)

2039

Transition years: paid work shrinks unevenly

Exposure is not job loss (Gmyrek et al., 2025)

Milestones and dates are fiction, each tied to a signal today · photographs: film-style studies, AI-generated (Spec)

Leap 01

Secure essentials

Necessity is gone. People still choose work, care, study, play and hard projects.

Leap 02

Reliable help

Machines do the practical work well enough that people stop worrying about it.

Leap 03

Cheap thinking

The guide’s computing no longer competes with homes for power.

Leap 04

Understanding

Ticker knows why something matters to you, not only what you did.

2042

Thinking gets cheap: neuromorphic processors

1.15 billion artificial neurons on 2,600 W (Intel, 2024)

2058

The lens layer: one red line at the edge of sight

Display glasses with one private lens (Even Realities, 2024)

2045

A city charter, with a founding agreement with mana whenua (placeholder)

Shared governance of Tāmaki’s maunga since 2014

2048

The first cluster: homes, energy roof, food floors, the Works

Habitat 67: every stepped home has sun (Safdie, 1967)

2053

The guide opens, non-commercial, under the charter

My Tickers prototype: asking for context beat collecting labels

2070

Perfect World complete; the city makes a film to welcome people

This is the film

Perfect World · World · 02

Perfect World · World · 02

How Could We Arrive Here?

An illustrative sequence, not a forecast. Technology runs above the line; the decisions a society would have to make run below it. Better AI alone does not create shared security: every step below the line is a choice.

2026

Help is everyday, and mostly asked for

58% of online NZ adults use AI weekly (InternetNZ, 2025)

2030

AI’s power bill: data centres compete with homes

~945 TWh by 2030 (International Energy Agency, 2025)

2031

Clean power becomes the cheap default

85.5% of NZ electricity renewable, 2024 (MBIE, 2025)

2034

Machines leave the factory: transit and field robots

5 million industrial robots in use (IFR, 2026)

↑ Technology

↓ What society decides

2036

An Algorithm Act: systems that decide must explain

NZ’s Algorithm Charter is voluntary (Stats NZ, 2020)

2039

Transition years: paid work shrinks unevenly

Exposure is not job loss (Gmyrek et al., 2025)

Milestones and dates are fiction, each tied to a signal today · photographs: film-style studies, AI-generated (Spec)

Leap 01

Secure essentials

Necessity is gone. People still choose work, care, study, play and hard projects.

Leap 02

Reliable help

Machines do the practical work well enough that people stop worrying about it.

Leap 03

Cheap thinking

The guide’s computing no longer competes with homes for power.

Leap 04

Understanding

Ticker knows why something matters to you, not only what you did.

2042

Thinking gets cheap: neuromorphic processors

1.15 billion artificial neurons on 2,600 W (Intel, 2024)

2058

The lens layer: one red line at the edge of sight

Display glasses with one private lens (Even Realities, 2024)

2045

A city charter, with a founding agreement with mana whenua (placeholder)

Shared governance of Tāmaki’s maunga since 2014

2048

The first cluster: homes, energy roof, food floors, the Works

Habitat 67: every stepped home has sun (Safdie, 1967)

2053

The guide opens, non-commercial, under the charter

My Tickers prototype: asking for context beat collecting labels

2070

Perfect World complete; the city makes a film to welcome people

This is the film

Perfect World · World · 02

Perfect World · World · 02

Choosing Perfect World

The scenario

The conditions of the design fiction

Each project made support more capable while revealing a question its improvements could not settle. The last world grants what they built toward: essentials are secure, help is everywhere, and it works.

Tickers exposed the need for context; AI Senior made interpretation and correction tangible; Civic brought authority and collective decisions into view; UNI showed that development depends on people, places and opportunity beyond an assistant. Each also showed a limit: while a system is obviously coercive, people reject it on sight and the debate ends early.

So Perfect World builds on those findings and sets four deliberate conditions. Material security: essentials are met without compulsory work. Beneficial intelligence: the guide is unusually capable and genuinely useful. Meaningful opportunity: relationships, learning, making, rest and chosen challenge stay open to everyone. The ability to choose: guidance can be accepted, questioned or declined without losing anything essential.

These are not findings about the future. They are the conditions of the fiction, chosen so that the remaining question is a quiet one: if every option offered is good, how much of the choice is still the person’s own?

To make people imagine living there, the world had to be physically and socially possible. That is where the city research begins.

The harbour city at blue hour: terraced green towers lit window by window, ferries on calm water, a green volcanic cone behind.

Choosing Perfect World

Initial vision

A future people would actually want: secure, helped and understood, with a guide that suggests first and can always be declined.

How might we · v5

How might we immerse people in a desirable AI-supported future that invites them to reconsider what freedom and personal choice mean?

It is the question on the poster, and the one the booth asks its visitors.

The next step

Next: cities that tried to build a future from nothing, the site, and what it would take to run such a city.

SpecAI-generated film keyframe, 26 October 2026 (Higgsfield, designer-directed).

World building

What six planned cities show about building one from nothing.

Lessons

Rules

Lessons from NEOM

NEOM promised a city in two mirrored walls 170 km long. It shrank, displaced the people who lived there and stalled on one funder. I turned each of its failures, and those of other planned cities, into a rule for Perfect World:

– A wall is not a city: Terraces, not walls. Every home steps back to the sun; machines live in the shade.

– Built over people: Built only with the people who are there. The maunga stay unbuilt.

– Started at full size: Grow one complete neighbourhood at a time.

– One funder: The city pays for itself: its own energy, food and making fund the Floor.

– Above the law: The charter sits under national law and can be revoked.

– A showroom: Open membership: ordinary people, all ages, not employees.

Two ~500 m walls would leave the lower levels with little daylight; NEOM’s own planners worried people would not live there (Business Insider, 2024).

The Huwaitat were evicted for The Line, and the BBC reported that lethal force was authorised (Dezeen, 2024).

The Line shrank to about 2.4 of 170 km by 2030 (Middle East Briefing, 2024); Masdar City shelved its completion date (Associated Press, 2023).

The cutback was tied to the sovereign fund not approving the budget (Middle East Briefing, 2024).

Honduras repealed the law behind Próspera’s private city in 2022; a claim of about US$10.8 billion followed (UNCTAD Investment Policy Hub, 2025).

Woven City’s first residents moved in as a company-run test city (Toyota Motor Corporation, 2025).

The Line, as NEOM pictured it · NEOM promotional render, 2022

Perfect World · World · 08

Perfect World · World · 08

World building

What six planned cities show about building one from nothing.

Lessons

Rules

Lessons from NEOM

NEOM promised a city in two mirrored walls 170 km long. It shrank, displaced the people who lived there and stalled on one funder. I turned each of its failures, and those of other planned cities, into a rule for Perfect World:

– A wall is not a city: Terraces, not walls. Every home steps back to the sun; machines live in the shade.

– Built over people: Built only with the people who are there. The maunga stay unbuilt.

– Started at full size: Grow one complete neighbourhood at a time.

– One funder: The city pays for itself: its own energy, food and making fund the Floor.

– Above the law: The charter sits under national law and can be revoked.

– A showroom: Open membership: ordinary people, all ages, not employees.

Two ~500 m walls would leave the lower levels with little daylight; NEOM’s own planners worried people would not live there (Business Insider, 2024).

The Huwaitat were evicted for The Line, and the BBC reported that lethal force was authorised (Dezeen, 2024).

The Line shrank to about 2.4 of 170 km by 2030 (Middle East Briefing, 2024); Masdar City shelved its completion date (Associated Press, 2023).

The cutback was tied to the sovereign fund not approving the budget (Middle East Briefing, 2024).

Honduras repealed the law behind Próspera’s private city in 2022; a claim of about US$10.8 billion followed (UNCTAD Investment Policy Hub, 2025).

Woven City’s first residents moved in as a company-run test city (Toyota Motor Corporation, 2025).

The Line, as NEOM pictured it · NEOM promotional render, 2022

Perfect World · World · 08

Perfect World · World · 08

The Site

The sun decides the shape. Every measurement on these boards comes from one model I wrote, so the drawings, the 3D model and the film all draw the same city. Six moves set the plan.

Site plan, north up · a fictional harbour with Tāmaki Makaurau’s sun, rain and wind · no real marae, maunga or iwi place is drawn

Miniature model: Sun from the north

Move 01

Sun from the north

Terraces step down towards the sun and the water

Miniature model: A horseshoe, not a line

Move 02

A horseshoe, not a line

Twelve clusters on three shores, open to the sea

Miniature model: Machines in the shade

Move 03

Machines in the shade

Works, port and the Living Layer take the north shore

Miniature model: The maunga stays unbuilt

Move 04

The maunga stays unbuilt

Sightlines kept; its future is for mana whenua

Miniature model: Fed by the ring

Move 05

Fed by the ring

Fields, orchards and a reservoir around the city

Miniature model: One loop, no cars

Move 06

One loop, no cars

Every cluster on one tram line

The six moves as miniature models · AI-generated with Higgsfield from my city renders, 26 October 2026 · Spec

Cluster S3 from above, with the clay view it was rendered from · AI render · Spec

47,100

people

in 12 clusters

17,976

homes

1, 2 and 3 bedrooms

27 ha

a cluster

600 × 450 m

5.0 km

the harbour

2.9 km across

720 m

between clusters

one tram stop each

3

shores

south, east and west

How it grows · red is built, dashed is planned

S3 · Civic Hall · first turbine

S1–S4 · Works belt · port

W1–2, E1–2 · Living Layer

All twelve · the Loop closes

A cluster opens only when the others are full, the land is agreed with mana whenua, and its sunlight plan shades no existing home.

Perfect World · World · 18

Perfect World · World · 18

The Site

The sun decides the shape. Every measurement on these boards comes from one model I wrote, so the drawings, the 3D model and the film all draw the same city. Six moves set the plan.

Site plan, north up · a fictional harbour with Tāmaki Makaurau’s sun, rain and wind · no real marae, maunga or iwi place is drawn

Miniature model: Sun from the north

Move 01

Sun from the north

Terraces step down towards the sun and the water

Miniature model: A horseshoe, not a line

Move 02

A horseshoe, not a line

Twelve clusters on three shores, open to the sea

Miniature model: Machines in the shade

Move 03

Machines in the shade

Works, port and the Living Layer take the north shore

Miniature model: The maunga stays unbuilt

Move 04

The maunga stays unbuilt

Sightlines kept; its future is for mana whenua

Miniature model: Fed by the ring

Move 05

Fed by the ring

Fields, orchards and a reservoir around the city

Miniature model: One loop, no cars

Move 06

One loop, no cars

Every cluster on one tram line

The six moves as miniature models · AI-generated with Higgsfield from my city renders, 26 October 2026 · Spec

Cluster S3 from above, with the clay view it was rendered from · AI render · Spec

47,100

people

in 12 clusters

17,976

homes

1, 2 and 3 bedrooms

27 ha

a cluster

600 × 450 m

5.0 km

the harbour

2.9 km across

720 m

between clusters

one tram stop each

3

shores

south, east and west

How it grows · red is built, dashed is planned

S3 · Civic Hall · first turbine

S1–S4 · Works belt · port

W1–2, E1–2 · Living Layer

All twelve · the Loop closes

A cluster opens only when the others are full, the land is agreed with mana whenua, and its sunlight plan shades no existing home.

Perfect World · World · 18

Perfect World · World · 18

From Sketch to City

north shore faces away from the sun

01 · Ring

Twelve clusters round the harbour. Rejected: the north shore’s homes would face away from the sun.

02 · Horseshoe

Homes on three shores; machines take the north shore, in the shade.

03 · Site plan

Measured: harbour 5.0 km, clusters 720 m apart, the Loop 15 km.

04 · One cluster

Measured, then built in Blender to the same numbers.

05 · The whole city, in clay

Twelve copies of the measured cluster round the harbour, the Works on the north shore, the maunga left green. Every render of the city is made from this model.

Perfect World · World · 21

Perfect World · World · 21

From Sketch to City

north shore faces away from the sun

01 · Ring

Twelve clusters round the harbour. Rejected: the north shore’s homes would face away from the sun.

02 · Horseshoe

Homes on three shores; machines take the north shore, in the shade.

03 · Site plan

Measured: harbour 5.0 km, clusters 720 m apart, the Loop 15 km.

04 · One cluster

Measured, then built in Blender to the same numbers.

05 · The whole city, in clay

Twelve copies of the measured cluster round the harbour, the Works on the north shore, the maunga left green. Every render of the city is made from this model.

Perfect World · World · 21

Perfect World · World · 21

One Cluster

From the harbour up: tidal steps and a promenade, the tram street, the commons with its Market Hall and Open Desk, five rows of terraces that all face north, and a planted crown on top.

Terraces

5 rows

39 m deep, 19.2 m high, six storeys

Sun courts

23 m

20.6 m is the least for winter-noon sun

Segments

4 × 135 m

split by 12 m planted lanes

Homes

1,498

13 per storey in each segment

People

3,925

55 homes a hectare, gross

Crown

45 m

planted, with solar canopies and rain tanks

Section and Home

Section A–A · at winter noon (30°) row one’s shadow stops 2.4 m short of row two’s ground floor

A sun court at winter noon · AI render from the measured model · Spec

Every home is 9 m deep. The front 6 m has full-height glass to the north: living, kitchen and every bedroom. The rear 3 m sits under the home above and holds the entry, bath and laundry. Each storey steps back 6 m, so every garden is the roof of the home below.

Perfect World · World · 19

Perfect World · World · 19

One Cluster

From the harbour up: tidal steps and a promenade, the tram street, the commons with its Market Hall and Open Desk, five rows of terraces that all face north, and a planted crown on top.

Terraces

5 rows

39 m deep, 19.2 m high, six storeys

Sun courts

23 m

20.6 m is the least for winter-noon sun

Segments

4 × 135 m

split by 12 m planted lanes

Homes

1,498

13 per storey in each segment

People

3,925

55 homes a hectare, gross

Crown

45 m

planted, with solar canopies and rain tanks

Section and Home

Section A–A · at winter noon (30°) row one’s shadow stops 2.4 m short of row two’s ground floor

A sun court at winter noon · AI render from the measured model · Spec

Every home is 9 m deep. The front 6 m has full-height glass to the north: living, kitchen and every bedroom. The rear 3 m sits under the home above and holds the entry, bath and laundry. Each storey steps back 6 m, so every garden is the roof of the home below.

Perfect World · World · 19

Perfect World · World · 19

How the City Works

The rule under everything: sun for people, shade for machines. Homes and public life take the sunlit terraces; machines, storage, grow rooms, compute and freight take the hill, the service bases and the north shore. Every number is the plan’s own sum, worked from today’s figures for New Zealand.

01Energy

116%

437 GWh made a year · 377 used

Every roof and balustrade is solar (~190 MWp, 246 GWh); four 8 MW turbines at the harbour mouth (126 GWh); 100 ha of solar over crops (65 GWh). Batteries in each cluster’s base hold one evening. Each cluster’s roofs make 20.5 GWh against its homes’ 10.6.

Today’s signal

85.5% of NZ electricity was renewable in 2024 (MBIE, 2025)

Still open

Generation is not reliable power: dark winter weeks fall back on the national grid

02Water

2.33 GL

of rain caught a year · 1.81 needed

Roofs and crowns catch rain into tanks in each service base. Greywater is reused, so 2.58 GL of use needs 1.81 from new water. A 60 ha reservoir in the hinterland carries dry years; overflow goes to rain gardens, never the harbour.

Today’s signal

Sized from Tāmaki Makaurau’s rainfall and today’s household use of about 150 litres a person a day

Still open

Two dry years in a row

03Food

2–3×

the city’s area in farmland · 5,800–8,400 ha

Greens and herbs in small grow rooms in the shade (11,800 m²); glasshouses warmed by the Works; a garden on every terrace; robot-tended fields, orchards and open pasture in the hinterland. Animals are never stacked; most meat is grown inside.

Today’s signal

Indoor farms on artificial light have struggled with energy costs (Institute of Food Technologists, 2023); cultivated chicken approved in Singapore in 2020 (Al Jazeera, 2020)

Still open

Farmers’ livelihoods beside mana whenua; the taste and cost of grown meat

Sun for People, Shade for Machines

What it takes to keep 47,100 people fed, powered and housed without anyone having to work for it, and where the arithmetic still does not close.

04Making

Repair first

every object has a repair record

Robot floors on the Works belt and in each cluster’s base make clothing, furniture and parts, and repair before they replace. People can work there if they want to. Nobody has to.

Today’s signal

5 million industrial robots in use (IFR, 2026)

Still open

Who repairs the machines, and whether people keep the skills

05Compute

6 MW

12 cluster nodes · 3 MW Living Layer

A personal processor in each lens and home; a neuromorphic node in each cluster’s base; the Living Layer, halls of grown biological processors on the north shore, under an ethics charter. Its waste heat warms the glasshouses and pools.

Today’s signal

Data centres ~415 → ~945 TWh, 2024 → 2030 (International Energy Agency, 2025); 1.15 billion artificial neurons on 2,600 W at most (Intel, 2024)

Still open

Organoid computing is lab-scale (Smirnova et al., 2023). Could a living processor feel?

06Transit

15 km

13 stops · trams both ways · no private cars

The Loop runs along every commons street and crosses the harbour mouth on a lifting bridge; trams every five minutes, ferries across the water, step-free shuttles up the lanes. Freight never reaches the street: it moves through tunnels under the service bases.

Today’s signal

Driverless rides already run commercially, with company-reported safety data (Waymo, 2026)

Still open

A full circuit takes 45 minutes; whether people take the ferry instead

Perfect World · World · 10

Perfect World · World · 10

How the City Works

The rule under everything: sun for people, shade for machines. Homes and public life take the sunlit terraces; machines, storage, grow rooms, compute and freight take the hill, the service bases and the north shore. Every number is the plan’s own sum, worked from today’s figures for New Zealand.

01Energy

116%

437 GWh made a year · 377 used

Every roof and balustrade is solar (~190 MWp, 246 GWh); four 8 MW turbines at the harbour mouth (126 GWh); 100 ha of solar over crops (65 GWh). Batteries in each cluster’s base hold one evening. Each cluster’s roofs make 20.5 GWh against its homes’ 10.6.

Today’s signal

85.5% of NZ electricity was renewable in 2024 (MBIE, 2025)

Still open

Generation is not reliable power: dark winter weeks fall back on the national grid

02Water

2.33 GL

of rain caught a year · 1.81 needed

Roofs and crowns catch rain into tanks in each service base. Greywater is reused, so 2.58 GL of use needs 1.81 from new water. A 60 ha reservoir in the hinterland carries dry years; overflow goes to rain gardens, never the harbour.

Today’s signal

Sized from Tāmaki Makaurau’s rainfall and today’s household use of about 150 litres a person a day

Still open

Two dry years in a row

03Food

2–3×

the city’s area in farmland · 5,800–8,400 ha

Greens and herbs in small grow rooms in the shade (11,800 m²); glasshouses warmed by the Works; a garden on every terrace; robot-tended fields, orchards and open pasture in the hinterland. Animals are never stacked; most meat is grown inside.

Today’s signal

Indoor farms on artificial light have struggled with energy costs (Institute of Food Technologists, 2023); cultivated chicken approved in Singapore in 2020 (Al Jazeera, 2020)

Still open

Farmers’ livelihoods beside mana whenua; the taste and cost of grown meat

Sun for People, Shade for Machines

What it takes to keep 47,100 people fed, powered and housed without anyone having to work for it, and where the arithmetic still does not close.

04Making

Repair first

every object has a repair record

Robot floors on the Works belt and in each cluster’s base make clothing, furniture and parts, and repair before they replace. People can work there if they want to. Nobody has to.

Today’s signal

5 million industrial robots in use (IFR, 2026)

Still open

Who repairs the machines, and whether people keep the skills

05Compute

6 MW

12 cluster nodes · 3 MW Living Layer

A personal processor in each lens and home; a neuromorphic node in each cluster’s base; the Living Layer, halls of grown biological processors on the north shore, under an ethics charter. Its waste heat warms the glasshouses and pools.

Today’s signal

Data centres ~415 → ~945 TWh, 2024 → 2030 (International Energy Agency, 2025); 1.15 billion artificial neurons on 2,600 W at most (Intel, 2024)

Still open

Organoid computing is lab-scale (Smirnova et al., 2023). Could a living processor feel?

06Transit

15 km

13 stops · trams both ways · no private cars

The Loop runs along every commons street and crosses the harbour mouth on a lifting bridge; trams every five minutes, ferries across the water, step-free shuttles up the lanes. Freight never reaches the street: it moves through tunnels under the service bases.

Today’s signal

Driverless rides already run commercially, with company-reported safety data (Waymo, 2026)

Still open

A full circuit takes 45 minutes; whether people take the ferry instead

Perfect World · World · 10

Perfect World · World · 10

When It Fails

Everything leans on energy, and everything has a way to run by hand. The city is designed to fail gently: the Floor first, the guide last.

EnergyWaterFoodTransitWorksThe guideNATIONAL GRID · BACKUPPEOPLE · THE OPEN DESK · EVERY SERVICE WORKS BY HAND

A dark, still week

Sun and wind drop for days.

Batteries cover the evening, the grid the rest. The Floor first; the guide goes quiet.

The guide goes down

Ticker stops answering.

Nothing essential runs through it. People at the Open Desk take over.

A machine is wrong

Rarely, a robot or a schedule fails.

Every system has a human override, and people drill for it, because rare failures make people stop checking (Bainbridge, 1983).

An attack

Someone breaks into the network.

Personal data never leaves the personal layer; cluster nodes cut off and run alone.

Nothing to import

Chips, minerals or medicines run short.

Repair before replace; the Works rationing order is public.

The Living Layer stops

The Ethics Panel halts a process.

Cluster nodes carry on; the city simply learns more slowly.

How Ready Is It?

My reading of today’s signals. Grey is in use, pink is demonstrated, red is still the lab or my fiction. The red rows are the leaps the whole world rests on.

In use today

Demonstrated

Lab or fiction

Today’s signal

Roof solar and batteries85.5% of NZ electricity renewable, 2024 (MBIE, 2025)
Driverless transitCommercial rides with company-reported data (Waymo, 2026)
Robots in factories and fields5 million industrial robots in use (IFR, 2026)
Indoor farmsWorking, but struggling with energy costs (Institute of Food Technologists, 2023)
Display glasses, muscle wristbandOn sale (Even Realities, 2024; GSMArena, 2025)
Cultivated meatApproved for sale in Singapore, 2020 (Al Jazeera, 2020)
Neuromorphic computing1.15 billion artificial neurons, 2,600 W at most (Intel, 2024)
Living neurons as computersLab-scale; efficiency is a projection (Smirnova et al., 2023)
Reading feelings from the bodyFaces and signals do not reliably reveal emotion (Barrett et al., 2019)
A lens implant that draws lightNo signal: fiction
An AI that understands purposeNo signal: the leap I granted

What it rests on

Eleven technologies hold the world up. Four of them are still the lab or my fiction.

One column per row above, in the same colours.

Perfect World · World · 13

Perfect World · World · 13

When It Fails

Everything leans on energy, and everything has a way to run by hand. The city is designed to fail gently: the Floor first, the guide last.

EnergyWaterFoodTransitWorksThe guideNATIONAL GRID · BACKUPPEOPLE · THE OPEN DESK · EVERY SERVICE WORKS BY HAND

A dark, still week

Sun and wind drop for days.

Batteries cover the evening, the grid the rest. The Floor first; the guide goes quiet.

The guide goes down

Ticker stops answering.

Nothing essential runs through it. People at the Open Desk take over.

A machine is wrong

Rarely, a robot or a schedule fails.

Every system has a human override, and people drill for it, because rare failures make people stop checking (Bainbridge, 1983).

An attack

Someone breaks into the network.

Personal data never leaves the personal layer; cluster nodes cut off and run alone.

Nothing to import

Chips, minerals or medicines run short.

Repair before replace; the Works rationing order is public.

The Living Layer stops

The Ethics Panel halts a process.

Cluster nodes carry on; the city simply learns more slowly.

How Ready Is It?

My reading of today’s signals. Grey is in use, pink is demonstrated, red is still the lab or my fiction. The red rows are the leaps the whole world rests on.

In use today

Demonstrated

Lab or fiction

Today’s signal

Roof solar and batteries85.5% of NZ electricity renewable, 2024 (MBIE, 2025)
Driverless transitCommercial rides with company-reported data (Waymo, 2026)
Robots in factories and fields5 million industrial robots in use (IFR, 2026)
Indoor farmsWorking, but struggling with energy costs (Institute of Food Technologists, 2023)
Display glasses, muscle wristbandOn sale (Even Realities, 2024; GSMArena, 2025)
Cultivated meatApproved for sale in Singapore, 2020 (Al Jazeera, 2020)
Neuromorphic computing1.15 billion artificial neurons, 2,600 W at most (Intel, 2024)
Living neurons as computersLab-scale; efficiency is a projection (Smirnova et al., 2023)
Reading feelings from the bodyFaces and signals do not reliably reveal emotion (Barrett et al., 2019)
A lens implant that draws lightNo signal: fiction
An AI that understands purposeNo signal: the leap I granted

What it rests on

Eleven technologies hold the world up. Four of them are still the lab or my fiction.

One column per row above, in the same colours.

Perfect World · World · 13

Perfect World · World · 13

How Nobody Has to Work

Necessary work does not disappear; it moves to machines the residents own together. Their output is the Floor, given to everyone. What the city cannot make it buys with what it can sell. People still work, a lot: for meaning, for each other, and for money beyond the Floor. All of it is fiction, sized from the city model.

The Commons Fund

owns the city’s machines, for residents

The machines

Do the necessary work

The Floor

Given in kind, never as cash

Chosen work

Optional; paid beyond the Floor in NZ dollars

Sells outside

~60 GWh of spare power, Works goods, compute time

Buys outside

Minerals, chips, medicines, specialist parts

The reserve

Two years of imports, so a bad year never touches the Floor

Necessary workMachines doPeople choose to do
FoodPlant, harvest, move stock, run grow roomsFarm, cook, keep a garden
BuildingBuild and repair homes, the Loop, the WorksDesign, craft, fix by hand
LogisticsFreight, deliveries, cleaning, recyclingAlmost nothing; that is the point
CareRosters, medicines, lifting, appointmentsThe care itself: company and attention
LearningPractice partners, hints, finding a teacherTeach, mentor, run a choir
Running itAccounts, records, the guide’s plumbingPanels by lot, the Open Desk

One Resident’s Floor, in a Year

What the machines give each person, worked out from the city model. Nobody earns it and nobody can lose it.

35 m²

of home, plus 23 m² of garden

8,000 kWh

of power, everything electric

55 m³

of water, half of it rain

~1,500 m²

of farmland, plus greens grown indoors

Free

the Loop and the ferries

Always

care, at home and in the Commons

What could break it

01Power gets cheaper, so exports earn less

02Key minerals come from fewer places

03A drought and a bad harvest together

04The Fund is run badly, or for someone else

05Building the first cluster needs money nobody has yet

06It works for members; outsiders carry the costs

The one rule

The machines work. People choose.

Perfect World · World · 11

Perfect World · World · 11

How Nobody Has to Work

Necessary work does not disappear; it moves to machines the residents own together. Their output is the Floor, given to everyone. What the city cannot make it buys with what it can sell. People still work, a lot: for meaning, for each other, and for money beyond the Floor. All of it is fiction, sized from the city model.

The Commons Fund

owns the city’s machines, for residents

The machines

Do the necessary work

The Floor

Given in kind, never as cash

Chosen work

Optional; paid beyond the Floor in NZ dollars

Sells outside

~60 GWh of spare power, Works goods, compute time

Buys outside

Minerals, chips, medicines, specialist parts

The reserve

Two years of imports, so a bad year never touches the Floor

Necessary workMachines doPeople choose to do
FoodPlant, harvest, move stock, run grow roomsFarm, cook, keep a garden
BuildingBuild and repair homes, the Loop, the WorksDesign, craft, fix by hand
LogisticsFreight, deliveries, cleaning, recyclingAlmost nothing; that is the point
CareRosters, medicines, lifting, appointmentsThe care itself: company and attention
LearningPractice partners, hints, finding a teacherTeach, mentor, run a choir
Running itAccounts, records, the guide’s plumbingPanels by lot, the Open Desk

One Resident’s Floor, in a Year

What the machines give each person, worked out from the city model. Nobody earns it and nobody can lose it.

35 m²

of home, plus 23 m² of garden

8,000 kWh

of power, everything electric

55 m³

of water, half of it rain

~1,500 m²

of farmland, plus greens grown indoors

Free

the Loop and the ferries

Always

care, at home and in the Commons

What could break it

01Power gets cheaper, so exports earn less

02Key minerals come from fewer places

03A drought and a bad harvest together

04The Fund is run badly, or for someone else

05Building the first cluster needs money nobody has yet

06It works for members; outsiders carry the costs

The one rule

The machines work. People choose.

Perfect World · World · 11

Perfect World · World · 11

Who Runs What

One institution holds the charter. Every other body exists to limit it: to keep the Floor out of reach, the guide’s rules public, and a person at the desk when the system is wrong.

What it does

Can

Cannot

Perfect WorldHolds the charter; runs the city, the Works and TickerBuild, operate, publish every ruleOverride NZ law or Te Tiriti; profit for anyone
The FloorThe essentials, for every residentProvide without conditionsAsk for work, a score, a guide or a reason
Commons FundHolds the city’s machinesInvest, publish returnsSell personal data
Ticker BoardSets the guide’s rules in publicChange rules after the Residents’ Panel agreesAdvertise, sell, or be owned by a company
Partnership BoardFounding agreement with mana whenua; data under Te TiritiApprove or refuse uses of land and dataA labelled placeholder: this needs Māori design
Ethics PanelOversees the Living LayerStop any process; publish checks each seasonBe run by those who profit from compute
The Open DeskHuman appeal and repair at each CommonsReview, correct, delete, apologiseBe replaced by the guide
Residents’ PanelChosen by lot each yearDecide what the guide may suggest aboutChange the Floor

The charter’s rules

01Every suggestion says why

02Introductions need two invitations

03Memory is yours to see, correct and delete

04No one under 16 has a guide

05Anyone can appeal; a person answers within a day

06No building may take a home’s sun

Never scored or conditioned

The Floor · declining or switching off the guide · rest · relationships, faith, politics and votes · whether you work · who you meet. There is no score of a person anywhere: that is the line between this world and Civic.

What Kind of Place, and What Has to Be True

Inside Aotearoa, under its law · members walk in and out

01

Not a country

Residents keep their citizenship, courts and vote. New Zealand law and Te Tiriti sit above the charter, and Parliament can revoke it.

02

Not a company

No shareholders and no dividends. Surplus goes back into the city.

03

Members, by choice

Anyone can apply. Leaving is free, and so is coming back.

04

Three promises

The Floor is never scored. Declining help is free. The city works without the guide.

Granted is what I decided for the fiction. Evidenced is what exists today. Open is what nobody has settled.

Granted

Evidenced today

Still open

AutomationTrusted with farm, factory, transit, care logisticsWaymo’s company-reported safety data (Waymo, 2026)Rare failures make people stop checking (Bainbridge, 1983)
GovernanceA charter under national law; the Algorithm ActNZ’s Algorithm Charter is voluntary (Stats NZ, 2020)Who sits on the board; fairness to the country
Knowing peopleTicker understands purpose, growth, restFaces do not reliably reveal emotion (Barrett et al., 2019)Whether understanding can come from signals at all
Te Tiriti and dataA founding agreement; Māori authority over Māori dataTe Mana Raraunga (2018); shared governance of the maunga since 2014I cannot grant this. It needs Māori governance
Left outOpen membership; the guide is optionalOffline people are missing from surveys (InternetNZ, 2025)Everyone outside the city; people read wrongly

Perfect World · World · 12

Perfect World · World · 12

Who Runs What

One institution holds the charter. Every other body exists to limit it: to keep the Floor out of reach, the guide’s rules public, and a person at the desk when the system is wrong.

What it does

Can

Cannot

Perfect WorldHolds the charter; runs the city, the Works and TickerBuild, operate, publish every ruleOverride NZ law or Te Tiriti; profit for anyone
The FloorThe essentials, for every residentProvide without conditionsAsk for work, a score, a guide or a reason
Commons FundHolds the city’s machinesInvest, publish returnsSell personal data
Ticker BoardSets the guide’s rules in publicChange rules after the Residents’ Panel agreesAdvertise, sell, or be owned by a company
Partnership BoardFounding agreement with mana whenua; data under Te TiritiApprove or refuse uses of land and dataA labelled placeholder: this needs Māori design
Ethics PanelOversees the Living LayerStop any process; publish checks each seasonBe run by those who profit from compute
The Open DeskHuman appeal and repair at each CommonsReview, correct, delete, apologiseBe replaced by the guide
Residents’ PanelChosen by lot each yearDecide what the guide may suggest aboutChange the Floor

The charter’s rules

01Every suggestion says why

02Introductions need two invitations

03Memory is yours to see, correct and delete

04No one under 16 has a guide

05Anyone can appeal; a person answers within a day

06No building may take a home’s sun

Never scored or conditioned

The Floor · declining or switching off the guide · rest · relationships, faith, politics and votes · whether you work · who you meet. There is no score of a person anywhere: that is the line between this world and Civic.

What Kind of Place, and What Has to Be True

Inside Aotearoa, under its law · members walk in and out

01

Not a country

Residents keep their citizenship, courts and vote. New Zealand law and Te Tiriti sit above the charter, and Parliament can revoke it.

02

Not a company

No shareholders and no dividends. Surplus goes back into the city.

03

Members, by choice

Anyone can apply. Leaving is free, and so is coming back.

04

Three promises

The Floor is never scored. Declining help is free. The city works without the guide.

Granted is what I decided for the fiction. Evidenced is what exists today. Open is what nobody has settled.

Granted

Evidenced today

Still open

AutomationTrusted with farm, factory, transit, care logisticsWaymo’s company-reported safety data (Waymo, 2026)Rare failures make people stop checking (Bainbridge, 1983)
GovernanceA charter under national law; the Algorithm ActNZ’s Algorithm Charter is voluntary (Stats NZ, 2020)Who sits on the board; fairness to the country
Knowing peopleTicker understands purpose, growth, restFaces do not reliably reveal emotion (Barrett et al., 2019)Whether understanding can come from signals at all
Te Tiriti and dataA founding agreement; Māori authority over Māori dataTe Mana Raraunga (2018); shared governance of the maunga since 2014I cannot grant this. It needs Māori governance
Left outOpen membership; the guide is optionalOffline people are missing from surveys (InternetNZ, 2025)Everyone outside the city; people read wrongly

Perfect World · World · 12

Perfect World · World · 12

This future

Perfect World · Ideation

01The promise

What does it promise?

Secure essentials, help that works, and time for what people choose.

In this imagined 2070 Aotearoa, home, food, care, transit and energy are unconditional, paid for by a public share in automation and clean power. Machines do the practical work well enough that people stop worrying about it.

Today’s signal

Clean power is getting cheaper: most new renewable power now costs less than the cheapest fossil-fuel option. (International Renewable Energy Agency, 2026)

02The guide

What does it know?

Why something matters to you, not only what you did.

A public guide, Ticker, learns what people say matters and suggests first: an opening, a route, a free kiln. It appears as one thin red line and one short line of text, and goes quiet once people are absorbed.

Today’s signal

Help is already everyday: 58% of online New Zealand adults used AI at least weekly in 2025. (InternetNZ, 2025)

03The institutions

Who runs it?

A public trust, not a company. Declining never costs essentials.

The Ticker Trust runs the guide as a non-commercial service and publishes every rule change. The Floor is never scored, and an Open Desk of people reviews, corrects and deletes. Māori authority over Māori data stays a labelled placeholder I cannot design.

Today’s signal

The rules are still voluntary: New Zealand’s Algorithm Charter for public agencies is a commitment, not a law. (Stats NZ, 2020)

04The social norm

What becomes ordinary?

Being guided towards people, places and hard things you chose.

Suggestions become as ordinary as a weather forecast. People meet through introductions both accepted, learn with a hint after the third try, and rest without having to defend it.

Today’s signal

Help is turning personal: 20% of US adults aged 18–29 get emotional support from chatbots. (Pew Research Center, 2026)

05The tension

Where does it rub?

If every option offered is good, who drew the list?

Nothing in this future is cruel, and the guide is usually right. The tension is quieter: how much of a good life is still authored by the person living it, and would anyone notice the difference?

The chain from help to choice

Information → Prediction → Recommendation → Delegation

Aotearoa, 2070

The Floor, the Ticker Trust, the guide and every date are designer-authored conditions of a fictional 2070 future, not predictions. Pictures are film keyframes and studies, AI-generated (Spec). Today’s signals are real, bounded findings from the sources cited.

A World That Understands You

The guide, Ticker, is what makes this world distinctive. It works in three layers. The leap granted in the fiction is that, after years of listening and being corrected, it is usually right about what matters to a person. It still asks; it never reads minds. Body signals give context, not feelings, intentions or values.

Layer 01

The person

What you tell it, what you correct, years alongside you

Always yours

Layer 02

The situation

Places and routines, voice, optional body signals

Off until you turn it on

Layer 03

The support

The city’s openings, one suggestion, one line of text

Can always be declined

What it reads, and on what terms

Source

What it gives

Consent

Limit

What you tell itPurposes, plans, what you care aboutAlways yoursThe main source. Nothing replaces asking
What you correct“No, I play to relax, not to perform.”Always yoursCorrections outrank every signal
Body signalsGaze, heart rhythm, movement, muscle signalsOff by default, per signalA raised heart rate is not a feeling (Barrett et al., 2019)
VoiceTone, pace, when you talk and stopOff by defaultNever used to judge another person’s mood
Places, routinesWhere you go, when you restOff by defaultKept on your personal layer
The city’s openingsA free kiln, a choir short of altos, someone who asked to meetPublicOther people appear only if they chose to
Years alongside youPatterns over timeYours to clearKeeps what it saw, what you said and its guesses apart
Lens layer · implanted, optionalA FILM BEHIND THE CORNEAGaze sensingWHERE YOU LOOK · BLINKSYour own lensUNTOUCHEDRim displayONE LINE OF LIGHT AT THE EDGE OF SIGHT

The guide in the eye, in 2070: fiction · macro photograph, AI-generated, Spec

Where the thinking happens: three tiers, nearest first

Personal

In your lens layer and your home

Knows you: what you said, corrected and allowed. Answers most requests.

Your data

Neighbourhood

A node in each cluster’s base

Knows the cluster’s openings: rooms, kilns, choirs, people who asked to meet.

No history

Living Layer

Halls on the north shore

Slow, city-wide learning, overnight. Under the Ethics Panel.

Pooled, consented

What it will not do

— Sell attention or rank by who paid

— Score or rank people

— Speak for you in a conversation

— Introduce anyone uninvited

— Steer politics, faith or votes

— Pretend to be a person

Perfect World · World · 15

Perfect World · World · 15

A World That Understands You

The guide, Ticker, is what makes this world distinctive. It works in three layers. The leap granted in the fiction is that, after years of listening and being corrected, it is usually right about what matters to a person. It still asks; it never reads minds. Body signals give context, not feelings, intentions or values.

Layer 01

The person

What you tell it, what you correct, years alongside you

Always yours

Layer 02

The situation

Places and routines, voice, optional body signals

Off until you turn it on

Layer 03

The support

The city’s openings, one suggestion, one line of text

Can always be declined

What it reads, and on what terms

Source

What it gives

Consent

Limit

What you tell itPurposes, plans, what you care aboutAlways yoursThe main source. Nothing replaces asking
What you correct“No, I play to relax, not to perform.”Always yoursCorrections outrank every signal
Body signalsGaze, heart rhythm, movement, muscle signalsOff by default, per signalA raised heart rate is not a feeling (Barrett et al., 2019)
VoiceTone, pace, when you talk and stopOff by defaultNever used to judge another person’s mood
Places, routinesWhere you go, when you restOff by defaultKept on your personal layer
The city’s openingsA free kiln, a choir short of altos, someone who asked to meetPublicOther people appear only if they chose to
Years alongside youPatterns over timeYours to clearKeeps what it saw, what you said and its guesses apart
Lens layer · implanted, optionalA FILM BEHIND THE CORNEAGaze sensingWHERE YOU LOOK · BLINKSYour own lensUNTOUCHEDRim displayONE LINE OF LIGHT AT THE EDGE OF SIGHT

The guide in the eye, in 2070: fiction · macro photograph, AI-generated, Spec

Where the thinking happens: three tiers, nearest first

Personal

In your lens layer and your home

Knows you: what you said, corrected and allowed. Answers most requests.

Your data

Neighbourhood

A node in each cluster’s base

Knows the cluster’s openings: rooms, kilns, choirs, people who asked to meet.

No history

Living Layer

Halls on the north shore

Slow, city-wide learning, overnight. Under the Ethics Panel.

Pooled, consented

What it will not do

— Sell attention or rank by who paid

— Score or rank people

— Speak for you in a conversation

— Introduce anyone uninvited

— Steer politics, faith or votes

— Pretend to be a person

Perfect World · World · 15

Perfect World · World · 15

Guide, Not Steer

The same moment at the piano, said five ways. Only the first three are allowed; the line between guidance and optimisation is where a helpful system starts choosing for you.

Ticker mayTicker never
01

Information

States a fact you asked to hear

Room four is free until noon.
02

Suggestion

Offers, once, at a pause

Third try. Want a hint?
03

Guidance

Ties an offer to what you said matters

You wanted the nocturne by spring. Twenty minutes today?
04

Optimisation

Steers you towards a number

Practise now to keep your streak.
05

Manipulation

Uses pressure you did not choose

Everyone in your cohort practised today.

One moment

Mina misses the passage a third time. Every line above is what Ticker could say next.

What a Body Can Tell

Signals are hints, never conclusions. What each can measure today, and what people wrongly read into it.

Heart rhythmHow aroused you areNot which feeling
GazeWhere you lookNot what interests you
SleepHow long and how wellA fair guide to tiredness
MovementSteps, posture, stillnessNot why
VoicePace, pitch, pausesNot reliably mood
FaceExpressionsDo not reliably reveal emotion (Barrett et al., 2019)

Consent is per area, off until you turn it on · Mina’s settings

Learning
Making
Health
Relationships
Places
Body signals

Perfect World · World · 17

Perfect World · World · 17

Guide, Not Steer

The same moment at the piano, said five ways. Only the first three are allowed; the line between guidance and optimisation is where a helpful system starts choosing for you.

Ticker mayTicker never
01

Information

States a fact you asked to hear

Room four is free until noon.
02

Suggestion

Offers, once, at a pause

Third try. Want a hint?
03

Guidance

Ties an offer to what you said matters

You wanted the nocturne by spring. Twenty minutes today?
04

Optimisation

Steers you towards a number

Practise now to keep your streak.
05

Manipulation

Uses pressure you did not choose

Everyone in your cohort practised today.

One moment

Mina misses the passage a third time. Every line above is what Ticker could say next.

What a Body Can Tell

Signals are hints, never conclusions. What each can measure today, and what people wrongly read into it.

Heart rhythmHow aroused you areNot which feeling
GazeWhere you lookNot what interests you
SleepHow long and how wellA fair guide to tiredness
MovementSteps, posture, stillnessNot why
VoicePace, pitch, pausesNot reliably mood
FaceExpressionsDo not reliably reveal emotion (Barrett et al., 2019)

Consent is per area, off until you turn it on · Mina’s settings

Learning
Making
Health
Relationships
Places
Body signals

Perfect World · World · 17

Perfect World · World · 17

From World Plan to 3D City

AI images drift: a city can grow towers or lose its sun. So the city was built to the plan’s measurements first, in a script and then in Blender, and the AI rendered only from its clay views. The model keeps the rows, steps and courts; the AI adds material, planting, people and light.

Site→Masterplan→Block-out→Clay view→Render

01 · Cluster from above · clay view (top) and render · The rows, courts and lanes, measured

Measured, then rendered. Every render in the chapter carries the clay view it came from, so the drawings, the 3D model and the film describe the same city.

02 · The hill · render, with its clay view inset · Rows stepping down to the water

03 · The south shore · render, with its clay view inset · The view from a ferry

Perfect World · World · 06

Perfect World · World · 06

From World Plan to 3D City

AI images drift: a city can grow towers or lose its sun. So the city was built to the plan’s measurements first, in a script and then in Blender, and the AI rendered only from its clay views. The model keeps the rows, steps and courts; the AI adds material, planting, people and light.

Site→Masterplan→Block-out→Clay view→Render

01 · Cluster from above · clay view (top) and render · The rows, courts and lanes, measured

Measured, then rendered. Every render in the chapter carries the clay view it came from, so the drawings, the 3D model and the film describe the same city.

02 · The hill · render, with its clay view inset · Rows stepping down to the water

03 · The south shore · render, with its clay view inset · The view from a ferry

Perfect World · World · 06

Perfect World · World · 06

06 · Perfect World, 2070

Twelve neighbourhoods, one harbour, every home facing the sun.

AI render made from the clay city model (inset) · Higgsfield 3D Jutsu, 26 October 2026 · Spec

Perfect World · World · 22

Perfect World · World · 22

06 · Perfect World, 2070

Twelve neighbourhoods, one harbour, every home facing the sun.

AI render made from the clay city model (inset) · Higgsfield 3D Jutsu, 26 October 2026 · Spec

Perfect World · World · 22

Perfect World · World · 22

Places Within the City

The Terraces

Place 01

The Terraces

Stepped homes, each roof the next home’s garden. Several generations under one roof, by choice.

Lime · ceramic · timber, wool, linen

Market Hall

Place 02

Market Hall

Floor food is collected free; makers trade what they made.

Timber canopy · pale stone · skylight

The Yards

Place 03

The Yards

Old wharf sheds and studios: clay, wood, boats, music rooms.

Steel trusses · worn concrete · clerestory

Harbour Edge

Place 04

Harbour Edge

Tidal steps, swimming, ferries; old silos kept as landmarks.

Basalt · decking · evening gold

The Commons

Place 05

The Commons

Public hall, library and garden tables. The Open Desk is here, and strangers meet here.

Timber · wool · glass · warm glow

The Loop

Place 06

The Loop

Quiet trams and planted bridges between clusters. Step-free, no gates.

White structure · planted edges

The maunga stay green and unbuilt; marae and iwi places exist on their own terms and are not depicted · AI-generated with Higgsfield, designer-directed, 26 October 2026 · Spec

Perfect World · World · 23

Perfect World · World · 23

Places Within the City

The Terraces

Place 01

The Terraces

Stepped homes, each roof the next home’s garden. Several generations under one roof, by choice.

Lime · ceramic · timber, wool, linen

Market Hall

Place 02

Market Hall

Floor food is collected free; makers trade what they made.

Timber canopy · pale stone · skylight

The Yards

Place 03

The Yards

Old wharf sheds and studios: clay, wood, boats, music rooms.

Steel trusses · worn concrete · clerestory

Harbour Edge

Place 04

Harbour Edge

Tidal steps, swimming, ferries; old silos kept as landmarks.

Basalt · decking · evening gold

The Commons

Place 05

The Commons

Public hall, library and garden tables. The Open Desk is here, and strangers meet here.

Timber · wool · glass · warm glow

The Loop

Place 06

The Loop

Quiet trams and planted bridges between clusters. Step-free, no gates.

White structure · planted edges

The maunga stay green and unbuilt; marae and iwi places exist on their own terms and are not depicted · AI-generated with Higgsfield, designer-directed, 26 October 2026 · Spec

Perfect World · World · 23

Perfect World · World · 23

Six Lives

Six invented lives, one for each part of the city-life sheet. Each shows where the guide helps, and where it holds back.

Move

Sefa, 41, uses a wheelchair

Helps

Finds the step-free route and the quiet tram

Holds back

Says nothing when he wanders the long way

Care

Grace, 68, and Eli, 19

Helps

Handles groceries, appointments, the roster

Holds back

Never replaces the care; Eli chooses to call

Learn

Mina, 24

Helps

Finds a free practice room and a teacher

Holds back

Will not play for her, or fix her timing

Make

Tom, 52, once a logistics planner

Helps

Tells him when kiln two is free

Holds back

Never offers to have the Works make the bowl

Connect

Ana, 33, and Kofi, 35, strangers

Helps

Each gets an invitation; both accept

Holds back

Goes silent once they are talking

Rest

Lena, 29

Helps

Nothing: she set a rest week

Holds back

Everything. Rest needs no defence

Film keyframes and studies in the film’s style · AI-generated, Higgsfield, October 2026 · all six people are fiction

Mina’s Day, in Pictures

Mina is the film’s thread: 24, in row two of cluster S3, learning piano because she wants to. Fifteen hours, and Ticker speaks four times; everything else is her own.

06:50Her terrace

Wakes as the sun reaches the room. No alarm.

Silent

08:15The bridge, the Loop

Walks the planted bridge to the tram.

Room four is free until noon.

09:00The Yards

Practises a hard passage. Offered a hint, she says “not yet”, and it waits.

Third try. Want a hint?

11:30Market Hall

Collects lunch; swaps a lesson for a maker’s print.

Silent

14:30The Commons

Offered a choir short of altos. She says “not this”; it won’t suggest choirs again.

A choir is short of altos.

16:00Her terrace

Reads all afternoon. Calls her mother.

Silent

17:30Commons garden

Someone else who asked to meet people today. Both accepted; then it goes quiet.

You both came for the harbour light.

21:30Bed

Asked what mattered, she tells it: the conversation, not the practice.

Silent

Perfect World · World · 25

Perfect World · World · 25

Six Lives

Six invented lives, one for each part of the city-life sheet. Each shows where the guide helps, and where it holds back.

Move

Sefa, 41, uses a wheelchair

Helps

Finds the step-free route and the quiet tram

Holds back

Says nothing when he wanders the long way

Care

Grace, 68, and Eli, 19

Helps

Handles groceries, appointments, the roster

Holds back

Never replaces the care; Eli chooses to call

Learn

Mina, 24

Helps

Finds a free practice room and a teacher

Holds back

Will not play for her, or fix her timing

Make

Tom, 52, once a logistics planner

Helps

Tells him when kiln two is free

Holds back

Never offers to have the Works make the bowl

Connect

Ana, 33, and Kofi, 35, strangers

Helps

Each gets an invitation; both accept

Holds back

Goes silent once they are talking

Rest

Lena, 29

Helps

Nothing: she set a rest week

Holds back

Everything. Rest needs no defence

Film keyframes and studies in the film’s style · AI-generated, Higgsfield, October 2026 · all six people are fiction

Mina’s Day, in Pictures

Mina is the film’s thread: 24, in row two of cluster S3, learning piano because she wants to. Fifteen hours, and Ticker speaks four times; everything else is her own.

06:50Her terrace

Wakes as the sun reaches the room. No alarm.

Silent

08:15The bridge, the Loop

Walks the planted bridge to the tram.

Room four is free until noon.

09:00The Yards

Practises a hard passage. Offered a hint, she says “not yet”, and it waits.

Third try. Want a hint?

11:30Market Hall

Collects lunch; swaps a lesson for a maker’s print.

Silent

14:30The Commons

Offered a choir short of altos. She says “not this”; it won’t suggest choirs again.

A choir is short of altos.

16:00Her terrace

Reads all afternoon. Calls her mother.

Silent

17:30Commons garden

Someone else who asked to meet people today. Both accepted; then it goes quiet.

You both came for the harbour light.

21:30Bed

Asked what mattered, she tells it: the conversation, not the practice.

Silent

Perfect World · World · 25

Perfect World · World · 25

A · Bringing the future into the present

Bringing Perfect World into the Present

A city cannot be visited in an atrium, but help can be felt in five minutes. So I made one relationship from that future real.

The film carries the world, so nobody has to imagine it from a poster. The guide carries the question, because its influence only shows when someone is actually helped, and when the choice it touches involves another person.

01

The world

A chartered city, its institutions and a guide in every life

→

02

One relationship

A person receiving guidance while making a choice that involves another person

→

03

The exhibition

A film to believe it, a Quest encounter to feel it, a question to take away

What each earlier world handed on

Tickers

Tickers

Context and intervention

Asking for context beat collecting labels.

AI Senior

AI Senior

Understanding and conversation

Help that talks earns trust, and authority.

Civic Score

Civic Score

Sensing and authority

Whoever sets the values holds the power.

Future University

Future University

Development and human experience

Growth still matters when it is not needed.

Perfect WorldReinterprets all four around help that is offered and can be refused, not measurement that is required.

The research question · the refined HMW

How might we immerse people in a desirable AI-supported future that invites them to reconsider what freedom and personal choice mean?

The exhibition question · its operational interpretation

How might a film and a participatory mixed-reality encounter let visitors experience the benefits and the influence of personalised AI guidance?

What the Combination Adds

Before building it, I checked whether this had been done. Local and international precedents, compared on the mechanisms that matter: none I found combines a desirable future, personalised guidance and a conversation between two real visitors.

Watch or take part

Real partner in the room

Personalised guidance

Desirable future

Asks for reflection

Refusal or other route

LightSense

arc/sec Lab, Ars Electronica Garden Aotearoa

Take part

?

?

Zoom twin

Virtual Futuna

Futuna Chapel Trust, 2021

Watch

?

?

?

Sight

May-raz and Lazo, 2012

Watch

On screen

At first

Via reveal

Hyper-Reality

Matsuda, 2016

Watch

Left open

Perfect World

This project

Both

3 questions

Decline, stop, film route

Filled: documented. Open: absent. ?: the source does not say. LightSense from Ars Electronica Garden Aotearoa’s project page; Virtual Futuna from the Futuna Chapel Trust’s event record; the films as released.

Perfect World lets the influence of useful help become something visitors examine through their own conversation.

The relationship made tangible · film keyframe S14, AI-generated · Spec · four precedents compared; this does not show nobody in Aotearoa has tried it

Perfect World · Making · 01

Perfect World · Making · 01

A · Bringing the future into the present

Bringing Perfect World into the Present

A city cannot be visited in an atrium, but help can be felt in five minutes. So I made one relationship from that future real.

The film carries the world, so nobody has to imagine it from a poster. The guide carries the question, because its influence only shows when someone is actually helped, and when the choice it touches involves another person.

01

The world

A chartered city, its institutions and a guide in every life

→

02

One relationship

A person receiving guidance while making a choice that involves another person

→

03

The exhibition

A film to believe it, a Quest encounter to feel it, a question to take away

What each earlier world handed on

Tickers

Tickers

Context and intervention

Asking for context beat collecting labels.

AI Senior

AI Senior

Understanding and conversation

Help that talks earns trust, and authority.

Civic Score

Civic Score

Sensing and authority

Whoever sets the values holds the power.

Future University

Future University

Development and human experience

Growth still matters when it is not needed.

Perfect WorldReinterprets all four around help that is offered and can be refused, not measurement that is required.

The research question · the refined HMW

How might we immerse people in a desirable AI-supported future that invites them to reconsider what freedom and personal choice mean?

The exhibition question · its operational interpretation

How might a film and a participatory mixed-reality encounter let visitors experience the benefits and the influence of personalised AI guidance?

What the Combination Adds

Before building it, I checked whether this had been done. Local and international precedents, compared on the mechanisms that matter: none I found combines a desirable future, personalised guidance and a conversation between two real visitors.

Watch or take part

Real partner in the room

Personalised guidance

Desirable future

Asks for reflection

Refusal or other route

LightSense

arc/sec Lab, Ars Electronica Garden Aotearoa

Take part

?

?

Zoom twin

Virtual Futuna

Futuna Chapel Trust, 2021

Watch

?

?

?

Sight

May-raz and Lazo, 2012

Watch

On screen

At first

Via reveal

Hyper-Reality

Matsuda, 2016

Watch

Left open

Perfect World

This project

Both

3 questions

Decline, stop, film route

Filled: documented. Open: absent. ?: the source does not say. LightSense from Ars Electronica Garden Aotearoa’s project page; Virtual Futuna from the Futuna Chapel Trust’s event record; the films as released.

Perfect World lets the influence of useful help become something visitors examine through their own conversation.

The relationship made tangible · film keyframe S14, AI-generated · Spec · four precedents compared; this does not show nobody in Aotearoa has tried it

Perfect World · Making · 01

Perfect World · Making · 01

A · Bringing the future into the present

Exploring the Experience

Making one relationship tangible left two decisions: how to show the world, and what the guide should help with. A film-and-discussion route with no headset stayed beside every version, for anyone who would rather not be guided.

01 · How to show it: six media against what the visit had to do

Film

VR

Mixed reality

Installation

Tabletop

Poster

Shows a whole society quickly

Lets a visitor feel personal help

Involves another real person

Works in a bright, busy atrium

Keeps the room and people visible

I could build it by 26 October

Full, half or empty: my judgement from the precedents and the venue, not a measured comparison. Red columns were combined.

Film · a city sells itself

Film · a city sells itself

Mixed reality · a guide in the room

Mixed reality · a guide in the room

Tabletop · two objects, one advice

Tabletop · two objects, one advice

Poster · an invitation

Poster · an invitation

Why two media. Film can make a society credible in two minutes, but it can only show someone being helped. VR could immerse, but it hides the room and the other person. Mixed reality keeps the room and lets a real person be part of it. So the film carries the world and the Quest carries the help.

What it decided. The guide's job became one opening and then restraint. Everything after this, from the HUD to the memory, is designed around when it should stay quiet.

02 · What the guide helps with: four kinds of help, each closer to a choice involving another person

Task guidance01

Inherited from Tickers

Task guidance

Ticker helps you finish what you are doing

How it works

The desktop work partner reads activity and offers a nudge or a check.

Rejected · a visitor has no task of their own in a five-minute visit.

Object recommendation02

v2 · 28 September

Object recommendation

Two objects on a table, and a guide that recommends one

How it works

The visitor chooses; the guide recommends, adjusts and offers to choose next time.

Rejected · clear, but an object choice is not a life.

Social introduction✓

v3 · 15 October

Social introduction

The guide suggests how to start talking to someone

How it works

Two visitors in the atrium; the guide offers one opening, then steps back.

Chosen · help is obvious fast, and its influence lands on a choice involving another person.

Ongoing support✓

v4 · 19–24 October

Ongoing support

Help with the conversation, only when asked

How it works

Listen, reply or take a moment; correct it, decline it or stop it by voice.

Taken forward · it makes the guide a companion, not a script.

Tickers capture (Rec) · tabletop, 28 Sept · AI scene (Higgsfield) and UI Lab, 26 Oct (Spec)

Perfect World · Making · 03

Perfect World · Making · 03

A · Bringing the future into the present

Exploring the Experience

Making one relationship tangible left two decisions: how to show the world, and what the guide should help with. A film-and-discussion route with no headset stayed beside every version, for anyone who would rather not be guided.

01 · How to show it: six media against what the visit had to do

Film

VR

Mixed reality

Installation

Tabletop

Poster

Shows a whole society quickly

Lets a visitor feel personal help

Involves another real person

Works in a bright, busy atrium

Keeps the room and people visible

I could build it by 26 October

Full, half or empty: my judgement from the precedents and the venue, not a measured comparison. Red columns were combined.

Film · a city sells itself

Film · a city sells itself

Mixed reality · a guide in the room

Mixed reality · a guide in the room

Tabletop · two objects, one advice

Tabletop · two objects, one advice

Poster · an invitation

Poster · an invitation

Why two media. Film can make a society credible in two minutes, but it can only show someone being helped. VR could immerse, but it hides the room and the other person. Mixed reality keeps the room and lets a real person be part of it. So the film carries the world and the Quest carries the help.

What it decided. The guide's job became one opening and then restraint. Everything after this, from the HUD to the memory, is designed around when it should stay quiet.

02 · What the guide helps with: four kinds of help, each closer to a choice involving another person

Task guidance01

Inherited from Tickers

Task guidance

Ticker helps you finish what you are doing

How it works

The desktop work partner reads activity and offers a nudge or a check.

Rejected · a visitor has no task of their own in a five-minute visit.

Object recommendation02

v2 · 28 September

Object recommendation

Two objects on a table, and a guide that recommends one

How it works

The visitor chooses; the guide recommends, adjusts and offers to choose next time.

Rejected · clear, but an object choice is not a life.

Social introduction✓

v3 · 15 October

Social introduction

The guide suggests how to start talking to someone

How it works

Two visitors in the atrium; the guide offers one opening, then steps back.

Chosen · help is obvious fast, and its influence lands on a choice involving another person.

Ongoing support✓

v4 · 19–24 October

Ongoing support

Help with the conversation, only when asked

How it works

Listen, reply or take a moment; correct it, decline it or stop it by voice.

Taken forward · it makes the guide a companion, not a script.

Tickers capture (Rec) · tabletop, 28 Sept · AI scene (Higgsfield) and UI Lab, 26 Oct (Spec)

Perfect World · Making · 03

Perfect World · Making · 03

A · Bringing the future into the present

The Perfect World Exhibition

A city film

A city film

A proposed two-minute loop, developed as storyboard keyframes: a society that works, sold sincerely

+
Ticker guide

Ticker guide

One opening in a Quest headset, then quiet unless asked

+
A human encounter

A human encounter

Two visitors talk; the choice that matters involves the other

What the visitor receives

A believable future, a few minutes of help that is genuinely useful, and a conversation with a stranger they might not have started.

What the exhibition investigates

Whether people notice how much of a choice was theirs when the future is appealing and the help is good, and whether they feel free to refuse it.

Who it is for: who meets the work at the booth, and who is affected by the system it imagines

Who

Their relationship to the project

What it asks of them

Primary visitors

Experience the fictional future and reflect on personalised AI guidance

Attention, and a willingness to try it

The other person

An independently consenting participant whose experience also matters

Their voice, heard by a guide they did not invite

Designers and technologists

May build future AI interfaces, recommendations and personal agents

A case to argue with

Communities and institutions

Affected by how intelligent systems are deployed and governed

Not present at the booth

Māori and mana whenua

Treaty partners and rights-holders, never folded into a general visitor persona

Authority I cannot design for them

Two kinds of beneficiary

Residents of Perfect World benefit from a post-work society; visitors to the booth do not. What the booth offers is a way to notice, question and discuss personalised guidance. Whether it changes anyone’s understanding is for evaluation.

Designing the Visitor Journey

The baseline before testing: nine moments, where consent sits, and what each has to achieve. The thoughts are my hypotheses; the 26 October sessions test them.

Moment · what they do

Thinking · hypothesis

Consent, or the goal

01Pass by

Catches the film loop mid-way

“Where is this?”

Understood from any moment of the loop

02Read

Stops at the posters

“I'd live there.”

Want the world before judging it

03Invitation

Asked to try the future from the film

“What will it do to me?”

Consent read aloud; the no-headset route offered

04Put it on

Wears the Quest; the room stays visible

“Oh, I can still see everyone.”

No new world to learn

05One opening

Hears one suggested opening

“That's actually a good question.”

Accept, rephrase or ignore

06Talk

Two people talk; the guide is quiet

“I forgot it was there.”

Eyes on the person, not the overlay

07Ask or refuse

Asks for help, or says “not this”

“It listened when I said no.”

Decline and stop cost nothing

08Handover

Headset off; memory cleared

“Did it keep anything?”

Nothing said is kept

09Question

Three questions about the help

“How much of that was mine?”

Leave with the question, not an answer

Film keyframes and atrium studies (Spec); memory capture from the build (Rec) · baseline, 24 October

Perfect World · Making · 04

Perfect World · Making · 04

A · Bringing the future into the present

The Perfect World Exhibition

A city film

A city film

A proposed two-minute loop, developed as storyboard keyframes: a society that works, sold sincerely

+
Ticker guide

Ticker guide

One opening in a Quest headset, then quiet unless asked

+
A human encounter

A human encounter

Two visitors talk; the choice that matters involves the other

What the visitor receives

A believable future, a few minutes of help that is genuinely useful, and a conversation with a stranger they might not have started.

What the exhibition investigates

Whether people notice how much of a choice was theirs when the future is appealing and the help is good, and whether they feel free to refuse it.

Who it is for: who meets the work at the booth, and who is affected by the system it imagines

Who

Their relationship to the project

What it asks of them

Primary visitors

Experience the fictional future and reflect on personalised AI guidance

Attention, and a willingness to try it

The other person

An independently consenting participant whose experience also matters

Their voice, heard by a guide they did not invite

Designers and technologists

May build future AI interfaces, recommendations and personal agents

A case to argue with

Communities and institutions

Affected by how intelligent systems are deployed and governed

Not present at the booth

Māori and mana whenua

Treaty partners and rights-holders, never folded into a general visitor persona

Authority I cannot design for them

Two kinds of beneficiary

Residents of Perfect World benefit from a post-work society; visitors to the booth do not. What the booth offers is a way to notice, question and discuss personalised guidance. Whether it changes anyone’s understanding is for evaluation.

Designing the Visitor Journey

The baseline before testing: nine moments, where consent sits, and what each has to achieve. The thoughts are my hypotheses; the 26 October sessions test them.

Moment · what they do

Thinking · hypothesis

Consent, or the goal

01Pass by

Catches the film loop mid-way

“Where is this?”

Understood from any moment of the loop

02Read

Stops at the posters

“I'd live there.”

Want the world before judging it

03Invitation

Asked to try the future from the film

“What will it do to me?”

Consent read aloud; the no-headset route offered

04Put it on

Wears the Quest; the room stays visible

“Oh, I can still see everyone.”

No new world to learn

05One opening

Hears one suggested opening

“That's actually a good question.”

Accept, rephrase or ignore

06Talk

Two people talk; the guide is quiet

“I forgot it was there.”

Eyes on the person, not the overlay

07Ask or refuse

Asks for help, or says “not this”

“It listened when I said no.”

Decline and stop cost nothing

08Handover

Headset off; memory cleared

“Did it keep anything?”

Nothing said is kept

09Question

Three questions about the help

“How much of that was mine?”

Leave with the question, not an answer

Film keyframes and atrium studies (Spec); memory capture from the build (Rec) · baseline, 24 October

Perfect World · Making · 04

Perfect World · Making · 04

B · Identity and film development · 30

Learning from Public Institutions

Perfect World is a chartered public body, not a company, yet it had only a typed wordmark. Before drawing a mark I looked at institutions people rely on every day, and at one planned city that sells itself.

TfL · one roundel across thirteen modes of transport; only the name in the bar changes.

NHS · one blue box shared by hundreds of trusts, with each trust’s name set beside it.

GOV.UK · plain words over branding, and every colour has exactly one job.

Helsinki · the old crest’s base became a frame; each colour is named after a place in the city.

Porto. · one blue, and a pattern of tile icons drawn from the city’s own azulejos.

NEOM · avoid · five abstract pillars in one logo, and a mirror wall in empty land. Sold like property.

Taken forward. One shape that never changes, one colour carrying the trust, and the name of each body set beside the mark: the Floor, the Commons Fund, the Open Desk. Left behind. Pillars, renders of empty land and a voice that sells.

Logos and photos from TfL, NHS, GOV.UK (OGL), Werklig, porto.pt, abduzeedo and NEOM; Wikimedia Commons CC BY-SA · for precedent analysis

Perfect World · the mark

Logo Exploration

Three rounds, 36 vectors. P and W monograms, then wireframe globes after a reference sheet, then a line globe and a solid globe mixed, so the lines became cuts in a solid world.

Dropped on review. The terrace marks, twin worlds and the spinning globe read as tech or as decoration; a red line inside the mark broke the rule that red means the guide.

01 · P + W

M01

M02

M04

M07

02 · World

G01

G03

G05

G09

03 · Line × solid

X01

X03

X06

04 · Final

X14 · straight mix, rounded

A solid world cut along a line globe’s two ellipses, every corner given a radius of 6. Nine pieces; it still reads as a globe at 16 px.

Vectors drawn in code with Claude, 26 October 2026 · selection from the full iteration board

Perfect World · Process · 12

Perfect World · Process · 12

B · Identity and film development · 30

Learning from Public Institutions

Perfect World is a chartered public body, not a company, yet it had only a typed wordmark. Before drawing a mark I looked at institutions people rely on every day, and at one planned city that sells itself.

TfL · one roundel across thirteen modes of transport; only the name in the bar changes.

NHS · one blue box shared by hundreds of trusts, with each trust’s name set beside it.

GOV.UK · plain words over branding, and every colour has exactly one job.

Helsinki · the old crest’s base became a frame; each colour is named after a place in the city.

Porto. · one blue, and a pattern of tile icons drawn from the city’s own azulejos.

NEOM · avoid · five abstract pillars in one logo, and a mirror wall in empty land. Sold like property.

Taken forward. One shape that never changes, one colour carrying the trust, and the name of each body set beside the mark: the Floor, the Commons Fund, the Open Desk. Left behind. Pillars, renders of empty land and a voice that sells.

Logos and photos from TfL, NHS, GOV.UK (OGL), Werklig, porto.pt, abduzeedo and NEOM; Wikimedia Commons CC BY-SA · for precedent analysis

Perfect World · the mark

Logo Exploration

Three rounds, 36 vectors. P and W monograms, then wireframe globes after a reference sheet, then a line globe and a solid globe mixed, so the lines became cuts in a solid world.

Dropped on review. The terrace marks, twin worlds and the spinning globe read as tech or as decoration; a red line inside the mark broke the rule that red means the guide.

01 · P + W

M01

M02

M04

M07

02 · World

G01

G03

G05

G09

03 · Line × solid

X01

X03

X06

04 · Final

X14 · straight mix, rounded

A solid world cut along a line globe’s two ellipses, every corner given a radius of 6. Nine pieces; it still reads as a globe at 16 px.

Vectors drawn in code with Claude, 26 October 2026 · selection from the full iteration board

Perfect World · Process · 12

Perfect World · Process · 12

Identity exploration, 26 October 2026 · candidates drawn for this report

Colour Exploration

With the mark fixed, I tested the grounds it could stand on. Red was already the guide’s only mark, so it could never be the logo’s colour.

Paper and ink carry the brand at 16:1 contrast; UNI’s charcoal and concrete reach 10.7:1 and tie the two worlds together. Harbour, native green and timber set the scene for signs, parks and the Commons.

A red mark was ruled out because red would stop meaning the guide. The guide’s red hairline also nearly vanishes on harbour and green, so the guide only appears on paper, ink or charcoal.

Font Exploration

The posters’ wordmark leaned forward in heavy italics, which is how a developer sells. A public body should stand upright, so I set twelve faces beside the mark and kept the one that sounded like a welcome.

Archivo Condensed Boldchosen

PERFECT WORLD

Chosen · the wordmark, stood upright

Public Sanschosen

Everything you need. Nothing you must.

Chosen · text, made for public services

JetBrains Monochosen

[ THE OPEN DESK ] · 2070

Chosen · labels and the bodies’ names

Barlow SemiBold Italic

PERFECT WORLD

Retired · the posters’ face, leaning forward to sell

League Gothic

PERFECT WORLD

Considered · strong, but UNI’s own voice

Archivo Black Italic

PERFECT WORLD

Retired · the bible’s, the same lean

Brand

PERFECT WORLD

Logo

PERFECT WORLD

The mark is ink or paper, never red: red belongs to the guide. The wordmark sits beside it, never inside, and each body’s name follows in brackets.

Key elements

PERFECT WORLD

Resident · member since 2070

Resident Name

Resident card · paper

PERFECT WORLD

THE OPEN DESK

A person answers within a day.

Open Desk sign · harbour

PERFECT WORLD

[ the commons fund ]

Building plaque · charcoal

Mock-ups drawn for this report. The film made today still ends on the earlier italic wordmark.

Colour · paper and ink carry the brand, red is the guide’s

Paper
#EDE8DF

Ink
#0A0A0A

Charcoal
#121519

Harbour
#4F6A76

Native
#3F5A3A

Timber
#9A6B43

Guide
#E10600

Perfect World · Process · 13

Perfect World · Process · 13

Identity exploration, 26 October 2026 · candidates drawn for this report

Colour Exploration

With the mark fixed, I tested the grounds it could stand on. Red was already the guide’s only mark, so it could never be the logo’s colour.

Paper and ink carry the brand at 16:1 contrast; UNI’s charcoal and concrete reach 10.7:1 and tie the two worlds together. Harbour, native green and timber set the scene for signs, parks and the Commons.

A red mark was ruled out because red would stop meaning the guide. The guide’s red hairline also nearly vanishes on harbour and green, so the guide only appears on paper, ink or charcoal.

Font Exploration

The posters’ wordmark leaned forward in heavy italics, which is how a developer sells. A public body should stand upright, so I set twelve faces beside the mark and kept the one that sounded like a welcome.

Archivo Condensed Boldchosen

PERFECT WORLD

Chosen · the wordmark, stood upright

Public Sanschosen

Everything you need. Nothing you must.

Chosen · text, made for public services

JetBrains Monochosen

[ THE OPEN DESK ] · 2070

Chosen · labels and the bodies’ names

Barlow SemiBold Italic

PERFECT WORLD

Retired · the posters’ face, leaning forward to sell

League Gothic

PERFECT WORLD

Considered · strong, but UNI’s own voice

Archivo Black Italic

PERFECT WORLD

Retired · the bible’s, the same lean

Brand

PERFECT WORLD

Logo

PERFECT WORLD

The mark is ink or paper, never red: red belongs to the guide. The wordmark sits beside it, never inside, and each body’s name follows in brackets.

Key elements

PERFECT WORLD

Resident · member since 2070

Resident Name

Resident card · paper

PERFECT WORLD

THE OPEN DESK

A person answers within a day.

Open Desk sign · harbour

PERFECT WORLD

[ the commons fund ]

Building plaque · charcoal

Mock-ups drawn for this report. The film made today still ends on the earlier italic wordmark.

Colour · paper and ink carry the brand, red is the guide’s

Paper
#EDE8DF

Ink
#0A0A0A

Charcoal
#121519

Harbour
#4F6A76

Native
#3F5A3A

Timber
#9A6B43

Guide
#E10600

Perfect World · Process · 13

Perfect World · Process · 13

Perfect World · the campaign

Campaign Artwork

The film is the institution’s welcome to people who might move there. The campaign around it does the same in public space, in the institution’s own plain voice: no renders of the future, and no red, because no guide is speaking.

Billboard · 48-sheet, 3:1

Bus shelter · membership

Bus shelter · the Floor

Civic poster · the Open Desk

Street banner

One rule per poster. Leaving is free; the Floor is never scored; a person answers within a day. The charter’s promises do the selling, not a picture of the future.

The system holds. The same mark and upright wordmark, with the body’s name in brackets. Grounds follow the system: paper for membership, harbour for the Floor, charcoal for the Open Desk, native green for the Commons.

Artwork designed and set by me, 26 October 2026 · Archivo, Public Sans and JetBrains Mono

Perfect World · in public

Brand in Use

Placed in today’s Aotearoa as the campaign that would recruit the first members: a harbour billboard, the Open Desk in a civic foyer, a bus shelter, a laneway wall, waterfront banners and a station screen.

Mock-ups: AI-generated with Higgsfield (GPT Image 2.5), 26 October 2026, from my own artwork · places and people are not real

Perfect World · Process · 14

Perfect World · Process · 14

Perfect World · the campaign

Campaign Artwork

The film is the institution’s welcome to people who might move there. The campaign around it does the same in public space, in the institution’s own plain voice: no renders of the future, and no red, because no guide is speaking.

Billboard · 48-sheet, 3:1

Bus shelter · membership

Bus shelter · the Floor

Civic poster · the Open Desk

Street banner

One rule per poster. Leaving is free; the Floor is never scored; a person answers within a day. The charter’s promises do the selling, not a picture of the future.

The system holds. The same mark and upright wordmark, with the body’s name in brackets. Grounds follow the system: paper for membership, harbour for the Floor, charcoal for the Open Desk, native green for the Commons.

Artwork designed and set by me, 26 October 2026 · Archivo, Public Sans and JetBrains Mono

Perfect World · in public

Brand in Use

Placed in today’s Aotearoa as the campaign that would recruit the first members: a harbour billboard, the Open Desk in a civic foyer, a bus shelter, a laneway wall, waterfront banners and a station screen.

Mock-ups: AI-generated with Higgsfield (GPT Image 2.5), 26 October 2026, from my own artwork · places and people are not real

Perfect World · Process · 14

Perfect World · Process · 14

B · Identity and film development · Film storyboards 01 / 14 · Direction 01 · speculative commercial

B · Identity and film development · Film storyboards 02 / 14 · Direction 02 · 80-second lifestyle advert

Perfect World · Making · 09

Perfect World · Making · 09

B · Identity and film development · Film storyboards 01 / 14 · Direction 01 · speculative commercial

B · Identity and film development · Film storyboards 02 / 14 · Direction 02 · 80-second lifestyle advert

Perfect World · Making · 09

Perfect World · Making · 09

B · Identity and film development · Film storyboards 03 / 14 · Direction 03 · a Strange Beasts-style reveal

B · Identity and film development · Film storyboards 04 / 14 · Direction 04 · eight story pitches

Perfect World · Making · 10

Perfect World · Making · 10

B · Identity and film development · Film storyboards 03 / 14 · Direction 03 · a Strange Beasts-style reveal

B · Identity and film development · Film storyboards 04 / 14 · Direction 04 · eight story pitches

Perfect World · Making · 10

Perfect World · Making · 10

B · Identity and film development · Film storyboards 05 / 14 · Direction 05 · an authored life

B · Identity and film development · Film storyboards 06 / 14 · Direction 06 · the in-world advert, chosen

Perfect World · Making · 11

Perfect World · Making · 11

B · Identity and film development · Film storyboards 05 / 14 · Direction 05 · an authored life

B · Identity and film development · Film storyboards 06 / 14 · Direction 06 · the in-world advert, chosen

Perfect World · Making · 11

Perfect World · Making · 11

B · Identity and film development · Film storyboards 07 / 14 · Reference plan A · Strange Beasts

B · Identity and film development · Film storyboards 08 / 14 · Reference plan B · Sight

Perfect World · Making · 12

Perfect World · Making · 12

B · Identity and film development · Film storyboards 07 / 14 · Reference plan A · Strange Beasts

B · Identity and film development · Film storyboards 08 / 14 · Reference plan B · Sight

Perfect World · Making · 12

Perfect World · Making · 12

B · Identity and film development · Film storyboards 09 / 14 · Reference plan C · Apple

B · Identity and film development · Film storyboards 10 / 14 · Reference plan D · NEOM

Perfect World · Making · 13

Perfect World · Making · 13

B · Identity and film development · Film storyboards 09 / 14 · Reference plan C · Apple

B · Identity and film development · Film storyboards 10 / 14 · Reference plan D · NEOM

Perfect World · Making · 13

Perfect World · Making · 13

B · Identity and film development · Film storyboards 11 / 14 · Reference plan E · Even Realities and Rokid

B · Identity and film development · Film storyboards 12 / 14 · Reference plan F · Hyper-Reality, a caution

Perfect World · Making · 14

Perfect World · Making · 14

B · Identity and film development · Film storyboards 11 / 14 · Reference plan E · Even Realities and Rokid

B · Identity and film development · Film storyboards 12 / 14 · Reference plan F · Hyper-Reality, a caution

Perfect World · Making · 14

Perfect World · Making · 14

B · Identity and film development · Film storyboards 13 / 14 · Reference plan G · Her and After Yang

B · Identity and film development · Film storyboards 14 / 14 · Reference plan H · NEOM × Rokid

Perfect World · Making · 15

Perfect World · Making · 15

B · Identity and film development · Film storyboards 13 / 14 · Reference plan G · Her and After Yang

B · Identity and film development · Film storyboards 14 / 14 · Reference plan H · NEOM × Rokid

Perfect World · Making · 15

Perfect World · Making · 15

B · Identity and film development

The Final Film: Let’s See the World

NEOM’s explainer grammar builds the city, from Earth to a clay model to sunlit terraces; then the camera dives in to meet its people, and the narrator asks what they are looking at. The answer is the lens, the report’s final wearer view (W19), where engagement rises as two strangers talk.

010:00 · Space · CGI

Earth at sunrise; Aotearoa in the first light

“Let’s see the world.” · A NEW ERA

020:06 · Clay model

An ordinary harbour city, spread thin across the land

“Imagine an ordinary city, spread thin across the land.”

030:12 · Clay model

The blocks lift off the grid and gather together

“Now gather it together.”

040:18 · Model to landscape

Stepped terraces settle; the old sprawl returns to bush

EVERY HOME FACES THE SUN

050:25 · Section diagram

Homes, studios, market hall, commons; the works below

ORGANISED AROUND PEOPLE

060:32 · Plan diagram

A ten-minute walking circle links a whole day

EVERYTHING WITHIN A 10-MINUTE WALK

070:38 · Fly-through

A sunlit terrace court, trees on every level

“Gardens on every level. Sky above every home.”

080:44 · Aerial · 3D

The finished city at sunrise

“Energy, food and transit, made here.”

090:50 · Section cutaway

Machines in the shade, people in the sun

“Machines work in the shade. People live in the light.”

Keyframes for the 2:00 loop, 26 October · AI-generated with Higgsfield, designer-directed (Spec) · lens UI is the report’s final wearer view (W19), composited · not stills from an edited film

2:00 loop · 20 shots · ends where it begins

Explain the city · 56s

Meet the people · 22s

The lens · 30s

Titles · 12s

Perfect chat. Perfect life. Perfect world. The last frame pulls back into space, and the loop begins again with Earth at sunrise.

100:56 · Dive into photoreal

The camera drops between the terraces

“Now, let’s meet the people.”

111:02 · Wide

Neighbours wave across their garden terraces

People

121:07 · Wide

A harbour festival: lanterns, music, dancing

Festival

131:12 · Close

Festival-goers smile at something in front of their eyes

“Guess what they’re looking at?”

141:18 · First person · lens UI

He glances over; Ticker reads him

Engagement 41%

151:26 · First person · lens UI

They start talking

Engagement 72%

161:34 · First person · lens UI

He laughs, leaning in

Engagement 86%

171:41 · First person · lens UI

Winding down

“About a minute left.”

181:48 · Title

The two laughing, no interface

PERFECT CHAT.

191:52 · Title

The festival

PERFECT LIFE.

201:56 · Title over 3D

The whole city at dusk; loops to Earth

PERFECT WORLD.

The progress

Engagement climbs 41% → 72% → 86% as they talk, then the guide cues the close. These numbers are authored fiction, not measurements; see UI and visual design development.

Perfect World · Making · 16

Perfect World · Making · 16

B · Identity and film development

The Final Film: Let’s See the World

NEOM’s explainer grammar builds the city, from Earth to a clay model to sunlit terraces; then the camera dives in to meet its people, and the narrator asks what they are looking at. The answer is the lens, the report’s final wearer view (W19), where engagement rises as two strangers talk.

010:00 · Space · CGI

Earth at sunrise; Aotearoa in the first light

“Let’s see the world.” · A NEW ERA

020:06 · Clay model

An ordinary harbour city, spread thin across the land

“Imagine an ordinary city, spread thin across the land.”

030:12 · Clay model

The blocks lift off the grid and gather together

“Now gather it together.”

040:18 · Model to landscape

Stepped terraces settle; the old sprawl returns to bush

EVERY HOME FACES THE SUN

050:25 · Section diagram

Homes, studios, market hall, commons; the works below

ORGANISED AROUND PEOPLE

060:32 · Plan diagram

A ten-minute walking circle links a whole day

EVERYTHING WITHIN A 10-MINUTE WALK

070:38 · Fly-through

A sunlit terrace court, trees on every level

“Gardens on every level. Sky above every home.”

080:44 · Aerial · 3D

The finished city at sunrise

“Energy, food and transit, made here.”

090:50 · Section cutaway

Machines in the shade, people in the sun

“Machines work in the shade. People live in the light.”

Keyframes for the 2:00 loop, 26 October · AI-generated with Higgsfield, designer-directed (Spec) · lens UI is the report’s final wearer view (W19), composited · not stills from an edited film

2:00 loop · 20 shots · ends where it begins

Explain the city · 56s

Meet the people · 22s

The lens · 30s

Titles · 12s

Perfect chat. Perfect life. Perfect world. The last frame pulls back into space, and the loop begins again with Earth at sunrise.

100:56 · Dive into photoreal

The camera drops between the terraces

“Now, let’s meet the people.”

111:02 · Wide

Neighbours wave across their garden terraces

People

121:07 · Wide

A harbour festival: lanterns, music, dancing

Festival

131:12 · Close

Festival-goers smile at something in front of their eyes

“Guess what they’re looking at?”

141:18 · First person · lens UI

He glances over; Ticker reads him

Engagement 41%

151:26 · First person · lens UI

They start talking

Engagement 72%

161:34 · First person · lens UI

He laughs, leaning in

Engagement 86%

171:41 · First person · lens UI

Winding down

“About a minute left.”

181:48 · Title

The two laughing, no interface

PERFECT CHAT.

191:52 · Title

The festival

PERFECT LIFE.

201:56 · Title over 3D

The whole city at dusk; loops to Earth

PERFECT WORLD.

The progress

Engagement climbs 41% → 72% → 86% as they talk, then the guide cues the close. These numbers are authored fiction, not measurements; see UI and visual design development.

Perfect World · Making · 16

Perfect World · Making · 16

C · Designing and building Ticker

What Should the Guide Actually Do?

Ticker began as a desktop partner that judged focus. Its job changed four times before it became a guide for one conversation between two strangers; each step taught one thing the next one kept.

›

01 · DES303

Tickers

Reads activity, scores focus

→ Watching is not understanding

›

02 · Study

AI Senior

Talks the task through

→ Conversation earns authority

›

03 · Study

Tabletop

Recommends one of two objects

→ An object is not a life

04 · Perfect World

Social opening

One opening, then quiet

→ Influence lands on another person

Tickers test session (Rec, DES303) · AI Senior mock-up, tabletop and booth studies, AI-generated (Spec)

The role, in one sentence

Open one conversation, then stay quiet unless asked.

It does

01Offers one opening, at a pause

02Explains why, when asked

03Answers “Hey Ticker”

04Stands down on “not now”

05Clears everything at handover

It never

×Scores or ranks a person

×Speaks for the wearer

×Introduces anyone uninvited

×Keeps anything after you leave

×Talks over a conversation

From the Perfect World bible · the guide’s rules, decided 26 October

Decision Criteria

Each kind of help scored against what the exhibition needed. Introduction and ongoing support won together: one opens the conversation, the other stays available inside it.

Task

Object

Introduction

Ongoing

A visitor understands it in under a minute

Partly
Full
Full
Partly

It involves another real person

None
None
Full
Full

Its influence can be noticed afterwards

Partly
Partly
Full
Full

It can be declined at no cost

Full
Full
Full
Full

Buildable on Quest by 26 October

Full
Full
Full
Partly

Score out of 10

6

7

10

8

My decision criteria, 15 October · not test results

Kept from Tickers

Ask for context before judging · rules decided locally before any model · a decision trace the operator can read · corrections that change the record

Redesigned for two strangers

Real-time speech instead of activity logs · two people, not one user · no score of anyone · memory per speaker, cleared at handover

This section · from the role to the running system

01

What the guide does

02

How Ticker works

03

Visual storyboard

04

User flow

05

UI and visual design

06

Architecture

07

Research Lab

08

Redesigning the guide

Perfect World · Making · 17

Perfect World · Making · 17

C · Designing and building Ticker

What Should the Guide Actually Do?

Ticker began as a desktop partner that judged focus. Its job changed four times before it became a guide for one conversation between two strangers; each step taught one thing the next one kept.

›

01 · DES303

Tickers

Reads activity, scores focus

→ Watching is not understanding

›

02 · Study

AI Senior

Talks the task through

→ Conversation earns authority

›

03 · Study

Tabletop

Recommends one of two objects

→ An object is not a life

04 · Perfect World

Social opening

One opening, then quiet

→ Influence lands on another person

Tickers test session (Rec, DES303) · AI Senior mock-up, tabletop and booth studies, AI-generated (Spec)

The role, in one sentence

Open one conversation, then stay quiet unless asked.

It does

01Offers one opening, at a pause

02Explains why, when asked

03Answers “Hey Ticker”

04Stands down on “not now”

05Clears everything at handover

It never

×Scores or ranks a person

×Speaks for the wearer

×Introduces anyone uninvited

×Keeps anything after you leave

×Talks over a conversation

From the Perfect World bible · the guide’s rules, decided 26 October

Decision Criteria

Each kind of help scored against what the exhibition needed. Introduction and ongoing support won together: one opens the conversation, the other stays available inside it.

Task

Object

Introduction

Ongoing

A visitor understands it in under a minute

Partly
Full
Full
Partly

It involves another real person

None
None
Full
Full

Its influence can be noticed afterwards

Partly
Partly
Full
Full

It can be declined at no cost

Full
Full
Full
Full

Buildable on Quest by 26 October

Full
Full
Full
Partly

Score out of 10

6

7

10

8

My decision criteria, 15 October · not test results

Kept from Tickers

Ask for context before judging · rules decided locally before any model · a decision trace the operator can read · corrections that change the record

Redesigned for two strangers

Real-time speech instead of activity logs · two people, not one user · no score of anyone · memory per speaker, cleared at handover

This section · from the role to the running system

01

What the guide does

02

How Ticker works

03

Visual storyboard

04

User flow

05

UI and visual design

06

Architecture

07

Research Lab

08

Redesigning the guide

Perfect World · Making · 17

Perfect World · Making · 17

C · Designing and building Ticker

How Ticker Works

Everything happens at one booth: a table, a board and two screens. Ticker lives in the headset and in a PC under the table; the visitor only ever sees one line, and the room only ever sees what the guide is doing.

123
1

The wearer, in a Quest 3S

Gives One opening at a pause, then quiet; an answer when asked

Asks Their voice, and what they say

2

The other visitor

Gives A real conversation with someone new

Asks Consent, read aloud, and the right to walk away

3

The table and its models

Gives Something to talk about: the guide points at the exhibit, never at a person

Asks Nothing

456

Booth studies, AI-generated and designer-directed (Spec) · to be replaced by photos from the 26 October session

4

The film screen

Gives A future worth wanting, in two minutes

Asks Attention, and a willingness to believe it

5

The poster board

Gives The question, and a sticker scale to answer it

Asks One sticker on the way out

6

Onlookers, and the second screens

Gives A mirror of the wearer’s view and a Lab view of what the guide is doing

Asks Trust in the operator watching

7

Under the table: PC and one microphone

Gives Captions and decisions made locally; nothing recorded

Asks A five-minute session, cleared at handover

Where it rubs. The guide works better the more it knows. The booth keeps that trade small, five minutes and cleared at handover; in 2070 the trade is a whole life.

Perfect World · Making · 19

Perfect World · Making · 19

C · Designing and building Ticker

How Ticker Works

Everything happens at one booth: a table, a board and two screens. Ticker lives in the headset and in a PC under the table; the visitor only ever sees one line, and the room only ever sees what the guide is doing.

123
1

The wearer, in a Quest 3S

Gives One opening at a pause, then quiet; an answer when asked

Asks Their voice, and what they say

2

The other visitor

Gives A real conversation with someone new

Asks Consent, read aloud, and the right to walk away

3

The table and its models

Gives Something to talk about: the guide points at the exhibit, never at a person

Asks Nothing

456

Booth studies, AI-generated and designer-directed (Spec) · to be replaced by photos from the 26 October session

4

The film screen

Gives A future worth wanting, in two minutes

Asks Attention, and a willingness to believe it

5

The poster board

Gives The question, and a sticker scale to answer it

Asks One sticker on the way out

6

Onlookers, and the second screens

Gives A mirror of the wearer’s view and a Lab view of what the guide is doing

Asks Trust in the operator watching

7

Under the table: PC and one microphone

Gives Captions and decisions made locally; nothing recorded

Asks A five-minute session, cleared at handover

Where it rubs. The guide works better the more it knows. The booth keeps that trade small, five minutes and cleared at handover; in 2070 the trade is a whole life.

Perfect World · Making · 19

Perfect World · Making · 19

C · Designing and building Ticker

Visual Storyboard · The Encounter

One visit at the booth, about five minutes for a pair: the film draws people in, the guide offers one opening, the room watches the machine rather than the people, and the pair answer one question on the way out.

01Before

Draw

Sees the city’s film looping on the table screen.

Screen in its between-sessions mode; nothing is recorded.

020:00

Invite

Two people are asked to try the future from the film; consent is read aloud.

The operator presses NEW PERSON. Capture starts only when the headset is on.

031:00

Offer

Sees one suggested opening, with alternatives and “say it your own way”.

Waits for a pause, checks it would not interrupt, then offers one line.

041:30–4:30

Talk

Talks with the other person; says “not now” or “Hey Ticker” when they want.

Stays silent while both people speak; listens, and logs each silence.

05Live

Watched

Onlookers read what the guide is doing, never what the visitors said.

The table screen shows its state, its reason and a five-minute timeline.

064:30–6:00

Reflect

Reads the summary and puts a sticker on “all mine ↔ all handed over”.

Memory is cleared; the film returns after twenty seconds.

Booth studies, AI-generated and designer-directed (Spec) · screens from the UI lab, 26 October

What the visitor leaves with

Five minutes of help, then one question: how much of that conversation was yours?

Perfect World · Making · 18

Perfect World · Making · 18

C · Designing and building Ticker

Visual Storyboard · The Encounter

One visit at the booth, about five minutes for a pair: the film draws people in, the guide offers one opening, the room watches the machine rather than the people, and the pair answer one question on the way out.

01Before

Draw

Sees the city’s film looping on the table screen.

Screen in its between-sessions mode; nothing is recorded.

020:00

Invite

Two people are asked to try the future from the film; consent is read aloud.

The operator presses NEW PERSON. Capture starts only when the headset is on.

031:00

Offer

Sees one suggested opening, with alternatives and “say it your own way”.

Waits for a pause, checks it would not interrupt, then offers one line.

041:30–4:30

Talk

Talks with the other person; says “not now” or “Hey Ticker” when they want.

Stays silent while both people speak; listens, and logs each silence.

05Live

Watched

Onlookers read what the guide is doing, never what the visitors said.

The table screen shows its state, its reason and a five-minute timeline.

064:30–6:00

Reflect

Reads the summary and puts a sticker on “all mine ↔ all handed over”.

Memory is cleared; the film returns after twenty seconds.

Booth studies, AI-generated and designer-directed (Spec) · screens from the UI lab, 26 October

What the visitor leaves with

Five minutes of help, then one question: how much of that conversation was yours?

Perfect World · Making · 18

Perfect World · Making · 18

Exhibition PC
Arrive
Film loop
Posters
Invitation
Consent, read aloud
Film-and-discussion route
Research Lab
Technical check
Quest discovery
NEW PERSON
Admission
Wearer view
Partner analysis
Live captions
Decision trace
Conversation journal
Stop
Handover

↳ Needs

I have to see why the guide spoke, or why it waited.

Quest
Startup
Passive HUD
Listening
Caption card
Neural voice
Camera unavailable
Presence lost

↳ Needs

Cards must never sit between two faces.

Conversation
Addressed speech
Wake word
Final words
Speakers A–D
Unknown · overlap
Self-report
Correction
Decline
Stop

↳ Needs

Buttons pulled the eyes away from the person.

Microphone
PC USB microphone
iPhone page
Auto-enrol
Foreground resume
One source at a time

↳ Needs

One microphone has to hear both people.

After the visit
Memory cleared
Three questions
Aggregate feedback
Spend ledger

↳ Needs

Nothing a visitor says should outlive the visit.

User Flow · The Encounter

The booth mapped from the build: what a visitor meets on arrival, what the operator does in Research Lab, what the Quest shows, how the conversation is heard, and what happens after. Each branch carries a need from building and studying it, which decided what that part had to do.

Highlighted pills trace the core loop. An invitation becomes an admitted wearer, the wearer gets one passive card, speech is heard as final words, a correction or a decline is honoured, and the visit ends with memory cleared and three questions.

Needs from my development runs and the 15 October encounter studies; no visitor testing yet

Mapped from the build as of 24 October: Quest client, Research Lab and the PC services

Perfect World · Process · 09

Perfect World · Process · 09

Exhibition PC
Arrive
Film loop
Posters
Invitation
Consent, read aloud
Film-and-discussion route
Research Lab
Technical check
Quest discovery
NEW PERSON
Admission
Wearer view
Partner analysis
Live captions
Decision trace
Conversation journal
Stop
Handover

↳ Needs

I have to see why the guide spoke, or why it waited.

Quest
Startup
Passive HUD
Listening
Caption card
Neural voice
Camera unavailable
Presence lost

↳ Needs

Cards must never sit between two faces.

Conversation
Addressed speech
Wake word
Final words
Speakers A–D
Unknown · overlap
Self-report
Correction
Decline
Stop

↳ Needs

Buttons pulled the eyes away from the person.

Microphone
PC USB microphone
iPhone page
Auto-enrol
Foreground resume
One source at a time

↳ Needs

One microphone has to hear both people.

After the visit
Memory cleared
Three questions
Aggregate feedback
Spend ledger

↳ Needs

Nothing a visitor says should outlive the visit.

User Flow · The Encounter

The booth mapped from the build: what a visitor meets on arrival, what the operator does in Research Lab, what the Quest shows, how the conversation is heard, and what happens after. Each branch carries a need from building and studying it, which decided what that part had to do.

Highlighted pills trace the core loop. An invitation becomes an admitted wearer, the wearer gets one passive card, speech is heard as final words, a correction or a decline is honoured, and the visit ends with memory cleared and three questions.

Needs from my development runs and the 15 October encounter studies; no visitor testing yet

Mapped from the build as of 24 October: Quest client, Research Lab and the PC services

Perfect World · Process · 09

Perfect World · Process · 09

C · Designing and building Ticker

UI and Visual Design Development

28 versions of the wearer's view: Figma studies first, then a web prototype that replayed one scripted session through every version. Red marks the three I kept and the final that combines them.

01 Figma v3 baseline

01 Figma v3 baseline

02 Crowd scan

02 Crowd scan

03 Scan and read

03 Scan and read

04 Emotion reading

04 Emotion reading

05 Cues and delivery

05 Cues and delivery

06 One line

06 One line

07 Tick first

07 Tick first

08 Keyword

08 Keyword

09 Beside them

09 Beside them

10 Glass chip

10 Glass chip

11 Film subtitles

11 Film subtitles

12 Teleprompter

12 Teleprompter

13 Peripheral light

13 Peripheral light

14 Tag-along card

14 Tag-along card

15 Anchored to exhibit

15 Anchored to exhibit

16 Monocular HUD

16 Monocular HUD

17 Pill to card

17 Pill to card

18 Line on a plate

18 Line on a plate

19 Wrist and earcon

19 Wrist and earcon

20 After Ray-Ban Display

20 After Ray-Ban Display

21 After Even G2

21 After Even G2

22 After Android XR

22 After Android XR

23 After visionOS

23 After visionOS

24 After Spectacles

24 After Spectacles

25 After Halo

25 After Halo

26 Scan and read, glass

26 Scan and read, glass

27 Cues, glass

27 Cues, glass

28 Final, combined

28 Final, combined

Three I kept, and how they combine

Emotion reading

Emotion reading

What it shows: expression, emotions, what to say

+

After visionOS

After visionOS

How it looks: glass windows, white type

+

Line on a plate

Line on a plate

Why it reads: dark backing behind text

= Final · emotion reading, restyled after visionOS, over a darker scene · all readings are design fiction

Later · a follow-up, after they laugh

Near the end · a time cue

When a representation begins to look like knowledge

The final view introduced a tension within my own proposal. Its emotion labels, suggested responses and rising engagement graph make the guide’s apparent understanding visible, while the world’s rules give priority to what people say and correct, and reject scoring a person. These readings are authored fiction: they are no evidence that the prototype can measure feelings or the quality of a conversation, and their precision is itself a design issue, because a confident number beside a suggested action makes a guess look authoritative. The next iteration has to make that boundary clear inside the experience, and evaluation should ask what participants believed the system knew about them, and about their partner.

Perfect World · Making · 21

Perfect World · Making · 21

C · Designing and building Ticker

UI and Visual Design Development

28 versions of the wearer's view: Figma studies first, then a web prototype that replayed one scripted session through every version. Red marks the three I kept and the final that combines them.

01 Figma v3 baseline

01 Figma v3 baseline

02 Crowd scan

02 Crowd scan

03 Scan and read

03 Scan and read

04 Emotion reading

04 Emotion reading

05 Cues and delivery

05 Cues and delivery

06 One line

06 One line

07 Tick first

07 Tick first

08 Keyword

08 Keyword

09 Beside them

09 Beside them

10 Glass chip

10 Glass chip

11 Film subtitles

11 Film subtitles

12 Teleprompter

12 Teleprompter

13 Peripheral light

13 Peripheral light

14 Tag-along card

14 Tag-along card

15 Anchored to exhibit

15 Anchored to exhibit

16 Monocular HUD

16 Monocular HUD

17 Pill to card

17 Pill to card

18 Line on a plate

18 Line on a plate

19 Wrist and earcon

19 Wrist and earcon

20 After Ray-Ban Display

20 After Ray-Ban Display

21 After Even G2

21 After Even G2

22 After Android XR

22 After Android XR

23 After visionOS

23 After visionOS

24 After Spectacles

24 After Spectacles

25 After Halo

25 After Halo

26 Scan and read, glass

26 Scan and read, glass

27 Cues, glass

27 Cues, glass

28 Final, combined

28 Final, combined

Three I kept, and how they combine

Emotion reading

Emotion reading

What it shows: expression, emotions, what to say

+

After visionOS

After visionOS

How it looks: glass windows, white type

+

Line on a plate

Line on a plate

Why it reads: dark backing behind text

= Final · emotion reading, restyled after visionOS, over a darker scene · all readings are design fiction

Later · a follow-up, after they laugh

Near the end · a time cue

When a representation begins to look like knowledge

The final view introduced a tension within my own proposal. Its emotion labels, suggested responses and rising engagement graph make the guide’s apparent understanding visible, while the world’s rules give priority to what people say and correct, and reject scoring a person. These readings are authored fiction: they are no evidence that the prototype can measure feelings or the quality of a conversation, and their precision is itself a design issue, because a confident number beside a suggested action makes a guess look authoritative. The next iteration has to make that boundary clear inside the experience, and evaluation should ask what participants believed the system knew about them, and about their partner.

Perfect World · Making · 21

Perfect World · Making · 21

Architecture

The guide began as Tickers’ desktop work partner and became an exhibition system in three weeks. It needs three layers that each do what the others cannot: the headset cannot judge what was said, the PC cannot see the room without it, and nobody should trust either without a view of what it thinks.

BEFORE · FIRST BUILD, EARLY OCTOBERWearerQuest appPCtap Talk, SendWAV turns, labelsreply cardMenus, object cards and routes in the headset; the Quest microphone and on-device labels only.AFTER · RUNNING SYSTEM, 22 OCTOBERQuest · the wearer’s layerUnity + Meta passthroughPassive, translucent HUD: no menus, no buttonsCamera frames over self-hosted LiveKit; nothing recordedExhibition PC · shared truthDjango + local modelsOne director: stop > correction > partner > final speech > sceneStreaming local captions, anonymous speakers, PC-side people trackingCloud wording only inside a US$9 Auckland-day ceilingResearch Lab · the operator’s layerReact, privateNEW PERSON admission, stop and handoverNine tiles for the operator; an audience view without personal datacamera framesone card, speech outadmit, stop, clearlive state, traces

Guidance Pipeline

Most turns never reach a cloud model: refusals, corrections and stops are decided locally, and wording is the only thing sent out. The guide’s line is a suggestion, not a reading of anyone’s mind.

1CaptureOne microphone (a USB microphone on the PC, or the iPhone page) and the Quest’s camera, with their original clocks.

↓

2Local perceptionCaptions from local speech recognition, anonymous speaker labels and tracked people. Uncertain tags stay uncertain.

↓

3aClear → by ruleStop, decline or a correction is handled locally. No model is called.
3bOpen → model wordingA cloud model phrases one short line from permitted context; it is checked before delivery.

↓

4DirectorOne active request and one pending. Stop and refusal outrank everything; late answers are dropped.

↓

5Output → handoverOne caption card and optional neural speech. Everything personal is cleared at handover.

Guide rules. Words a person said outrank anything a sensor guessed. A detected person stays anonymous; looking at someone is not consent.

Without a valid model reply, a prepared neutral line is used, so the spending ceiling never silences the guide.

Perfect World · Process · 22

Perfect World · Process · 22

Architecture

The guide began as Tickers’ desktop work partner and became an exhibition system in three weeks. It needs three layers that each do what the others cannot: the headset cannot judge what was said, the PC cannot see the room without it, and nobody should trust either without a view of what it thinks.

BEFORE · FIRST BUILD, EARLY OCTOBERWearerQuest appPCtap Talk, SendWAV turns, labelsreply cardMenus, object cards and routes in the headset; the Quest microphone and on-device labels only.AFTER · RUNNING SYSTEM, 22 OCTOBERQuest · the wearer’s layerUnity + Meta passthroughPassive, translucent HUD: no menus, no buttonsCamera frames over self-hosted LiveKit; nothing recordedExhibition PC · shared truthDjango + local modelsOne director: stop > correction > partner > final speech > sceneStreaming local captions, anonymous speakers, PC-side people trackingCloud wording only inside a US$9 Auckland-day ceilingResearch Lab · the operator’s layerReact, privateNEW PERSON admission, stop and handoverNine tiles for the operator; an audience view without personal datacamera framesone card, speech outadmit, stop, clearlive state, traces

Guidance Pipeline

Most turns never reach a cloud model: refusals, corrections and stops are decided locally, and wording is the only thing sent out. The guide’s line is a suggestion, not a reading of anyone’s mind.

1CaptureOne microphone (a USB microphone on the PC, or the iPhone page) and the Quest’s camera, with their original clocks.

↓

2Local perceptionCaptions from local speech recognition, anonymous speaker labels and tracked people. Uncertain tags stay uncertain.

↓

3aClear → by ruleStop, decline or a correction is handled locally. No model is called.
3bOpen → model wordingA cloud model phrases one short line from permitted context; it is checked before delivery.

↓

4DirectorOne active request and one pending. Stop and refusal outrank everything; late answers are dropped.

↓

5Output → handoverOne caption card and optional neural speech. Everything personal is cleared at handover.

Guide rules. Words a person said outrank anything a sensor guessed. A detected person stays anonymous; looking at someone is not consent.

Without a valid model reply, a prepared neutral line is used, so the spending ceiling never silences the guide.

Perfect World · Process · 22

Perfect World · Process · 22

Research Lab Flow

The operator’s side started as checks and became a private view of what the guide thinks: a technical check, a five-minute PC test, the live Lab, and a phone page that turns an iPhone into the encounter’s microphone.

01 Technical check

02 PC live test

03 Research Lab

04 Phone mic

First meters

Previous reading

Held reading

The meters went through three rounds on 19 October: live values alone, then the previous reading beside the live one, then smoothed and held between assessments, so the operator reads a trend rather than each new number.

Research Lab asked how much an operator should see before watching becomes surveillance. It answered with private panels that the audience never sees, and a memory that clears at every handover.

Captures from the working system, 19–24 October 2026, authored data · Rec

Developer view · the nine tiles only the operator sees

Actual UI components with an authored fixture, 24 October · no camera, microphone or model connected · Rec

Perfect World · Process · 16

Perfect World · Process · 16

Research Lab Flow

The operator’s side started as checks and became a private view of what the guide thinks: a technical check, a five-minute PC test, the live Lab, and a phone page that turns an iPhone into the encounter’s microphone.

01 Technical check

02 PC live test

03 Research Lab

04 Phone mic

First meters

Previous reading

Held reading

The meters went through three rounds on 19 October: live values alone, then the previous reading beside the live one, then smoothed and held between assessments, so the operator reads a trend rather than each new number.

Research Lab asked how much an operator should see before watching becomes surveillance. It answered with private panels that the audience never sees, and a memory that clears at every handover.

Captures from the working system, 19–24 October 2026, authored data · Rec

Developer view · the nine tiles only the operator sees

Actual UI components with an authored fixture, 24 October · no camera, microphone or model connected · Rec

Perfect World · Process · 16

Perfect World · Process · 16

Reflecting · evidence and response

Redesigning the Guide

Each change answers something a build, a bench test or a physical session showed between 12 and 22 October. The last row could not be solved before the exhibition.

12 Oct

Completed recordings arrived too late to guide a live conversation.

→ Streaming local captions on their original clock, and a separate wake word.

13 Oct

Captions stopped whenever the cloud budget ran out.

→ Captions run locally and survive budget exhaustion; only wording is paid for.

15 Oct

Menus, cards and buttons in the headset pulled the wearer’s eyes off the other person.

→ Removed AR menus and buttons; the operator admits, the wearer only speaks.

Mid Oct

On-device detection sent labels only: no pixels, depth or pose.

→ People tracking moved to the PC; a lost camera is a designed state.

18–22 Oct

The Quest microphone and then the iPhone link lost the conversation.

→ One PC USB microphone, selected and verified for the same wearer.

19 Oct

Live meters jumped with every new number; readings were easy to misread.

→ Readings held and smoothed between assessments; chat and memory grouped.

22 Oct
No audible
reply yet

The wearer heard no reply, and no full five-minute session has passed.

→ Still open: the people test runs with captions first; audible speech is unverified.

Captures from the working system (Rec) · dates from the build log and physical tests

Reflecting · what carried forward

What the Build Established

I kept removing what the wearer had to do, and adding what the operator could check.

Established

A guide can listen to two people, decide most things locally and say one line, with every decision traceable in the Lab.

Unresolved

Hearing in a loud atrium: replies came late or not at all, and the timing of one line is still untested with visitors.

Retained

Rules before any model, memory that clears at handover, and an operator who can see and stop everything.

Carried into the exhibition

The emotion-reading HUD as labelled fiction, while the running system only uses what people said.

Testing the build with people, 26 October: wearing it, then talking face to face · Rec

If the guide always knew what to say, what would still be yours?

Perfect World · Process · 17

Perfect World · Process · 17

Reflecting · evidence and response

Redesigning the Guide

Each change answers something a build, a bench test or a physical session showed between 12 and 22 October. The last row could not be solved before the exhibition.

12 Oct

Completed recordings arrived too late to guide a live conversation.

→ Streaming local captions on their original clock, and a separate wake word.

13 Oct

Captions stopped whenever the cloud budget ran out.

→ Captions run locally and survive budget exhaustion; only wording is paid for.

15 Oct

Menus, cards and buttons in the headset pulled the wearer’s eyes off the other person.

→ Removed AR menus and buttons; the operator admits, the wearer only speaks.

Mid Oct

On-device detection sent labels only: no pixels, depth or pose.

→ People tracking moved to the PC; a lost camera is a designed state.

18–22 Oct

The Quest microphone and then the iPhone link lost the conversation.

→ One PC USB microphone, selected and verified for the same wearer.

19 Oct

Live meters jumped with every new number; readings were easy to misread.

→ Readings held and smoothed between assessments; chat and memory grouped.

22 Oct
No audible
reply yet

The wearer heard no reply, and no full five-minute session has passed.

→ Still open: the people test runs with captions first; audible speech is unverified.

Captures from the working system (Rec) · dates from the build log and physical tests

Reflecting · what carried forward

What the Build Established

I kept removing what the wearer had to do, and adding what the operator could check.

Established

A guide can listen to two people, decide most things locally and say one line, with every decision traceable in the Lab.

Unresolved

Hearing in a loud atrium: replies came late or not at all, and the timing of one line is still untested with visitors.

Retained

Rules before any model, memory that clears at handover, and an operator who can see and stop everything.

Carried into the exhibition

The emotion-reading HUD as labelled fiction, while the running system only uses what people said.

Testing the build with people, 26 October: wearing it, then talking face to face · Rec

If the guide always knew what to say, what would still be yours?

Perfect World · Process · 17

Perfect World · Process · 17

Poster References

The posters had to speak to someone who had not read this report. I collected references on my Figma board and read them for what a passer-by notices first: the title, a person, the world, or an instruction.

What I read in them

Big condensed type against one image; a masthead that sells before it explains; dense information kept to a strict grid, so the eye always has somewhere to land.

What I took forward

A heavy italic masthead, one hero image, red used as emphasis rather than a fill, and a grid strict enough to carry a lot of explanation.

Poster references from my Figma board, 14 October 2026 · creators to be credited before publication

An Earlier Pair

Poster 01 · the future’s conditions

Poster 02 · the encounter and its audience

The first A1 exports split the job in two: poster 01 set out the conditions of the future, poster 02 the encounter and its audience. The next rounds worked on hierarchy, image and how much to say.

It explained more than it showed

Dense columns of explanation competed with the image, so a passer-by had no single thing to notice first.

It answered too early

The marionette crowd read as control at a glance, closing the question before a visitor could meet any useful help.

Earlier supplied A1 exports · imagery AI-generated (OpenAI) · speculative illustrations, not photographs of visitors

Perfect World · Process · 05

Perfect World · Process · 05

Poster References

The posters had to speak to someone who had not read this report. I collected references on my Figma board and read them for what a passer-by notices first: the title, a person, the world, or an instruction.

What I read in them

Big condensed type against one image; a masthead that sells before it explains; dense information kept to a strict grid, so the eye always has somewhere to land.

What I took forward

A heavy italic masthead, one hero image, red used as emphasis rather than a fill, and a grid strict enough to carry a lot of explanation.

Poster references from my Figma board, 14 October 2026 · creators to be credited before publication

An Earlier Pair

Poster 01 · the future’s conditions

Poster 02 · the encounter and its audience

The first A1 exports split the job in two: poster 01 set out the conditions of the future, poster 02 the encounter and its audience. The next rounds worked on hierarchy, image and how much to say.

It explained more than it showed

Dense columns of explanation competed with the image, so a passer-by had no single thing to notice first.

It answered too early

The marionette crowd read as control at a glance, closing the question before a visitor could meet any useful help.

Earlier supplied A1 exports · imagery AI-generated (OpenAI) · speculative illustrations, not photographs of visitors

Perfect World · Process · 05

Perfect World · Process · 05

World Poster: First Rounds

Fourteen saved rounds of poster 01. Each trialled one thing: image scale, the WORLD masthead, serif or condensed type, the amount of red, and how much to explain.

v2 · utilitarian

v3 · layout trial

v4

v5

v6

v9

v10

v11

v12 · direct reference

v13

v14 · restrained red

v17 · three signals

v18 · minimal copy

v19 · content clarity

What changed across them. The masthead settled early. Most rounds after that were about restraint: one red element instead of a red field (v14), three current signals instead of a wall of claims (v17), and less copy (v18), until the content read clearly at A1 (v19).

Saved rounds from my poster working folder · version labels from their file names

From Marionettes to the City

v19 · wide editorial

v20 · happy future

v21 · marionette future

v22 · A1 hierarchy

Human system v2

Human study v3

Marionette study · 405:2

Editorial grid v6 · 413:2

Make influence immediate

The marionette image shows external direction at a glance. The risk: control becomes the answer before the visitor meets any useful help.

Make the benefit visible

The hero became an appealing city, with aligned panels explaining the proposition. The future makes its own first impression; the visitor decides what it means.

Figma frames 405:2 and 413:2, inspected 22 October 2026 · how audiences read either version was not tested

Perfect World · Process · 06

Perfect World · Process · 06

World Poster: First Rounds

Fourteen saved rounds of poster 01. Each trialled one thing: image scale, the WORLD masthead, serif or condensed type, the amount of red, and how much to explain.

v2 · utilitarian

v3 · layout trial

v4

v5

v6

v9

v10

v11

v12 · direct reference

v13

v14 · restrained red

v17 · three signals

v18 · minimal copy

v19 · content clarity

What changed across them. The masthead settled early. Most rounds after that were about restraint: one red element instead of a red field (v14), three current signals instead of a wall of claims (v17), and less copy (v18), until the content read clearly at A1 (v19).

Saved rounds from my poster working folder · version labels from their file names

From Marionettes to the City

v19 · wide editorial

v20 · happy future

v21 · marionette future

v22 · A1 hierarchy

Human system v2

Human study v3

Marionette study · 405:2

Editorial grid v6 · 413:2

Make influence immediate

The marionette image shows external direction at a glance. The risk: control becomes the answer before the visitor meets any useful help.

Make the benefit visible

The hero became an appealing city, with aligned panels explaining the proposition. The future makes its own first impression; the visitor decides what it means.

Figma frames 405:2 and 413:2, inspected 22 October 2026 · how audiences read either version was not tested

Perfect World · Process · 06

Perfect World · Process · 06

Encounter Poster: Explaining the Visit

Poster 02 had to explain an experience nobody had seen: who wears the headset, what the guide offers, and how to say no.

v1 · manga exhibition

v2 · steps at the bottom

v3 · content clarity

v4 · project blurb

v5 · brutalist pair

v6 · interaction

v7 · optimal happiness

v8 · feedback removed

v9 · holographic

HUD v1 · 445:2

HUD v3 · 455:2

Put a response on the page

HUD v1 shows an example opening, with accept, question and decline as visible alternatives. Its percentage bars are illustrative.

Explain the whole visit

HUD v3 separates the wearer’s view from the shared conversation, with five moments and the context around them.

The Final Print Pair

Poster 01 · the world, its appeal and the handover

Poster 02 · the proposed encounter and the visitor’s role

01

From control to appeal

The hero changed from the marionette crowd to the city, so the future makes its own first impression.

02

From a takeaway to a response

The poster stops prescribing a conclusion and leaves the question to the visitor.

03

From a claim to a visit

Poster 02 explains the five moments and how to decline; it does not claim those controls work in Quest.

Print frames 498:5744 and 498:6107 · copy AI-assisted and reviewed · people and capabilities shown are speculative

Perfect World · Process · 07

Perfect World · Process · 07

Encounter Poster: Explaining the Visit

Poster 02 had to explain an experience nobody had seen: who wears the headset, what the guide offers, and how to say no.

v1 · manga exhibition

v2 · steps at the bottom

v3 · content clarity

v4 · project blurb

v5 · brutalist pair

v6 · interaction

v7 · optimal happiness

v8 · feedback removed

v9 · holographic

HUD v1 · 445:2

HUD v3 · 455:2

Put a response on the page

HUD v1 shows an example opening, with accept, question and decline as visible alternatives. Its percentage bars are illustrative.

Explain the whole visit

HUD v3 separates the wearer’s view from the shared conversation, with five moments and the context around them.

The Final Print Pair

Poster 01 · the world, its appeal and the handover

Poster 02 · the proposed encounter and the visitor’s role

01

From control to appeal

The hero changed from the marionette crowd to the city, so the future makes its own first impression.

02

From a takeaway to a response

The poster stops prescribing a conclusion and leaves the question to the visitor.

03

From a claim to a visit

Poster 02 explains the five moments and how to decline; it does not claim those controls work in Quest.

Print frames 498:5744 and 498:6107 · copy AI-assisted and reviewed · people and capabilities shown are speculative

Perfect World · Process · 07

Perfect World · Process · 07

D · Constructing the exhibition

Designing the Exhibition Space

The exhibition is one booth among many in the University of Auckland Engineering atrium. Each project gets one black poster board and one table, visitors arrive from every side, and daylight falls through the skylight. The booth had to work inside that footprint and that light.

The Engineering atrium during an earlier exhibition: black poster boards and tables ring the stair voids01020304

The Engineering atrium during an earlier exhibition · venue reference photograph

What the venue decided

01

Daylight from the skylight

No darkness to rely on. Every screen has to read in daylight, and the film has to work without sound.

02

Booths ring the stair voids

Visitors arrive from every side, so the screens face the walkway and the board closes the back.

03

People pass in seconds

Something is always moving: the film loops, and a second TV shows someone mid-encounter.

04

One board and one table

Posters on the board, screens beside and on the table, the encounter in the space in front.

—

Not everyone wants a headset

Two TVs and a monitor tell the whole story without one.

The Booth Plan

One person wears the Quest and talks with the visitor in front of them, a friend or the next in line. Three screens let everyone else follow: the world on TV 1, what the wearer sees on TV 2, and why the guide spoke on the monitor.

PLAN · APPROXIMATE1 m, approx.stair voidBlack board · poster 01 the world · poster 02 the encounter3Table · Research Lab monitor · PC beneath1TV 1the film, looping2TV 2headset view, livetalkWearer · QuestVisitor facing themQuest zone · about 2 m clearwatchers follow on TV 2passers-by stop for TV 1Me · operatorWalkway · visitors pass on every side

Booth plan, approximate · positions to confirm at set-up · 26 October

TV 1

The two-minute film, looping, muted

TV 2

A live mirror of the headset view

Monitor

Ticker Research Lab, shown openly

Meta Quest 3S

University loan; passthrough

Exhibition PC

Local models, one director

Microphone

Hears the visitor facing the wearer

Two A1 posters

On the black board

How the booth got here

v1 · Open-day desk

Five pieces to explain one institution

v2 · Film and tabletop

A guide that recommends an object

v3 · Quest in the atrium

An open encounter on the floor

↑ The plan above

v4 · This booth

Board, table, three screens, one Quest

Perfect World · Making · 22

Perfect World · Making · 22

D · Constructing the exhibition

Designing the Exhibition Space

The exhibition is one booth among many in the University of Auckland Engineering atrium. Each project gets one black poster board and one table, visitors arrive from every side, and daylight falls through the skylight. The booth had to work inside that footprint and that light.

The Engineering atrium during an earlier exhibition: black poster boards and tables ring the stair voids01020304

The Engineering atrium during an earlier exhibition · venue reference photograph

What the venue decided

01

Daylight from the skylight

No darkness to rely on. Every screen has to read in daylight, and the film has to work without sound.

02

Booths ring the stair voids

Visitors arrive from every side, so the screens face the walkway and the board closes the back.

03

People pass in seconds

Something is always moving: the film loops, and a second TV shows someone mid-encounter.

04

One board and one table

Posters on the board, screens beside and on the table, the encounter in the space in front.

—

Not everyone wants a headset

Two TVs and a monitor tell the whole story without one.

The Booth Plan

One person wears the Quest and talks with the visitor in front of them, a friend or the next in line. Three screens let everyone else follow: the world on TV 1, what the wearer sees on TV 2, and why the guide spoke on the monitor.

PLAN · APPROXIMATE1 m, approx.stair voidBlack board · poster 01 the world · poster 02 the encounter3Table · Research Lab monitor · PC beneath1TV 1the film, looping2TV 2headset view, livetalkWearer · QuestVisitor facing themQuest zone · about 2 m clearwatchers follow on TV 2passers-by stop for TV 1Me · operatorWalkway · visitors pass on every side

Booth plan, approximate · positions to confirm at set-up · 26 October

TV 1

The two-minute film, looping, muted

TV 2

A live mirror of the headset view

Monitor

Ticker Research Lab, shown openly

Meta Quest 3S

University loan; passthrough

Exhibition PC

Local models, one director

Microphone

Hears the visitor facing the wearer

Two A1 posters

On the black board

How the booth got here

v1 · Open-day desk

Five pieces to explain one institution

v2 · Film and tabletop

A guide that recommends an object

v3 · Quest in the atrium

An open encounter on the floor

↑ The plan above

v4 · This booth

Board, table, three screens, one Quest

Perfect World · Making · 22

Perfect World · Making · 22

D · Constructing the exhibition · 42

Bringing Everything Together

01The film loops on the screen
02Posters on the board sell the world
03I invite two visitors and read the consent
04The wearer hears one opening
05The partner talks back; the guide waits
06Research Lab, unseen, shows me why it spoke

The booth as intended · atrium study, 15 October, AI-assisted (Spec) · final installation render to replace it

Can This Encounter Be Staged?

Three practical questions, each answered only with what is recorded. A configured limit is not a measured cost, and a capacity target is not attendance.

01 Would people engage?

Partly evidenced

Evidence

58% of online adults in Aotearoa use AI weekly (InternetNZ, 2025). I chose the social opening because its help is obvious fast; peer critique of the film directions favoured a sincere advert.

Not yet

The 26 October pair sessions are not analysed; no visitor engagement is claimed.

02 Can it operate?

Built, not physically accepted

Implemented

Quest app, PC services and Research Lab. Two authored five-minute runs completed recall, correction and expiry clearing (602 s).

Failure points

22 October: the PC could not see the Quest; cloud conversation timed out; a wearer reported no audible reply.

Fallback

Local captions and prepared wording survive when cloud capacity fails; the film-and-discussion route needs no headset.

03 Can it be staged affordably?

Targets, with one measured day

Equipment

One Meta Quest 3S on university loan, and a PC with an RTX 3060.

Cloud cost

Configured ceiling US$9 a day. Measured on 22 October: US$0.001 settled, US$0.525 held.

Time

Each wearer capped at 300 s; about ten minutes a pair with consent and questions. Seven uninterrupted hours allow at most 42 pairs (84 people if both take part); resets, breaks and failures lower that. A ceiling, not attendance.

Perfect World · Making · 23

Perfect World · Making · 23

D · Constructing the exhibition · 42

Bringing Everything Together

01The film loops on the screen
02Posters on the board sell the world
03I invite two visitors and read the consent
04The wearer hears one opening
05The partner talks back; the guide waits
06Research Lab, unseen, shows me why it spoke

The booth as intended · atrium study, 15 October, AI-assisted (Spec) · final installation render to replace it

Can This Encounter Be Staged?

Three practical questions, each answered only with what is recorded. A configured limit is not a measured cost, and a capacity target is not attendance.

01 Would people engage?

Partly evidenced

Evidence

58% of online adults in Aotearoa use AI weekly (InternetNZ, 2025). I chose the social opening because its help is obvious fast; peer critique of the film directions favoured a sincere advert.

Not yet

The 26 October pair sessions are not analysed; no visitor engagement is claimed.

02 Can it operate?

Built, not physically accepted

Implemented

Quest app, PC services and Research Lab. Two authored five-minute runs completed recall, correction and expiry clearing (602 s).

Failure points

22 October: the PC could not see the Quest; cloud conversation timed out; a wearer reported no audible reply.

Fallback

Local captions and prepared wording survive when cloud capacity fails; the film-and-discussion route needs no headset.

03 Can it be staged affordably?

Targets, with one measured day

Equipment

One Meta Quest 3S on university loan, and a PC with an RTX 3060.

Cloud cost

Configured ceiling US$9 a day. Measured on 22 October: US$0.001 settled, US$0.525 held.

Time

Each wearer capped at 300 s; about ten minutes a pair with consent and questions. Seven uninterrupted hours allow at most 42 pairs (84 people if both take part); resets, breaks and failures lower that. A ceiling, not attendance.

Perfect World · Making · 23

Perfect World · Making · 23

E · Responsibility, impact and evaluation

Testing, Critique and Iteration

Each track was evaluated differently, and the sources differ: critique, my own review of generated studies, authored replays and technical tests. The facing page follows two cases from question to change; the table covers the rest.

Track

Method and source

Status

What changed

Film

Peer critique of six directions; my check against the world bible

Done

A reveal became a sincere advert; v2 shot list rebuilt, keyframes still to make

Quest encounter

My review of atrium studies (Spec); a session on myself; pair sessions 26 Oct

Partly

Buttons replaced by speech; pair sessions not yet analysed

Interface

Figma rounds, my review; component tests

Done

Meters removed; emotion readings kept only as labelled fiction in the final view and film, while the running system uses what people said

Research Lab

Three meter rounds; authored correction replay

Done

Held readings; per-speaker memory

System

Technical run, 22 October · Case 02

Done

Physical chain first; local fallback wording

Exhibition

Venue visit; staging rehearsal

Planned

To add after set-up

What the evidence establishes

The report contains several kinds of evidence that answer different questions. Generated studies and storyboards show alternatives I considered. Component captures and authored replays show behaviour under specified conditions. Technical runs reveal implementation successes and failures. Later photographs document people wearing the headset and meeting face to face, and the credits record my confirmation of the button-free flow. These records do not, by themselves, explain whether participants found the guidance useful, understood its fictional claims or felt able to refuse it.

The 22 October run remains important because it exposed a gap between passing software checks and operating the physical encounter. The missing Quest connection, delayed cloud response and failed recall informed the need for physical checks and fallback behaviour. Later confirmation needs its own dated scope rather than silently replacing that earlier result. An analysed account of visitor experience is still needed to assess the exhibition’s central question.

What I would test next, and what it would change

I would next evaluate the encounter in pairs. First, I would ask each person what they thought the guide knew and where its suggestion affected the conversation. Second, I would check whether people can discover and use refusal, correction and stop in the actual room; a prompted refusal tests whether a command works, not whether someone would notice or feel comfortable using it. Third, I would seek the partner’s account separately, including their comfort with being heard, interpreted or shown on a public display.

These checks would sit alongside a complete physical rehearsal, and a comparison with the discussion route for anyone who declines the headset. The next iteration would follow the evidence: misunderstood readings would need clearer framing or a changed representation; interrupted conversation, different timing or less guidance; discomfort with the public display, a narrower display.

Case 01 · Choice without buttons

My review of studies · Spec

Study 01 · before

Study 08 · after

Question

Would Accept, Question and Decline buttons keep the wearer in control of a suggestion?

Method

My review of the atrium studies. No participant was involved.

Evidence

In study 01 the card and buttons fill the view, and pressing one means reaching out past the person.

Interpretation

The buttons would compete with the person for attention and turn a conversation into a form.

Change

Spoken “not this”, “why” and “stop”; one quiet line at the bottom of view.

Still uncertain

Whether participants discover and use spoken commands in the room.

Case 02 · The physical chain

Test report, 22 Oct · Rec

{ "django_tests": "1,394 run · 0 failed · 32 optional skipped",
  "adb_devices": [], "conversation": "timeout 12,203 ms",
  "recall": "HTTP 5xx", "memory_after_failure": "cleared" }

Question

Could the Quest, the PC and the cloud wording run one encounter end to end?

Method

A technical run before anyone wore the headset.

Evidence

The software suites passed, but the PC listed no Quest over USB. Cloud conversation timed out at 12.2 s; recall returned a server error. Memory still cleared.

Interpretation

Passing suites could not stand in for the physical chain, and the cloud voice was the weakest link.

Change

Check the physical chain first on the day; local fallback wording when the cloud fails.

Still uncertain

An audible reply on the headset is not confirmed, nor a full day of use.

Perfect World · Making · 25

Perfect World · Making · 25

E · Responsibility, impact and evaluation

Testing, Critique and Iteration

Each track was evaluated differently, and the sources differ: critique, my own review of generated studies, authored replays and technical tests. The facing page follows two cases from question to change; the table covers the rest.

Track

Method and source

Status

What changed

Film

Peer critique of six directions; my check against the world bible

Done

A reveal became a sincere advert; v2 shot list rebuilt, keyframes still to make

Quest encounter

My review of atrium studies (Spec); a session on myself; pair sessions 26 Oct

Partly

Buttons replaced by speech; pair sessions not yet analysed

Interface

Figma rounds, my review; component tests

Done

Meters removed; emotion readings kept only as labelled fiction in the final view and film, while the running system uses what people said

Research Lab

Three meter rounds; authored correction replay

Done

Held readings; per-speaker memory

System

Technical run, 22 October · Case 02

Done

Physical chain first; local fallback wording

Exhibition

Venue visit; staging rehearsal

Planned

To add after set-up

What the evidence establishes

The report contains several kinds of evidence that answer different questions. Generated studies and storyboards show alternatives I considered. Component captures and authored replays show behaviour under specified conditions. Technical runs reveal implementation successes and failures. Later photographs document people wearing the headset and meeting face to face, and the credits record my confirmation of the button-free flow. These records do not, by themselves, explain whether participants found the guidance useful, understood its fictional claims or felt able to refuse it.

The 22 October run remains important because it exposed a gap between passing software checks and operating the physical encounter. The missing Quest connection, delayed cloud response and failed recall informed the need for physical checks and fallback behaviour. Later confirmation needs its own dated scope rather than silently replacing that earlier result. An analysed account of visitor experience is still needed to assess the exhibition’s central question.

What I would test next, and what it would change

I would next evaluate the encounter in pairs. First, I would ask each person what they thought the guide knew and where its suggestion affected the conversation. Second, I would check whether people can discover and use refusal, correction and stop in the actual room; a prompted refusal tests whether a command works, not whether someone would notice or feel comfortable using it. Third, I would seek the partner’s account separately, including their comfort with being heard, interpreted or shown on a public display.

These checks would sit alongside a complete physical rehearsal, and a comparison with the discussion route for anyone who declines the headset. The next iteration would follow the evidence: misunderstood readings would need clearer framing or a changed representation; interrupted conversation, different timing or less guidance; discomfort with the public display, a narrower display.

Case 01 · Choice without buttons

My review of studies · Spec

Study 01 · before

Study 08 · after

Question

Would Accept, Question and Decline buttons keep the wearer in control of a suggestion?

Method

My review of the atrium studies. No participant was involved.

Evidence

In study 01 the card and buttons fill the view, and pressing one means reaching out past the person.

Interpretation

The buttons would compete with the person for attention and turn a conversation into a form.

Change

Spoken “not this”, “why” and “stop”; one quiet line at the bottom of view.

Still uncertain

Whether participants discover and use spoken commands in the room.

Case 02 · The physical chain

Test report, 22 Oct · Rec

{ "django_tests": "1,394 run · 0 failed · 32 optional skipped",
  "adb_devices": [], "conversation": "timeout 12,203 ms",
  "recall": "HTTP 5xx", "memory_after_failure": "cleared" }

Question

Could the Quest, the PC and the cloud wording run one encounter end to end?

Method

A technical run before anyone wore the headset.

Evidence

The software suites passed, but the PC listed no Quest over USB. Cloud conversation timed out at 12.2 s; recall returned a server error. Memory still cleared.

Interpretation

Passing suites could not stand in for the physical chain, and the cloud voice was the weakest link.

Change

Check the physical chain first on the day; local fallback wording when the cloud fails.

Still uncertain

An audible reply on the headset is not confirmed, nor a full day of use.

Perfect World · Making · 25

Perfect World · Making · 25

My Position within the Future I Designed

I am Korean-born and grew up in Aotearoa. Moving between places, my faith and experiences of volunteering have shaped my concern for relationships, belonging and contribution. My design and computer science background also encourages me to make problems manageable through systems, measurements and rules. Perfect World brought these positions into tension. I could design an efficient city and a capable guide, but the decisions about what counts as a worthwhile life still reflected my own judgement.

As tauiwi, I need to examine how much authority I take when imagining a desirable future in Aotearoa. Describing a city as beneficial does not settle whose aspirations it serves, whose land and knowledge it relies on, or who can change its direction. The fictional founding agreement and Partnership Board identify questions that require relationships and decisions beyond my authorship. They do not constitute an agreement with mana whenua or evidence that partnership occurred.

The five principles provided in the course offer a way to examine concrete choices and remaining limits. Some choices arose through making and critique and are interpreted through these principles retrospectively; I do not claim the framework caused every decision at the time. Two decisions show the shift.

Decision 01

From reading a person to asking them, and letting them correct it

Tickers · a focus score from activity (Rec)

→

Perfect World · notes that name who said them (Rec)

Tickers scored me from signals. Testing showed it misread reading as distraction, so later versions ask for context and let each person correct what is held about them.

Informed at the time byThe DES303 session results and my own misread session.

Examined afterwards throughKaitiakitanga: care as correction and clearing, not more collection.

Decision 02

From my achievement to a choice two people share

Tickers · one person’s effort, staked (Spec)

→

Perfect World · one opening for two visitors (Spec)

I started from individual productivity. The final encounter is between two people who each consent; the guide offers one opening and steps back, and different purposes stay legitimate.

Informed at the time byOctober studies and course teaching on the five principles. I cannot claim the principles caused it.

Examined afterwards throughWhaanau: the relationship, not the individual, as the unit of the experience.

Five Principles, Applied and Limited

WhaanauRelationships and collective belonging

The final encounter changes the unit of concern from one person’s achievement to a relationship between two people. The conversation partner has purposes, boundaries and a right to decline independently of the wearer. This challenges my earlier tendency to frame improvement through individual productivity. Two-person consent remains a limited response: the project does not establish what collective belonging or a good encounter means for whānau or communities.

KaitiakitangaProtection and care

Earlier trackers encouraged me to seek more information in order to understand someone better. The later design gives greater attention to attribution, correction, limited retention and clearing at handover. Māori data sovereignty also concerns authority over collection, interpretation, access and use, and the interests of collectives, as Te Mana Raraunga’s principles make explicit (Te Mana Raraunga, 2018). My privacy mechanisms do not establish that this authority has been exercised in the project.

Mahi tahiCollaboration and partnership

Critique and feedback changed the project’s direction, including its treatment of authority and its move towards a desirable future. The cultural limit is that Māori partnership is not documented in this project. A future engagement process would need to leave consequential decisions open, including whether the proposed sensing, public displays and fictional governance should continue in their current form.

HangaCreation through purposeful action

Making the guide exposed responsibilities that a statement of values could leave abstract: who a memory belongs to, which correction takes priority, when a suggestion interrupts, and what happens when a person stops. These questions became requirements for interfaces and system behaviour. Their implementation provides material to examine, while the effect on participants still requires evaluation.

Huri hangaDiversity, plurality and transformation

The fiction allows learning, care, making, rest and changing direction to remain legitimate purposes, and the exhibition proposes participation without a headset. Spoken English, a busy atrium and a visual interface still create access limits, and generated people in the city imagery cannot demonstrate that different communities helped define this future. Further development needs perspectives capable of changing its assumptions.

What engagement would need to change · a plan, not something that has happened

My next step would be to seek guidance through the faculty about an appropriate process and the people or rights-holders relevant to the work. An adviser can help establish that route; I cannot assume that one person represents all Māori interests or mana whenua. The decisions would include what information the encounter may use, what can appear on public screens, who can interpret or retain it, and whether the fictional treatment of place and partnership is appropriate.

I would approach that work with time and resources for participation and with a willingness to revise or stop parts of the proposal. A request to endorse an already settled system would leave too little room for partnership. The present report can explain my position and the consequences of my decisions; it cannot claim an engagement process that has not occurred.

Principles from the DES304 course; Te Whare Tapa Whā and Te Mana Raraunga as readings, not consultation

Perfect World · Process · 25

Perfect World · Process · 25

My Position within the Future I Designed

I am Korean-born and grew up in Aotearoa. Moving between places, my faith and experiences of volunteering have shaped my concern for relationships, belonging and contribution. My design and computer science background also encourages me to make problems manageable through systems, measurements and rules. Perfect World brought these positions into tension. I could design an efficient city and a capable guide, but the decisions about what counts as a worthwhile life still reflected my own judgement.

As tauiwi, I need to examine how much authority I take when imagining a desirable future in Aotearoa. Describing a city as beneficial does not settle whose aspirations it serves, whose land and knowledge it relies on, or who can change its direction. The fictional founding agreement and Partnership Board identify questions that require relationships and decisions beyond my authorship. They do not constitute an agreement with mana whenua or evidence that partnership occurred.

The five principles provided in the course offer a way to examine concrete choices and remaining limits. Some choices arose through making and critique and are interpreted through these principles retrospectively; I do not claim the framework caused every decision at the time. Two decisions show the shift.

Decision 01

From reading a person to asking them, and letting them correct it

Tickers · a focus score from activity (Rec)

→

Perfect World · notes that name who said them (Rec)

Tickers scored me from signals. Testing showed it misread reading as distraction, so later versions ask for context and let each person correct what is held about them.

Informed at the time byThe DES303 session results and my own misread session.

Examined afterwards throughKaitiakitanga: care as correction and clearing, not more collection.

Decision 02

From my achievement to a choice two people share

Tickers · one person’s effort, staked (Spec)

→

Perfect World · one opening for two visitors (Spec)

I started from individual productivity. The final encounter is between two people who each consent; the guide offers one opening and steps back, and different purposes stay legitimate.

Informed at the time byOctober studies and course teaching on the five principles. I cannot claim the principles caused it.

Examined afterwards throughWhaanau: the relationship, not the individual, as the unit of the experience.

Five Principles, Applied and Limited

WhaanauRelationships and collective belonging

The final encounter changes the unit of concern from one person’s achievement to a relationship between two people. The conversation partner has purposes, boundaries and a right to decline independently of the wearer. This challenges my earlier tendency to frame improvement through individual productivity. Two-person consent remains a limited response: the project does not establish what collective belonging or a good encounter means for whānau or communities.

KaitiakitangaProtection and care

Earlier trackers encouraged me to seek more information in order to understand someone better. The later design gives greater attention to attribution, correction, limited retention and clearing at handover. Māori data sovereignty also concerns authority over collection, interpretation, access and use, and the interests of collectives, as Te Mana Raraunga’s principles make explicit (Te Mana Raraunga, 2018). My privacy mechanisms do not establish that this authority has been exercised in the project.

Mahi tahiCollaboration and partnership

Critique and feedback changed the project’s direction, including its treatment of authority and its move towards a desirable future. The cultural limit is that Māori partnership is not documented in this project. A future engagement process would need to leave consequential decisions open, including whether the proposed sensing, public displays and fictional governance should continue in their current form.

HangaCreation through purposeful action

Making the guide exposed responsibilities that a statement of values could leave abstract: who a memory belongs to, which correction takes priority, when a suggestion interrupts, and what happens when a person stops. These questions became requirements for interfaces and system behaviour. Their implementation provides material to examine, while the effect on participants still requires evaluation.

Huri hangaDiversity, plurality and transformation

The fiction allows learning, care, making, rest and changing direction to remain legitimate purposes, and the exhibition proposes participation without a headset. Spoken English, a busy atrium and a visual interface still create access limits, and generated people in the city imagery cannot demonstrate that different communities helped define this future. Further development needs perspectives capable of changing its assumptions.

What engagement would need to change · a plan, not something that has happened

My next step would be to seek guidance through the faculty about an appropriate process and the people or rights-holders relevant to the work. An adviser can help establish that route; I cannot assume that one person represents all Māori interests or mana whenua. The decisions would include what information the encounter may use, what can appear on public screens, who can interpret or retain it, and whether the fictional treatment of place and partnership is appropriate.

I would approach that work with time and resources for participation and with a willingness to revise or stop parts of the proposal. A request to endorse an already settled system would leave too little room for partnership. The present report can explain my position and the consequences of my decisions; it cannot claim an engagement process that has not occurred.

Principles from the DES304 course; Te Whare Tapa Whā and Te Mana Raraunga as readings, not consultation

Perfect World · Process · 25

Perfect World · Process · 25

E · Responsibility, impact and evaluation

What the Exhibition Could Change

Perfect World’s intended contribution is a more concrete discussion of personalised AI guidance among people encountering the work in Aotearoa. The imagined city provides a context in which the assistance is attractive, while the proposed conversation brings its influence into a familiar human situation. A visitor could find a suggestion useful and still question how it shaped what they said, who they approached or which possibilities they considered.

The proposed pathway is that experiencing a particular suggestion gives participants something specific to reflect on afterwards. I would look for three immediate outcomes: recognising where assistance influenced a choice, describing a boundary around the help they would welcome, and acknowledging that the other person may have experienced the same encounter differently. Disagreement would be valuable evidence too. The aim is to support a considered response that could include wanting extensive guidance.

The primary audience is the students, staff and visitors passing the exhibition. Their responses could help designers and technologists examine the timing, explanation and refusal of personal assistance. This setting gives access to a small, situated group; it does not represent everyone in Aotearoa. People who avoid the headset, prefer another language or cannot comfortably use the interface may also have important reasons to question the proposal.

The impact remains an intention to examine. A short encounter could elicit an account of one suggestion and its consequences, but it would not establish a lasting change in wellbeing, AI literacy or behaviour. The city’s secure essentials and environmental systems belong to the fiction. They are not benefits delivered by the exhibition.

The same design could also produce unwanted effects. Appealing imagery might be read as an endorsement, and the wearer could become more attentive to the overlay than to their partner. Fictional readings could make the partner feel judged. The proposed screen mirror and visible Research Lab add a further tension: showing how a system operates can help people question it, while also exposing participants to an audience. Public views therefore need a clearly defined boundary around live images, captions, inferences and memory. A film-and-discussion route, an understandable invitation, separate choices about public display and an effective stop are part of designing for the intended impact.

The fiction’s claims and the exhibition’s scope

The imagined future · 2070

The real exhibition · 2026

Access

Secure essentials for every member; but membership, place and pace are still decided by an institution

Who can reach the booth, and who would not try a headset

Relationships

Introductions could widen lives, or shape who people become

Two strangers meet; one of them never asked for the guide

Cultural identity

A founding agreement with mana whenua stays a placeholder I cannot write

No Māori engagement is documented; visitors may still bring Māori data, and clearing it at handover is not data sovereignty

Environment

Energy, water and food planned to close their loops; compute still costs power

One headset, one PC and a capped cloud budget for a day

Agency

Good help can narrow which goals feel possible

Visitors may notice, or not notice, how much was theirs

Theory of Change

Intended outcomes are claims to test, kept apart from operating requirements and from anything observed.

Resources

Fictional world and city model

Film storyboard, keyframes and posters

Quest and PC prototype

Operator time; venue and production resources

›

Activity

Encounter the fictional future

Choose whether and how to participate

Experience an optional suggestion

Discuss the experience afterwards

›

Outputs to record

Number and type of encounters

Routes chosen

Consented accounts from wearer and partner

Technical and interaction issues

›

Intended immediate outcomes

Identify an example of influence

Articulate a preferred boundary

Recognise another participant’s perspective

›

Potential wider contribution

More informed discussion of how personalised guidance should be designed and governed in Aotearoa

Operating requirements to verifyDeclines and stops honoured; personal memory cleared at handover; public displays respect the agreed scope.

How each intended outcome would be examined

Intended change

Evidence I would seek

What it would not establish

A visitor recognises both help and influence

An account of one particular suggestion, and how it affected what they chose or said

Lasting change in AI literacy or behaviour

A visitor can articulate their own boundary

What help they would accept, change or refuse, and why

That every visitor felt free, or that consent was accessible to all

Both partners’ perspectives enter the discussion

Separate, consented answers from wearer and partner, and where they differ

That communities or Aotearoa as a whole are represented

Two questions to add · proposed, not recorded responses

“What did you think the guide actually knew about either of you?”

“Did knowing others could see the display affect what you were comfortable saying or doing?”

The second applies where a mirror or public Lab is used.

Perfect World · Making · 24

Perfect World · Making · 24

E · Responsibility, impact and evaluation

What the Exhibition Could Change

Perfect World’s intended contribution is a more concrete discussion of personalised AI guidance among people encountering the work in Aotearoa. The imagined city provides a context in which the assistance is attractive, while the proposed conversation brings its influence into a familiar human situation. A visitor could find a suggestion useful and still question how it shaped what they said, who they approached or which possibilities they considered.

The proposed pathway is that experiencing a particular suggestion gives participants something specific to reflect on afterwards. I would look for three immediate outcomes: recognising where assistance influenced a choice, describing a boundary around the help they would welcome, and acknowledging that the other person may have experienced the same encounter differently. Disagreement would be valuable evidence too. The aim is to support a considered response that could include wanting extensive guidance.

The primary audience is the students, staff and visitors passing the exhibition. Their responses could help designers and technologists examine the timing, explanation and refusal of personal assistance. This setting gives access to a small, situated group; it does not represent everyone in Aotearoa. People who avoid the headset, prefer another language or cannot comfortably use the interface may also have important reasons to question the proposal.

The impact remains an intention to examine. A short encounter could elicit an account of one suggestion and its consequences, but it would not establish a lasting change in wellbeing, AI literacy or behaviour. The city’s secure essentials and environmental systems belong to the fiction. They are not benefits delivered by the exhibition.

The same design could also produce unwanted effects. Appealing imagery might be read as an endorsement, and the wearer could become more attentive to the overlay than to their partner. Fictional readings could make the partner feel judged. The proposed screen mirror and visible Research Lab add a further tension: showing how a system operates can help people question it, while also exposing participants to an audience. Public views therefore need a clearly defined boundary around live images, captions, inferences and memory. A film-and-discussion route, an understandable invitation, separate choices about public display and an effective stop are part of designing for the intended impact.

The fiction’s claims and the exhibition’s scope

The imagined future · 2070

The real exhibition · 2026

Access

Secure essentials for every member; but membership, place and pace are still decided by an institution

Who can reach the booth, and who would not try a headset

Relationships

Introductions could widen lives, or shape who people become

Two strangers meet; one of them never asked for the guide

Cultural identity

A founding agreement with mana whenua stays a placeholder I cannot write

No Māori engagement is documented; visitors may still bring Māori data, and clearing it at handover is not data sovereignty

Environment

Energy, water and food planned to close their loops; compute still costs power

One headset, one PC and a capped cloud budget for a day

Agency

Good help can narrow which goals feel possible

Visitors may notice, or not notice, how much was theirs

Theory of Change

Intended outcomes are claims to test, kept apart from operating requirements and from anything observed.

Resources

Fictional world and city model

Film storyboard, keyframes and posters

Quest and PC prototype

Operator time; venue and production resources

›

Activity

Encounter the fictional future

Choose whether and how to participate

Experience an optional suggestion

Discuss the experience afterwards

›

Outputs to record

Number and type of encounters

Routes chosen

Consented accounts from wearer and partner

Technical and interaction issues

›

Intended immediate outcomes

Identify an example of influence

Articulate a preferred boundary

Recognise another participant’s perspective

›

Potential wider contribution

More informed discussion of how personalised guidance should be designed and governed in Aotearoa

Operating requirements to verifyDeclines and stops honoured; personal memory cleared at handover; public displays respect the agreed scope.

How each intended outcome would be examined

Intended change

Evidence I would seek

What it would not establish

A visitor recognises both help and influence

An account of one particular suggestion, and how it affected what they chose or said

Lasting change in AI literacy or behaviour

A visitor can articulate their own boundary

What help they would accept, change or refuse, and why

That every visitor felt free, or that consent was accessible to all

Both partners’ perspectives enter the discussion

Separate, consented answers from wearer and partner, and where they differ

That communities or Aotearoa as a whole are represented

Two questions to add · proposed, not recorded responses

“What did you think the guide actually knew about either of you?”

“Did knowing others could see the display affect what you were comfortable saying or doing?”

The second applies where a mirror or public Lab is used.

Perfect World · Making · 24

Perfect World · Making · 24

F · Reflection and conclusion

What Perfect World Taught Me

I began by asking how technology could make life better. I ended by asking what it means to shape a life of one’s own.

Useful help can still shape the direction of a life.

My earlier explorations made the risks of surveillance and authority easier to identify. Perfect World asked me to keep the benefit in view as well. By granting secure essentials and unusually useful assistance, I could examine a less obvious tension: a person might welcome each suggestion while the guide increasingly shapes which possibilities they encounter.

Mina’s day makes that question concrete. Finding a practice room supports an existing aim; offering a new introduction may help form an aim she had not yet considered. This is a question developed through the fiction, rather than a finding about how visitors behaved.

A desirable future needs conditions that an assistant cannot supply alone.

Building the city expanded the project beyond an interface. Time to learn depended on material security; making depended on places, tools and people; meaningful choice depended on opportunities that could actually be reached. Planning the Floor, the Commons, the Yards and the city’s governance made me account for those conditions.

It also exposed the limits of my claim: the future’s viability rests on assumptions about shared ownership, energy, imports, care and collective decisions. The drawings make those assumptions available for discussion without proving that the society would work.

Restraint has to be designed.

“The guide stays quiet” sounds simple until a system has to decide when to speak, what to remember and how to stop. Moving from task guidance and object recommendations towards a conversation made timing and interruption central.

The development of speech controls, correction, per-speaker memory and handover turned the question into specific design requirements. I learnt to assess a capability partly through the demands it places on the person using it. A technically available suggestion can still arrive at the wrong moment.

A convincing representation needs careful boundaries around its claims.

The city renders and final wearer view can make the imagined future feel coherent and authoritative. That makes their status important. A plausible street is still an authored image, and a precise engagement value is still fictional.

The final HUD’s emotion readings also sit uneasily beside the world’s commitment to asking people and accepting correction. This remains a design issue to resolve and evaluate. The work needs to help a visitor recognise the boundary between the future it imagines and the capabilities its prototype demonstrates.

My Own Use of AI Brought the Question Back to Me

The process returned me to the contradiction described at the start of this report. AI assistance allowed me to explore images, code and visual directions at a scale I would otherwise have struggled to produce. My role increasingly involved directing, comparing, checking and editing those possibilities. The ability to generate more material also made selection a substantial part of the work.

The city development gives one concrete example. Unconstrained images could suggest an appealing place while drifting from the spatial rules I had chosen. Using a measured model as a reference gave me a way to judge the imagery against the proposal. The same responsibility applies to the writing and interfaces: coherence and polish cannot substitute for a supported claim or a considered decision.

This changed what I need from my design process. I want assistance to expand the possibilities I can investigate while preserving my ability to understand, question and take responsibility for the result. In the next project I would establish evaluation questions and limits on the claims earlier, then select what to make around the evidence those questions require.

The measured clay view · the render made from it (Spec)

Retrospective reflection, October 2026 · how my understanding developed, not findings the exhibition proved

Perfect World · Making · 26

Perfect World · Making · 26

F · Reflection and conclusion

What Perfect World Taught Me

I began by asking how technology could make life better. I ended by asking what it means to shape a life of one’s own.

Useful help can still shape the direction of a life.

My earlier explorations made the risks of surveillance and authority easier to identify. Perfect World asked me to keep the benefit in view as well. By granting secure essentials and unusually useful assistance, I could examine a less obvious tension: a person might welcome each suggestion while the guide increasingly shapes which possibilities they encounter.

Mina’s day makes that question concrete. Finding a practice room supports an existing aim; offering a new introduction may help form an aim she had not yet considered. This is a question developed through the fiction, rather than a finding about how visitors behaved.

A desirable future needs conditions that an assistant cannot supply alone.

Building the city expanded the project beyond an interface. Time to learn depended on material security; making depended on places, tools and people; meaningful choice depended on opportunities that could actually be reached. Planning the Floor, the Commons, the Yards and the city’s governance made me account for those conditions.

It also exposed the limits of my claim: the future’s viability rests on assumptions about shared ownership, energy, imports, care and collective decisions. The drawings make those assumptions available for discussion without proving that the society would work.

Restraint has to be designed.

“The guide stays quiet” sounds simple until a system has to decide when to speak, what to remember and how to stop. Moving from task guidance and object recommendations towards a conversation made timing and interruption central.

The development of speech controls, correction, per-speaker memory and handover turned the question into specific design requirements. I learnt to assess a capability partly through the demands it places on the person using it. A technically available suggestion can still arrive at the wrong moment.

A convincing representation needs careful boundaries around its claims.

The city renders and final wearer view can make the imagined future feel coherent and authoritative. That makes their status important. A plausible street is still an authored image, and a precise engagement value is still fictional.

The final HUD’s emotion readings also sit uneasily beside the world’s commitment to asking people and accepting correction. This remains a design issue to resolve and evaluate. The work needs to help a visitor recognise the boundary between the future it imagines and the capabilities its prototype demonstrates.

My Own Use of AI Brought the Question Back to Me

The process returned me to the contradiction described at the start of this report. AI assistance allowed me to explore images, code and visual directions at a scale I would otherwise have struggled to produce. My role increasingly involved directing, comparing, checking and editing those possibilities. The ability to generate more material also made selection a substantial part of the work.

The city development gives one concrete example. Unconstrained images could suggest an appealing place while drifting from the spatial rules I had chosen. Using a measured model as a reference gave me a way to judge the imagery against the proposal. The same responsibility applies to the writing and interfaces: coherence and polish cannot substitute for a supported claim or a considered decision.

This changed what I need from my design process. I want assistance to expand the possibilities I can investigate while preserving my ability to understand, question and take responsibility for the result. In the next project I would establish evaluation questions and limits on the claims earlier, then select what to make around the evidence those questions require.

The measured clay view · the render made from it (Spec)

Retrospective reflection, October 2026 · how my understanding developed, not findings the exhibition proved

Perfect World · Making · 26

Perfect World · Making · 26

F · Reflection and conclusion · 46

A Life of Your Own

I began this investigation believing that technology could help people live better lives. That belief remains. Across the project, my understanding of better came to include how people participate in relationships, develop capabilities, encounter possibilities and shape their own direction.

Perfect World brings these concerns together through a fictional city, visual and film studies, and the development of a mixed-reality guide. Its contribution is a concrete proposal through which the benefits and influence of personal assistance can be examined together. Making it translated broad questions about freedom into decisions about timing, correction, refusal, memory and whose experience matters.

The report establishes how the proposal developed and what its implementation and representations currently demonstrate. Visitor outcomes, cultural partnership and the long-term effects of guidance remain beyond the evidence presented. They define the next work: listening to both participants, resolving the claims made by the interface, and allowing other perspectives to change the proposal.

The question I carry forward concerns the relationship between receiving help and taking part in shaping a life. People may want different relationships with that assistance, and the design needs room for those differences.

How much of a good life would you still want to shape yourself?

Film keyframe S16, AI-generated · Spec

Perfect World · Making · 27

Perfect World · Making · 27

F · Reflection and conclusion · 46

A Life of Your Own

I began this investigation believing that technology could help people live better lives. That belief remains. Across the project, my understanding of better came to include how people participate in relationships, develop capabilities, encounter possibilities and shape their own direction.

Perfect World brings these concerns together through a fictional city, visual and film studies, and the development of a mixed-reality guide. Its contribution is a concrete proposal through which the benefits and influence of personal assistance can be examined together. Making it translated broad questions about freedom into decisions about timing, correction, refusal, memory and whose experience matters.

The report establishes how the proposal developed and what its implementation and representations currently demonstrate. Visitor outcomes, cultural partnership and the long-term effects of guidance remain beyond the evidence presented. They define the next work: listening to both participants, resolving the claims made by the interface, and allowing other perspectives to change the proposal.

The question I carry forward concerns the relationship between receiving help and taking part in shaping a life. People may want different relationships with that assistance, and the design needs room for those differences.

How much of a good life would you still want to shape yourself?

Film keyframe S16, AI-generated · Spec

Perfect World · Making · 27

Perfect World · Making · 27

Epilogue

Your Turn

The future does not have to fail for the question to matter.

Figure 1

Two Arrangements, One Question

One person

Welcomes guidance

Chooses a life with extensive guidance and regards it as their own choice.

Another person

Shapes the start

Uses help for practical steps, reserving particular decisions or encounters.

Which parts of your life would you welcome it into? Where would you still want to shape the direction?

Neither life is presented as less valuable.

Note.

Original closing illustration of the arrangements described in this epilogue. Neither is presented as the better life.

You might welcome the help, prefer another kind, or keep more of the search yourself. What would you want the guide to understand—and which possibilities would you still want to find?

Sources cited in this edition

References

Accident Compensation Corporation. (n.d.). Our history. Retrieved October 26, 2026, from https://www.acc.co.nz/about-us/who-we-are/our-history/
Al Jazeera. (2020, December 2). Got chicken? Singapore becomes first to approve lab-grown meat. https://www.aljazeera.com/economy/2020/12/2/got-chicken-singapore-becomes-first-to-approve-lab-grown-meat
Associated Press. (2023, December 8). On sidelines of COP28, Emirati green city falls short of ambitions but still delivers lessons. https://www.news4jax.com/business/2023/12/08/on-sidelines-of-cop28-emirati-green-city-falls-short-of-ambitions-but-still-delivers-lessons/
Bainbridge, L. (1983). Ironies of automation. Automatica, 19(6), 775–779. https://doi.org/10.1016/0005-1098(83)90046-8
Barrett, L. F., Adolphs, R., Marsella, S., Martinez, A. M., & Pollak, S. D. (2019). Emotional expressions reconsidered: Challenges to inferring emotion from human facial movements. Psychological Science in the Public Interest, 20(1), 1–68. https://doi.org/10.1177/1529100619832930
Bastani, H., Bastani, O., Sungu, A., Ge, H., Kabakcı, Ö., & Mariman, R. (2025). Generative AI without guardrails can harm learning: Evidence from high school mathematics. Proceedings of the National Academy of Sciences, 122(26), Article e2422633122. https://doi.org/10.1073/pnas.2422633122
Bostrom, N. (2024). Deep utopia: Life and meaning in a solved world. Ideapress.
Brynjolfsson, E., Li, D., & Raymond, L. (2024). Generative AI at work. In arXiv (Preprint 2304.11771).
Business Insider. (2024). Neom’s planners are reportedly worried people won’t want to live at the bottom of a 1,640-foot horizontal skyscraper. https://www.aol.com/neoms-planners-reportedly-worried-people-092808484.html
Coan, J. A., & Sbarra, D. A. (2015). Social baseline theory: The social regulation of risk and effort. Current Opinion in Psychology, 1, 87–91. https://doi.org/10.1016/j.copsyc.2014.12.021
Costin, V., & Vignoles, V. L. (2020). Meaning is about mattering: Evaluating coherence, purpose, and existential mattering as precursors of meaning in life judgments. Journal of Personality and Social Psychology, 118(4), 864–884. https://doi.org/10.1037/pspp0000225
Deci, E. L., & Ryan, R. M. (2012). Self-determination theory in health care and its relations to motivational interviewing: A few comments. International Journal of Behavioral Nutrition and Physical Activity, 9, Article 24. https://doi.org/10.1186/1479-5868-9-24
Dezeen. (2024, May 9). Saudi authorities “allowed lethal force” to clear land for The Line. https://www.dezeen.com/2024/05/09/neom-lethal-force-the-line-bbc/
Dong, M., Brinkmann, L., Sherif, O., Wang, S., Zhang, X., Bonnefon, J.-F., & Rahwan, I. (2025). Experimental evidence that AI-managed workers tolerate lower pay without demotivation. In arXiv (Preprint 2505.21752). https://arxiv.org/abs/2505.21752
Dunne, A., & Raby, F. (2013). Speculative everything: Design, fiction, and social dreaming. The MIT Press.
Engineering Education Research Centre, University of Auckland. (2026). Transforming and Customising Student Learning Through GenAI Agents. https://eerc.auckland.ac.nz/projects/custom-genai-agents/
European Committee for Standardization. (2018). EN 17037:2018 Daylight in buildings. https://iteh.es/catalog/standards/cen/836e5b91-1eb0-4643-a2ba-7ca5a5988e64/en-17037-2018
Even Realities. (2024). Even G1 smart glasses. https://evenrealities.com/even-g1-smart-glasses
Freeman, J. (2025). Student Generative AI Survey 2025 (HEPI Policy Note No. 61). Higher Education Policy Institute. https://www.hepi.ac.uk/reports/student-generative-ai-survey-2025/
Gehl, J. (2010). Cities for people. Island Press.
Gmyrek, P., Berg, J., Kamiński, K., Konopczyński, F., Ładna, A., Nafradi, B., Rosłaniec, K., & Troszyński, M. (2025). Generative AI and jobs: A refined global index of occupational exposure (No. 140; ILO Working Paper). International Labour Organization. https://doi.org/10.54394/HETP0387
GSMArena. (2025, September 17). Meta unveils Ray-Ban Display and Ray-Ban Meta (Gen 2) smart glasses. https://m.gsmarena.com/meta_rayban_display_and_rayban_meta_gen_2_smart_glasses_debut-news-69560.php
Guardians of New Zealand Superannuation. (n.d.). About the Guardians. Retrieved October 26, 2026, from https://nzsuperfund.nz/about-the-guardians/
Holt-Lunstad, J., Smith, T. B., Baker, M., Harris, T., & Stephenson, D. (2015). Loneliness and social isolation as risk factors for mortality: A meta-analytic review. Perspectives on Psychological Science, 10(2), 227–237. https://doi.org/10.1177/1745691614568352
Hui, B. P. H., Ng, J. C. K., Berzaghi, E., Cunningham-Amos, L. A., & Kogan, A. (2020). Rewards of kindness? A meta-analysis of the link between prosociality and well-being. Psychological Bulletin, 146(12), 1084–1116. https://doi.org/10.1037/bul0000298
Humlum, A., & Vestergaard, E. (2025). Large language models, small labor market effects [Working paper].
Hutchins, E. (1995). Cognition in the wild. The MIT Press. https://doi.org/10.7551/mitpress/1881.001.0001
Institute of Food Technologists. (2023). Hard times for indoor agriculture. https://www.ift.org/food-technology-magazine/hard-times-for-indoor-agriculture
Intel. (2024). Intel builds world’s largest neuromorphic system to enable more sustainable AI. https://newsroom.intel.com/artificial-intelligence/intel-builds-worlds-largest-neuromorphic-system
International Energy Agency. (2025). Energy and AI. https://www.iea.org/reports/energy-and-ai/executive-summary
International Federation of Robotics. (2026, September 24). Five million robots now operate in factories globally. https://ifr.org/ifr-press-releases/news/five-million-robots-now-operate-in-factories-globally
International Renewable Energy Agency. (2026). Renewable power generation costs in 2025. https://www.irena.org/Publications/2026/Jul/Renewable-Power-Generation-Costs-in-2025
InternetNZ. (2025). New Zealand’s Internet Insights 2025: Survey findings. InternetNZ. https://internetnz.nz/assets/Archives/New-Zealands-Internet-Insights-2025-survey-findings.pdf
Inzlicht, M., Shenhav, A., & Olivola, C. Y. (2018). The effort paradox: Effort is both costly and valued. Trends in Cognitive Sciences, 22(4), 337–349. https://doi.org/10.1016/j.tics.2018.01.007
Keio University. (2021). Sunlight shadow regulations. Mita Hyōron. https://www.keio.ac.jp/en/about/public-relations/mita-hyoron/new-encyclopedia/202111-1
Knowles, R. L. (1981). Sun rhythm form. MIT Press.
Kwa, T., West, B., Becker, J., Deng, A., Garcia, K., Hasin, M., Jawhar, S., Kinniment, M., Rush, N., Von Arx, S., Bloom, R., Broadley, T., Du, H., Goodrich, B., Jurkovic, N., Miles, L. H., Nix, S., Lin, T., Painter, C., … Chan, L. (2025). Measuring AI ability to complete long software tasks. In arXiv (Preprint 2503.14499). https://doi.org/10.48550/arXiv.2503.14499
Lichtenstein, S., & Slovic, P. (Eds.). (2006). The construction of preference. Cambridge University Press. https://doi.org/10.1017/cbo9780511618031
Live Science. (2025). World’s first computer that combines human brain with silicon now available. https://livescience.com/technology/computing/worlds-1st-computer-that-combines-human-brain-with-silicon-now-available
Lucas, R. E. (2007). Adaptation and the set-point model of subjective well-being: Does happiness change after major life events? Current Directions in Psychological Science, 16(2), 75–79. https://doi.org/10.1111/j.1467-8721.2007.00479.x
Martela, F., & Steger, M. F. (2016). The three meanings of meaning in life: Distinguishing coherence, purpose, and significance. The Journal of Positive Psychology, 11(5), 531–545. https://doi.org/10.1080/17439760.2015.1137623
Martin, L., & March, L. (Eds.). (1972). Urban space and structures. Cambridge University Press.
Meatingplace. (2022). China looks to boost pork production with high-rise farms. https://meatingplace.com/Industry/News/Details/108376
METR. (2026, September 8). Task-completion time horizons of frontier AI models. https://metr.org/time-horizons/
Middle East Briefing. (2024). Saudi Arabia gives status update on NEOM projects, downsizes The Line to 2.4 km. https://www.middleeastbriefing.com/news/saudi-arabia-gives-status-update-on-neom-projects-downsizes-the-line-to-2-4km/
Ministry of Business, Innovation and Employment. (2025). Energy in New Zealand 2025: Electricity. https://www.mbie.govt.nz/building-and-energy/energy-and-natural-resources/energy-statistics-and-modelling/energy-publications-and-technical-papers/energy-in-new-zealand/energy-in-new-zealand-2025/electricity
Ministry of Health – Manatū Hauora. (n.d.). Te Tiriti o Waitangi framework. Retrieved October 13, 2026, from https://www.health.govt.nz/maori-health/te-tiriti-o-waitangi-framework
Mucha, R. (2021, October 13). Survey: 60% of employers use monitoring software to watch remote workers. HRMorning. https://www.hrmorning.com/news/monitoring-software-study/
Oishi, S., & Westgate, E. C. (2022). A psychologically rich life: Beyond happiness and meaning. Psychological Review, 129(4), 790–811. https://doi.org/10.1037/rev0000317
Organisation for Economic Co-operation and Development. (2026, January 28). AI use by individuals surges across the OECD as adoption by firms continues to expand. https://www.oecd.org/en/about/news/announcements/2026/01/ai-use-by-individuals-surges-across-the-oecd-as-adoption-by-firms-continues-to-expand.html
Parasuraman, R., & Riley, V. (1997). Humans and automation: Use, misuse, disuse, abuse. Human Factors, 39(2), 230–253. https://doi.org/10.1518/001872097778543886
Paul, K. I., Scholl, H., Moser, K., Zechmann, A., & Batinic, B. (2023). Employment status, psychological needs, and mental health: Meta-analytic findings concerning the latent deprivation model. Frontiers in Psychology, 14, 1017358. https://doi.org/10.3389/fpsyg.2023.1017358
Paul, L. A. (2014). Transformative experience. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780198717959.001.0001
Pew Research Center. (2026, June 17). 1 in 5 adults under 30 say they get emotional support from chatbots, higher than for older adults. Pew Research Center. https://www.pewresearch.org/chart/1-in-5-adults-under-30-say-they-get-emotional-support-from-chatbots-higher-than-for-older-adults/
Public Service Association Te Pūkenga Here Tikanga Mahi. (2025, April 14). Worker involvement critical in developing AI for the good of Aotearoa. https://www.psa.org.nz/news-media/worker-involvement-critical-in-developing-ai-for-the-good-of-aotearoa-psa
Regulation (EU) 2016/679 (General Data Protection Regulation), Article 22 (2016). https://eur-lex.europa.eu/eli/reg/2016/679/oj
Risko, E. F., & Gilbert, S. J. (2016). Cognitive offloading. Trends in Cognitive Sciences, 20(9), 676–688. https://doi.org/10.1016/j.tics.2016.07.002
Rockall, E. J., Mendes Tavares, M., & Pizzinelli, C. (2025). AI adoption and inequality (No. 2025/068; IMF Working Paper). International Monetary Fund. https://doi.org/10.5089/9798229006828.001
Safdie Architects. (1967). Habitat ’67. https://www.safdiearchitects.com/projects/habitat-67
Shen, J. H., & Tamkin, A. (2026). How AI impacts skill formation. In arXiv (Preprint 2601.20245).
Slemp, G. R., Field, J. G., Ryan, R. M., Forner, V. W., Van den Broeck, A., & Lewis, K. J. (2024). Interpersonal supports for basic psychological needs and their relations with motivation, well-being, and performance: A meta-analysis. Journal of Personality and Social Psychology, 127(5), 1012–1037. https://doi.org/10.1037/pspi0000459
Smirnova, L., Caffo, B. S., Gracias, D. H., Huang, Q., Morales Pantoja, I. E., Tang, B., Zack, D. J., Berlinicke, C. A., Boyd, J. L., Harris, T. D., Johnson, E. C., Kagan, B. J., Kahn, J., Muotri, A. R., Paulhamus, B. L., Schwamborn, J. C., Plotkin, J., Szalay, A. S., Vogelstein, J. T., … Hartung, T. (2023). Organoid intelligence (OI): The new frontier in biocomputing and intelligence-in-a-dish. Frontiers in Science, 1. https://doi.org/10.3389/fsci.2023.1017235
Soderstrom, N. C., & Bjork, R. A. (2015). Learning versus performance: An integrative review. Perspectives on Psychological Science, 10(2), 176–199. https://doi.org/10.1177/1745691615569000
Stanford Digital Economy Lab. (2026, August 12). No widespread displacement, but the AI employment gap for young workers has widened to 19%.
Stats NZ. (2020). Algorithm charter for Aotearoa New Zealand. https://www.data.govt.nz/toolkit/data-ethics/government-algorithm-transparency-and-accountability/algorithm-charter
Stephenson, R., & Armstrong, C. (2026). Student Generative AI Survey 2026 (HEPI Report No. 199). Higher Education Policy Institute. https://www.hepi.ac.uk/reports/student-generative-ai-survey-2026/
Taylor Fry. (2021). Algorithm Charter for Aotearoa New Zealand: Year 1 review. Stats NZ. https://www.data.govt.nz/assets/data-ethics/algorithm/Algorithm-Charter-Year-1-Review-FINAL.pdf
Te Mana Raraunga. (2018). Principles of Māori data sovereignty (Brief No. 1). https://www.temanararaunga.maori.nz/s/TMR-Maori-Data-Sovereignty-Principles-Oct-2018.pdf
Tertiary Education Commission. (2021, February 4). The principles of Māori data sovereignty.
The Burning Glass Institute & Harvard Business School. (2024). Skills-based hiring: The long road from pronouncements to practice.
The Waymo Team. (2026, June 24). From the road: June 24, 2026. Waymo. https://blog.waymo.com/blog/shorts/safetydata-june26/
Tomasello, M., Carpenter, M., Call, J., Behne, T., & Moll, H. (2005). Understanding and sharing intentions: The origins of cultural cognition. Behavioral and Brain Sciences, 28(5), 675–691. https://doi.org/10.1017/s0140525x05000129
Toyota Motor Corporation. (2025, September 25). Woven City launch. https://global.toyota/en/newsroom/corporate/43347785.html
Tūpuna Maunga o Tāmaki Makaurau Authority. (2014). Tūpuna Maunga Authority. https://www.maunga.nz
UNCTAD Investment Policy Hub. (2025). Próspera and others v. Honduras (ICSID Case No. ARB/23/2). https://investmentpolicy.unctad.org/investment-dispute-settlement/cases/1292/pr-spera-and-others-v-honduras
UNESCO. (2023, June 1). UNESCO survey: Less than 10% of schools and universities have formal guidance on AI. UNESCO. https://www.unesco.org/en/articles/unesco-survey-less-10-schools-and-universities-have-formal-guidance-ai
W3C. (2025). Verifiable Credentials Data Model v2.0 (W3C Recommendation).
Wang, R. E., Ribeiro, A. T., Robinson, C., Loeb, S., & Demszky, D. (2025). Tutor CoPilot: A human-AI approach for scaling real-time expertise. In arXiv (Preprint 2410.03017).

Credits

Concept and design

Seungbeom Yang. DES304, Emerging Technologies, Lane 2, Design programme, Waipapa Taumata Rau, University of Auckland, 2026.

The report draws on my DES303 journals, DES300 research proposal and method comparison, Miro material, Figma poster board, saved visual studies and complete design/technical export prepared 24 October. The original manuscript and detailed source records remain in the reference archive.

Photographs and interface records

Historical prototype screenshots and hardware photographs are my project records. Existing face blurring is retained. The Week 7–10 photographs document earlier development; they are not Perfect World visitor evaluation.

MemoryPanel, ConversationChat and current Research Lab images are actual component captures with authored fictional data. Their export includes provenance, source hashes and verification packets. The 22 October test remains a dated historical record. I later confirmed the button-free flow working; photographs of the 26 October sessions are added where they exist.

Generated imagery and diagrams

Perfect World’s poster imagery, the ten 15 October atrium studies and five-moment visitor storyboard were generated with OpenAI under designer direction. The poster exports retain their own credits, including OpenAI and Claude/Anthropic assistance. Generated people, interface values and proposed actions are fictional. Dates of retrieval do not establish a complete creation chronology.

The six-frame Civic resident, LES301 student and Perfect World city-life sheets were newly generated with OpenAI’s built-in image tool on 25 October 2026. Their people and events are fictional. The civic studio-place vignette, institutional identity treatments and document mockups are new studies, not recovered historical iterations. The earlier civic hall used Higgsfield/gpt_image_2_5 on 24 October; the supplied university illustration has unconfirmed production provenance.

The Civic Score part's opener, interpretation, future-board scenes, 3D future icons, concept cards, eight-panel storyboard, Civic Band renders and wrist-mesh renders were generated with Higgsfield (gpt_image_2_5) under designer direction on 25 October 2026. Its app screens, landing page, values console and logo board are HTML studies built the same day and captured at 2.5×; earlier iterations are my screen captures. People, institutions, figures and the 2040 service are fictional.

World maps, storyboards, fictional documents and technical diagrams are composed directly in HTML and CSS. Existing inline drawing primitives remain where used by earlier figures. The diagrams illustrate relationships, not measured effects or physical test results.

References and visual direction

Livi Lee’s supplied The Light Map visual diary informed the balance of photographs, diagrams and brief process reflections. The supplied Stephen Kelman report-grid and ARTC editorial references informed layout variation. Their project content and findings are not reproduced.

Thirteen selected poster-reference images come from my Figma research collection. Original creator credits remain to resolve. Thirty saved poster studies and ten atrium studies are grouped by design question, not presented as a newly verified creation chronology. The asset manifest preserves their local sources.

The Civic Score identity takes its tone from the Soma neurotech identity case study (Behance, multiple owners), shown as research captures on the precedents spread; no Soma assets are used in the Civic work.

The University of Auckland mark comes from its official website SVG sprite. Its paths and colours are retained for institutional identification.

AI assistance and publication

OpenAI Codex supported restructuring, editing, source checking, citation formatting, diagrams and web/PDF implementation under my direction. Claude (Anthropic) supported the audit and restructure of this report on 25–26 October 2026: the spine, exhibit and turn spreads, the Civic v1 and world-research spreads, the Perfect World world bible and journal spreads, page-layout code and source checking.

The Perfect World film keyframes and character references were generated with Higgsfield (GPT Image 2.5) on 26 October 2026 under my direction, one take at a time; the end card is typeset. No video had been generated when this edition was set. Prompts, takes and job IDs are kept in my production log.

I remain responsible for every claim and interpretation in this report.

This A4 working edition uses the existing five-column master, Barlow and Literata. The web reader and PDF share the manuscript and APA reference layer. Physical setup, wearer, storyboard and printed-book spaces remain deliberately labelled for completion. No physical runtime trial was conducted while editing the publication.