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

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
- Act I · Why we began
- The Design Brief
- Positionality
- The Personal Contradiction
- Design Methodology
- One belief, an evolving inquiry
- Two courses, five worlds
- Five worlds, one changing question
- What carried forward, and what advanced
- World 01 · Tickers
- Tickers: the signal
- When Support Becomes Surveillance
- Brainstorming
- Imagined systems of judgement
- Aotearoa, 2031
- Making the future tangible
- Making: Learning from existing systems
- Making: How Tickers works
- Making: Visual storyboard
- Making: User flow, phone to desktop
- Making: Wireframes and visual identity
- Making: Mid-fidelity to code, mobile
- Making: Why a desktop app, and the pipeline
- Testing: What I wanted to test
- Testing: What the evidence showed
- Testing: When the system got it wrong
- Reflecting: Redesigning the judgement, and what carried forward
- Act II · Imagining the future
- World 02 · AI Senior
- Signal
- When Help Becomes Authority
- Brainstorming
- Aotearoa, 2037
- Making the future tangible
- The idea: Project idea and positioning
- The idea: Visual storyboard
- The idea: How the AI senior works
- Two surfaces: User flow, manager workspace
- Prototyping: Precedents
- Prototyping: Brand and UI components
- Prototyping: UI development, workspace to desktop
- Prototyping: Core design elements, workspace
- Prototyping: Core design elements, desktop senior
- R&D: Ticker Cube
- R&D: Why a living AI senior, and its architecture
- R&D: A stateful senior, and ethics as architecture
- Closing: System map
- The turn: what Crit 2 changed, and what carried forward
- World 03 · Civic Score
- Emerging signals of algorithmic governance
- Research: Societies already imagined
- What if AI could allocate opportunity?
- The first Civic Score proposal
- When the proposal was challenged
- Design response: A more desirable civic future
- The imagined society: What kind of society is this?
- The system: Who has power in Civic?
- How it becomes normal
- Living with it: Living under Civic Score
- Living with it: Two residents
- Brand: Civic brand and brand in use
- Exhibition: Making the future experienceable
- Exhibition: From scoring to changing the values
- Technical check: What could be demonstrated?
- What Civic Score changed
Perfect World / continued
- World 04 · LifeRPG / UNI
- AI helps practice and can harm the exam
- Imagining learning beyond scores
- LifeRPG: the first direction
- When personal progress becomes another score
- Research: What learning still needs
- Research: What future would need a new university?
- From an app to an institution
- UNI in one minute, and positioning
- The imagined society: Building UNI’s society
- How it becomes normal
- How the institution operates
- A learner’s route: enrol to credential
- Living and learning in UNI
- Exhibition: How could visitors experience UNI?
- Prototyping: Brand, and what was actually designed
- Evaluation: When the idea was challenged
- What UNI changed
- Life after necessity
- World 05 · Perfect World
- Help is turning personal
- What makes a life worth living?
- Opening up possible futures
- Futures we have already imagined
- How could we arrive here?
- Choosing Perfect World
- Act III · Building Perfect World
- Learning from future cities
- The site
- Planning the city
- One cluster, sun for every home
- What makes it possible
- When it fails, and how ready it is
- How nobody has to work
- Who runs Perfect World
- How it becomes ordinary
- A world that understands you
- Guide, not steer
- From world plan to 3D city
- Revealing Perfect World
- Act IV · Living in Perfect World
- Places within the city
- Six lives, and Mina’s day
- Act V · Experiencing and reflecting
- Bringing Perfect World into the present
- Exploring the experience
- The exhibition, who it is for, and the visitor journey
- Branding a public institution
- The mark, colour and type
- The brand in use
- Film storyboards: directions 01 and 02
- Film storyboards: directions 03 and 04
- Film storyboards: directions 05 and 06
- Film storyboards: reference plans A and B
- Film storyboards: reference plans C and D
- Film storyboards: reference plans E and F
- Film storyboards: reference plans G and H
- The final film: let’s see the world
- What should the guide actually do?
- How Ticker works
- Visual storyboard: the encounter
- UX and conversational guidance
- UI and visual design development
- Building the AI system
- Making the system inspectable
- Redesigning the guide
- Communicating the exhibition: references
- Communicating the exhibition: the world poster
- Communicating the exhibition: the encounter poster
- Designing the exhibition space
- Bringing everything together
- Testing, critique and iteration
- My position within the future I designed
- What the exhibition could change
- What Perfect World taught me
- A life of your own
- Your Turn
- References
- 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.

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.

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

01Explore · Vision and Opportunity
Investigate emerging technologies, identify opportunities, gather inspiration and develop initial speculative questions.
02Imagine · Future World-Building
Construct possible future conditions and explore how technological, social and cultural developments might reshape everyday life.
03Ideate · Speculative Interventions
Generate concepts and consider how a product, service, system or experience might exist within the imagined future.
04Develop · Making and Iteration
Translate ideas into tangible artefacts through sketches, branding, interface development, system diagrams, prototypes and technical experimentation.
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.
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?

01Effort
Tickers
02Work
AI Senior
03Civic life
Civic Score

04Learning
LifeRPG → UNI
05A whole life
Perfect World
The spine
How the question was worded
How the question was worded, brief to refined
The course brief
How might we use emerging technologies and speculative design to reimagine how people in Aotearoa connect, collaborate, and create?
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.
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.
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.
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.
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.
Earlier worldsBrief, research and tech demo
Tickers + Focus Integrity
TestFlightW7 formativeAI Senior + Ticker Cube
Crit 2Civic Score
LifeRPG / UNI
Perfect WorldPerfect World: world + film
Posters + encounter studies
Making it workQuest guide build + Research Lab
Report, installation, exhibition
Methodology / timelineDES304 Capstone
Dated in journals, commits or files
Approximate span, reconstructed
Critique, test or submission
- Future Work deck
- Critique redraw
- World reportUNI model v1.1
- Tabletop plan
- Studies
- Technical run
- 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

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.

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?

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?

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?

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
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.
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.
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.
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.
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
01 Tickers 
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.

02 AI Senior 
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.

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.

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.
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.

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

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

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

Then AI Senior: uncertain, so it asks the user 
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.

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?
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)
- Any generative AI use
- To help with assessed work
Also in the evidence
- 01Tools 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.
- 02Visibility changes a promise
Task apps keep a commitment private; once friends can see it, a commitment becomes something to prove (my DES303 precedent analysis).
- 03A record is not proof
A credential can show who issued a claim and its status without proving that the claim is true (W3C, 2025).
- 04Agents 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.
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)
UK undergraduates using generative AI, 2026
HEPIUS employers with remote staff using monitoring software
Vendor survey, 2021Schools and universities with formal guidance
UNESCO, 2023used generative AI to help with assessed work, 2026
used generative AI in 2024
of universities had formal guidance in 2023
of schools had formal guidance in 2023
US employers in the monitoring survey
schools and universities in the UNESCO survey
Figure 3: Why US employers use monitoring software (vendor survey of 1,250 employers, 2021)
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.

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).
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?
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
| Merits | Severance | The Circle | Tickers | |
|---|---|---|---|---|
| Who judges | The system and the audience | The employer | Colleagues and followers | Friends and a desktop agent |
| What is scored | Effort, as merits | The work self | Participation and sharing | Declared effort |
| Can the person answer back | Only by performing | No | Barely | Yes: explain and challenge |
| Why people accept it | It pays for life | A clean work-life split | Belonging and status | Trust 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
| Merits | Severance | The Circle | Tickers | |
|---|---|---|---|---|
| Who judges | The system and the audience | The employer | Colleagues and followers | Friends and a desktop agent |
| What is scored | Effort, as merits | The work self | Participation and sharing | Declared effort |
| Can the person answer back | Only by performing | No | Barely | Yes: explain and challenge |
| Why people accept it | It pays for life | A clean work-life split | Belonging and status | Trust 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.
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.
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.
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.
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.
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.
✓Taken forwardDigital · 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.
02Physical · 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
03Institutional · 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?
04Digital · 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.
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.
A conceptual comparison from the DES303 precedent analysis, not a measured ranking
Tickers · Prototyping · 07
Tickers · Prototyping · 07
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.
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
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.
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
↳ Needs
The score felt okay, but I wanted to know why it dropped.
↳ Needs
Reading looks inactive, so the system needs a pause or reading mode.
↳ Needs
Screen evidence feels strong, but I would not want raw screenshots shared.
↳ 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
↳ Needs
The score felt okay, but I wanted to know why it dropped.
↳ Needs
Reading looks inactive, so the system needs a pause or reading mode.
↳ Needs
Screen evidence feels strong, but I would not want raw screenshots shared.
↳ 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

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

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.
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.
↓
↓
↓
↓
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.
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.
↓
↓
↓
↓
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
- 1More evidence will make the score feel fairer.
- 2Low typing or low input means drift.
- 3Browser or YouTube use is usually distraction.
- 4Camera presence makes the score stronger.
- 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
- 1More evidence will make the score feel fairer.
- 2Low typing or low input means drift.
- 3Browser or YouTube use is usually distraction.
- 4Camera presence makes the score stronger.
- 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.
| Model | Evidence | Reads | Privacy cost | Score | Labels matched |
|---|---|---|---|---|---|
| M0 | Timer only | Session time | Lowest | 100 | 5/10 |
| MA | Metadata | App, window, input | Low | 93 | 5/10 |
| MS | Screen | Screen change | Medium | 86 | 6/10 |
| MB | Metadata + screen | App, input, screen | Medium | 90 | 5/10 |
| MAI | Metadata + screen + AI | Plus AI judge, 5 calls | Medium | 93 | 5/10 |
| CPRES | Camera presence | Face present | High | 85 | 5/10 |
| MPRES | Metadata + screen + presence | All of the above | Highest | 88 | 5/10 |

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
| Task | Model read | My label | Fair | Privacy | Trust | |
|---|---|---|---|---|---|---|
| P1 | Coding | Mostly aligned | Aligned | 4/5 | 4/5 | 3/5 |
| P2 | Essay writing | Partial | Aligned / unclear | 3/5 | 4/5 | 2/5 |
| P3 | Lecture watching | Distracted | Aligned | 2/5 | 3/5 | 1/5 |
| P4 | Research browsing | Partial | Partial | 4/5 | 2/5 | 2/5 |
“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
■ 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.
| Model | Evidence | Reads | Privacy cost | Score | Labels matched |
|---|---|---|---|---|---|
| M0 | Timer only | Session time | Lowest | 100 | 5/10 |
| MA | Metadata | App, window, input | Low | 93 | 5/10 |
| MS | Screen | Screen change | Medium | 86 | 6/10 |
| MB | Metadata + screen | App, input, screen | Medium | 90 | 5/10 |
| MAI | Metadata + screen + AI | Plus AI judge, 5 calls | Medium | 93 | 5/10 |
| CPRES | Camera presence | Face present | High | 85 | 5/10 |
| MPRES | Metadata + screen + presence | All of the above | Highest | 88 | 5/10 |

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
| Task | Model read | My label | Fair | Privacy | Trust | |
|---|---|---|---|---|---|---|
| P1 | Coding | Mostly aligned | Aligned | 4/5 | 4/5 | 3/5 |
| P2 | Essay writing | Partial | Aligned / unclear | 3/5 | 4/5 | 2/5 |
| P3 | Lecture watching | Distracted | Aligned | 2/5 | 3/5 | 1/5 |
| P4 | Research browsing | Partial | Partial | 4/5 | 2/5 | 2/5 |
“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
■ 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.

The segment drill-down I labelled sessions in; values above are from tick 34 of my own session
Same Signal, Opposite Meanings
| Segment | System saw | Model | My label | What it was |
|---|---|---|---|---|
| 03/10 | Chrome active, low typing, static screen | Drift risk | Aligned / partial | I was reading course material for the blog |
| 04/10 | YouTube open, no typing | Distracted | Partial | It could be a tutorial; the system lacked task context |
| 05/10 | Camera absent | Off-task | Unclear | I may have been writing notes on paper |
| The signal | What the system assumed | What it could also mean |
|---|---|---|
| Chrome is active | Browsing instead of working | Researching or checking references |
| YouTube is open | Distracted | A lecture, tutorial or design reference |
| Screen is static | Stopped working | Reading, thinking or analysing |
| Low typing | Idle | Planning, reading or reviewing |
| No face on camera | Left the session | Writing 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.

The segment drill-down I labelled sessions in; values above are from tick 34 of my own session
Same Signal, Opposite Meanings
| Segment | System saw | Model | My label | What it was |
|---|---|---|---|---|
| 03/10 | Chrome active, low typing, static screen | Drift risk | Aligned / partial | I was reading course material for the blog |
| 04/10 | YouTube open, no typing | Distracted | Partial | It could be a tutorial; the system lacked task context |
| 05/10 | Camera absent | Off-task | Unclear | I may have been writing notes on paper |
| The signal | What the system assumed | What it could also mean |
|---|---|---|
| Chrome is active | Browsing instead of working | Researching or checking references |
| YouTube is open | Distracted | A lecture, tutorial or design reference |
| Screen is static | Stopped working | Reading, thinking or analysing |
| Low typing | Idle | Planning, reading or reviewing |
| No face on camera | Left the session | Writing 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 showed | Source | What I changed |
|---|---|---|
| “The score felt okay, but I wanted to know why it dropped.” | P1 · four-person session | Show the reason beside the number |
| “Reading looks inactive, so the system needs a pause or reading mode.” | P2 · four-person session | A pause or reading state before any warning |
| “YouTube can be work depending on the task.” | P3 · four-person session | Judge evidence against the declared task, not app categories |
| Camera presence read as the most invasive evidence, and did not prove focus | Session debrief · P4 | Make the camera optional and separate from scoring |
| All four wanted correction, appeal or an uncertain state before a score counted | Session debrief | Segment labels, explanation and contestable results |
| Reading course material for my blog was scored drift_risk, 0.61 | My own session · tick 34 | Clarify 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 showed | Source | What I changed |
|---|---|---|
| “The score felt okay, but I wanted to know why it dropped.” | P1 · four-person session | Show the reason beside the number |
| “Reading looks inactive, so the system needs a pause or reading mode.” | P2 · four-person session | A pause or reading state before any warning |
| “YouTube can be work depending on the task.” | P3 · four-person session | Judge evidence against the declared task, not app categories |
| Camera presence read as the most invasive evidence, and did not prove focus | Session debrief · P4 | Make the camera optional and separate from scoring |
| All four wanted correction, appeal or an uncertain state before a score counted | Session debrief | Segment labels, explanation and contestable results |
| Reading course material for my blog was scored drift_risk, 0.61 | My own session · tick 34 | Clarify 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

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?
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)
25%jobs worldwide have some exposure to generative AI
ILO, 2025Also in the evidence
- 01Automation leaves the hard cases
Automation may leave people with difficult exceptions (Bainbridge, 1983), which is a different job from the one they had before.
- 02Use 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.
- 03Planning becomes judging
Once assistance helps define the task, it also helps define what counts as successful work.
- 04Agents 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.
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)
Workers worldwide in an occupation with some exposure
ILO, 2025Employment exposed in high-income countries
ILO, 2025Global employment in the highest-exposure category
ILO, 2025of women’s employment worldwide is in the highest-exposure category
of men’s employment worldwide is in that category
of women’s employment in high-income countries
of men’s employment in high-income countries
of OECD firms used AI in 2023
of OECD firms used AI in 2025
Figure 3: Share of employment in the highest-exposure category (ILO, 2025)
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.

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).
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?
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.
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.
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.
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.
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.
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.
✓Taken forwardDigital · 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.
02Spatial · 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
03Physical · 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.
04Institutional · 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?
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.
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?
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.
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
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.
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
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.
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
↳ Needs
Bring my tasks in from Motion; don't make me move.
↳ Needs
If AI picks who does what, who can say no?
↳ Needs
If it plans and assigns, isn't it just Motion?
↳ 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
↳ Needs
Bring my tasks in from Motion; don't make me move.
↳ Needs
If AI picks who does what, who can say no?
↳ Needs
If it plans and assigns, isn't it just Motion?
↳ 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

Mark · the voice orb
Idle
Understanding
SpeakingThree 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 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
Ask sheet
Is this still part of your task?
Chrome · competitor sites · 6 min
RELATEDNOT RELATEDPAUSEStates
LOCAL SUMMARIES ONLYASK_USERCOOLDOWNWARNLearned pattern
Chrome · competitor research
related: landing page
Permission block
Screen summaryON · LOCAL
ReadsChanged or static, app category
NeverRaw screenshots leave the device
Listening overlay · Week 10 build

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

Mark · the voice orb
Idle
Understanding
SpeakingThree 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 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
Ask sheet
Is this still part of your task?
Chrome · competitor sites · 6 min
RELATEDNOT RELATEDPAUSEStates
LOCAL SUMMARIES ONLYASK_USERCOOLDOWNWARNLearned pattern
Chrome · competitor research
related: landing page
Permission block
Screen summaryON · LOCAL
ReadsChanged or static, app category
NeverRaw screenshots leave the device
Listening overlay · Week 10 build

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.
| Area | Question | Status |
|---|---|---|
| Compute | Can it run the interface, store evidence, sync with the PC and process locally? | Raspberry Pi 5 chosen over ESP32 |
| Display | Can the worker see the task, timer, warning and correction quickly? | Touch display mounted |
| Camera | Can it read room-based work without feeling like hidden spying? | HQ camera, C-mount lens; untested |
| Controls | Can the worker dismiss, pause or correct without the screen? | Planned |
| Privacy | Can the worker see and control when sensing is on? | Indicator or shutter planned |
| Sync | Can the Cube send task-state summaries back to the desktop? | Planned |
| Offline | Does 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.
| Area | Question | Status |
|---|---|---|
| Compute | Can it run the interface, store evidence, sync with the PC and process locally? | Raspberry Pi 5 chosen over ESP32 |
| Display | Can the worker see the task, timer, warning and correction quickly? | Touch display mounted |
| Camera | Can it read room-based work without feeling like hidden spying? | HQ camera, C-mount lens; untested |
| Controls | Can the worker dismiss, pause or correct without the screen? | Planned |
| Privacy | Can the worker see and control when sensing is on? | Indicator or shutter planned |
| Sync | Can the Cube send task-state summaries back to the desktop? | Planned |
| Offline | Does 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
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.
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
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.
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.
↓
↓
↓
↓

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.
| Risk | Answered in the build by |
|---|---|
| Hidden collection | Every sensing layer is a permission block that says what it reads and what it never keeps. |
| Raw evidence exposed | Local summaries only by default; raw screen, audio and camera stay on the device. |
| Misjudgement | Ask when uncertain; Technical Evidence shows why the senior asked. |
| Power imbalance | Worker-facing first. Managers see limited task-level summaries and cannot switch monitoring on silently. |
| Learning in the dark | Learned patterns can be read, edited or forgotten. |
| Connector creep | Read-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.
↓
↓
↓
↓

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.
| Risk | Answered in the build by |
|---|---|
| Hidden collection | Every sensing layer is a permission block that says what it reads and what it never keeps. |
| Raw evidence exposed | Local summaries only by default; raw screen, audio and camera stay on the device. |
| Misjudgement | Ask when uncertain; Technical Evidence shows why the senior asked. |
| Power imbalance | Worker-facing first. Managers see limited task-level summaries and cannot switch monitoring on silently. |
| Learning in the dark | Learned patterns can be read, edited or forgotten. |
| Connector creep | Read-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
System Map
AI Senior · Prototyping · 19
AI Senior · Prototyping · 19
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
“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.
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
“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.
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

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?
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)
Also in the evidence
- 01A charter, not a rule
Aotearoa’s Algorithm Charter commits signatories to explanation, human oversight and challenge routes (Stats NZ, 2020).
- 02Data carries authority
Te Mana Raraunga foregrounds authority, context, collective benefit and guardianship in data (Te Mana Raraunga, 2018).
- 03Questions of partnership
The health-sector Te Tiriti framework adds questions of authority, equity, options and partnership (Ministry of Health – Manatū Hauora, n.d.).
- 04Benefits 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.
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)
Said AI was already used in their workplace
PSA, 2025Use AI personally
PSA, 2025AI users who had training
PSA, 2025of AI users said their use was self-initiated
said AI helped them do parts of their job more effectively
said AI helped them save time
agreed AI could streamline processes for better public outcomes
agencies had signed the voluntary Algorithm Charter within a year
of government agencies are covered by those signatories
Figure 3: What respondents said AI did for them (PSA, April 2025)
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
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?
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.

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

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

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
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?
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
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?
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.
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.
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.
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.
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.
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 plan | To show | What the evidence says | Verdict |
|---|---|---|---|
| Heart rate (wrist) | Stress and calm on the board | Some 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) | Stress | WESAD: about 80% across three states, 15 people, in a lab. Wrist EDA samples at 4 Hz. | Simulate Simulate. |
| Behaviour (camera and mic) | Interruptions, support | The 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, compliance | A 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 board | Students publicly judged | Each 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 plan | To show | What the evidence says | Verdict |
|---|---|---|---|
| Heart rate (wrist) | Stress and calm on the board | Some 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) | Stress | WESAD: about 80% across three states, 15 people, in a lab. Wrist EDA samples at 4 Hz. | Simulate Simulate. |
| Behaviour (camera and mic) | Interruptions, support | The 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, compliance | A 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 board | Students publicly judged | Each 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.
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.
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.
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.
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.
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.
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 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?
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)
Also in the evidence
- 01Performing is not learning
Immediate performance and lasting learning differ (Soderstrom & Bjork, 2015).
- 02Goals can be shared
Shared goals make participation relational (Tomasello et al., 2005), which a score on its own does not show.
- 03Two 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.
- 04A 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%.
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)
Used AI for assessed work, 2026
HEPI 2026Used AI in any form, 2026
HEPI 2026Said institution would spot AI use, 2025
HEPI 2025used AI for assessments in 2024
used AI for assessments in 2025
used AI in any form in 2024
said their institution’s AI policy was clear (2025)
practice gain, plain chat (field experiment)
practice gain, tutoring version
Figure 3: What students said about their institution, and what they did (HEPI 2025, 2026)
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
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?
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.
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
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.
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
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
LifeRPG · The turning point
Who makes learning real?
An AI can guide your learning. But who makes that learning real?
Future University · Process
Future University · Process
LifeRPG · What it exposed
When personal progress becomes another score
LifeRPG · The turning point
Who makes learning real?
An AI can guide your learning. But who makes that learning real?
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.


From each programme’s public description, simplified · bottom: AI-generated 2040 studies
Research · Synthesis
What AI provides, and what it doesn’t

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.


From each programme’s public description, simplified · bottom: AI-generated 2040 studies
Research · Synthesis
What AI provides, and what it doesn’t

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 ←
Restricted, unequal support
Broad support for learning and security→ Less paid work
Restricted, unequal support
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 ←
Restricted, unequal support
Broad support for learning and security→ Less paid work
Restricted, unequal support
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.

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.
A teacher. People design the learning; your AI explains inside it.
A place to practise. A funded, supervised place at a real host.
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.
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.
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.
A teacher. People design the learning; your AI explains inside it.
A place to practise. A funded, supervised place at a real host.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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?

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
The public pays for teaching and for supervised places, through the Practice Guarantee of 2036.
02
A place
A host gives a real problem, a named supervisor and funded hours. Hosts never award credit.
03
Credit
Only the Assessment Board can turn evidence into credit, and it gives its reasons.
04
Recognition
Employers and professional bodies decide for themselves whether to recognise it.
05
Access
A steward plans around money, time and care, and handles challenges to a decision.
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?

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
The public pays for teaching and for supervised places, through the Practice Guarantee of 2036.
02
A place
A host gives a real problem, a named supervisor and funded hours. Hosts never award credit.
03
Credit
Only the Assessment Board can turn evidence into credit, and it gives its reasons.
04
Recognition
Employers and professional bodies decide for themselves whether to recognise it.
05
Access
A steward plans around money, time and care, and handles challenges to a decision.
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
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
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
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
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.
Ari maps the semester around a part-time job before anything is recorded.
↳ Stewards plan around money, time and care.
A teacher frames the question; the AI explains inside that frame.
↳ Educators set how far the AI may help.
A letter names the depot, the problem and a senior who will supervise.
↳ UNI funds supervised places at real hosts.
Hands on the shuttle; the AI gives hints only after Ari tries.
↳ Hosts are paid to supervise, never to grade.

Moana, 67, and a first-year map the harbour for no grade at all.
↳ Open inquiry is never assessed, by design.
On the train, Ari picks what moves from private practice to evidence.
↳ Practice stays private unless Ari moves it.
Named tools only. An assessor asks why, then decides and gives reasons.
↳ Only people award credit, with reasons.
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.
Ari maps the semester around a part-time job before anything is recorded.
↳ Stewards plan around money, time and care.
A teacher frames the question; the AI explains inside that frame.
↳ Educators set how far the AI may help.
A letter names the depot, the problem and a senior who will supervise.
↳ UNI funds supervised places at real hosts.
Hands on the shuttle; the AI gives hints only after Ari tries.
↳ Hosts are paid to supervise, never to grade.

Moana, 67, and a first-year map the harbour for no grade at all.
↳ Open inquiry is never assessed, by design.
On the train, Ari picks what moves from private practice to evidence.
↳ Practice stays private unless Ari moves it.
Named tools only. An assessor asks why, then decides and gives reasons.
↳ Only people award credit, with reasons.
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?
Status as of October 2026 · images: AI-generated studies and renders, except the portal screenshot

Exhibition · Documented proposal
The Learning Transition Desk
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?
Status as of October 2026 · images: AI-generated studies and renders, except the portal screenshot

Exhibition · Documented proposal
The Learning Transition Desk
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”.

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
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”.

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
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
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
Future University · Process
Future University · Process
Evaluation · Critique to decision
When the idea was challenged
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
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.
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.
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.
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.
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.
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.
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.
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.
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
Tickers · Could AI make effort trustworthy?
Observing was not understanding.
AI Senior · Could AI understand and guide work?
Assistance introduced authority.
Civic Score · What if that authority reached society?
Fairer judgement was not justified judgement.
LifeRPG · Could AI help people develop instead of ranking them?
Personal progression could become another score.
Future University · What institutions does meaningful learning need?
Learning still served work and achievement.
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
Tickers · Could AI make effort trustworthy?
Observing was not understanding.
AI Senior · Could AI understand and guide work?
Assistance introduced authority.
Civic Score · What if that authority reached society?
Fairer judgement was not justified judgement.
LifeRPG · Could AI help people develop instead of ranking them?
Personal progression could become another score.
Future University · What institutions does meaningful learning need?
Learning still served work and achievement.
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

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?
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)
58%of online New Zealand adults use AI at least weekly
InternetNZ and Verian, 1,003 online adults aged 18+, Nov to Dec 2025Also in the evidence
- 01Connection 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).
- 02Preferences are constructed
Preferences can be built through context and choice (Lichtenstein & Slovic, 2006).
- 03Choosing without knowing
Transformative experiences complicate choosing with complete advance knowledge (L. A. Paul, 2014).
- 04Meaning 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.
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)
Online NZ adults using AI at least weekly
InternetNZ, 2025Online NZ adults using AI in the past year
InternetNZ, 2025OECD students aged 16+ using generative AI
OECD, 2025of employed people in the OECD used generative AI
of unemployed people
of people outside the labour market
of OECD firms used AI in 2025
of US adults aged 18–29 get emotional support from chatbots
of US adults aged 65+
Figure 3: Who uses generative AI in OECD countries, 2025 (OECD)
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.

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.

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.

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.

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.

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.

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.

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.

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
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?
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.

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?

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?
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?

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?

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?

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.

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?

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?
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?

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?

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?

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.

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

Move 01
Sun from the north
Terraces step down towards the sun and the water

Move 02
A horseshoe, not a line
Twelve clusters on three shores, open to the sea

Move 03
Machines in the shade
Works, port and the Living Layer take the north shore

Move 04
The maunga stays unbuilt
Sightlines kept; its future is for mana whenua

Move 05
Fed by the ring
Fields, orchards and a reservoir around the city

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
people
in 12 clusters
homes
1, 2 and 3 bedrooms
a cluster
600 × 450 m
the harbour
2.9 km across
between clusters
one tram stop each
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

Move 01
Sun from the north
Terraces step down towards the sun and the water

Move 02
A horseshoe, not a line
Twelve clusters on three shores, open to the sea

Move 03
Machines in the shade
Works, port and the Living Layer take the north shore

Move 04
The maunga stays unbuilt
Sightlines kept; its future is for mana whenua

Move 05
Fed by the ring
Fields, orchards and a reservoir around the city

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
people
in 12 clusters
homes
1, 2 and 3 bedrooms
a cluster
600 × 450 m
the harbour
2.9 km across
between clusters
one tram stop each
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
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
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.

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

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

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.

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

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?

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.

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

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

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.

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

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?

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.
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
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.
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
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
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
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
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
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
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
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.
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.
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.
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.
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?
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

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

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.
Information
States a fact you asked to hear
Suggestion
Offers, once, at a pause
Guidance
Ties an offer to what you said matters
Optimisation
Steers you towards a number
Manipulation
Uses pressure you did not choose
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.
Consent is per area, off until you turn it on · Mina’s settings
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.
Information
States a fact you asked to hear
Suggestion
Offers, once, at a pause
Guidance
Ties an offer to what you said matters
Optimisation
Steers you towards a number
Manipulation
Uses pressure you did not choose
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.
Consent is per area, off until you turn it on · Mina’s settings
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.


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.


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 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 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.

08:15The bridge, the Loop
Walks the planted bridge to the tram.

09:00The Yards
Practises a hard passage. Offered a hint, she says “not yet”, and it waits.

11:30Market Hall
Collects lunch; swaps a lesson for a maker’s print.

14:30The Commons
Offered a choir short of altos. She says “not this”; it won’t suggest choirs again.

16:00Her terrace
Reads all afternoon. Calls her mother.

17:30Commons garden
Someone else who asked to meet people today. Both accepted; then it goes quiet.

21:30Bed
Asked what mattered, she tells it: the conversation, not the practice.
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.

08:15The bridge, the Loop
Walks the planted bridge to the tram.

09:00The Yards
Practises a hard passage. Offered a hint, she says “not yet”, and it waits.

11:30Market Hall
Collects lunch; swaps a lesson for a maker’s print.

14:30The Commons
Offered a choir short of altos. She says “not this”; it won’t suggest choirs again.

16:00Her terrace
Reads all afternoon. Calls her mother.

17:30Commons garden
Someone else who asked to meet people today. Both accepted; then it goes quiet.

21:30Bed
Asked what mattered, she tells it: the conversation, not the practice.
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
Context and intervention
Asking for context beat collecting labels.

AI Senior
Understanding and conversation
Help that talks earns trust, and authority.

Civic Score
Sensing and authority
Whoever sets the values holds the power.

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
Context and intervention
Asking for context beat collecting labels.

AI Senior
Understanding and conversation
Help that talks earns trust, and authority.

Civic Score
Sensing and authority
Whoever sets the values holds the power.

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

Mixed reality · a guide in the room

Tabletop · two objects, one advice

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
01Inherited 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.
02v2 · 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.
✓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.
✓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

Mixed reality · a guide in the room

Tabletop · two objects, one advice

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
01Inherited 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.
02v2 · 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.
✓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.
✓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 proposed two-minute loop, developed as storyboard keyframes: a society that works, sold sincerely

Ticker guide
One opening in a Quest headset, then quiet unless asked

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 proposed two-minute loop, developed as storyboard keyframes: a society that works, sold sincerely

Ticker guide
One opening in a Quest headset, then quiet unless asked

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
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
Resident card · paper
Open Desk sign · harbour
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
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
Resident card · paper
Open Desk sign · harbour
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
It involves another real person
Its influence can be noticed afterwards
It can be declined at no cost
Buildable on Quest by 26 October
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
It involves another real person
Its influence can be noticed afterwards
It can be declined at no cost
Buildable on Quest by 26 October
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.

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
The other visitor
Gives A real conversation with someone new
Asks Consent, read aloud, and the right to walk away
The table and its models
Gives Something to talk about: the guide points at the exhibit, never at a person
Asks Nothing

Booth studies, AI-generated and designer-directed (Spec) · to be replaced by photos from the 26 October session
The film screen
Gives A future worth wanting, in two minutes
Asks Attention, and a willingness to believe it
The poster board
Gives The question, and a sticker scale to answer it
Asks One sticker on the way out
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
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.

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
The other visitor
Gives A real conversation with someone new
Asks Consent, read aloud, and the right to walk away
The table and its models
Gives Something to talk about: the guide points at the exhibit, never at a person
Asks Nothing

Booth studies, AI-generated and designer-directed (Spec) · to be replaced by photos from the 26 October session
The film screen
Gives A future worth wanting, in two minutes
Asks Attention, and a willingness to believe it
The poster board
Gives The question, and a sticker scale to answer it
Asks One sticker on the way out
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
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
↳ Needs
I have to see why the guide spoke, or why it waited.
↳ Needs
Cards must never sit between two faces.
↳ Needs
Buttons pulled the eyes away from the person.
↳ Needs
One microphone has to hear both people.
↳ 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
↳ Needs
I have to see why the guide spoke, or why it waited.
↳ Needs
Cards must never sit between two faces.
↳ Needs
Buttons pulled the eyes away from the person.
↳ Needs
One microphone has to hear both people.
↳ 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

02 Crowd scan

03 Scan and read

04 Emotion reading

05 Cues and delivery

06 One line

07 Tick first

08 Keyword

09 Beside them

10 Glass chip

11 Film subtitles

12 Teleprompter

13 Peripheral light

14 Tag-along card

15 Anchored to exhibit

16 Monocular HUD

17 Pill to card

18 Line on a plate

19 Wrist and earcon

20 After Ray-Ban Display

21 After Even G2

22 After Android XR

23 After visionOS

24 After Spectacles

25 After Halo

26 Scan and read, glass

27 Cues, glass

28 Final, combined
Three I kept, and how they combine

Emotion reading
What it shows: expression, emotions, what to say
+

After visionOS
How it looks: glass windows, white type
+

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

02 Crowd scan

03 Scan and read

04 Emotion reading

05 Cues and delivery

06 One line

07 Tick first

08 Keyword

09 Beside them

10 Glass chip

11 Film subtitles

12 Teleprompter

13 Peripheral light

14 Tag-along card

15 Anchored to exhibit

16 Monocular HUD

17 Pill to card

18 Line on a plate

19 Wrist and earcon

20 After Ray-Ban Display

21 After Even G2

22 After Android XR

23 After visionOS

24 After Spectacles

25 After Halo

26 Scan and read, glass

27 Cues, glass

28 Final, combined
Three I kept, and how they combine

Emotion reading
What it shows: expression, emotions, what to say
+

After visionOS
How it looks: glass windows, white type
+

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.
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.
↓
↓
↓
↓
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.
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.
↓
↓
↓
↓
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.

Completed recordings arrived too late to guide a live conversation.
→ Streaming local captions on their original clock, and a separate wake word.

Captions stopped whenever the cloud budget ran out.
→ Captions run locally and survive budget exhaustion; only wording is paid for.

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.

On-device detection sent labels only: no pixels, depth or pose.
→ People tracking moved to the PC; a lost camera is a designed state.

The Quest microphone and then the iPhone link lost the conversation.
→ One PC USB microphone, selected and verified for the same wearer.

Live meters jumped with every new number; readings were easy to misread.
→ Readings held and smoothed between assessments; chat and memory grouped.
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.

Completed recordings arrived too late to guide a live conversation.
→ Streaming local captions on their original clock, and a separate wake word.

Captions stopped whenever the cloud budget ran out.
→ Captions run locally and survive budget exhaustion; only wording is paid for.

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.

On-device detection sent labels only: no pixels, depth or pose.
→ People tracking moved to the PC; a lost camera is a designed state.

The Quest microphone and then the iPhone link lost the conversation.
→ One PC USB microphone, selected and verified for the same wearer.

Live meters jumped with every new number; readings were easy to misread.
→ Readings held and smoothed between assessments; chat and memory grouped.
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.














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








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.














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








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.











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
From control to appeal
The hero changed from the marionette crowd to the city, so the future makes its own first impression.
From a takeaway to a response
The poster stops prescribing a conclusion and leaves the question to the visitor.
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.











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
From control to appeal
The hero changed from the marionette crowd to the city, so the future makes its own first impression.
From a takeaway to a response
The poster stops prescribing a conclusion and leaves the question to the visitor.
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.
01020304The 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.
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
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.
01020304The 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.
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
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
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
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.
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
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.



































