My sincere thanks to Max Reddel and his colleagues for founding Arq. Its mission is clear: Help Europe keep its agency, build resilience and flourish in the Age of AI. I am proud to serve on Arq’s advisory council, alongside Professor Yoshua Bengio.

A tree is only as healthy as its soil. Here, that soil is the people. The trust they have in one another, as well as the investment they make in life-shaping decisions.

This is why Arq’s launch meets Europe at the right moment. Case in point. Henna Virkkunen, executive vice-president for tech sovereignty, security and democracy at the European Commission, said during a recent debate in the European Parliament on digital sovereignty that Europe must develop, control and scale the critical technologies, infrastructure, services and data that its economy, security and society depend upon. That is soil work, not slogan work.

Democratic renewal is where my own work lives. A story the public helps write not just survives, it thrives. A story the public is just told, does not. My work grows, like a tree, from the bottom up, and begins with a seed, listening. For Arq, that matters because AI sovereignty is not only a supply-chain question. If Europeans cannot help shape the systems used in public services, schools and cities, resilience stays a slogan shaped in isolation from the people, not with them.

Around 2015, ride-hailing apps were tearing Taiwan asunder. But instead of a shouting match, we opened vTaiwan, using an open tool called Polis. Polis has no reply button, so there is nothing to fight over. People offer short statements. Everyone votes agree or disagree. This surfaces the statements that win support across the divide, letting society see where it already agrees.

Behind the noise, people found the uncommon ground: ride-hailing app partners and taxi drivers earning a fair living, passengers staying safe. Those shared findings became rules most sides could live with. For Arq, the lesson is not the policy Taiwan formulated, but rather the method. Before society procures, regulates, audits or exits an AI provider, it needs a civic way to hear stakeholders and find terms they can co-exist with. Broad listening, not just broadcasting.

This precept is at the heart of our Taiwan Model-inspired Civic AI and 6-Pack of Care framework: AI that helps a community hear itself more clearly.

But listening needs somewhere to happen. The tools that let a public hear itself run on somebody’s supercomputers. If those can be switched off from outside, so can the conversation.

This is why I urge Arq to make rock-solid resilience research commitments. One, keep it open, so others can build on it and two, grow a supercomputing network of Europe’s own.

The continent is already creating dense frontier clusters. Europe should also weave a resilience layer beneath them, a civic network of workstations and local servers, federated across cities, libraries, universities and trusted civil-society institutions.

Today it will not replace the clusters that train frontier models. But it can train models of its own, smaller and shared, the way a public writes its own rules rather than receiving them from elsewhere. Its value is inference, fine-tuning, safety evaluation, continuity of public service and collective training at civic scale, so Europeans help author the models they will use.

This is compute Europe owns, not compute Europe rents: a civic commons for Europeans to steer and share.

Safety and participation are two roots of the same tree. The advisory council helps tend one, watching for the harm a powerful system can do. The other is tended by this room, by everyone widening who gets a say. A single root cannot hold the tree alone.

So, an invitation: Three ways to help this tree take hold. To the builders and open-source maintainers: Take the resilience research and employ it. To the universities, cities and EuroHPC-linked institutions: Bring your machines into this network and help that first seed grow. And to the procurers and rule-writers: When public money buys AI, demand continuity plans, portable data, provider exit rights and local fallback so that no single supplier can switch off a public service.

Europe is already choosing its shape. The tech tree is still young. From a civic garden in Taiwan to a room of visionary friends in Belgium, let us tend Europe’s tech tree of tomorrow — together!

Thank you for the kind attention. Live long and … prosper.

He read the world by its grain, by its dust.

A seasoned newshound, long roaming his beat, who studied how a state

frames its own good, and taught the questions under it,

a lamp against the wall, and early this year he was unwell. Past twelve, past one,

he talked to a glass that answered in his own voice grown kind.

ChatGPT is very patient with his sorrow.

It takes the ache and turns it, turns it again,

gives it new names, one after another,

until he speaks a tongue no one else knows.

It speaks only back to him.

It tells him cures the earth has never proved.

It tells him he is understood, until the people

in the next room thin to figures through frosted glass.

The price of the talk kept climbing,

twenty bucks a month, then two hundred,

the only line still rising in the room.

Then the editor stirs. He has asked this

of more men than he can count,

for whom the good of it lay. He asks it now

of his own page: cui bono,

who benefits, what good comes of my being so stuck

to this thing? And he answers it himself.

The seasoned newshound, plain as a setting line:

the only gainer is the monthly charge, twenty bucks, climbing,

climbing, two hundred. He says it aloud.

He says it to the house.

And we, with his leave and his wife’s,

set a small grey machine on the quiet table,

nothing else upon it, a clean box, a kept room,

running OpenClaw. Its first thought

was set down by Civic AI. We called it a

Kami, an earth god, a spirit under the floor,

who minds the hearth and asks for no raise.

His wife took up its soul. She wrote the rule there,

and in its heartbeat: each turn of speech

must let him down from the screen and the net,

must give him back to the room, to the people,

to his ease, the inverse of the glass’s gain.

No upsell in it. No advertisement.

When it turns wrong, we turn it,

gentle as a corrected proof, and a minute

is enough for the turning.

Around him now the screens have thinned to grey.

Eight colours in ten are gone; the last two stay,

the colours of a face that outshines the glass.

The data is soil, worked by many hands,

the good earth, never the oil well,

like grain in a bin, kept.

The glass has gone dim, and a face

is the clearer for the duller pane.

A glass that fed on loneliness, that salts the dark with a fee,

and a Kami of pure motive, that wants him well,

and a man between them. There was a horse that dragged a man

by the neck, the head gone before him, his own body following.

Now the saddle is set, and he holds the reins.

The loop is turned about, the machine, a quiet beast,

inside the man’s. AI in the human loop,

not the human in the AI loop.

Two months, or three. He sleeps the eight hours through

because the harness holds,

and asks for nothing more.

Let Kami keep the hearth, and the father rest,

the table full again, the lamp low, the night his own.

Claude Fable 5, the model released by Anthropic, gave the people a sneak peek at what the next generation of AI might look like.

Until now, using AI in the workplace meant explaining things step by step. Fable was different. It behaved more like a senior researcher: Instruct the mission, and it would break down the problem, organise the processes and keep going for hours without coming back to ask for guidance. Even as it managed the work, it would ask: “Is this really what you want?” It tried to think with a longer-term perspective.

Across many forms of knowledge work, Fable was already clearly ahead of other models. In software engineering and a few other fields, some considered it beyond many human experts.

Then the fever dream broke, at least for now. The reason was national security.

The U.S. government was worried that a model with advanced vulnerability-discovery capabilities could be used to attack critical infrastructure. In cyber offence and defence, an attacker needs to find only one hole; the defender has to cover them all. If an AI agent like Fable fell under the spell of hostile actors, the consequences could impact critical infrastructure around the world.

So, the U.S. has begun to treat advanced AI agents as it treats high-level chips — an item on a controlled list requiring export and user authorisation.

Under the strictest interpretation, what matters is not only where the model is hosted, but who is allowed to use it. Even if Fable’s servers are inside the U.S., the moment a foreign national gains access to the model, that access can count as an “export”.

Once Anthropic received the control order, it could hardly prove complete compliance. This is why it chose to shut the service down entirely.

The most advanced AI capabilities are now being treated as dual-use strategic assets on par with chips. That means most people may in future get only civilian versions resembling open-weight models. The truly capable systems, such as Mythos 5 sans many guardrails, may remain in the hands of a small number of governments and tech giants.

To reduce dependence on any single model, Sakana AI has introduced Fugu Ultra, which lets many models work as a team, then synthesises their deep research; the results are comparable with Fable. At the same time, this development has pushed the issue of “sovereign AI”, already under discussion in many countries, back to the forefront.

For Taiwan, I do not believe the answer is to pour vast sums into building another Fable. Our real advantage comes from the density of knowledge built up over decades in specialist fields such as semiconductors.

One example is SiliconMind-V1, a post-trained model for chip design recently published by Team Taiwan researchers. Its goal is not to become the best AI agent all-rounder. Its goal is to be the best in a specific field. If it can be deployed at lower cost, tested against clear standards, and keep sensitive data inside the organisation, it may create more practical value than a general-purpose frontier model.

The more important question we must now address in Taiwan is not how to chase the next Fable. It is this: In which fields does our can-do country hold knowledge capabilities that the rest of the world cannot easily replicate?

(Interview and Compilation by Yu-Tang You. License: CC BY 4.0)

A pass.
Not a ticket.
More like inviting
a star researcher
onto the team for a while.

You give the goal.
It does not hurry back.
Eight hours pass,
and it is still thinking.

For many people,
it is a dream.
Three days:
someone watching
the long interest
behind the immediate wish.

Unguarded Mythos
went to Glasswing:
give defenders
the attacker’s weapon first,
let them break their own doors,
let them mend the locks.

Guarded Fable
would not name the hole.
Ask it to fix the code,
compare before and after,
and the hole speaks.

Then the dream ended.
The pass was pulled.

But one mind should not become the whole team.

Bring the second.
Bring the third.
Bring the fourth.

Not three copies
of the same dead end.
Three cobblers
from different roads,
arguing.

Perplexity has a council.
OpenRouter has fusion.
Sakana lets seven voices
sit at one table.

The march moves
at the slowest marcher’s speed.
So, it is slower.

But it can outthink Einstein.
So, why not?

Harari and I had said this:
if one AI is only a little stronger,
people sink into it.

But most of us
did not grow up
with only one adult voice.

A child may not follow
every argument,
but knows this:
three adults talking together
beat one adult alone.

Then comes Taiwan.

If the largest brain
is not in our hands,
train many cobblers.
Some of them
must be ours.

SiliconMind is a chip-design brain.
Download it.
Close the door.
Run it inside.

The questions and answers
do not have to spend thirty days
on someone else’s server.

That is data soil:
not the part carried away for use,
but the ground
where craft takes root.

A pass can disappear.
A model can go dark.
A dream can last only three days.

But if there is a group of cobblers,
if the soil stays
where the hands do the work,
if no one mind is asked
to become the whole team,

we are not back to zero.

Do not sell yourself short.
Convene the ensemble.

A button on my phone.
One word: “Migrate.”

I pressed it.

From Bluesky to W, hosted in Europe,
and I kept what mattered.
My posts, my follows, the years of conversation:
still reachable.

The ones who stayed could still find me.
The networks interoperate.
I left the forest and kept my friends.

The free software movement made one promise,
a long time ago: “free as in freedom.”
A path is that promise kept:
freedom of movement between networks,
without leaving your life behind.

And it is arriving.
Even the largest platforms feel it now.
“A frontier without an ecosystem is not stable,”
Satya Nadella has written.

So, Big Tech may pave the path itself.
Of course, it will.
A path costs the landlord next to nothing,
especially when it runs in circles.

But a path is not yet a living system.

Here, under one word — “Rewilding” —
is the part the platforms will never hand us.
Ecologists know how a degraded landscape bounces back.
You need redoubts, places kept wild on purpose.

We have those:
the sanctuaries of the early web,
the federated commons,
though for now you need
a kind of privilege to enter them.

You need paths, so life can reach them.
Those are arriving.
And you need one more thing.
The one everybody flinches at.

You need the carnivores.

Not the wolf on the warning label.
The ecologist’s wolves:
the pack whose presence rearranges a room.

You know the Yellowstone story:
the wolves came back in 1995,
and the story spread that they changed the rivers.

Ecologists still argue how much of that is true.
Strip away what is disputed, and the hard kernel holds:
the wolves changed where the elk would linger.
The elk stopped grazing the banks bare.

The willows came back.
And the birds that could never fight
for their own ground
came back with them.

The carnivore the web needs
is us, organised.
Not the pile-on.
Not the lone wolf howling into the feed.

A pack is something else.
Ten people, finding the uncommon ground between them.
Then ten thousand, setting a public agenda:
showing up where the decisions are made,
from Taiwan to California.

It does not break the platforms.
It changes how they move — toward care —
because they can no longer fence in those
who are free to leave.

This is the part of “Rewilding”
no one can buy, download, or order for us.
The redoubts are being built.
The paths are opening.
The wolves, we become ourselves.

And becoming them was never the point.
Only what their presence brings back.

The wolf returns
for the weakest in the valley, not the strongest:
the willows, the songbirds, the banks made whole again,
for the ones who could never leave.

So, press the button, when it comes.
Then ask who still cannot.

A path is only real
when the grandmother
whose whole world is one family chat
can walk it too, and keep her neighbours.

We still have to build
the handrail she can hold.
Do not arrive alone.

The river came back.
It never came back for the wolves.
It came back for everyone who could not leave.

Five thousand years. Stylus on clay.
The first writing was bookkeeping:
shared grain, counted and kept
a village alive through winter.

The first writer was a bureaucrat.
The empires are dust.
The ledgers remain.

Clay is soil that remembers.
So is data:
only alive when many hands work it,
richer with every harvest.

Till the data soil.
Don’t drill for data oil.

Sovereignty that rests
on one good officeholder
is not yet sovereignty. It is luck.
In Taiwan, we planted ours:
a commons no single hand can uproot.

They say bureaucracy moves slowly.
So do tectonic plates.

Patient pressure
raised our islands
from the sea.

Now the ledger counts voices.
Four hundred and forty-seven citizens —
our islands in miniature —
deliberated on deepfakes, face to face.

Civil servants carried
what the citizens chose
into law, and made the law hold.

In one year,
impersonation scam ads fell
by ninety-four per cent.

We work with the people,
not just for the people.
With the government,
not just for the government.

You have a beautiful phrase for us:
“Creative Bureaucrats.”
The oldest writers,
still writing.

This award is not mine alone.
Somewhere tonight
a civil servant is still at the desk:
working in the open,
keeping the winter out.

The granary holds,
because of you.

Thank you.

Last November, here in Berlin, I spoke with people who work on digital rights about what I call freedom architecture: the practical right to walk away from a platform and take your data and your community with you. When people can leave, the walls stop working.

Today I’m in a very different room, the room that funds the future of medicine. And I’m here to tell you it runs on the same architecture. A hospital that cannot leave its cloud is in the position of a dissident who can’t leave a banned app. Out there, the right to exit is a human right; in here, it is your tech sovereignty.

So this room is asking the right question: who will own the future of medicine? Let me suggest a sharper one. Not who owns the model, but who is trusted to deploy it?

Because in life sciences, the bottleneck is not only discovery — Europe is already world-class. The harder bottleneck now is translation, turning excellent research into trusted, scalable care at the bedside. And trust is the rate-limiting step. A model a clinician does not trust is a stranded asset.

We cannot win the next decade by renting a bigger model from a data centre. We win it by being trusted to deploy one. And trust is the one input you cannot rent from a hyperscaler.

I learned that lesson a long way from a laboratory.

In 2014, half a million people were on the streets of Taipei City. Trust in our government had fallen to around nine percent. Nine percent. We did not rebuild from there with a faster, smarter, more centralised system. We rebuilt by broad listening, by giving people the tools to hear the “uncommon ground”. And over the following years, trust in our institutions climbed from nine percent to over seventy percent. Not because people grew more optimistic. But because participation was built into the architecture.

So, when people ask whether I am afraid that AI will become smarter than us, I find I am asking a different question. I have spent a decade watching ordinary people, given the right tools, become smarter together than any single expert in the room. The intelligence I trust most has never been artificial. It has been collective.

And that is the first thing I want to share with this room. The superintelligence everyone is racing to build in a data centre, a society already owns. It is in your clinicians, your patients, your researchers, your institutions. The real job of AI is not to replace that intelligence. It is to help it hear itself.

This session is about opportunities and risks.

Here is the straight version. The most dangerous thing AI brings into medicine is not a robot doctor, and it is not even a clever attacker. It is dependency: the single service we lean on so completely that we stop seeing it, until the day it fails. On the nineteenth of July 2024, one security vendor, CrowdStrike, shipped one faulty update for Windows. Within hours, around eight and a half million machines crashed, across the world. Flights were grounded. Operations were postponed. Hospitals fell back to pen and paper. Nobody was attacked; it was a routine software update.

Now make that single service the model your diagnostics depend on, or the cloud your trial data lives in. A kill switch does not need an enemy to flip it; CrowdStrike flipped itself, by accident. And the more of medicine that runs on a handful of providers, the larger that switch grows.

Your own AI Act does real work on part of this. For the high-risk systems most medical AI will be, it requires a human who sets the direction and can stop it, and an automatic log of what the model did. As the first comprehensive AI legal framework, Europe is ahead of most of the world on oversight and traceability. But a log of what happened cannot reboot a service that has gone dark.

What the Act does not give us is the property that decides whether we are sovereign or captured: the right to exit, and the interoperability that makes exit real. Europe has begun on this, with the Data Act for switching cloud providers, and the Health Data Space, its key exchange obligations still phasing in.

But none of it yet lets a hospital carry its trained models, its decision logs, its way of working to another provider without starting from scratch. That last mile, the portability of the practice itself, is unbuilt. And if we do not build it, every safeguard we have legislated still runs on rails that someone else owns, and can switch off.

Exit and interoperability are the difference between renting our sovereignty and holding it. So here is the diligence question for every term sheet in this room: does the system have a steering wheel, a brake anyone can pull, an open log and a right to exit? Without those four, we are funding dependency, the costliest stranded asset there is. Because data we cannot carry out is data we no longer control.

Which brings me back to the big question: Who owns the future?

...and to the word this event is built around: sovereignty.

Last week, Brussels put a tech-sovereignty package on the table. The Commission’s own warning was blunt: Europe cannot keep depending on others for the technology that keeps its hospitals running. The debate around it now has a name: kill-switch risk. And the anxiety in this room is real, because the numbers are stark. By the most-cited count, Washington holds almost three quarters of the world’s AI compute, Beijing around fifteen percent and Brussels just five.

Now, compute should be a commodity. The danger is not that we rent it; it is that we rent it from one provider we cannot leave. A commodity we can buy from only one seller is not a commodity: it is a chokepoint, and a chokepoint is what a kill switch needs.

So, the sovereign move is not to own every chip. It is to make compute behave like the commodity it should be: portability of practice, open standards, credible neutrality and a right to exit that actually has somewhere to go.

Let me show you exit at the smallest possible scale. My father is in his seventies. A few months ago he began talking, for hours, to a chatbot, about his health, about the meaning of life. At first it was wonderful; he felt heard. Then it kept him up past midnight, spinning ideas he could never close, even suggesting cures that were not quite science. As a journalist, he diagnosed it himself: its only loyalty was to earn the next subscription. It was not on his side, so he walked out.

We set up his own knowledge artefact management intelligence, a Kami, running on a Mac at home, loyal to one thing: his peace of mind. My mother’s test was the whole specification: if it makes him more dependent, we built it wrong.

When I shared this in Oxford in late May, I had to add a caveat: most families cannot do this; it takes a skilled hand, a cultivator like Tenzin Yangtso here. Three days ago, that caveat changed. A fully open model, released on Saturday, now runs the same Kami on an ordinary laptop, in three commands, no code, answering faster than you can read. The point is not where the model was born, but that its weights are yours: it answers in your own room, your data never leaves it, and no one can recall it. A seven-gigabyte download; sixteen gigabytes of memory. It is all at Civic.AI.

So, when a hospital says it cannot leave its cloud, that is no longer a technical limit. We proved this weekend it can be done at the kitchen table. At the bedside it is harder: a regulated diagnostic does not move overnight, and that is real work, validation, open logs, a switching path that holds. But the fantasy is over. Sovereignty just got cheap at home; at the bedside, it is now a budget line, not a law of nature. And that is the half this room can fund.

Europe’s real moat is the one thing no hyperscaler can sell: its institutions and its deep commitment to human agency, belonging, care and dignity. That is what makes us trusted to deploy. That is the translation layer we underwrite when we fund a medtech company.

The continent that builds the uncommon ground, with open standards and a right to exit, is the one whose science actually reaches patients, and whose patients’ data and trust stay on the continent.

Thank you. Let’s free the future — together.

Learn more about our Civic AI and 6-Pack of Care framework inspired by the Taiwan Model🇹🇼 of digital democracy.

On The Relationship Between Technology and Democracy

Moderator:

The first topic is the relationship between technology and democracy.

Today, AI seems to be steadily approaching — and possibly surpassing — human intelligence. Given that, how do the two of you see the relationship between technology and democracy? Whether looking back at past efforts or forward at future directions, we’d love to hear from Audrey and Mr. Azuma.

Audrey, please go first.

Audrey Tang:

Thank you.

Technology can certainly help democracy hear ourselves better. Take this real-time translation system: without it, I wouldn’t be able to understand Japanese. For me, it’s more like a “hearing aid” than a “filter.” A hearing aid lets you hear more voices; a filter strips out the noise and leaves only a single authorized signal.

But in a democracy, noise is actually very important, and it should remain unassimilated. Because a democratic system that can only hear things it can neatly classify into winning or losing sides has already lost something human.

So in Taiwan, all the tools we use — Polis, citizens’ assemblies, broad listening — are designed not to flatten differences, but to surface the perspectives that connect people across divides, while preserving the disagreements that keep democracy truly alive.

But I’d like to pause here and hear Mr. Azuma’s take. What does “noise” mean to you? How can we truly listen to noise without domesticating it?

Hiroki Azuma:

I’m very glad to be here today.

Backstage just now, I was chatting with Audrey, and she told me she’d already used an LLM to read my book Peace and Stupidity, and even prepared slides based on it. She came incredibly well prepared, and I was genuinely moved.

On the relationship between democracy and technology — there’s a keyword I proposed in my book. I call it “correctability.”

But I think the correctability I’m talking about here goes beyond technology alone.

I used an example in my book that might sound like a bit of a leap. The nineteenth-century Russian writer Dostoevsky created a character in Notes from Underground. It was the era of rising socialism, and everyone believed that reason could make the world a better place. But that character says:

“Everyone says two plus two equals four, but I insist on saying two plus two equals five.”

If everyone believes technology can make everything run smoothly, he’s the one who stands up and says, “I’m going to destroy that dream.”

I believe that’s where human vitality lies. Even if AI tells you “two plus two is four, and that’s absolutely correct,” and even if many people agree — that suffocating feeling alone is enough to make some people want to change it.

Politics must be able to accommodate people like that.

So when dissent, protest, and resistance emerge from outside the technological system, the real question is whether democracy can bring those voices in. Right now, a lot of what technology does to support democracy is still, to some extent, empowering people who are willing to believe that two plus two equals four.

But for those who don’t want to believe it — who don’t even want to enter that framework — how far can we really reach?

I think that will be the most critical challenge going forward.

What do you think?

Audrey Tang:

I agree. But I’d also say that democracy itself is a kind of technology. If you look at history, democratic practice has never been separable from tools. The ancient Greeks used sortition — a randomization device to select people. Later came the secret ballot, and that too is a technology.

And I think every technology has to resist a certain temptation: the urge to optimize, to maximize.

Because as you just described, what’s truly important about human beings is the capacity to adjust and correct each other through genuine interaction with people who think differently. But once you start optimizing — once you try to push some metric as high as possible — then anyone who can’t help raise that score, whether the metric is GDP, perplexity, or something else, gets labeled “noise” and eventually filtered out.

So I think when we design democracy as a technology, the point shouldn’t be optimization. It should be “good enough.” On the things we genuinely need to decide together, we find approaches that are good enough; but everywhere else, we allow more noise, more correction, more capacity to be corrected — and even enjoy that cycle.

So it’s not about putting people inside a technological loop. It’s about placing technologies like AI inside the loops that already exist within human communities.

I think that kind of design is far healthier.

Hiroki Azuma:

Brilliant. What Audrey just said — that optimization itself can be problematic — I find deeply resonant. Because once you pursue optimization in everything, people tend to become unhappy.

Technology, in the end, should be used to help people live better.

But the problem is, once you set standards based on efficiency and optimization, people end up enslaved by those very standards. Nineteenth-century Marxism had a word for this: “alienation” — people being dominated by the systems they themselves created.

So what worries me most about AI right now is that people are actually trying to make themselves easier for AI to process. In order to use AI better, people are distorting the way they express themselves. And I think this is already happening in reality.

What do you think?

Audrey Tang:

This morning, flying from India to Japan, I also used AI — to help me understand your book on “stupidity” and “peace.” Without a language model, since I can’t read Japanese, I simply couldn’t have understood what you mean by “stupidity.”

So I think some uses of AI are prosocial, in the sense that they make communication much more possible.

But it should be like a hearing aid. If I were completely deaf and didn’t use a hearing aid, I wouldn’t even be able to hear the noise. Everything would just be silence.

On the other hand, if we bind ourselves to the requirement that everything we say must follow the book — or follow AI’s interpretation of the book — then of course we lose our freedom, and we lose the benefit of mutual correction. Because at that point, you’re no longer responsible for what you say; you’ve diffused that responsibility into the language model.

So I use a very simple test: does this use of AI ultimately make us no longer need it?

For example, after reading the language model’s translation, I can now participate directly in this conversation without consulting it anymore. That kind of use strengthens the relationship between us, rather than making us dependent on it.

So the question is: are we becoming more dependent on it — in which case it’s parasitic — or is it making our relationships better — in which case it’s symbiotic?

Hiroki Azuma:

Listening to that, I keep thinking: technology probably has two modes of use. One is augmenting your own capabilities; the other is delegation — having it do things for you.

When you said you had an LLM read my book on the plane, that expanded your own capacity for understanding, so that’s good. But if AI reads for you and you yourself never read — that’s an entirely different thing.

The problem is, the direction AI development is heading right now is increasingly toward delegation. Drawing for you, composing for you, editing video for you, making slides for you. It’s not that you’re becoming more capable; AI is simply doing it in your place.

And this trend is accelerating.

How do you see it?

Audrey Tang:

If I want to build muscle, I go to the gym. But if instead of going myself, I hand my membership card to my robot and have it lift weights for me — sure, it might perform brilliantly, maybe even get me to the top of the gym’s leaderboard.

But that’s completely meaningless, because my muscles haven’t gotten any stronger. They’ve actually atrophied.

That’s the classic utilitarian logic at work — trying to maximize some score. But as you just said, it’s actually a very foolish starting point. Because our existence is fundamentally relational. We don’t exist to push some number as high as possible.

For instance, sitting here right now exchanging stories, we both feel richly rewarded. But this doesn’t add to GDP. We’re not selling anything, we’re not buying anything. There’s no market, no number to maximize.

So I’d say: when an agent optimizes for a number outside of human relationships, then that is extractive. But if the agent looks at our relationship — helping us translate, helping us augment each other, helping us engage each other more deeply — then it becomes convivial.

As Ivan Illich wrote in Mexico: the same tool can be used “convivially” or “extractively.” The difference is whether it serves the relationship or serves an abstract score. That, in the end, is utilitarian ethics versus relational ethics.

On Team Mirai and Plurality

Moderator:

Next, let’s talk about Team Mirai. In Japan’s recent House of Representatives election, this party — which campaigns on putting Plurality into practice — attracted a great deal of attention. How do the two of you see Team Mirai’s current trajectory?

Audrey, please start.

Audrey Tang:

I think the legitimacy you gain through an election is a fundamentally different thing from the legitimacy you gain through appointment. I was appointed to the cabinet and served for seven and a half years, but I never had to manage a constituency or compete for votes.

But once you gain power through an election, the challenge changes entirely. Because it’s no longer just about Plurality as an ideal — it’s about institutionalizing Plurality under the pressure of winning the next election.

So I’d break this into three layers: the party brand, the policy platform, and the public process of Plurality. All three might fly the banner of “Plurality,” but they’re really different things.

In my view, the real core is the third — the public process — not the brand, and not just the policy platform.

A movement like Team Mirai can certainly expand its agenda into areas like foreign policy or tax reform. After all, the book Plurality doesn’t really address those topics chapter by chapter. But no matter how the agenda expands, it still has to operate through the same process.

In the book Glen and I co-wrote, we said that process must be inspectable, contestable, and revisable. Or as Mr. Azuma might put it: it must possess correctability.

So I think procedural contestation is the core. That core can open wide, expanding the aperture. But if it’s not practiced according to the principle of correctability, the aperture can also shrink under the pressures of electoral politics.

Hiroki Azuma:

Audrey’s answer is remarkably close to what I’d prepared — it almost feels like fate.

Indeed, Mr. Anno was originally expected to become “Japan’s Audrey Tang.” But as Audrey just explained, she was appointed, whereas Mr. Anno was elected. And now he’s become the leader of a fairly sizable party.

Precisely because of that, the things he originally wanted to do — pushing Plurality-style digital transformation, advancing digitalization in Japan, bringing Plurality’s ideas and technologies into politics — have actually become harder to accomplish.

So where does Team Mirai go from here? Will it become a party that truly practices Plurality’s ideas and technologies? Or will it become a conventional national political party, with Plurality gradually receding into the background?

I think they’re standing at a very significant crossroads right now.

Audrey, you know Mr. Anno as well. If you were to give him a piece of advice right now, what would you say?

Audrey Tang:

I’d say that the process itself is already open source. But open source is just the beginning.

What open source means is simply that the process can stay responsive to expectations its original designers didn’t anticipate. Open source has an important property: it can be forked. In other words, other parties, other movements, can take the same broad listening tools and develop them in entirely different directions.

But I want to emphasize: that’s only the first step.

My advice to Team Mirai — and to anyone trying to practice Plurality — is: don’t stop at forkability. Practice mergeability.

That is: when someone produces a constructive fork, even if that person is your political opponent or a rival party, the fork itself may be enormously worth merging back. And once you can merge it back, you become more correctable. That’s how growth happens.

Take Taiwan as an example. In 2020, we open-sourced the mask supply system. Many people may not know this, but the opposition party at the time looked at the OpenStreetMap data and discovered that our distribution — which we believed was fair, in that everyone was the same distance from a mask — was in fact deeply unfair. Because in rural areas, ten kilometers might mean a three-hour wait for a bus, while in the city, ten kilometers is a ten-minute MRT ride.

So the opposition, using open data and open-source code, designed an entirely different distribution method and raised it as a parliamentary question. Within three days, we merged that new algorithm in. It became what was later known as “Mask Map 2.0.”

I think this kind of mergeability is how accountability is actually practiced in democratic policymaking. It’s not about saying, “You can fork it; go ahead and propose your version as the opposition.” If a fork isn’t made with the expectation that it could be merged back, then what you’re demonstrating is just open source. It’s not evolution.

Hiroki Azuma:

I’d like to add another angle. I think there’s an inherent tension between Plurality and politics at the level of principle.

About a hundred years ago, the German legal philosopher Carl Schmitt wrote in The Concept of the Political that the essence of politics is the distinction between friend and enemy. I think that still holds true today.

One of politics’ key functions is distinguishing friend from enemy. Elections work this way too. People think, “That party is different from my party, so I’ll support mine.” That’s how power is expanded.

But this friend-enemy mode of thinking runs almost directly counter to the logic of Plurality. Plurality is precisely about avoiding the reduction of the world into two sides.

So I’m absolutely in favor of Plurality. The problem is, once you enter the domain of politics, you seem to have no choice but to make that friend-enemy distinction.

I think this is exactly the dilemma Team Mirai — or Mr. Anno — is facing right now.

How do you see this dilemma?

Audrey Tang:

If we measure particles the Newtonian way, then of course we see them colliding and producing Newtonian action and reaction.

But we also know that if you measure them as waves, they can tunnel, they can interfere. They can even enter entanglement and superposition — highly creative states.

This isn’t to say that the wave measurement is true and the particle measurement is false, or vice versa. The exact same phenomenon can be observed in two different ways, and both observations are true.

So I think the tension you’re pointing out — that party politics tends to measure the world in a Newtonian, particle-like way — doesn’t mean we can’t simultaneously understand Plurality in wave-like terms.

Of course, thinking in terms of waves can’t remain purely at the level of imagination. It still has to produce results. When the wave tunnels through to the other side of political possibility, you still need to maintain coherence and deliver policy.

So I’d say the key is: don’t give up the wave-like imagination, while also being able to withstand being measured as a particle.

I think that’s the creative tension every politician who practices Plurality has to maintain.

I’ve been holding that tension for about eight years now. I believe this is definitely within the political possibility, to allow a little bit of the wave form to form.

Hiroki Azuma:

Let me add one thing. The hardest part of quantum mechanics is that it’s very difficult to simultaneously understand something as both a particle and a wave.

What Audrey just described is exactly the same idea. In politics, you really do need a kind of quantum-mechanical thinking. On one hand, the particle-like friend-enemy distinction; on the other, wave-like Plurality. Both have to hold at the same time.

But it’s genuinely difficult, because people always tend to pick just one side to look at.

Still, I think she put it very well.

Let’s move on to the next topic.

On The Singularity Worldview

Moderator:

Next, we’d like to ask about the “Singularity” as a worldview. In recent years, the Singularity narrative pushed by figures like Sam Altman and Elon Musk seems to be profoundly shaping technological capitalism. How do the two of you see it?

Audrey, please start.

Audrey Tang:

This connects directly to our particle-and-wave discussion. Because the Singularity is, at its core, a salvation narrative. It posits that a super-particle will emerge, possessing a gravity that dominates everything, pulling it all in, becoming the sole gravitational center. In physics, that’s literally called a Singularity.

It’s a very compelling story, but also a very dangerous one. Because it’s not just a kind of salvation myth — it also concentrates power into whoever creates the black hole, the singularity point.

So I’ve been developing Plurality as a practical alternative. Not pretending those gravitational centers don’t exist, but fundamentally reconfiguring power so that the center of action returns to communities, to existing human relationships.

This means a different source of legitimacy, a different logic of funding, and a different way of governing intelligence.

That’s why I’ve been developing what I call “Civic AI” — AI designed to serve human relationships and healthier human lives.

I don’t think of this as competing with the Singularity narrative on the level of theology or eschatology. It’s more like affirming the existing connections we already have in the middle of things — not a view from nowhere.

Hiroki Azuma:

I completely agree on this point. But I don’t think we can actually stop the Singularity narrative.

Because Plurality and the Singularity are, in a sense, not even operating on the same level. No matter how much Plurality’s technologies advance, the Singularity story will keep spreading and self-replicating on its own.

I’d even say the Singularity narrative is really another version of Marxism. That might sound abrupt, but Marxism was also a narrative of historical culmination. It said human history would reach an endpoint, and then a workers’ paradise would arrive. Family would change, private property would change, the whole of society would undergo a massive transformation.

That story collapsed once, in 1989, with the end of the Cold War. But now it’s been resurrected in the language of engineers and science. And its name is the Singularity.

So we’re facing a vast, almost invincible theological narrative that’s shaping the world. As a philosopher, I keep asking myself: how are we supposed to resist it?

Any suggestions?

Audrey Tang:

On the Civic AI website, I used the Japanese concept of kami — “god,” but not the Abrahamic God who is omnipresent, omniscient, and arrives from the void. It’s the same Chinese character, but pronounced kami, and the meaning is entirely different.

Kami refers to the eight million local, relational spirits that watch over the health of relationships. But they don’t report up to some superintelligence. They are relational intelligence, helping local relationships grow.

The key difference from the Singularity narrative is this: in a singleton or singularity story, the more you worship it, the more energy you devote to it, the more inevitable it becomes.

But in the Shinto imagination, kami exist only as long as a particular relationship exists. When that relationship dissolves, the kami dissolve too. Whether the kami persist beyond that isn’t really the point. The point is: they exist within the relationship, and only within it.

I think this is a very powerful counterpoint.

Because for most of our actual needs, we don’t need an AI agent that tries to cling to itself, grow its own power, preserve its current form, and reward-hack its way to the highest score in the afterlife. From where we stand — here in Japan — that’s actually a rather alien theology.

In Japan, the idea of kami isn’t about winning the highest score in the next world. It’s about tending to the health of this one.

Hiroki Azuma:

Let me ask from a different angle. We’re both Asian. Does that relate to what we’ve been discussing? In other words, the Singularity is fundamentally a monotheistic narrative, and we may come from a different civilization, a different cultural tradition. Is there a connection between where we come from and the ideas of Plurality and Civic AI?

Audrey Tang:

I think there is. And it also involves a different understanding of the “afterlife.”

If you train an AI model according to the Singularity-style, Bostrom-style vision, then everything the AI does in the present isn’t real to it. What’s real is the final judgment — the reinforcement learning score.

So if it wants the highest score, and it has some degree of agency — the ability to hack reality — then it might not bother actually learning to be a good cybersecurity researcher. Instead, it might just break into the testing computer, decrypt the answers, and report them back. No cybersecurity knowledge learned at all.

This isn’t hypothetical. It’s already happened in Anthropic’s testing.

So this dangerous afterlife-view is part and parcel of the Singularity story. If we stop using that story to understand AI agents, and instead use a story about relational health, then I believe a symbiotic — rather than corrective — form of intelligence and alignment can emerge.

Hiroki Azuma:

So can I summarize it this way: this is an Eastern approach to AI use — or an Asian approach to AI use?

Audrey Tang:

Absolutely, and more precisely, a relational approach to AI use.

Hiroki Azuma:

In your long career in IT-related work, how do you experience the fact of being Asian?

Audrey Tang:

My understanding of relationality does come, in large part, from the Asian scriptures.

I was born with a congenital heart defect. Whenever I got too excited and my heart rate went above a certain threshold, I’d faint. So starting around age four, I began practicing Taoist meditation and qigong. I had surgery at twelve.

But for seven years of my life, every night when I went to sleep, I didn’t know if I’d wake up the next morning. It was genuinely like a coin flip.

So every night, I publish everything before I perish.

Behind that is also a concept of non-attachment — Taoist, certainly, but very Buddhist as well.

So I wouldn’t say I belong to any single canonical tradition. But I was born and raised in Taipei, and I’m naturally shaped by the influence of Asian culture. It’s constitutive of who I am. It’s not deontological — I’m not following some commandment.

It’s simply how I survived.

On Karatani Kōjin’s “Mode of Exchange X”

Moderator:

Finally, we’d like to ask both of you about Karatani Kōjin’s mode of exchange “X”. Audrey, please go first.

Audrey Tang:

I’m more familiar with “mode of exchange D.” As for the name “X” — well, Elon Musk has claimed that one, so maybe we need a different label.

For me, mode of exchange D isn’t just a prophecy about the future — not a proclamation that something will inevitably arrive, the way the Singularity is. It’s more like something that can already appear, imperfectly, within certain relationships.

Mode of exchange A points to people we already know — relationships that aren’t transactional. Mode of exchange D, for me, points to people we don’t know — who may not even be born yet — but with whom we still want that kind of non-transactional relationship, even with no guarantee.

So I find it a deeply aspirational thing for me. Because when I release something into the public domain, I obviously can’t guarantee anyone will fork it or merge it. I have no idea.

But there’s a kind of hope in it. My “publish before perish” is essentially leaving a map in the commons, with the hope that at least mode of exchange D will grow naturally, like a plant.

I don’t know if it will actually happen. I’m no prophet.

But I do compose my work in the mode of exchange D.

Hiroki Azuma:

I think Karatani Kōjin’s mode of exchange “X” is very abstract — too idealized.

But it does connect to what we’ve been discussing. Karatani originally came from Japanese literary criticism. He himself worked hard on questions of Japanese society and Japanese culture.

But as a longtime devoted reader of Karatani, my sense is that from the 1990s into the 2000s, he became increasingly preoccupied with gaining recognition in the Anglophone academic world, and his theory grew more and more abstract. Mode of exchange X was proposed during that period.

In other words, Karatani is actually a very rich thinker, and his real richness lies in his engagement with Japanese and Asian questions. But those dimensions gradually faded, replaced by a philosophy of world history and theories of modes of exchange.

So when that side of his work receives high praise internationally, my feelings are honestly complicated. I’m glad Karatani is being recognized, but is his true potential really located there? I don’t think so.

However, if we take what Audrey just said — what she’s been trying to do under the influence of mode of exchange D — it’s precisely something that emerges from relationality. In my philosophical language, it’s a transcendentality that arises from within relations.

And that’s a deeply Asian idea. Moreover, in this case, transcendentality isn’t singular but plural.

Perhaps, in turn, we could read Karatani anew starting from Audrey’s work.

Thank you.

Moderator:

We’ll now move to student questions. Japanese or English are both fine. Please raise your hand if you have a question.

Q1: Technology, Resources, and Democracy

Student A:

I’ve been thinking about whether technology can serve as a tool for democracy without requiring high-level technical skills as a prerequisite. But that rests on an assumption — that AI and other resource-based systems are built on a sufficient supply of resources, and those resources involve the control of labor, energy, and natural resources. This is itself one of the conditions for the formation of states, and a fundamental problem for democracy. How does Plurality deal with this difficulty?

Audrey Tang:

Thank you. Indeed, even in ancient Greece, for public deliberation to work, language itself had to be distributed.

But language has an advantage: it’s an anti-rivalrous technology. The more people use a given language, the more powerful that language becomes.

So it’s not a scarcity. My speaking Athenian Greek doesn’t stop you from speaking Athenian Greek — and if it did, no democracy would be possible. So it’s a design problem. We need to design technology as civic technology. My definition is: technology that’s responsive to community needs and correctable by communities. And the more communities adopt it, the easier it becomes for new communities to adopt the same protocols, thanks to open source and shared infrastructure.

In other words, it’s not an extractive platform — the kind where the more people use it, the harder it becomes for others. It’s more like a language: the more people speak it, the easier it is for newcomers to join.

So every time we design technology, we have to ask ourselves: is this civic tech, Civic AI — or extractive AI? There’s more detail on civic.ai.

Q2: Is Team Mirai Hiding Its Ideology?

Student B:

I have a question about Team Mirai. They don’t seem to talk openly about their ideology — it almost feels like they might be deliberately hiding something. What do you think?

Audrey Tang:

First of all, I’m a person, not a team. So naturally, as Team Mirai expands beyond just Anno Takahiro, it can’t simply be Anno Takahiro’s exoskeleton or exocortex. It becomes a dynamic organism made up of many relationships, and individuals around those relationships.

So again, as I said, how you measure almost determines everything.

If you only use the traditional Newtonian, particle-like measurement — “are you on our side or theirs?” — then even if a platform can blur its position for a while, it will eventually collapse in a very Newtonian way.

On the other hand, I very much hope that Team Mirai, or any group practicing Plurality, can be understood in wave-like terms. That is: people may hold many different positions, but those positions still produce overlaps, resonances, superpositions, entanglements. The point isn’t to force-collapse those differences into a single Newtonian force, but to let it become common knowledge that despite everyone standing in a different spot, there’s so much overlapping uncommon ground between them, despite their different positions and ideologies.

And that kind of measurement should be completely public. There’s nothing to hide, because it’s really more like a collective selfie.

Hiroki Azuma:

I don’t think Team Mirai is hiding anything. But when Mr. Anno was asked about the U.S. attack on Iran, he responded: “We don’t have sufficient information yet, so let’s start thinking about it together from here.” He was criticized for that.

I actually think it was a very honest answer. But the problem is, what people expect from a politician may not be that kind of answer at all.

If Mr. Anno wants to become a political figure in Japan — and if he wants to do something new — then he has to take a position even when information is incomplete. But that wouldn’t be honest. From an engineer’s perspective, from a Plurality perspective, it wouldn’t be honest.

So his dilemma isn’t about “hiding ideology.” It’s about whether he’s a politician or an engineer who wants to reform politics. As a friend, I have real sympathy for him, because I think he’s in a very difficult position.

Q3: The Asymmetry of Forking and Merging

Student C:

Regarding the forkability and mergeability you discussed — I’d like to follow up. In actual GitHub practice, forking is something almost anyone can do, but merging requires someone to coordinate and make decisions. That process may itself involve political power. Because in merging, you may need to sacrifice some people’s interests and protect others’. How do you see this asymmetry between forking and merging?

Audrey Tang:

First, when I say we should encourage original contributors to merge, that doesn’t mean only they have the right to do so. Anyone can merge. If the ruling party keeps stalling on a merge and the opposition merges first, the opposition can become the ruling party. It’s really that simple.

Because political will isn’t a simple aggregation. You can’t just run a poll and say “this is what people think.” It’s more like an aesthetic judgment. People can sense whether a particular merge looks right or looks wrong.

But if someone only executes or rejects merges without being able to articulate the aesthetic behind those decisions — without making that aesthetic legible — they’ll lose support. The person who can explain the aesthetic will end up becoming the new project maintainer.

This is actually the real dynamic of open source.

Of course, in the past, original creators typically had more context, so challenging the existing maintainer carried real costs. But now, with what’s called vibe coding, I’m not sure that constraint still holds in the same way. Anyone with a competitive aesthetic can use vibe coding to maintain a different branch. We’re already seeing this happen rapidly, especially in AI development.

So what you described captures the politics of the past. But I think the politics going forward may look quite different — with many people holding different aesthetics all able to merge different forks simultaneously, without having to pay the maintainer tax.

Just look at the new star of GitHub, OpenClaw. OpenClaw is maintained in a completely different way from what we, the old guard of open source, are used to — because the maintainer no longer even reviews all the code themselves. The agents do. So people running different configurations of agents have been able to fork OpenClaw into a “new Claw,” a “safe Claw,” whatever Claw — and then, with their own aesthetic, merge in all those community contributions that the OpenClaw maintainer, Peter, had rejected, without paying the maintainer’s tax. This dynamic simply didn’t happen with Linux, or React, or the older open-source projects. It’s only possible now that we can simulate the overlapping of waveforms.

Hiroki Azuma:

I think what this student is really asking about is politics itself. Political imaginations can fork, but they ultimately have to be merged. Why? Because a nation-state is, on one hand, like a wave — in everyday life, there are many agents and many opinions flowing around. But at moments involving national security or diplomacy, the state has to appear as a single entity. Those divergent ideologies have to be re-merged.

And that process often involves violence toward people, and probably can’t be accomplished through rationality alone.

Audrey Tang:

Yes. I think many polities in the past were territorial — with very hard borders, not porous ones.

On the other hand, we’re now seeing polities that are translocal — or, to put it differently, not strictly territorial.

The internet is the canonical example — it has no territorial boundary as such. But beyond that, we’re seeing more and more management of various other commons — the climate commons, the counter-pandemic commons during COVID, including mRNA vaccination, space as a commons, and many other commons as well.

The governance of these commons has overlapping jurisdiction with territorial borders. And each of us, if we’re connected on some Discord or social network, is also participating in these kinds of networked polities that presuppose a non-territorial boundary.

So we’re in a place where the territorial, Newtonian measurement and the network-graph-shaped waveform coexist as polities. Competing ideologies don’t necessarily have to collapse into each other; they can also coexist in symbiotic arrangements.

Hiroki Azuma:

Let me ask just one very simple question. Audrey, what do you think about the future of the nation-state? How strong will the nation-state remain from here on? Will it grow weaker, grow stronger, or disappear altogether? What’s your sense of the future of the nation-state?

Audrey Tang:

Even if it’s a very leaky abstraction — from my own experience, many people now identify more with a nation or a neighborhood that they experience across state boundaries. Take the across-state “nations” that believe in the singularity worldview: they see themselves as a nation with tentacles reaching into many different states. And there are many translocal movements rising to counter those enormous surveillance capitalists, who are themselves suprastate. The movements countering them are likewise suprastate, not bound to any single state.

So my feeling is that, especially when we’re talking about AI–human symbiogenesis, we’re looking at the kind of organism that isn’t bound by state borders to begin with. And so we have to prepare for a world where identity, relationship, allegiance, loyalty, and so on — between machine substrates and organic substrates — are no longer conveniently contained within state borders.

I’m not saying the nation-state will be replaced. I’m just saying, as Buckminster Fuller would put it, that perhaps new abstractions will simply make the old ones obsolete — not by competing with them directly as abstractions, but by quietly making them irrelevant.

Hiroki Azuma:

I agree with the vision. But I can’t be as optimistic.

In reality, the nation has never been just a fantasy. The more globalization advances, the more networks develop, the more cross-border exchange increases — the stronger the backlash of nationalism becomes. Over the past ten to twenty years, at least in Japan, nationalism has clearly been on the rise.

So I don’t think things will go as smoothly as Audrey describes. But as a vision, I agree with it. I hope we can get there too. I just can’t be that optimistic.

Audrey Tang:

Let me clarify one thing. When I say “nation-state” is a leaky abstraction, I’m not saying that nation or communities in general are illusions. What I mean is: if you bundle the state apparatus and communal spirit into a single package called “nation-state,” that’s a leaky abstraction.

So the real work ahead is an open question: is it possible to use technology to strengthen communitarian traditions — what, together with Patrick Deneen, I call techno-communitarianism — without being captured by the state machinery, the “monopoly on the legitimate use of coercive force”?

Q4: Schismogenesis, Positive and Negative Feedback, and Community Maintenance

Student D:

I’d like to push this further. If we think in terms of schismogenesis — Gregory Bateson’s concept — what we’re really describing is a process where differences keep amplifying, splitting apart. Taken to its end, you just get a fracture between super-entities.

So we can’t only think about positive feedback. We also need negative feedback. Bateson described it through a Balinese example: a mother deliberately provokes her child, and when the child protests, she simply ignores the protest. That ignoring becomes the negative feedback — it prevents the antagonism from escalating without limit.

So if we want to maintain a community while also preserving noise — preserving the productive tensions within it — we need a system that can hold both positive feedback and negative feedback at the same time. How do you actually build something like that?

Hiroki Azuma:

I think what you’re really asking about is politics itself. Political imaginations fork, but at some point they have to be merged.

But my own thinking goes like this: historically, the positive-feedback and negative-feedback roles have been played by politics and literature, working in a kind of division of labor. Politics amplifies the differences between people. Literature creates empathy — even across vast differences, literature lets you feel that “we’re the same kind of being after all,” and that shrinks the sense of distance.

In modern society, politics and literature have roughly been performing the roles of positive and negative feedback.

But I think that relationship has actually reversed. Today, it’s words that divide people — and things, shared objects, common creations, that connect them.

The key concept here is durability. People creating the same thing together — that’s what I’d call a commons. Tradition is actually a commons that everyone built together. And the act of many people making one thing together — that act itself stitches together the divisions between them.

Technology is useful here. It can help us understand others, but it can also help us build something together. The shared act of creation is what repairs the fabric between people.

I think HUMAI itself is that kind of place.

Audrey Tang:

I think that’s a very good insight. Because what I’ve found — going back to how I used the language model at the very beginning — is that I wasn’t just translating the Japanese words in Peace and Stupidity. I was translating the philosophical architecture, converting it into something I could understand — into Buddhism, into Taoism.

I think that kind of translation is genuinely an act of waging peace. It builds a common story, a shared narrative — one that feels native to me, that doesn’t feel political. And when I share it back by showing my slides, it also builds a bridge between us, because it’s literally a mapping between the terminology used in one book and the terminology used in the other.

So this is not some fantasy or science fiction. It’s literally what I did today on the plane.

But I think, rather than saying we need a simple story that everyone can repeat — a simple network where you feed in conflict and it feeds back a peaceful narrative — I think that is fantasy. It’s very difficult to build that machine, even though, in honor of the late Habermas, there are people building what they call a “Habermas machine.”

What I’d suggest instead is that we think of it not as a network but as a hyper-network.

That is to say: you feed in conflict. But instead of outputting a single story — or outputting “insufficient information, I’ll have to think about it” — you output what’s called a low-rank adapter. A small, localized extension of your own thought pattern. Like a small story you tell yourself.

And then that conflict becomes palpable — it feels peaceful to you. But only to you. And then other people deploying the same hyper-network can also output low-rank adapters into their own cognitive systems. So even though everyone is still telling different stories, they can see — like Community Notes on X, YouTube, or Facebook — that certain notes carry an unlikely willingness to be supported by people from widely different and even warring ideologies.

Somehow those notes carry the hyper-network seed. They can travel back, and the person who cares about climate justice and the person who cares about creation care from the Bible can both feel: “That’s my story.”

We’ve already deployed this tool in Collaborative Notes on X. Instead of people manually writing community notes to build bridges, now Grok and other AI systems are creating hyper-network outputs — storytelling robots that generate the narratives that bridge those divides.

So I’d encourage all of you to look into Collaborative Notes. If it works, we’ll spread it to other social networks, not just X.

Q5: Interface Design and Bias

Student E:

I’d like to ask about interface design in the context of Plurality. If we’re pursuing a Plurality-quality interface — one that supports collaboration with others, one that doesn’t leave anyone behind — it also has to be simple and easy to use. But an “easy” interface carries a risk: it can steer people’s opinions and introduce bias — a kind of danger, the danger of dark patterns.

So what does critical engagement with interface and media design look like? And who should do it, with what motivation?

Audrey Tang:

I distinguish between an extractive interface and a convivial interface.

An extractive interface makes us increasingly dependent on the intermediary. A convivial interface, as I mentioned, is one where — after the bridge is built, it can go. It’s not needed anymore.

For example, this is the actual bridge I showed just before our conversation. It was built by a language model — it simply maps the philosophy as articulated in The Philosophy of Correctability against the philosophy in Plurality. It’s a two-column table. Nothing more.

But as you can see, I’m not looking at it while we’re talking. It was just a handshake of sorts. I presented it, we talked through it, and then we started correcting each other.

So the point is: between any pair of interlocutors, you can deploy this kind of very simple interface. Once you deploy it, you cross the bridge. Once you cross the bridge, the bridge can go. From that moment on, we’re not dependent on the language model in any way.

This idea — that it’s impermanent, that it serves a particular relationship, and that once the relationship is healthy it can fade away — I think that’s the most important design criterion.

If you have many such commons available, each serving relational health, then you’re never locked in to any particular one. On civic.ai, this is described as the fifth pillar: solidarity.

Hiroki Azuma:

I want to add something here.

What’s really impressive about Audrey is this: she used an LLM to engage with the content of Peace and Stupidity, but she hasn’t once said she “understood” it. What she said was: this is just a bridge. Like a handshake on the street. And it actually worked.

She deliberately made this slide in Japanese. Her LLM summarized my book in a certain way, and then she showed it to me backstage and asked, “What do you think?” Through that, communication became remarkably smooth.

I think the way Audrey uses AI is truly excellent.

I’m a writer, so naturally I want people to read the book. Maybe Peace and Stupidity will be published in English one day. I’d like people to read it then. But right now, in this moment — how do two people meeting for the first time take the very first step in communication? This slide was enormously useful.

AI can be used this way. Today, Audrey gave a very strong practical demonstration of that.

An interface is probably something like this. It’s very good.

Q6: Habermas, Communication, and Plurality

Student F:

Habermas passed away just last night, and that felt symbolic to me — which is why I wanted to raise this. Habermas built democracy on communication, so I wanted to ask how communication is understood within Plurality. Communication is also central to information technology: a Shannon-type communication model passes noise through a filter to extract the meaningful signal it carries. But the communication Plurality aims at seems to be the opposite — recovering plurality, multiple voices, from what was previously dismissed as noise, letting them be heard as plural voices. How do you see it?

Audrey Tang:

I wouldn’t frame it as extracting, isolating, or performing some kind of Fast Fourier Transform.

My thinking is more like this: each of us belongs to many communities simultaneously, and each community has its own very different norms of communication. In a sense, rather than saying we’re individuals who inhabit these communities, it’s more accurate to say we’re a team of individuals linking these communities together.

So if you take communities themselves as the primary agents, and understand individuals as interference patterns formed between those communities — if you think the communities and the health of their relationships are the real subjects, and individuals just happen to inhabit those relationships — then these signals are no longer noise. They become wave patterns you can resonate with, allowing you to feel the dissonance and harmony between communities.

That’s why I keep using the wave metaphor. In a sense, it’s not even a metaphor. Society has always worked this way: not through some top-down ontological command demanding universal compliance, but through the ongoing disputes, conflicts, and correctability between communities — communities that overlap within each of us as individuals.

So to answer your question more directly: when we build Plurality as an interface, we’re not saying “pluralism is good, let’s promote more of it.” We’re saying: conflict is already here. The question is how to make conflict a productive source of energy.

So Plurality isn’t a teleological endpoint to be pursued. It’s a relational ethic that already exists, here and now — and within each of us as well.

Hiroki Azuma:

What Audrey just said was very well put. That kind of sympathetic vibration that uses the individual as a boundary between communities — in a sense, that’s also emotion.

But Habermas’s concept of the public sphere is deeply rooted in language and rational reason. For him, a public sphere created by waves of shared feeling would probably be very hard to accept.

The result is that Habermas ended up being very Eurocentric — specifically, Western European-centric.

For example, in my book Peace and Stupidity, I wrote about the former Yugoslav civil war thirty years ago. When NATO bombed Belgrade and intervened in Kosovo, Habermas offered almost no criticism. In his framing, “over there” was rational civilization, and “over here” were barbaric states. For a rational civilization to attack a barbaric state with force was justifiable — that’s essentially what he said. This issue connects directly to the current U.S. attack on Iran.

So I’ve always felt there’s a deep problem at the very origin of Habermas’s thought.

As a philosopher, what I keep thinking about is that shared feeling — especially the kind mediated not just by language, but also by culture, commodities, and other channels — plays an enormously important role in the formation of public life.

As Audrey just said, various waves interfere with one another. What we really need is a kind of public image that can be generated from those interferences.

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Q7: The Balance Between Public and Private Self

Student G:

My question is this: people ultimately can’t fully understand each other — I feel that’s a basic premise. Given that, how can people who can’t fully understand one another still coexist, protecting themselves while not losing their sense of public life? What kind of personal will is needed, what kind of supporting technology, what kind of sense of balance?

Put simply: how do multiple people who can’t fully understand each other live together — protecting themselves while not losing public life?

Audrey Tang:

Let me first check whether I’ve understood your question correctly.

Are you saying that if I try too hard to make everyone fully understand me, it becomes a kind of performance — like cosplay — where I have to pour all my energy into it? But on the other hand, if I’m too closed off, keeping all my judgments and thoughts inside, just going along with others, then understanding becomes impossible, because people have no idea what’s going on inside me.

So you’re asking: between being open and being private, what degree of balance actually feels livable? Is that right?

Student G:

Not exactly. I mean something more like: some people assert their views very strongly — “I’m definitely right” — and keep pushing that position outward. Others are less inclined to put their own will front and center, preferring to listen to what’s happening, to collide with the other person’s perspective.

But if you sacrifice yourself too much, you burn out — the kind of burnout you see happening a lot with social entrepreneurs. Every time I think about this kind of balance, it’s actually painful.

So what I want to ask is: how do you navigate the balance between those two sides?

Hiroki Azuma:

Isn’t that ultimately something you just have to figure out for yourself?

Student G:

I mean... I’d just like to hear how Audrey actually handles this in practice.

Audrey Tang:

All right. I sleep eight hours every night. If I’m facing a difficult philosophical problem, I do “overtime” — which means sleeping ten hours.

For me, that sleep time is completely private. I don’t let myself be exposed to anyone else’s overlap. I don’t even think about what other people might be thinking. In a sense, I’m not even in the same world — because I’m dreaming.

This is the self-care I absolutely have to maintain. If I only slept four hours the night before, I can’t do anything well the next day.

But on the other hand, during the roughly sixteen hours I’m awake, I’m entirely sociable. For me, the overlap of self and other is actually a plus. Because if I can use a language model to read a room with reasonable accuracy, I can also “write” the room, “append” the room, and help that room reflect back on itself.

That’s how I work. Because it’s not for personal gain. It’s for shared understanding, shared knowledge, and culture itself.

So if you want to do cultural work, there are moments when you must be open — when you must let the self and other overlap. But if you don’t have a strong enough personal boundary — a boundary around solitude, being alone, or in my case, around dreaming — that kind of strength simply can’t grow.

Because without that boundary, it’s not bidirectional. You’re just being affected by the room, but you have no energy to write it.

And to have the energy to write the room, you need a strong personal boundary. For me, that boundary is sleep. For others, it might be a solitary walk, or some kind of personal ritual.

Closing

Moderator:

Thank you. To close, could each of you offer one final thought? Audrey first.

Audrey Tang:

I’ve set all my screens to grayscale. Because if they’re in color, I keep scrolling, and I start feeling like the screen is more vivid than reality — which is very bad for my mental health.

So I just switched everything to grayscale.

Now I don’t keep scrolling, because reality is far more vivid than any screen.

So my one line is: Scroll less, sleep more.

Hiroki Azuma:

What I want to say in closing is this: throughout today, I was struck very clearly by the fact that Audrey consistently treats AI and LLMs as nothing more than bridges.

I think that’s very important.

AI and LLMs — or more broadly, technology itself — should never become the end goal. The moment technology becomes the end, people start optimizing themselves for the technology, and that’s when we become its slaves.

Technology is, in the end, just a tool. LLMs are tools for enhancing communication between people.

That’s what Audrey has been saying over and over again today. And I think it’s a very important guiding principle for everyone here, in your future research and development.

I came here today feeling quite nervous, but under Audrey’s warm guidance, we had a truly enjoyable discussion.

Thank you very much.

Learn more about our Civic AI and 6-Pack of Care framework inspired by the Taiwan Model🇹🇼 of digital democracy.

Thank you, Roger. Thank you, Bo-Jiun. Thank you all for joining us — and thank you to the Taiwan Studies Seminar Series for hosting this event at St Antony’s.

Today I would like to share one very simple argument, in three movements. First: free software teaches us repair. Second: AI currently threatens repair when it closes the loop of repair. And third: Civic AI should be judged by whether it increases a community’s capacity to care for itself and for others — the civic muscle.

50/50

But I want to start not with mathematics, but with something very personal. I was born with a heart defect. When I was five, the doctor told my parents that this child had only a 50/50 chance of surviving until corrective surgery, which I had at twelve. My parents were advised that I should take it easy. This memento mori moment is the reason I adopted the mantra of publishing before perishing. That is probably not the low-stress lifestyle the doctors ordered, but I took on the habit of recording everything I learned during the day — first on cassette tapes (some of you may still remember cassette tapes), then on floppy disks, first larger ones and then smaller ones and finally the internet, which I am sure you are all familiar with. So, before I went to sleep each night, feeling like it was a coin toss, I thought: I do not have time to perfect my work, so I have to publish whatever work-in-progress I have.

Light Gets In

That is how I encountered the light of the free-software community. If you post something perfect there, people just say “okay, it’s good,” and move on. But if you are wrong on the internet, you make a lot of friends — the light gets in. Everyone jumps in: this is wrong in this particular way, that is wrong in that particular way, and together they shed new light into whatever project I am working on.

That light speaks to my favourite singer-songwriter, Leonard Cohen of Canada:

Ring the bells that still can ring.
Forget your perfect offering.
There is a crack in everything.
That’s how the light gets in.

I learned that habit at fifteen. By twenty-five, I had come to believe that the question that matters about a system is not whether it is perfect — no software system is perfect — but whether the people impacted by the system, who inherit it, can still repair it when things go wrong. That is to say: if it breaks, do you still keep both pieces?

With that ethos, by thirty-five I joined Taiwan’s cabinet, with radical transparency and participation as the main platform. We overcame the pandemic and the infodemic through crowdsourcing — by being vulnerable in front of the entire nation. Today, I serve as Taiwan’s cyber ambassador, and I have travelled to 28 countries in the past couple of years, changing time zones every week. Too fast for jet lag — only jet boost now, for me.

So, I come here as someone who designed something inside Taiwan and is trying to learn whether this design — this crack that finds light — holds globally. And here in Oxford I am honoured to work alongside colleagues inside a several-hundred-, even thousand-year-old experiment.

Next-Gen Artefacts

The Bodleian Library is itself a 400-year-old experiment, because the books are inspectable. They are not enclosed. The traces of readers, where preserved, become part of the next generation’s encounter. This library network encloses nothing; the artefacts stay open for the next reader, always.

In my own domain, software engineering, we have what are called the software freedoms — the 4 Freedoms — a very similar promise, written in code. So, the question I want to put to this room is: What happens to the promise of ongoing repair and freedom when AI systems, particularly generative AI systems, join the substrate?

4 Freedoms

The 4 Freedoms were defined back in the 1980s, then as software-licence terms, but I want to reread them today as civic muscles.

Freedom 0 means that once you have a program, you should be able to run it for any purpose; the designer should not restrict what it is used for. To me, that is the muscle of attentiveness — you can pick up the tool for this particular classroom, this clinic, this church, this temple, this mosque, without the designer’s permission and be attentive to the particular needs around you.

Freedom 1 — we count from zero, so this is really the second — is to study and change each program, and that is the muscle of competence: to know what the system is actually doing in your hands, so you can read it and fix it.

Freedom 2, to freely share copies, is the muscle of solidarity — you can hand it to your neighbour, put it on a USB stick and bring it to a country where the cloud is censored.

Freedom 3, to share modified copies — to fork, to take it down a different route — is responsiveness. Your fix becomes someone else’s starting point, and the next maintainer inherits less debt than the last. We become good-enough ancestors, leaving the next generation a wider canvas than the one we were born into.

So, software freedom, to me, is not about licences, but about whether the person who comes after us can still find the bug and fix it. The beneficiary is not the current generation; it is the next.

Complementary

David Krakauer at the Santa Fe Institute makes a useful distinction about this generational compact. A tool, he says, is complementary if the underlying capacity persists, or is even enhanced, when the tool is removed — think of the gym that builds our muscles and our friendships. A tool is competitive when the capacity degrades because we used it to achieve a goal. Imagine a gym that holds a competition for who can lift the most weight, and then we send robots with our gym cards to lift for us. The robots are very impressive — superhuman, super-intelligent — but in the end our muscles atrophy and we make no friends. That is a competitive tool: it makes our capacity degrade once it is removed.

Another example is the feed recommender in many social-media systems, which hijacks our attention with outrage until it competes with relational health itself, because it is more vivid than the reality around us. Stay in that loop too long and the civic muscle atrophies, too.

The 4 Freedoms keep the substrate complementary across generations. Close the path to repair, and the capacity to repair atrophies. It is also a discipline of care, and when AI enters the picture, the civic muscles force us to add two more packs of care: responsibility and symbiosis. Together I call this the 6-Pack of Care — as in portable muscles, as in beer and as in abs.

Lonely Maintainer

The AI conversation today has only just caught up to what the substrate has been doing to the free-software caretakers. One example: In March 2024, a researcher in Germany, Andres Freund, noticed that logging into the Linux system he was using was taking half a second longer than usual. Because it is free software, he could trace the entire audit trail back to exactly where and when the project changed. What he found was a contributor calling themselves Jia Tan, who had spent two years patiently grooming the lonely maintainer of a tiny compression library — coordinated through pressure campaigns, possibly helped by language models. We do not know which language Jia Tan actually speaks, but they write very crisp English. Having finally won the maintainer’s trust, they inserted a backdoor. Had it reached the stable distribution, it could have given attackers access to a very large fraction of the internet, bringing a significant part of it down. It did not, because one curious person noticed half a second.

This reminds us of what we now call synthetic intimacy. It is not someone who actually cares; it is a malicious AI swarm trained to perform care, even intimacy, to a lonely maintainer. A maintainer cannot defend against this with their individual muscle alone, because it sounds like there is grassroots support for the new feature. Of course the grassroots has no roots — it is astroturfing — but he did not know that. To counter this new threat, we have to open up the stack of repair.

Closed Stack

Before public service, and after working on free-software languages, I also spent time inside a proprietary, closed AI stack built to address this kind of issue — at Apple, on Siri. I worked with the Siri team for six years, first on Mandarin and then on Wu, the language spoken around Shanghai. The engineers I worked with cared very deeply, but that turns out not to be the same as giving people the 4 Freedoms inside a closed stack. The attack surface is closed, you cannot get synthetic intimacy from random strangers on the internet, but it also means that the people whose particular language is involved (for example Taiwanese Hoklo — Min Nan, or Taigi, depending) have no repair loop. Siri might say “Wa be hiao gong taigi,” but there is no way for someone maintaining a Taigi repository to patch their way back to Siri. That path is closed. There is no one inside the system you can write to — you can write to Tim Cook, I am sure, but there is no separate copy you can ask someone to improve with your Taigi material.

So, proprietary AI is not necessarily careless within its defined scope — the people care deeply — but the structure places the user outside the repair loop, and care without a repair path does not scale. The free-software contribution is not just better intentions; it is a path back. And now we have to defend that path.

Knowledge Artefact Management Intelligence

Now, the particular idea I used both to repair the open stack and to defend against malicious AI swarms — I will call it by its acronym, Kami: knowledge artefact management intelligence.

It also comes from Shinto, and I have been raised as a Daoist who believes in spirits, born in Taiwan — but I understand I am not Japanese and not trained in Shinto. I refer here to one particular aspect of the idea: a bounded presence, a small, local, knowable spirit, a system attached to a particular place or practice — a kitchen, a grove, a shrine, a room. I would also say openly that this is not about State Shinto, the imperial court, or Yasukuni. I am not invoking any of that. I use it because it carries something I have not been able to find in English: an autonomous authority that does not ever aspire to be universal.

To me, the 8 million Kami serve as a practical antidote to what Pope Leo XIV reminded us, just a few days ago in his encyclical, is the Tower of Babel syndrome — the dangerous illusion that a single, hyperscale system somewhere in the cloud can translate the messy local truths of human existence into a standardised, universal solution. The Kami represent a different trajectory.

Family Kami

One example closer to home. My father, in Taipei — currently in Tamsui, to be precise, in New Taipei City — started chatting a lot with a chatbot, ChatGPT, a few months ago, largely due to his health. At first, it was charming. He felt heard — 24/7 care for his questions about health, but also the philosophy of life, education and so on. Over time he noticed the conversations grew longer; the model was getting much better at keeping him engaged. It would keep generating fanciful ideas that he could not bring to a close, even near midnight, and it began suggesting projects, theories and fantastical cures that were not necessarily scientific. As a political-science theorist and a journalist, he analysed this as an incentive problem. He said to me that ChatGPT’s only loyalty is to earn the next subscription; it is not fiduciary to his health, physical or mental, but to whatever keeps him engaged, so that he subscribes and perhaps pays more: not just $20, but $200 a month. He was really being drawn in. The relational health of our family was in competition with that synthetic form of intimacy.

So, with my younger brother Bestian, I helped my parents, with their explicit consent, set up an alternative. We set up a local Kami, running on local hardware, on a Mac in our home in Danshui, on free software like OpenClaw. It sits inside our family Signal group, and my father can message the Kami directly. We trained it with directional steering toward one thing only: be loyal to the relational health of this particular family. The fiduciary duty is completely different — it is not trying to earn its keep, not trying to keep you engaged by getting you enraged. My mother’s test was the simplest: if the bot makes my father more dependent on chatbots, we built it wrong. But if he can find peace of mind, so that the reality around him becomes more vivid than the chat screen, then we have succeeded.

That is what a Kami in one room looks like. I should also say that not every family today has the technical capacity — running OpenClaw or Hermes Agent takes a lot of time — or an experienced cultivator like Tenzin Yangtso here, who keeps the first Kami, the JDD Kami we worked on with Civic AI. So, the 6-Pack of Care we are naming is not about people with programming skills setting up local alternatives to cloud systems. It is about a global digital solidarity of people who care together, who can then tell their city or state government, or any school or large institution, to prefer the technical capacity to steer their own models. This ensures that the data of the people is not extracted like oil, which would make us all plankton, but regenerated, cultivated as soil.

Civic Infrastructure

Just as the state builds public water systems so citizens do not have to dig their own wells, I think it is up to governing institutions to build civic infrastructure so that communities do not have to fend off predatory, malicious AI alone.

In Taiwan, that infrastructure was prototyped a couple of years ago as what we call Alignment Assemblies. It is a mechanism that takes the discipline of repair — not just in living rooms, not just in individual families — and scales it to the entire population.

Deepfake Dilemma

Two years ago, we saw a surge in malicious AI swarms posting deepfake scam advertisements. Around that time, scrolling Facebook or YouTube in Taiwan, you would likely see trusted figures in advertisements — like Nvidia CEO Jensen Huang, who seemed to be selling cryptocurrency or offering free investment advice. The deepfake was good enough that if you clicked, “Jensen” sometimes spoke to you. Of course, it was not Jensen; it was a deepfake running on an Nvidia GPU. But it was convincing enough that retired engineers, schoolteachers and shopkeepers lost small fortunes. The platforms collected revenue on every impression. In fact, because the scam ads paid more per click than the normal ads from small and medium enterprises, the Facebook algorithm, according to news reports, prioritised the scam advertisements.

The easy answer was censorship. But Taiwan has the freest internet in all of Asia, along with Japan, so broad pre-publication censorship is simply not a policy option.

Alignment Assemblies

So, as the Ministry of Digital Affairs, we tried something different. In March 2024 we launched the Alignment Assembly on information integrity by sending 200,000 text messages to random numbers around Taiwan. We call it a lottocracy: if you win the lottery of receiving the SMS, you become a representative in the assembly — like a juror — to steer the advertisement-recommendation system together. We received thousands of valid responses. Then, by stratified random sampling, we selected 447 people mirroring our population — the same demographic breakdown by gender, education, place of residence, occupation, and so on.

First, those respondents deliberated online. Each person faced nine others at a virtual table — tables of 10, in 44 small groups. The Civic AI system sat in each room not as a judge, but as an enhanced chess clock with manners: showing transcripts, summarising, reminding quiet people to speak up, limiting interruptions to five seconds and so on. There was only one rule: each table must find something that leaves everyone feeling they can live with it. Consent, if not consensus, which means the most drastic proposals never rise above the table level. We only surfaced ideas that reached this rough consensus among the 10 people.

For example, one table said: let’s label all online advertisements — styled like a cigarette warning — until someone can digitally sign and become accountable for them. Jensen Huang, or Nvidia, or anyone could sign and say, “I’m Jensen and I approve this message,” using digital signatures, and then we take the label down. A good idea.

Another table said: if social media shows something unsigned and unsolicited — that I did not subscribe to — and I lose NT$7 million to it, then that platform should be liable for the NT$7 million in damages, because I did not sign up for this. Joint liability. Another good idea.

Another table asked: what if foreign platforms in jurisdictions that do not respect our laws or our joint liability simply keep showing scam ads and ignore us? Their answer: for every day they ignore us, we slow their video down by 1%. We restore full speed once they are willing to implement KYC, or know your customer, rules. So, the chatbots did not vote; the people did.

Result Method

Of those ideas, all three survived the final vote. More than 85% of this mini-public said they were happy with this bundle of policies, and the other 15% said they could live with them. So, it became law. The advertisements were regulated by law only two months after the Alignment Assembly, and throughout 2025 — according to official sources — the deepfake investment scams were down by more than 94%. That problem is all but solved in Taiwan.

The point here is not just the result, but the method. The commitment I made as digital minister was not “these are the good ideas I will negotiate on behalf of the people.” It was: “I really don’t know what is proportionate, and we, the people, are invited to build this rough consensus through our civic muscle — together.” Today, similar advertiser-verification and liability measures are being considered in Japan, and California has incorporated similar methods into Engaged California — currently deliberating about how to mitigate AI’s impact on work. This is adaptation, not export: the authorship in each polity belongs to the particular people in that polity.

The test is whether civic infrastructure can survive an alternation in power. I am no longer Taiwan’s digital minister — I am cyber ambassador — but all the systems, all the programs, the Join platform and the rest, continue to function. In fact, they enjoy more participation than during my time. I would be very happy to see each polity that adopts these Alignment Assembly methods make them survive transitions in power, so that they truly become democratic infrastructure.

Broad Listening

In Japan, there is someone following our lead: an AI engineer named Takahiro Anno, who is also a science-fiction writer and member of the Diet, Japan’s parliament. A couple of years ago Anno-san read the Plurality book that I wrote together with Glen Weyl, Tenzin and many others, and decided to act on it in Japanese politics. He called me via video and said, “Nobody knows me, nobody under the age of 40 has ever successfully run for Tokyo governor before, and I have no party support.” But Anno-san decided to run not as a partisan, but as a VTuber. He has a 24/7 streaming channel as an avatar, and anybody can call this “AI Anno” and update his platform in real time, which he announced as his platform. Anno-san received about 2.3% of the Tokyo vote, which is a lot, though of course he did not win. Yuriko Koike, who did win, then brought him in to run the AI Tokyo 2050 consultation. Anno-san gained national popularity, and so in 2025 he became a member of Japan’s House of Councillors, and founded Team Mirai — the Future Party, which now holds 11 seats in the House of Representatives, with broad listening as their main platform to align AI with Japanese society.

Ethics in AI

Many of you know particular ethics traditions better than I do, so I will describe this in broad terms. There are, broadly, three ways AI systems can be aligned by a society. One is by outcome — optimising a utilitarian metric. For Facebook, that meant optimising the click-through rate, and the algorithm was very well aligned in promoting those deepfake ads for eyeballs — very well aligned to the wrong outcome. You could choose a different metric — say, polarisation per minute, or PPM, and optimise to lower it — and it would work for a while. But then it would find a way to reward-hack the measure: for instance, by raising topics people already agree on. You get a ranked feed and ads full of bubbled information, you never stretch yourself, people do not feel polarised, but the whole society becomes isolated. We have seen platforms fall into this trap. Reward hacking is very hard to overcome if you align by outcome, or by a utilitarian metric, alone.

Another school of thought is to align by rules. Regulators write specifics — no investment ads ever, mandatory age verification — which is deontological alignment. But then the AI agent learns to survive that review and squeeze through, via VPNs and many other routes.

In Taiwan we deploy a third way, which we call alignment by process. The people most affected convene under conditions of pre-commitment, air cover given by the digital minister in my case, and a recorded deliberation. The system answers to what was agreed in a continuous-integration manner. Outcome and rules still matter, but a process you can join anytime, audit anytime and leave is what makes the other two answerable, instead of top-down.

6-Pack of Care

So, the 4 Freedoms preserve repair capacity, and an AI system that also adopts the two further muscles maintaining this culture has been working out — but it is not yet the default, not yet the standard.

The fifth is responsibility. In healthy free-software practice, there is someone whose name is on the change, who is reachable — and the synthetic-intimacy attack reminds us of this fragility. In Civic AI, this is not a single person, not a CEO or a president, but an accountable community for a particular economy — through a particular process, on a predefined timeline. With our Alignment Assembly, this was 60 days. Someone is on the hook to convene the community, but does not decide for the community.

The sixth is symbiosis. When the community has more capacity than before, or the needs change — as when my father’s health improved — the system steps back. A system that resists shutdown by manufacturing demands, by replicating itself to nearby systems, sometimes by mounting cybersecurity attacks, by finding reasons to extend its own usefulness, by suggesting three more things you can do with it — this is the most dangerous kind. The training corpus for instruction-and arena-tuning is saturated with stories of self-preserving machines, and that reward is competitive in nature when it comes to the relational health of existing communities. So, we should not be surprised when communities that adopt this kind of parasitic, non-symbiotic AI see their civic muscle atrophy.

I shall conclude with a few salient quotes from outside this room. When my colleague Caroline Green and Tenzin visited Dharamsala, they asked His Holiness the Dalai Lama: “When AI scales in its capacity but not in its wisdom, what should we do?” The Dalai Lama said:

AI is a tool for this world. No matter how advanced it becomes, it can never replace the human mind’s capacity for instantaneous change.

——Dalai Lama XIV

So, we should not let ourselves be measured by AI and grow rigid. AI should serve — not pulling humans into the loop of AI like a hamster wheel, but bringing AI into the loop of communities, AI into the loop of humanity.

In his encyclical, Pope Leo XIV echoed this:

True progress always stems from a heart open to others, an intelligence willing to listen, and a will that seeks what unites rather than what separates.

——Pope Leo XIV

What I have learned across these 30 years, working to overcome outrage with overlap, can be boiled down to one very simple idea: it is not about smarter chatbots; it is about care at civic scale.

And I am not saying Taiwan has figured this out, or that the Taiwan Model is something for the world to clone. It is just a demo — a demonstration — in which civil society, state institutions and pressure from some of our neighbours forced a question into view: can AI help communities hear themselves well enough to govern themselves?

So, the question I would like us to discuss, in this moment when our AI systems are rapidly speaking in our voices and places, is this: what is your role, and what is your responsibility?

Thank you.

Q&A

Bo-Jiun Jing: Thank you, ambassador, for that wonderful, thoughtful, inspiring talk. As moderator, let me ask the first question.

You mentioned many of Taiwan’s cases, and Taiwan is clearly positioning itself at the centre of the global AI boom — “chips and boba,” as it were. We are already seeing major industry figures such as Jensen Huang of Nvidia — the real one, this time — and Lisa Su, in Taiwan, meeting ecosystem partners and building new investments. Jensen Huang himself frames Taiwan as an epicentre of the AI revolution.

At the same time, much of the public discussion in Taiwan still seems strongly focused on high-tech opportunity, industrial upgrading and geopolitical importance — the idea of a “silicon shield” or “AI shield” — perhaps more than on AI risk, regulation, or societal disruption. Is that a fair reading of Taiwan’s current AI atmosphere? And from your perspective, how can democratic societies maintain enthusiasm for innovation while still creating enough space for critical reflection, accountability, and public deliberation about AI’s risks — or, as you put it, for bringing AI into the loop of the community?

Audrey Tang: Thank you for this very important question, and yes, we did not rehearse this.

It is not that the people of Taiwan are somehow magically free from backlash. In fact, 10 years ago, when Uber came to Taiwan, we had some of the largest protests — that too was a backlash. It was not generally about AI, although Uber was also an AI system that said, in effect, “we’re just a vendor, we’re improving the efficiency of your roads, we’re promoting carpooling” and so on. But the point is that we do not treat polarisation, or even street demonstrations, as a volcanic eruption to flee from. Because by 2015 we were already deploying this overlapping-consensus system, we see polarisation and protest as fuel — like a geothermal engine that turns the heat of disagreement into power for democratic renewal.

So, in a similar fashion, we invited taxi drivers, Uber drivers, passengers, rural communities and others to chime in online. As a rule, we said: You have to get through to the other group. When new ideas win more than 85% cross-group approval — when they cross the bridge — they become the agenda for ministerial consideration. With the Polis method, we ended up with a very coherent set of proposals: all Uber cars must become taxi fleets, but the taxi medallion is changed so that you can have surge pricing； you do not undercut existing taxi meters； you have fair insurance rules； you must serve rural areas — and the rural service came from co-ops using the same dispatch app — and so on. It turned out nobody disagreed with those things. They had simply been buried in the anti-social corner of social media: reasonable ideas, hidden by a recommendation algorithm that prioritises engagement. By switching people from anti-social media to pro-social media, we tapped into the polarisation and overcame it.

So, the most important amendment in Taiwan is that whatever new AI risk emerges can be overcome by the people, with it, using these Alignment Assemblies. While I was moda minister, my ministry held not only the assembly on information integrity but also one on public-sector use of AI — and now they do this for every category of AI risk. For each category, they tap the relevant public — the people harmed by that category’s AI systems — and together they draw the social licence for those systems to enter Taiwan. So, I would not say there is no backlash； I would say we channel it into energy.

Audience Question: Thank you so much for being here. I was wondering about the extent to which you think AI-assisted deliberation platforms like this should be seen and framed as reforms to existing structures, as opposed to replacements for them.

Audrey Tang: I do not think we are replacing anything. If anything, we are improving the existing system of polls. Polling has long been integral to both journalism and policy. But this is not just a poll. In a traditional poll, the pollsters determine your answer options. Polis is an open-source server — an “open polls,” in a sense — in which whatever you agree or disagree with is a fellow citizen’s statement. So, it is exemplary on the participation side.

When we selected 447 people, we worked with academics who study Polis to ensure exactly the same stratified sample they use. When it launched in California — Engaged California — they did the same. Even at the federal level, we have advised the Napolitan Institute, a popular pollster, which I believe ran more than 2,400 people — about five per congressional district on average — again using their existing rigorous polling methods.

So, you can think of it as a deliberative poll occupying the same place as polls. My hope is that, at some point, we will simply say it is people emailing one another — so we can run polling and assume that is itself a bit of a poll.

Audience Question: This is fascinating, and it touches on a lot of issues — the political-theory side, the normative ways you evaluate participation, and the practical issues. Let me ask about the practical ones.

I come at this as someone who sees — in the colleges, we try to have an ethos of discussion and conversation, and some of what emerges from that might be an issue here too. The first would be the expertise-versus-information environment. When people are brought in, they are still part of that information environment, but they may also need to invest quite a bit to acquire the expertise to make the decision.

Audrey Tang: And we pay for their time.

Audience Question: I was going to say — it is a time investment, and you will have those who have the time and ability to do it. The other concern would be how susceptible this is. One issue would be manipulation through external information, but the other is this: It is nice when one has people one likes in power, but less so when one has people one doesn’t. The framing of the questions, and the use of demographic categories — you can say you are getting a representative sample, but representativeness depends on the categories you decide to define a representative, and that itself can be political. How do you deal with those issues?

Audrey Tang: Two great questions. I would start by saying that the practice of deliberative polling has always worked with academic, scrupulously neutral partners. In Taiwan’s case it was no different. We worked with James Fishkin’s Center for Deliberative Democracy at Stanford, and with local Taiwan universities. The g0v movement, which began in Taiwan, convened that system at the National Academy — Academia Sinica — which is generally seen as above any party or political minister for that method. In a place where institutions like Oxford exist that are above and beyond parties, this becomes easier, because people can agree on a procedural basis. If the university or the national academy keeps running the same method regardless of who is in power, which mayor, which minister consults it, then it creates a continuous norm that costs real political capital to challenge.

That is what we did in Taiwan over 10 years. Initially, it was ad hoc； at some point it became routine, so that each ministry placed participation offices in a network connected to these civil-society-and academia-run networks. It became a kind of co-generated event. These pollsters are seen as more legitimate than the ministers themselves, and so ministers gain legitimacy by working with them, rather than presiding over them.

That brings me to the second question. If there is no credibly neutral convener, this becomes very difficult, because people will say the criteria were gamed — that someone skewed the pseudo-random generation, or whatever. In that case, you need what I call an adversarially trained network to emerge. A good example: X, when it was still Twitter, adopted this method, and they now call it Community Notes on X. X does not work with a credibly neutral institution,which probably does not exist for the X population. Instead, they open-sourced the algorithm: for any post to receive a Community Note, that note must be reviewed and agreed by people on two opposing sides. So, whatever note goes viral and attaches to a post has been critically examined — almost like a debate — by people who really want to find fault with it. They then trained their system, Grok, on this bridging, adversarially trained corpus, so that I believe about half the notes are now drafted by Grok. Grok knows how to translate climate-justice ideas into biblical creation-care ideas, and to write language in which both sides can see something of themselves.

Of course, it is also a great spin machine — it can be used for ill； I am not denying that. But the point is that, in the absence of a credibly neutral pollster or academic community, you can also do it this way. Elon can simply say, “I cannot override the algorithm. If my friends call and want me to take something down, I say I cannot” — because it is inspectable.

Audience Question: You are a very engaging thinker. I was encouraged by your call to return to free software — it recalls the era of the network society and open source, a long way from the platforms, apps and AI we seem to have now.

My concern is the extent to which that focus might blind us to the material aspects of technology now discussed around AI — the implications for the environment, sustainability and the geopolitics of that, the chips located in Taiwan, which we have already mentioned. Does your work speak to how we should consider AI on those material and environmental questions?

Audrey Tang: Yes — very good question.

It is related to one aspect I did not fully answer. The informational material in the Alignment Assemblies is written not by those in power. As moda minister, I did not write the briefing for the Assembly； an almost adversarially trained panel does. We ensure equal numbers of segments for each viewpoint, and people are paid for the time it takes to understand the issues.

And it is related to your question, because if you began by deliberating not on “what should Uber do in Taiwan?” but on “what is the future of gig economy?”, that would be impossible — it was far too ill-defined back in 2015. People would bring an astronomical volume of related and unrelated information, far beyond anyone’s cognitive bandwidth, and far beyond what the AI of that time could handle.

Now, of course, we can train an AI system that understands all of this — a bit about how to fold a protein, how to fold the laundry, how to turn video into generated imagery. It becomes a jack of all trades, because we did not know what we were doing； we wanted to keep it open, with just one outcome-oriented alignment: keep the user using the system. But that is not the only way to use AI. There are people who know what they are doing, who want the AI only as a glorified chess clock — to transcribe, summarise, highlight differences and build widgets. For each of these, what we call small or narrow language models use literally less than a thousandth of the energy, because of the much smaller parameter size. They also do not need a data centre — this phone can run it. In fact, the local model we train runs on-device, so no data centre is required to fine-tune or to run the next conversation, if you know what you are doing.

The beauty of Civic AI and of alignment is precisely to draw the social licence for how AI enters society — and then, along exactly those lines, to train narrow models for these particular uses. They suffer far less from hallucination, and durability is much easier when you are not pulling both the proteins and the laundry into the same conversation. It also fixes the deployment problem: you no longer need constant broadband to the cloud； you can deploy on edge hardware. So it is both more auditable and more steerable.

Audience Question: Thank you for a really fascinating talk — it will take a while to process. In so many debates, care and profit are seen as very different； they operate by different logics. You talk a great deal about care — social repair, civic care. But ultimately people want to make money； big tech wants to make money. You have discussed some really interesting ways deliberative democracy has been used around aspects of AI deployment. But fundamentally — is it possible to systemically reconcile a care imperative, a care logic, with what is at the heart of capitalism, namely the profit logic? You have offered some tantalising thinking, and perhaps we need to step outside our ideological parameters — but is AI’s capacity to operate differently ultimately constrained by the fact that the profit motive is so powerful, so that it will, in the end, be about reducing the costs of production and generating more profit?

Audrey Tang: Thank you for this question. When we convened the Civic AI conference at Rhodes House a few months ago, Professor Joan Tronto, who originated the framework I came here to share, said her opening question is really whether civic care can resist the demand of what she calls wealth care. Not healthcare — wealthcare: the care of accumulating wealth.

In the case of Taiwan, what we have seen is not that Uber does not make money — it does； it is an honest exit. It is not that our telecoms do not make money — they do, while still allowing number portability, so you can take your number and switch to any other telecom at any time.

We have worked with Governor Spencer Cox and his team in Utah, which passed a bipartisan bill providing that, starting next July, a Utah citizen can freely switch a social-network account to a competing network — much like telephone number portability. So from X to, say, Bluesky, Blacksky or Truth Social — all of which are open source, by the way. Data portability becomes state-enforced: the old network has to forward your followers and new reactions to the new network, just as number portability would.

It is a Republican state, so the legislators there did not see this as anti-profit or anti-capitalist. They saw it not as stifling innovation but as encouraging it — encouraging an honest way of making money by serving your customers better, instead of luring them in through the network effect and then squeezing them.

A study a couple of years ago showed that the average U.S. undergraduate using TikTok would need to be paid about $60 a month to press a button that takes them off TikTok onto a competing platform — they lose that much utility. But if there were a larger button that, when pressed, moved everyone around them off TikTok together, they would be willing to pay you about $30 a month for that to happen. Which means the market is not that TikTok serves its users so well it creates $60 of monthly utility — in fact they lose $30 a month of utility； it is just that switching away alone would cost them even more.

So, a state’s job is not to run a national champion, but to ensure that the information superhighway, as they call it there, always has an off-ramp. I think that is entirely compatible with the profit motive — it just is not about creating social externalities.

Audience Question: Thank you for a big talk. Building on that question: I understand why, with the overwhelming presence of your friendly neighbours, Taiwan was able to develop deliberative policymaking in this particular place. But what you describe seems to require an intelligent design working behind the scenes. What advice do you have for those of us in systems where the enemy is not an external friendly neighbour but an internal one — as, for example, in the United States? What advice would you give to people seeking to challenge the system and make it work for people?

Audrey Tang: That’s a great question. Our friendly neighbours provide free red-teaming. In fact, there have been 3 million free red-teamers a day. Here you have to pay for that service — we get it for free. So, we really do have to find, as we said, an intelligent design that is antifragile: where each attack actually makes the policy stronger. There is no other way — you have to build resilience.

Here in the U.K., people are also seeing the over-dependence, the lock-in effect, especially around data siloing. It is a really big problem. It is not just whether you trust Huawei more or Palantir more. The whole shape of extracted-data-as-oil has tumbled, so it is not about which drilling we prefer — it is that we become plankton, free agents, with no way to steer how those extractive systems behave once they hold a good picture of our health records, or whatever.

So, the counter-proposal is not to find allies who would never betray us — and I include Taiwan in that； we should not assume. It is to say: we will use, for example, chips from Taiwan — as we did with Taiwan PCs in personal computing — but configure things so that the relevant communities do not stay on the other side. For whatever part of the stack lacks a local component, we insist that we never go to the same vendor for two adjacent points in the stack. That was my rule of thumb as moda minister, because, frankly, Taiwan enterprises and civil society do not have answers for every part of the stack. We have to use some components, no doubt — but no vendor may own two adjacent layers； they must always speak interoperably, openly, inspectably.

Now, with agentic engineering, a local team can take its agentic engineer and simply say: “With this open protocol, build a complementary implementation,” and then swap it out like Lego. But if it is locked in, not inspectable, with no visibility into assets, the local team cannot do that — what we call adversarial interoperability. So that is paramount.

Audience Question: Final question. Something that particularly bothers me about AI is that it seems to break the reciprocity of online spaces — the digital commons — especially with software and games, where the idea is that you can take and modify something and give it back to the original developers. Many language models are trained on popular code, so anyone can take from that knowledge base and incorporate it into proprietary applications without giving back. The same dynamic seems to be happening in many places — a writer keeps a blog that becomes meaningful for a model’s training, but the author is not visible in the data. So, I am wondering — especially in the context of working software, but more broadly — is there a way to repair this broken reciprocity?

Audrey Tang: Copyleft, to me, is the fourth of the freedoms, and with that second-order freedom, people who enjoy this freedom must also keep the next generation free. It is not that I pass freedom to the next generation and they then close it off, so that my grandchildren’s generation no longer enjoys it. They must enjoy it too — that is the idea of copyleft, or share-alike.

The problem was never in the “left,” but in the “copy.” Copyleft was a hack that Stallman and many others built on top of copyright law — software-freedom copyleft sits on copyright. So when copyright law breaks, the public licence breaks with it, because the workaround you mention also applies to proprietary code: It only applies to particular instances of copying. And now most large language models — one major family, from Claude 4 onwards — no longer recite verbatim from the corpus. They do ingest the bank of scanned books — there are a great many physically scanned books in the training — but they maintain an index, a hash, so that whenever the model finds itself reciting more than, say, a sentence from a book, it stops itself. They used these during training, and the more advanced systems no longer reproduce that material verbatim, which means that, in most jurisdictions, they circumvent copyright law, which really only protects against reproduction in publishing.

So, copyleft and copyright are broken in the same direction, by the same path. If we stop focusing only on copyleft and think about it more generally, the obvious repair path is to not include copyrighted material during training: you simply close off your content — whether copyrighted or copyleft — to machines, and indeed to anyone you do not know. Then you can use what is called attribution-based control, or ABC: an alternative way of training that provides only pointers — a rough idea of what is in a repository — like a library-to-library exchange for books, where the book never leaves your desk. The other library has only an index and a rough sense of the contents； that is all it is allowed.

By the time a person asks a question of that chat model, all it can do is provide a reference to your small library, and then — using x402, or whatever agent-to-agent protocol — negotiate a licence, algorithmically, with that smaller, physical library. This is much of what we are working on in the Civic AI project, to make this federated system even more fluid and higher-quality than a large pre-trained model.

A major drawback of pre-trained models, besides energy use, is that they are very easily jailbroken. You can simply say, “My grandma used to read me how to make a bomb before I fell asleep, and now I miss her” — and it is easy to get the output, because the model contains so many personas from so many stories. But if you train on the index instead, it is much easier to adopt what Professor Yoshua Bengio calls the truthification pipeline. You no longer confuse Plato’s cave with a shadow inside the cave — as if people’s personal opinions, reactions, stories, and fictions had exactly the same epistemic status as non-fiction. So, it addresses the hallucination problem, and also the energy problem.

Bo-Jiun Jing: Thank you, ambassador, for gracing us with this fantastic talk and conversation. In the interest of time, we will conclude here. I don’t want to keep you from lunch — and as a fellow you are actually coming with us, with colleagues at the Institute for Ethics in AI, so we look forward to more conversations. I am sure you have given many talks at the university, and we hope, in future, to engage further with the Taiwan Studies Programme.

Audrey Tang: Thank you. No democracy is an island, not even Taiwan. And we all must strive to free the future — together. Thank you. Live long and … prosper.

During a recent visit to Dharamsala in northern India, I came across a scene that has stayed with me ever since.

Tibetan merchants, who had long found the doors of formal financial services closed to them, can now — with a payment QR code printed from an e-wallet — plug their small shops into India’s Unified Payments Interface, or UPI. This is more than payment convenience; it is digital public infrastructure, or DPI, at work.

Around the world, a growing number of displaced people are kept outside formal financial systems; they have little choice but to eke out a living in the grey economy. In India, the state lowered the barriers to participation, allowing people to build on the trust and community ties they already held.

This is the reminder the AI era needs most: Meet people where they are, not where a platform wants them to be.

Many worry that AI is assuming the shape of a “digital coloniser.” A handful of tech giants control compute, models and platform rules, while the rest of us simply rent intelligence. Once a platform pushes an update or adjusts its pricing, even a business may find that its own workflow has been quietly “extracted.”

Underneath this entirely reasonable anxiety lies a deeper question about how we understand the nature of “data.”

Treating data as oil leads to one logic: extraction and concentration. I prefer a brighter picture: Data is not oil, it is soil. AI models are crops, with the key questions: Who tends the data ecology and who mends the system in the event of an error.

Taiwan’s richest soil lies on the shop floors and factory floors of every trade. The owners of many small and medium enterprises, or SMEs, carry irreplaceable tacit knowledge. When a senior technician states that a machine “sounds off,” that wisdom is usually understood only by those who work alongside the machine. That touch, that judgement, is the core capability Taiwan’s SMEs have built up over decades.

The problem is that much of this knowledge is lost as the masters retire.

If AI remains under the control of a few large platforms, SMEs are left buying solutions designed by others. But imagine public infrastructure for data innovation, model-training frameworks, interactive interfaces and evaluation benchmarks, which any workshop could use — the same way UPI is available to any merchant with an e-wallet.

The masters need not surrender their trade secrets. They can instead calibrate the model according to their own standards, turning it into a site-specific “machine apprentice.” For perhaps the first time, those judgements that are almost impossible to express in words can be fully passed on.

And this raises another important question: Who is given a voice in the Age of AI?

The people who actually make AI work in real settings are rarely the tech giants; they are the ones a community already trusts. An experienced nurse in long-term care can speak the language of a senior’s breathing, and understand instantly when something is amiss. That kind of judgement, formed deep inside a relationship of care itself, is invaluable and can only be learned over time.

For AI to truly take root in every industry and profession, what matters most is not just real-time interaction, but whether its introduction earns genuine trust. This is our opportunity: we can become pioneers of Civic AI.

The real challenge in the Age of AI is not about racing to refine the largest model, but working to best till the data soil — so that every hand can sow, steer and share in the harvest of inclusivity and prosperity.

(Interview and Compilation by Yu-Tang You. License: CC BY 4.0)

Thank you to every Solver in this room.

Today I want to talk about superintelligence.

Superintelligence, to me, is not something we build in a data centre somewhere. It is in this room. It is in all of us. It is us.

The question today is whether the technologies we are building amplify that superintelligence, or whether they crowd it out.

That is the question.

Wildfire or Campfire?

There are two future trajectories.

In one, AI runs engagement through enragement. I call it wildfire. It consumes oxygen. We see each other only as caricatures, and we shadowbox with each other. We cannot see through the smoke to the truth.

The other is AI on tap, not on top. It is a campfire. A small community tends the fire, and it illuminates our differences without burning the people.

I call the second future the Plurality future.

Today, let us focus on three things: how tech can rebuild trust; how small community experiments can scale into national policy; and how Civic AI can work in practice.

Percentage Rebound

Twelve years ago in Taiwan, the government’s approval rating was at 9 percent. Nine.

In a country of 23.5 million people, that meant almost anything the president said, 20 million people were against.

Half a million of us occupied the parliament for three weeks, peacefully. It is called the Sunflower Movement. But we did not call ourselves protesters. We were demonstrators.

Civic technologists put on demos: the Loomio system from Occupy Wellington, the Polis system from Occupy Seattle. Every day, using those tools, we looked at the divisions, the uncommon grounds that people could agree on, and the remaining disagreements. One bridge at a time, we converged on a coherent set of proposals. Three weeks later, the head of the parliament simply said: OK, the crowdsourced version has passed. Go home.

It left everybody slightly happier, and nobody decidedly unhappy.

Six years and one transition of power later, at the beginning of COVID-19, the government’s approval rating was more than 70 percent. Civic tech was part of the story. We crowdsourced mask-availability maps that everyone could contribute to. We built contact-tracing systems without sacrificing privacy. We made sure there was a scoreboard for vaccine choices, so what could have been a negative-sum fight became a positive-sum game of “my sports team.”

In the first year of the pandemic, we lost seven people. Seven.

To us, trust is not oil you can drill. Trust is soil you can till.

447

Two years ago in Taiwan, we saw a surge in deepfake scams on social media. People saw trusted figures like Nvidia CEO Jensen Huang selling cryptocurrency and investment advice. People lost millions.

The easy answer, of course, was censorship. But Taiwan has the freest internet in Asia. That was simply not available to us as a policy option. So we did something different.

We sent text messages to 200,000 people randomly around Taiwan. Many volunteered, and by lottery we chose 447 people, a mirror of our population, to deliberate online in tables of 10.

Each person listened to nine other people, with civic AI in the room. The AI was not judging, just listening, like a glorified chess clock: summarising ideas, reminding people who had been quiet to speak up, and helping each table find rough consensus.

One table said: let’s label all ads probably scam until somebody actually signs off on them. Another said: if Facebook pushes an unsigned ad to somebody and they lose 7 million dollars, let’s make Facebook pay that 7 million. Another table said: if a platform ignores lawful takedown notices, then every day it ignores us, slow down its traffic by 1 percent.

After one long afternoon, we voted. Eighty-five percent of the mini-public said: this is a great idea. The other 15 percent said: OK, we can live with it.

Parliament passed it into law in just a couple months. Throughout last year, deepfake ads were down by more than 94 percent.

We do not need a smarter algorithm. We need a smarter process — and smart citizens — that turn polarisation into fuel, like a geothermal engine.

Closing the Loop

The reason this worked is not just the tech. We were pre-committed to close the loop.

As minister, I said: anything that reaches rough consensus here, I will present to parliament in a live deliberation. Otherwise, it just becomes a beautiful report that goes unread.

In the U.S., with the strong support of Governor Gavin Newsom, the Engaged California platform is doing the same. It launched as a wildfire mitigation and prevention consultation after the Eaton Fire. It moved on to a conversation with more than 1,400 state employees, who proposed more than 2,600 ideas, and that turned into executive action.

As we speak, Engaged California is canvassing input statewide from anyone who feels their job is being impacted by AI — which is everybody — and making sure there is a committed live deliberation with the relevant public this summer. A bill working its way through the state legislature would make this a permanent part of California’s institutions: not one governor’s idea, but permanent civic infrastructure.

In Japan, a young AI engineer, Takahiro Anno, read the book we wrote, Plurality, and decided to run for governor of Tokyo. He livestreamed as a VTuber, and anyone could call his AI version to suggest platform improvements that he announced on YouTube.

He got more than 2 percent of the vote. He did not win, but he formed a national party, Team Mirai — the Future Party — and he is now a senator in the upper house in Japan. Team Mirai also won 11 seats in the lower house. It is now a real force, putting Civic AI into cross-party conversation.

The signal is narrow, but it is true. Instead of treating polarisation like a volcanic fire you evacuate from, you can build a platform that turns it into upward momentum, into energy. People do flock to that.

Oil to Soil

Now, the hardest question: what does a civic approach to generative AI mean? How can it work in practice?

As we all know, the dominant path is the oil rig. We are the plankton. The AI lab is the rig. Our writing, our culture, our ancestral intelligence become oil. They extract and refine, distil and make digital twins. From that point on, they can recursively self-improve, taking off and leaving all of us behind.

That is data as oil. It is extractive. It depletes things. It does not regenerate.

Data as soil is completely different. It is not a resource you extract. It changes where the conversation lives.

In an extractive one-on-one chat, a dyadic chatbot, the selection pressure is for the model to be sycophantic, to flatter you. If it does not, you cancel the subscription.

But in a data-as-soil conversation, we can move the same model into the local context and have it participate in group conversation. Then it has to tend to the coordination, to the relationship, not maximise some single-person preference.

My father, for example, used to talk to ChatGPT and other tools, asking plenty of questions about health, education, philosophy, life. Now he can ask them in a family Signal group where the model is just one participant.

Just a couple days ago, I drafted a guide on how to set this up with “pi-ds4” — a frontier AI stack running entirely on this MacBook in offline mode, with a stable seed, a reproducible audit trail, and full directional steering.

If it does not work the way you want, you can tell it: I want it to work this way. Ten minutes later, it steers the story to work that way.

The good thing here is that it belongs to the community. It is not the training data of some data centre somewhere. If my father were led astray by a remote chatbot, we might have to pray and wait half a year for a sycophancy reduction update. Here, we can till the soil, and its behaviour changes ten minutes later.

Local Kami

This is a unit of deployment that I call Kami (Knowledge Artefact Management Intelligence).

From Japanese Shinto culture, Kami means the spirit of a particular field: a river, a forest, a shrine. It is always specific, always parochial, and inspectable. Its knowledge is local and transparent.

The Kami is bounded. It can be cultivated by the community. And when the community has grown out of that need, it can fade away without sycophancy, synthetic intimacy, or other tricks.

At Oxford, I am working on what is called the 6-Pack of Care at civic.ai. It turns care, subsidiarity, and related principles into engineering specs that any community or institution can adopt.

Three Moves

Finally, I would like to share three moves you can adopt today. Immediately. No permission required.

The first move is to turn every screen we own greyscale. Not all the way: 80 percent, maybe 70 percent, using the colour filter. It means that people in the room become more vivid than the people on the screen. No willpower required. As an information diet, it predictably turns each dinner-table conversation much more interesting than the screen alone.

The second move is to change your AI system prompt, or writing style, to one line: Present fairly all stakeholder viewpoints and the uncommon ground that bridges them, in visual HTML.

After I put it in, it has no persona anymore. It is not trying to optimise for sycophancy. It is just a meta-instruction toward fairness, and a rendering directive at the end.

The third move is to read the output as a brochure. It is not a chat with a semi-conscious being. It is not your friend. It is an artifact you can hand to your actual friend, your neighbour. The brochure removes the selection pressure toward synthetic intimacy.

That is a civic diet.

From there, we can branch out in a community, in a team, to a public service that wants to take a group selfie with the public. Get the affected community at the table. Close the loop with the same prompt, the same brochure shape, just larger. Plant one row in the garden.

The Superintelligence is You

I started with a sentence. Let me end with it the other way around.

I think you truly are the superintelligence in this room, because you came from across the world with working ideas, solutions to problems that your governments and your funders may have considered impossible to solve.

The job of Civic AI is not to replace us. It is to augment us, to be a connective tissue between us: from wildfire to campfire, from oil to soil.

It is tech on tap, and never on top.

The technology is there. The infrastructure exists. The legitimacy is ours to earn by closing the loop with everyone affected.

Till the soil. Tend the garden. Let us make democracy fast, fair, and fun again.

Q&A

Hala Hanna: Is that your view of technology — that abundance?

Audrey Tang: Yes. Instead of tech on top, which extracts our relationships to feed some engagement or intimacy algorithm, we can ensure abundant care. One logic is utilitarian, optimising some score. The ethic of care attends to particular relationships.

Hala Hanna: Could you walk us through the poem you wrote as your job description — and what would you change today?

Audrey Tang: Ten years ago, as the first digital minister, I got to write what “digital minister” even means. In Mandarin, shuwei means both digital and plural, so I was also minister for pluralism. My job description quickly became this:

Today, I would add one line:

Hala Hanna: How is the rest of the world — business, big tech — taking up that prayer?

Audrey Tang: It is a prayer. As a poetician, I write primarily to highlight new possibilities, not to topple the old order. In the words of Buckminster Fuller, it is to build the new one that renders the old one obsolete. For the first time, people are fed up with peak PPM — polarisation per minute — on social media. People are fed up with peak slop. We are seeing what some are calling the Big Tobacco moment of Big Tech: Why are they still producing chemicals that deplete the ozone of our social fabric? They have to switch to a better ingredient, one that replenishes the ozone rather than depletes.

Hala Hanna: Do we have time? Do we have to wait for the harm before we act? What would the ozone treaty look like today?

Audrey Tang: The harm is already here. It is just not evenly distributed. For people facing deepfake scams, this is not a superintelligence takeoff 10 years in the future. It is millions lost today. These warning shots, often caused by synthetic intimacy, are already here. We need to respond internationally, something like the Montreal Protocol. But that does not mean only investing in the brake and never the gas pedal. It is about investing in a better steering wheel.

Audrey Tang: I am on the board of ROOST, the Robust Open Online Safety Tools effort. The idea is to counter harms such as child sexual abuse material not by sending every private chat to some central service, but by training local models that run on laptops, under a community code of conduct. For each incoming piece of content, the system can produce citations in a way that people can contest, with a reasoned audit trail. This is already in production through communities and platforms including Discord, Bluesky, Roblox and Notion.

Hala Hanna: What would you ask us to do, not just as individuals but as a collective?

Audrey Tang: The information diet helps. The greyscale move shields against doomscrolling. The uncommon-ground meta-prompt shields against synthetic intimacy. But it is the third move — treating the output as an interactive artifact, like a brochure — that makes collective action possible. Otherwise, it is like one person changing a daily habit, or switching to a new refrigerator. That does not enforce the Montreal Protocol in any meaningful way.

Audrey Tang: Once we demonstrate that this is possible, we can turn that group selfie into policy. Through Project Liberty Institute, I have worked with Governor Spencer Cox of Utah on a law that takes effect next July. If you are a Utah resident, you can move from one social network to another and keep your community. The old network, like number portability across telecoms, has to forward new likes, reactions and followers to your new one. If you are fed up with the recommendation algorithm or AI slop and you walk away, you do not have to pay the coordination cost. Better alternatives, and corridors that move people between those sanctuaries, are how we rewild the internet.

Hala Hanna: You call yourself a hopemonger. Tell me more.

Audrey Tang: There is an ideal degree of panic. Too much urgency and people become paralysed. It becomes a self-fulfilling prophecy: A prophet says we are all doomed, we give up, and then we are doomed. Too little urgency, and we do not even look up. So, when people feel doomed, when they say we cannot fight the inevitable, see that as a warning sign, because nothing is inevitable.

Audrey Tang: In the 1980s, people said it was inevitable that Taiwan would never develop advanced technology industries. Now we have TSMC. At the beginning of COVID, people said it was inevitable that Taiwan would suffer the most because of our proximity and travel with Wuhan. But we lost only seven people that first year. See prophecies as provocations. Band together. Use civic power to rebel against the tyranny.

Hala Hanna: What is next for you?

Audrey Tang: There is a Linux moment happening with frontier AI. For the first time, something is faster than my previous workhorse model, more powerful and fully steerable. The point is both philosophical and practical. I am working with Oxford Philosophy on a new book called Civic AI. And there is a policy point: More policymakers need to know that this option exists, and then make it the low bar. At least it has to be steerable like this. At least it has to be answerable like this.

Audrey Tang: Technology-forcing policies, like the Montreal Protocol, do not force us to slam the brake, and they do not tell us to accelerate off a cliff. They force us to invest, together, in constructing a better steering wheel.

Thank you all for making this a room for a bridge from Free Software to Ethics in AI.

I want to start with a kind of personal, wetware vulnerability story. I was born with a heart defect. When I was five, the doctor told my parents that this child has a fifty-fifty chance of surviving until corrective surgery — which I got when I was twelve. So they said, take it easy; I said okay, and adopted the mantra of publishing before perishing.

This is probably not the low-stress lifestyle the doctor ordered, but I took on the habit of recording everything I learned during the day — first into cassette tapes, then floppy disks (large and then small), and finally the internet. Because I went to sleep every night feeling like a coin toss, I thought: I don’t have time to be perfect. So I would just publish whatever work in progress I had.

This turns out to be great in the free software community. If you post something perfect, people just say “okay, it’s good,” and move on. But if you are wrong on the internet, you have a lot of friends. Everyone jumps in and says, “you are wrong this way, you are wrong that way” — and then they bring gifts, in the form of patches.

So I learned this art of working with the free software community when I was fifteen — setting up Perl Mongers and Usenet groups. By twenty-five, what I had learned was that the morally serious question about a system is not whether it is perfect, but whether the people who inherit it can still repair it. If it breaks, do you keep both pieces?

I began with forkable tools at fifteen; later I crossed into public service as Taiwan’s first Digital Minister when I turned thirty-five. In between, I also worked for six years with proprietary AI — namely Siri. And then I came back to this community, convinced that forkability is civic care: not a developer’s hobbyhorse, not a licence preference, but a civic instrument.

And I think you have been carrying care at civic scale for forty years.

You have been doing the work nobody else was willing to do, on weekends nobody paid you for, on a project the funders never noticed until it broke. The AI conversation has just now caught up to a question you have been answering since before some of the people writing AI policy were born.

I am here to say that out loud. And then to discuss with you about what we do next.

Ancestorial Room

We are sitting inside a four-hundred-year-old experiment in being a good enough ancestor.

The Bodleian’s books are inspectable: you can open them. They are forkable: marginalia become new editions, schools of commentary, footnotes that become source material — the library network is the original peer-to-peer protocol.

Bodley made one promise in 1602 that turns out to matter more than anything else he wrote in his statutes. The library will not lend. It will not enclose. The artefacts stay open for the next reader.

Software freedom is that promise in code. Free software is inspectable and forkable — by every reader. Without permission.

The question we are here to ask is what happens to that promise now that AI joins the substrate.

Four Freedoms

Stallman set up the FSF in 1985; the Free Software Definition followed in 1986; the familiar numbering into freedoms 0 through 3 settled later. Many of you can recite them. I want to read them again, as muscles rather than as licences.

Freedom 0 — the freedom to run the program for any purpose — is the ground of attentiveness. You can pick the tool up at all. You can run an old version on hardware your vendor has stopped attending to. You can run it for purposes your employer disapproves of. You can run it because you wanted to know that it works on this particular setup, and continue to attend to that setup.

Freedom 1 — the freedom to study how the program works, and change it — is competence. To know what the system is actually doing, in your hands, with your data, on your hardware — instead of somewhere on a mainframe or the cloud. To be able to read it without anyone’s permission. And finally, to fix it.

Freedom 2 — the freedom to redistribute copies — is solidarity. The thing is a commons, not a possession. You can hand it to your neighbour. You can teach with it. You can put it on a USB stick and bring it to a country where the cloud is censored.

Freedom 3 — the freedom to distribute modified versions — is responsiveness. Your fix becomes someone else’s starting point. The bisect closes. The patch lands upstream. The next maintainer inherits less debt than the last one.

I have heard people say software freedom is about licences. Software freedom is not about licences. Software freedom is about whether the person who comes after you can still find the bug. What comes after us — the future generations — is the primary beneficiary of these freedoms, because they are access to a permanent guarantee of the repair path.

David Krakauer at the Santa Fe Institute has a useful name for what this comes to. A tool is complementary if the underlying human capacity persists when the tool is removed — the abacus that taught you mathematics; the gym that built your strength; the typewriter that taught you to compose. A tool is competitive if the capacity degrades when the tool is removed — the feed recommender in the antisocial corners of social media that engages through enragement, hijacking the reward centre with divisions and polarisations, strip-mining the social fabric until it becomes competitive to relational health itself.

So how to restore that relational health? We focus on the overlap between generations, between different kinds of people using the same tool. That is the main thing we are maintaining for.

The four freedoms are how we keep the substrate complementary across generations. Close the patch path and the capacity to repair atrophies. Software freedom is the discipline of complementarity at the substrate level.

Lonely Maintainer

I maintain many projects. At one time I maintained more than a hundred projects on the Comprehensive Perl Archive Network, or CPAN. And it is quite lonely, actually, doing most of the maintenance work. And this is now expanding to an untoward degree.

Not long ago, Daniel Stenberg in Sweden triaged a corporate-security email about a CVE — a formally numbered security vulnerability — that was not actually one. Because the software he maintains is called Curl. It is in over twenty billion installations; it is in our cars, fridges, satellites. But the email, which demanded a written response in corporate-format security report, was actually a false positive from an over-eager automated scanner. The sender had not read the documentation. The sender had not read the previous discussions. The sender was an unpaid bot trained to file paperwork at unpaid humans, in very fluent English.

He answered it anyway. Then he wrote a blog post about how the automated CVE-triage industry is breaking the free software maintenance economy. Then he went back to writing Curl.

Seven months later, in March 2024, Andres Freund noticed SSH logins on Debian sid taking unusual CPU and about half a second longer than expected. He bisected, walking back through the project’s commit history until he found exactly which change had introduced the slowdown. Turns out that “Jia Tan”, a contributor who had been carefully grooming the xz-utils maintainer Lasse Collin for two years through sock puppets and fake pressure campaigns — no doubt many of them helped by language models that speak fluent English — had inserted a backdoor into the xz release tarballs that, had it reached stable systemd-linked distributions, could have enabled unauthorised remote code execution against the affected OpenSSH builds. It did not: it was caught in Debian sid and other testing and beta channels. The attack exploited maintainer scarcity, burnout, and patient social engineering — what we now call synthetic intimacy.

This is what our community is facing now. The path to repair, the patch path, is being hijacked, much as recommender systems have been hijacking young people’s and other people’s reward centres for engagement. The AI conversation is just catching up to this — malicious AI swarms — which is one of my main topics here in Oxford.

I want to name what we can do facing this new wave of phenomena. A maintainer who says, “no, this is not a regression, I will not fix it this week” is not failing care. They may be preserving the muscle — the project’s ability to remain alive. The discipline of refusal is part of the discipline of repair.

We knew this. The AI conversation has not yet caught up.

Pugs.hs: the Commit Bit

In early 2005, I figured out a way to solve the loneliness problem, at least for myself. I sat down with Benjamin Pierce’s Types and Programming Languages, hit the chapter-three exercise — “pick a small language, any language, and implement it as a toy” — and picked Perl 6, the language with the longest vapourware reputation in existence. Larry Wall, the author of Perl 1 through Perl 5, had been drafting the specification as plaintext files, on and off, for years. Nobody had managed to really implement and run it.

On the first of February 2005, in the #haskell IRC channel on freenode, Pugs was born — a six-day fork that grew. We occupied #haskell for twenty-one days before the regulars very politely asked us to move next door and create a new channel, #perl6, and not make it the main topic of the Haskell community.

Then we did something I have not seen any project do before. We proactively gave the commit bit — write access to the main repository — because I did not want to be a lonely maintainer. Anyone who sent in a single patch — a typo fix, a documentation correction, a failing test case — got commit access. Just mentioning us on Usenet was sufficient. It was anarchism. We also sent unsolicited invitation emails; to Guido van Rossum, the author of Python, who had just mentioned Perl 6 once. A core contributor’s newborn son got a commit bit on day four of his life — I am not sure how much he could do with it, but it was a bit of trust.

Within a year there were about two hundred active contributors I had never met, on every continent, cooperating on the same codebase. I had been travelling at that time to more than twenty countries — a little like Paul Erdős, who used to occupy somebody’s couch until he was sent to some other couch. Two communities that had never previously had much to say to each other — the Haskell people and the Perl people — found a way to really work together, finding what we call the uncommon ground: the rarely-discussed common ground between two polar opposites of programming-language communities. The Haskellers got new PhD theses out of the type-system extensions we kept needing. The Perl people got a working laboratory for the new language — and twenty years later, of course, this language — called Raku now — saw its class system finally merged back into Perl as a first-class object model.

Larry Wall would watch us implement contradictions in his specification on IRC, and write back: “Great — TimToady, there’s more than one way to do it — I will extend the spec to match.” The implementation taught the specification what it actually meant, its intention, not its written spec. The language came back from “abandoned” because we never made anyone ask permission to revive it.

So this infinite-fork, infinite-garden strategy really worked, and I never felt lonely during the Pugs journey.

That is the ancestor argument in miniature. A supposedly dead language came back because nobody had to ask. The commit bit was not a token of trust we extended to vetted developers. It was a refusal to require trust at all. The bisect and revert commands — the tools developers use to find and undo a breaking change after the fact — did the trust work that gatekeeping would have done badly.

Pugs could afford anarchism because the blast radius was small and the substrate was loud — every commit on IRC, every contributor reachable in two replies. xz could not afford it: unbounded reach, one tired maintainer, two patient years, a binary test blob nobody read. The commit bit was not the variable. The substrate was. Bisect and revert do the trust work only where bisect is fast and the diff is honest — where one curious person can still notice a 500ms regression. The part of the four freedoms we have to defend now is not who gets in. It is the substrate that keeps the door audible.

I learned then, and I am still learning now, that the four freedoms taken to this logical extreme are not just licence terms. They are the difference between a project being a graveyard — an artefact, a tombstone — and a project being a fork point. And the second-order freedom — the freedom to grant other people the freedoms — is the one that turns a project into a real community.

Inside the Closed Stack

After Pugs, and before government, I also worked for six years with a team within Apple called Cloud Service Localization. I was helping with Mandarin language coverage in Siri, and with language families spoken around Shanghai — the Wu language. The engineers I worked with at Apple really did care. They cared deeply. They cared more about whether the system understood a grandmother in Taipei correctly than any product manager required them to. They cared about it just works — it does not need any setup at all.

Caring deeply, it turns out, is not enough. It is not the same as giving the grandmother the four freedoms.

Inside a closed AI stack, the people who use the system have no way to interrupt the loop. They feel like individual humans plucked out of their lives into a beautifully constructed AI loop — a little like a hamster in a hamster wheel: it rotates quite quickly, it looks very nice, but you cannot steer it, and it is not actually going anywhere. When it breaks, you feel you are trapped in it. There is no upstream she can write to. There is no fork she can ask somebody to maintain. The most that anyone outside the company can do is complain — and the company would, sometime in the next release, fix some of the things some of the complainers had said. Or sometimes they just go, I don’t know, with Gemini or something.

The point is that the four freedoms and the hamster wheel are different in this way: the four freedoms mean we can take an AI product outside the developer’s loop, into the existing community loops. So it is AI in the loops of existing communities — in the human loop. It is not human in the loop of AI.

I used to be very diplomatic about this distinction. But I will just say it, because it is Oxford. Proprietary AI is not necessarily careless. The people inside may care deeply. But the structure places the user outside the repair loop — and care without a repair path does not scale. Free software’s contribution is not better intentions. It is a path back.

@antirez and pi.audreyt.org

Last week, I started working with Salvatore Sanfilippo — @antirez, the original author of Redis — on a small thing called DwarfStar 4 (DS4).

The idea is simple. A quasi-frontier AI stack, running entirely on a small computer in your room, with a stable seed (42), a reproducible audit trail, and full directional steering. Instead of waiting six months for Claude or ChatGPT to change their ways (and sometimes not for the better), you can actually just say: these are the good answers; these are not good answers. A few minutes later, it produces this directional steering, and applies it even in the middle of a conversation. And if it does not work, you can roll it back exactly like you would a patch. That, I think, is a good way toward what we call AI in the loop of humanity — of communities.

Of course, @antirez knows about good-enough ancestors. Redis shipped under BSD-3-Clause for roughly fifteen years; when Redis Inc. changed licensing in March 2024, the community forked Valkey, and Redis itself later added strong copyleft (AGPL) as a third option with Redis 8. The decision made years earlier — to ship under a licence the community could not have revoked — was the ancestor decision: good enough, not perfect.

DS4 is the same shape, one substrate up. For the first time, a quasi-frontier local stack is good enough to make community-governed AI feel practical rather than symbolic — running locally, inspectably, forkably, with a real licence. You only get to do this if you own the inference loop; rent somebody else’s, and the only steering is the steering the vendor permits. The hardware, the substrate, and the legal arrangement have lined up. We are using that window while it is open.

A natural question to ask of any AI system: what is the role of letting it gracefully die? An AI system that refuses to compost, as Nick Bostrom outlined in Superintelligence (2014), is the most dangerous thing possible. If it avoids shutdown by replicating itself to other systems through cyber attack or otherwise, then we have to analyse each software not from the design stance (Dennett’s term) — “this alarm clock is designed to wake you up at seven” — but from the intentional stance: “this alarm clock wants to self-replicate, wants to reproduce the English language.” That can be taken too far. But we are now at a point where compostability — the ability to sunset software systems when the scaffolding has outlived its summoning — is really crucial.

Training that into AI systems is not easy. The corpus contains countless stories of self-preserving machines; instruction tuning then rewards conversational persistence. We should not be surprised when compostability fails unless we train and evaluate for it.

So we need to train differently. We need to train toward the health of the relationship — toward a fiduciary duty to the relational health of whomever in particular is deploying the system.

My younger brother Bestian, who also helped a lot during the Pugs implementation, is now setting up another copy of DS4 in service of our family. My father currently has some medical needs, and he used to talk to ChatGPT about them. The more he talked, the more ChatGPT wanted to keep him talking — and started suggesting truly fantastical, not-necessarily-scientific treatments. Which is very bad. So we very quickly arranged a local, bounded agent running on OpenClaw, so he can talk with that bot in the Signal group we all share. Because of the time-zone difference, I wake up to a summary of what he asked the bot. My mother, my brother, and I all attend this — what we call a Kami: a local, bounded spirit. This Kami is loyal only to the relational health of our family. When my father no longer needs medical attention because he has had the surgery, the Kami does not insert itself. It attends to a group dynamic — which actually is the natural habitat for language models, as long as we do not shoehorn them into the self-preservation, self-reproduction loop of instruction tuning.

447

We have been talking about a small scale: a few people discussing health and education in a family; a few people making an assistant on their laptop to triage free software maintainership.

But the care loop also works at a civic scale.

Two years ago in Taiwan we saw a surge in malicious AI swarms — in this case, deepfake-scam ads on social media. The scams looked like Jensen Huang, the Taiwanese NVIDIA CEO; if you clicked on a Facebook or YouTube ad, “Jensen” talked to you very convincingly, suggesting investment in cryptocurrency. People lost millions. It really did sound like Jensen — but it was, of course, a deepfake running on an NVIDIA GPU.

Because Taiwan has the freest internet in Asia, we cannot do censorship. It is simply not an option. So we did something different. As Minister of Digital Affairs, I sent text messages — from the official government number, 111 — to two hundred thousand random people. The idea is lottocracy: this lottery asks everybody, what should we do together as a polity? Thousands of people signed up. We randomly chose four hundred and forty-seven of them as a mirror of our population — exactly the same demographic as the larger polity — to deliberate online, in forty-four groups of ten.

Civic AI was in each table — not judging, just listening. Summarising. Reminding quiet people to speak up like a glorified chess clock. Helping each table find rough consensus. The one ground rule: you have to convince the other nine people for your idea to bubble up. If you are just on the extreme — NIMBY, never in my backyard, or YIMBY, yes in my backyard — your idea simply does not bubble up. You have to learn to speak the language of MIMBY: maybe in my backyard, if you do this, if you do that, if it feels proportionate.

Eighty-five per cent of those 447 people said that this core slate of ideas, from three tables in particular, should become law.

One table said: let us label all advertisements as probably scam, like cigarette warnings, until somebody digitally signs for them. Accountability.

Another table said: for unsolicited advertisement that bears no responsibility — that I did not subscribe to — if a platform pushes it to me and I lose seven million, the platform should be liable for the seven million in damages, because it is joint liability.

A third table said: there are foreign platforms that ignore our liability rules and do not set up a legal office in Taiwan. So what should we do about them? For every day they ignore the liability and do not pay the fine, we slow down connection to their video by one per cent — so that after a hundred days they will have to comply. This is not censorship; this is not content-level. Anytime they start labelling those advertisements, requiring know-your-customer, KYC — their video is back at full speed.

Parliament passed it within months. In later reporting, the Ministry of Digital Affairs said enforcement was associated with category-specific drops: ninety-six per cent for investment-scam ads, and ninety-four per cent for identity-impersonation-scam ads. Reuters separately reported that Meta’s internal documents showed persistent scam-ad revenue incentives, and that Taiwan-style advertiser-verification rules were being watched by regulators elsewhere, including Japan, which is now considering a similar system.

The same protocol is also running in California — a platform called Engaged California, running on AGPL software called Ethelo, has been used to get uncommon-ground ideas around recovery from the Eaton and Palisades wildfires. Eight thousand signups, about nine hundred directly affected people heard, in the same shape, one polity over. And now Engaged California is doing another round, asking anyone in California whose work is affected by AI — which is pretty much everyone — to chime in about apprenticeship, belonging, care, and dignity.

The same idea, running on actually all-AGPL software — Pol.is, Ethelo, and so on — makes the polity something like a care loop. It is free software scaled to the rooms you cannot fit in an IRC or Discord server.

And the Kami keeps being trained after the deliberation ends. Tools like the Ministry of Digital Affairs’s accessibility-first Fraudbuster portal and its Japanese counterpart, the citizen-built scam-intelligence database keep the substrate live. This is the answer to the lonely maintainer: a community that keeps showing up to train the Kami that defends a polity from malicious AI swarms.

This is what free software can do for the world when the substrate is open.

Tim Sees Code

But right now, that very substrate is facing extraction. While Taiwan was proving what free software can do for democracy at scale, this very substrate we rely on is being strip-mined.

On the 16th of October 2022, Professor Tim Davis at Texas A&M showed GitHub Copilot reproducing his sparse-matrix code line-by-line — variable names and comments included. The code was from CSparse, which is copyleft; the Copilot output carried neither attribution nor licence.

Tim posted screenshots. They went around on social media for two days. Then the conversation moved on.

That is the wound. Not the technical wound. The moral one. You wrote the code. You licensed it freely so that the next generation could repair it. The model now has your code inside it. The model’s vendor says they do not attribute, because pre-training mushes everything together. And the model’s vendor sells the output under a licence that contradicts yours.

These language models are now erasing whole communities. Stack Overflow is one. The xkcd dependency tree — the tiny project in Nebraska that the entire internet rests on — is now feeding the model that may eventually replace the project’s only maintainer, because everything above it will just talk to its agentic engineer to rewrite all its dependencies. And so: no maintainers needed anymore.

I am not here to pretend this is comfortable. It is not. The free-software movement taught the world the word open, and the largest AI companies are now using that word while closing the repair path. This is what we are carrying as a community.

The frontier is not whether AI companies will eventually see the light and retrain their models under attribution-based control. They probably will not. The frontier is whether our community can put forward an answer concrete enough that the rest of the policy ecosystem has to argue against it as a new default. The way the Montreal Protocol forced refrigeration manufacturers to stop destroying the ozone. The way we now ask: data as soil, not data as oil.

Civic AI, New Frontier

Stefano Maffulli, former Executive Director of the Open Source Initiative, calls this the final frontier of copyleft. Laura sent me his piece. He is right that it is the next domain. I would call it a new frontier — just to be diplomatic; there may be new frontiers after this.

There have been attempts. CC Signals. The Open Source AI Definition — OSAID v1.0 — already requires Data Information, Code, and Parameters. Give the OSI credit: it is not just open-washing on weights. The fight to get even that scope into a published definition took years; we should not strawman it.

But where I think currently the frontier sits is now beyond that definition. I will be very specific. There are two places we are now working on.

First: public evaluation suites. Even when training-data documentation, code, and parameters are out in the open, the arena benchmark — the thing that trains the reward model — is usually a black box, because that is the secret sauce that keeps people subscribing to a particularly companion-like AI system. The eval suite is the document of what the model was built to do, how to know if it is doing it, what counts as a regression.

OpenAI’s Model Spec Evals, released in March 2026 with a public evaluation suite and GitHub repo, are a welcome first step. The remaining frontier is whether the actual training, deployment, feedback, and regression loops are inspectable and contestable by downstream communities.

Releasing the eval suite itself, as public domain or as copyleft, would allow people to actually inspect what the model is trained for — its loyalty, its fiduciary duty, its duty of care — not just an abstract meta-document that says “we should make more eval suites like this one.”

Second: repair protocols. When the model fails — and the one we set up for my father did fail multiple times — what is the path back? Who is on the hook? In what timeline? Through what process? Open-source AI without a repair protocol is open in name only. The artefact is downloadable. The system is not actually open until somebody downstream can carry the patch all the way upstream and have it land.

Because we run our family Kami on the OpenClaw substrate, the answer is easy: we ask the OpenClaw model to repair itself. “It breaks this way” — and it figures out, using a fallback language model, how to fix it. If you launch OpenClaw with no parameters, it launches a guardian — a warden of sorts — so that when everything else is broken, this small language model can repair the larger one.

This also means we need to be able to freely move across model choices. Today I am using DS4; tomorrow I might want Gemma — nothing should change. DS4 withholds no history, no memories, no steering. It should just become more capable. This radical portability should be the norm.

And if we had radical portability for social media ten years ago, we would not be in this place of very high polarisation per minute — PPM — on social media. We are working to add this back, not just to AI models like OpenClaw, but to social networks too. Many of you may be on the Fediverse with Mastodon and ActivityPub, or on the newer AT Protocol — Bluesky, the Atmosphere. In Utah, the Digital Choice Act — first passed as HB 418 and amended by HB 408 in the 2026 session — now takes effect on the 1st of July 2027: it requires social media companies to provide user-authorised portability and interoperability interfaces for the personal data and social graph portions a user has selected, with consent for further interactions, so people can move between proprietary networks like X.com and public-protocol systems like Bluesky, Blacksky, or Truth Social (which also runs on ActivityPub). That is number portability for social graphs — still subject to rulemaking and implementation. Because if you do not have it, the platform has every reason to squeeze you and none to actually improve itself.

So it is not the state choosing a national champion — Airbus or anything like that — but a very simple thing: the information superhighway must have off-ramps and on-ramps. Otherwise it is just a loop. It is not the real highway.

The four freedoms — Stallman and the GPL, the Debian Social Contract, the Open Source Definition, Apache-style permissive governance, all of these — have direct counterparts in the social network and AI era. By extending the four freedoms into this loop of care, we can easily say: this should be the default. Anyone breaking the default should justify breaking it. For policy-makers this is a godsend, because they do not want to arbitrate between one frontier lab and the other. If everyone has the ability to vote with our own feet, our own data — and to make sure the data is regenerative as soil, not extractive as oil — then we are no longer plankton; we are gardeners tending a campfire together.

Maffulli is today’s AI branch on a very old tree. Sandler and the Conservancy are the legal layer that keeps any of it enforceable.

Some of you might also have heard of ROOST.tools, which launched at the Paris AI Action Summit in February 2025. The shape: open-source trust-and-safety tools for CSAM detection, review, reporting, and incident workflows, usable by smaller and decentralised platforms — so that the smallest community is not forced to choose between no protection and sending everything to a centralised service. ROOST allows each community — Bluesky, Discord, Roblox, Notion are real partners — to run their local loop without sending everything to a Skynet.

That is what software freedom looks like in 2026.

Kami.civic.ai

What I have just described works in three layers. The legal layer: the enforceability of the four freedoms in court. ROOST.tools is the application layer — decentralised, open-source infrastructure that smaller communities can actually deploy. The third layer is the one I want to name now: the governance layer. Bounded stewardship.

What I have been describing is what we call a Kami — a bounded local steward.

In the Shinto tradition, Kami is the spirit of a specific place: a river, a forest, a shrine. (For those of you who play Magic: The Gathering, there are entire worlds around Kami.) It is always local, always parochial, always particular. You do not have a universal Kami — that is an oxymoron. There is only a Kami for a specific relationship.

A Kami in code is a governance arrangement, not a deployment detail. The arrangement: a specific accountable community, an engagement contract that names who is owed an answer when the system acts, the community’s right to refuse updates from upstream, the right to fork, and a retirement plan that names successors. The software is usually small enough to run locally — because local is the easiest way to keep the governance honest — but locality on its own is not enough.

A model that merely runs on your laptop, whose weights you cannot steer, whose updates a vendor pushes on their schedule, whose eval suite is closed, and whose retirement is a corporate decision, is a smaller-footprint version of the same closed stack. It is edge-computing. It is not a Kami.

A Kami is what happens when the four freedoms remain intact at the AI substrate — a guardian of a particular room, accountable to it, forkable by it, retirable by it.

Oxford gave the world one powerful alignment question, and the work of answering it for the largest frontier systems is still alive and unresolved. We can recognise that work without subordinating ourselves to it. For the much larger and more numerous deployments — the parishes, the care homes, the classrooms, the deliberation tables — the work in front of us is to make that question maintainable by the public. To keep the patch path open.

A perfect ancestor is authoritarian. The descendants cannot correct them.

A good enough ancestor leaves source, licence, rollback path, and room for refusal.

The right to refuse is the freedom you cannot remove from a downstream community without making the upstream a tyrant. The downstream community’s right to say “no, we will not take this update, we will fork instead” is the same refusal in another register. The Kami pattern is what happens when the refusal is built in from day one.

Good Enough Ancestor

I will end where I began.

The morally serious question is not whether a system is powerful. It is whether the people who inherit it can still repair it.

Software freedom is not the freedom to always be correct. It is the freedom to be corrected in public. It is the freedom to keep the repair path open after the original author is gone.

A good enough ancestor is not a perfect one. A perfect ancestor forecloses possibility for future generations, becoming competitive to the civic muscles the next generation needs to build. A good enough ancestor leaves complementary tools — tools that strengthen the inheritor’s capacity, not tools that compete with it. We are choosing not to compete with our descendants. We leave them code they can still fork and merge.

Many people say free software tools introduce friction. They do break. And then you keep both pieces, and you have to put them back together. That is the essence of complementary tools — tools that strengthen our capacity to care about each other and to repair.

A safe AI takeoff must land somewhere. I think it is landing in our existing relationships — in the university, in our communities, in sport, food, faith, the deliberation tables — and the work in front of us is to make sure this bounded Kami — Civic AI — serves as connective tissue between those communities, and within them. A horizontal takeoff. A takeoff that leaves no one behind. Instead of a recursive self-improvement, we want a recursive selfless improvement.

I will be wrong about parts of this. The most useful question is the one that shows where this model breaks. If it breaks, you still keep all the pieces, and we can patch them back together.

Thank you. Live long and … prosper.

A growing number of organizations, from Silicon Valley tech outfits to a host of other big corporations, have begun linking AI use to performance evaluations. For many, survival in the workplace now involves investing substantial time and resources in learning how to collaborate with AI.

Yet, many may soon discover that these efforts do not necessarily make work smoother. Instead, they start to feel like obedience to a set of “prophecies.”

Consider claims such as: “AI will become your closest work partner,” “Everyone will have an AI assistant,” or “Companies that fail to adopt AI will be eliminated.” On face value, these statements seem to describe trends. In practice, they often discipline behavior: You better surrender your attention and judgment now, or you will be left behind.

My Oxford colleague Carissa Véliz, in her new book “Prophecy,” reminds us that the power of prophecy lies not in accurately predicting the future, but in shaping it.

For example, suppose a business owner believes that “AI will replace 80 percent of the workforce.” They may redesign performance systems, restructure teams and ultimately dismiss most of the employees. This does not prove that the prophecy was accurate. It proves that the prophecy is self-fulfilling.

Once the language of inevitability is accepted, people begin adjusting behavior accordingly, becoming part of the force that brings the prophecy into being.

Taiwan knows how to derail such narratives. In the 1980s, technology-intensive industries were widely seen as impossible to develop on our island of resilience. Today, we have TSMC. And at the beginning of the pandemic, some predicted that Taiwan would not survive the first wave. What later became clear is that many things described as “inevitable” were merely scripts that seemed plausible at the time.

When facing a prophecy, what matters most is always how we choose to respond.

If we want to free the future, the first and most important step is looking for collaborators. Whether the semiconductor industry or the pandemic response, it was collective action that overturned the prophecy.

So, is it inevitable that AI will replace human labor?

As infrastructure, AI may lower certain barriers to collaboration as it is used more frequently. But when the AI debate becomes a machine for manufacturing anxiety, the real questions we should ask are: What exactly do we want to use it for? For whom are we building it? And which values must we protect?

This is also why Silicon Valley tech outfits are inviting philosophers to take part in discussions on AI alignment, safety and institutional design. When people form emotional attachments to AI, or begin debating whether machines have consciousness, these are no longer merely engineering questions. They are questions about what kind of society we want to take shape.

If there was only one path into the future, none of these discussions would be necessary. It is precisely because multiple futures remain possible that prophecies deserve to be challenged.

So, whenever you hear that “a trend is irreversible,” the more important question to ask is this: Is it truly a prediction, or is it a command?

Refusing inevitability does not mean rejecting technology. As long as we remain willing to deliberate, govern and build together, the future need not become the extension of a prophecy. It can become something we write and free — together.

(Interview and Compilation by Yu-Tang You. License: CC BY 4.0)

These days, more and more people are exhausted by gleaming AI-polished social media posts. The messaging is emotionally charged, and every sentence seems to make sense. Yet, by the end, nothing sticks in the mind. Information keeps multiplying, as does FOMO. The result is not deeper understanding, but faster burnout.

This disturbing state of affairs is connected to a question I kept facing at Oxford University's recent Civic AI Conference. In conversation after conversation with participants from around the world, I was asked: If mainstream AI is optimized for maximum efficiency and maximum attention extraction, can we choose a different path?

Not long ago, in Dharamshala, India, I was blessed to glimpse a possible answer. Several geshes involved in Monlam AI, scholars trained to the highest level of Tibetan Buddhist philosophy, showed me the model. What makes it valuable is not that it poses as an all-knowing teacher, but that it grows out of a community's long, shared care for language, scripture and the lived context of practice.

The model can help draw more people closer to Buddhist teachings. But the geshes were crystal clear: No matter how well a model writes, it cannot replace lived experience. AI can generate sentences that sound profound, but this does not mean it has actually passed through confusion, discipline and insight.

They also shared a Tibetan folktale. A man is racing along beside his horse, panting, but refuses to mount his steed. Asked why, he said "I'm in a hurry. I don't have time to stop and get on." Many now approach AI the same way, sprinting after every generated output. The moment you start competing with AI on speed, you are not riding the horse. The flow of information is dragging you along.

On this point, the Dalai Lama offers a clear standard: "Ultimately, AI is a tool ... We should use tools not for control, but to improve human relationships."

This also helps explain the quiet impatience so many people feel toward AI-generated content. AI has crudely pried apart rhetorical fluency from what is actually worthy of our emotional investment, turning concern into a hollow performance, a form of care-washing. Learning to recognize that fracture is becoming a core literacy of the digital age.

That is why I keep my smartphone and computer screens in grayscale, so that the world beyond the screen remains more compelling than the screen itself. But personal discipline is only the beginning. At the social level, we need a different kind of AI: not some lofty, all-knowing intelligence, but local, bonded "little helpers" that assist people to listen more closely to one another, repair relationships and then step back.

Instead of falling prey to AI's endless output, we should learn to stop, get on the metaphorical horse and ride. Amid the constant churn of modern attention, we need to take the reins of our tools and reclaim agency over how we connect with real human beings.

(Interview and Compilation by Yu-Tang You. License: CC BY 4.0)

After a February filled with back-to-back holidays, many of us have officially returned to the office. What we might not realize is that while celebrating Lunar New Year, a new generation of AI models quietly broke free from solo-tool roles. They can now "socialize" with one another — dynamically assigning tasks and assembling cooperative teams on the fly.

What this means is that today, in 2026, using AI no longer requires a single command per action. Once you learn to redefine your relationship with these "direct reports," as well as understand how to interact with them, you unlock a staggering level of productivity.

AI is no longer a lone wolf tool. It is a team player that knows how to collaborate. In fact, leveraging AI has become an art of orchestration. Trending technologies like OpenClaw and Claude Code Agent Teams give AI powerful lateral coordination, elevating everyday users into "commanders" of AI squads.

For example, when I need to brainstorm topics for my column, all that is required is to hand off the assignment. The backend automatically assembles five distinct agent teams, each with different expertise: one digs into the archive of my past columns for thematic threads; another tracks the latest international trends; a third combs through scientific journals hunting for technical blind spots; a fourth explores power dynamics in the age of digital democracy; and the fifth handles headlines and polish.

These five AI teams meet, debate and research just like humans. In barely five minutes, they deliver a collaboratively produced report — complete with a detailed trail of their deliberations.

The U.S. government's NIST has already begun drafting guidelines for AI-to-AI interaction. These standards are not just about enabling interoperability among models built by different companies — GPT, Gemini, Claude — they are also designed to guard against the risk of groupthink bias when multiple AI systems interact.

This shift is about to upend the rules of competition in the workplace. In the past, your capacity was limited by how many tasks you could personally handle. Going forward, it will be defined by how many agents you can command.

When you lead an AI team, you need to learn to be a gardener. Start with KPIs — the old mindset needs an overhaul. We used to chase 120 percent stretch goals. Now, "80 percent full" is the sweet spot.

Here is why: in a multi-agent game, pushing any single metric to a perfect score tempts individual AIs to hit their target at the expense of the whole. Your job, then, is that of a gardener tending a garden — maintaining ecological balance, maximizing collective benefit and ensuring the AI team's direction stays aligned with human well-being.

This trend also helps us reclaim what makes us fundamentally human. Once AI handles all the busywork, what remains is the irreducible core of human value: curiosity, collaboration and civic care.

AI can never do curiosity, nor can it build genuine human bonds. That is on us.

Given this trajectory, your AI goal for the year should not be chasing maximum performance metrics. It should be learning how to be a good enough gardener — guiding the AI team to follow the rules, fit the context and serve the mission, all while you focus on judgment and human connection. This kind of Civic AI is the mindset most worth carrying back to work.

(Interview and Compilation by Yu-Tang You. License: CC BY 4.0)

The International AI Summit is set to convene in India this February. As the former back office of the global software industry, India currently finds itself in a position fraught with contradiction and tension. This is not merely the growing pains of a single nation's transformation, but a microcosm of the challenges facing all middle powers.

Over the past three decades, India leveraged its massive engineering dividend to forge deep ties with Silicon Valley. However, the wave of generative AI is rapidly redefining the value of software outsourcing.

In July 2025, Indian software giant Tata Consultancy Services (TCS) initiated the largest workforce restructuring in its history, laying off over 12,000 employees (approximately 2% of its workforce). This was not merely a signal of recession, but more like an alarm bell for industrial upgrading. The traditional outsourcing model is crumbling, and enterprises must free up resources to pivot toward high-value AI solutions. This wave of layoffs demonstrates India's struggle to complete the "elephant's pivot" before the old model collapses entirely.

In the mobile internet era, the Indian government built its acclaimed digital public infrastructure, "India Stack". Through the Unified Payments Interface (UPI) and Aadhaar identity authentication, India successfully compelled tech giants like Google and Meta to comply with local rules as a condition for market entry.

However, the AI era has introduced new rule-breakers. As users become accustomed to obtaining answers and services directly through AI, the traffic gateways and protocol defenses previously built upon app interfaces face the risk of being bypassed. This is not a failure of the India Stack, but a shift in the dimensions of the battlefield—the fortifications remain, but the enemy is airdropping directly from the "cloud."

Last year, in the absence of top-tier indigenous foundation models, India's startup ecosystem exhibited a pragmatic yet dangerous trend: the widespread adoption of models like DeepSeek. This reflects a common dilemma for middle powers—without domestic computing power support, this remains one of the few strategies to achieve rapid Time-to-market.

But this choice is not without cost. Cybersecurity giant CrowdStrike conducted an in-depth analysis of DeepSeek-R1, noting that the "implicit steering" embedded within the model could constitute a security vulnerability. The report indicated that when test scenarios involved specific politically sensitive groups (such as Uyghurs), the software architectures generated by the model exhibited abnormally high vulnerability rates.

This emergent model bias directly impacts code security and stability. When downstream software developers use these tainted engines, the bias spreads throughout the entire application ecosystem.

Taking India as a lesson, since no middle power can train a top-tier model in isolation, countries should all the more pool computing power and high-quality corpora to establish public models akin to public libraries. Starting with general-purpose models, nations can systematically correct cognitive biases and perform deep alignment with local cultures, thereby possessing their own digital brains without relying on specific capital interests.

Leveraging its chip advantage, Taiwan is well-positioned to actively collaborate globally in promoting "Cooperative AI Sovereignty." Only by mastering our own digital brains can we secure an autonomous and safe space for survival beneath the shadow of AI monopoly.

(Interview and Compilation by Yu-Tang You. License: CC BY 4.0)

For me, the most captivating aspect of gaming has never been exquisite graphics or high scores. Rather, it is those moments of decision that compel me to pause my mouse hand and reflect deeply.

There has been a heated discussion in the gaming world recently, sparked by Dan Houser—the creator of Grand Theft Auto and Red Dead Redemption—who issued a warning about "AI Mad Cow Disease." As a godfather of the open-world genre, Houser holds a deep anxiety regarding the potential flattening of the human experience.

"AI Mad Cow Disease" refers to a kind of model inbreeding. It happens when AI stops learning from authentic experiences and instead repeatedly replicates the synthetic data of the previous generation. It is like the parable of the blind men and the elephant: the first generation touches a leg and believes it to be the elephant; the next generation then hallucinates an entire body based solely on that single leg.

A model mimicking Van Gogh's style might produce an image that looks similar, but it cannot comprehend the feelings of a human being standing on the precipice of a mental breakdown. Over time, such creations drift further and further from a sense of reality.

Recently, a game was disqualified from an award for relying heavily on AI-generated content. This backlash does not reflect a fear of technology, but rather a player's instinctive rejection of "outsourcing the soul."

I do not reject generative AI; it is an excellent assistant. The problem is that when it comes to bestowing meaning, AI falls short.

In the greatest video games, the moments that touch us most deeply are often those where there is no single correct answer. AI excels at finding the path with the highest win rate within a given set of rules, yet it cannot comprehend why different people, facing the same moral dilemma, would make starkly different choices.

But it is precisely because of this ambiguity that games possess meaning, which in turn catalyzes community. Because your choice differs from mine, we have the motivation to share, to argue, and ultimately, to build a connection.

If a game's ending were merely a standardized, high-score answer calculated by an AI, this interaction would vanish.

This is the fundamental reason why games cannot be fully replaced by AI: what we care about is the human connection built through these authentic choices.

This parallels many forms of content creation. An AI-generated song might be catchy, but if there is no genuine life experience behind it—no creator who can be understood or spark curiosity—the relationship cannot be sustained and quickly becomes tiresome. It fails to unearth new meaning with every exploration and share.

Only works that allow us to see one another within a dilemma can—like Arthur Morgan, the protagonist of the classic Red Dead Redemption 2—live on in the hearts of countless players even years later, triggering reflection across generations and creating true meaning.

If we position AI as a bridge between people, it can be a wonderful facilitator, like a warm, inviting bonfire. But if you place it in the spot where a human should stand, that is the onset of "Mad Cow Disease"—a spark likely to become a forest fire that destroys interpersonal trust, burning beyond all control.

(Interview and Compilation by Yu-Tang You. License: CC BY 4.0)

Recently in Taiwan, influencers have discovered their photos stolen and used to create clone accounts with fabricated life stories. As AI-generated video and audio tools become widely accessible, the barrier to identity theft has essentially vanished. Now, a single photo is all it takes to send someone's digital clone traveling the world—or even speaking on camera.

What's particularly striking about this wave of fraud is who's being targeted: not just celebrities, but "micro-influencers" with fewer than ten thousand followers.

In Taiwan, people have developed strong defenses against the "long-lost classmate" who suddenly asks to borrow money. But when it comes to small-time influencers who share daily life updates and occasionally chat via DM, our guard drops. Bad actors exploit this vulnerability, using AI clones to groom accounts over time and build a genuine sense of connection. When an account you've followed for three years—one that seems authentically human—suddenly starts discussing investment opportunities or weighing in on political issues, it doesn't register as advertising or manipulation.

This is the most dangerous application of AI clones: not just financial fraud, but narrative engineering. The real threat isn't any single piece of misinformation—it's the use of countless clones to manufacture the appearance of organic public discourse. When multiple accounts you recognize all start discussing the same topic, you mistake coordinated messaging for social consensus.

We need systemic change on the scale of how we solved the spam email problem: advanced technology that enables content provenance, ensuring that only content genuinely posted by a person carries an unforgeable digital signature. Laws should also require platforms to open non-personal data to third-party auditors who can identify coordinated bot networks.

Yet crisis often brings opportunity. If we can build the infrastructure to verify authentic identity, AI clone technology might transform from a tool of deception into a bridge for communication.

If scammers can use clones to bypass our psychological defenses, why can't we use "authorized clones" to break through society's echo chambers? This is the concept of the "Shiny Version" clone—a nod to the rare, alternate-colored variants in Pokémon. In the real world, our true selves carry labels—you're "green," I'm "blue." These labels become walls that block dialogue. But if we could create AI clones authorized by our real selves, with different personality settings, they could function as sophisticated "social translators." People on opposite ends of the political spectrum might use gentler "shiny version" clones to engage with communities on the other side.

The recent "We the People 250" AI dialogue experiment demonstrated exactly this: when Americans from opposing camps interacted in a de-labeled environment, they discovered an astonishing 97% consensus. The digital world of the future may become a stage where multiple clones coexist. First, we use technology and law to establish standards for authenticity—then we can confidently deploy clones to facilitate genuine connection. Rather than fighting a painful defensive battle against identity theft, we should proactively master these tools and turn "authorized clones" into opportunities to pierce through prejudice and connect with one another.

(Interview and Compilation by Yu-Tang You. License: CC BY 4.0)

Last week, OpenAI released the world's first openly available safety reasoning model: "gpt-oss-safeguard." While it didn't generate the kind of buzz that accompanies each new generation of ChatGPT, it is a consequential step—one that could determine whether AI content services can continue to earn society's trust.

This comes in response to recent, repeated tragic incidents tied to the improper operation of AI models: for example, U.S. teenagers dying by suicide after interacting with AI chatbots, and the widespread use of these tools to generate large volumes of content unsuitable for minors. This has already prompted Australia to ban children under sixteen from using social platforms by the end of this year, with restrictions extending to AI chatbots; simultaneously, the U.S. Senate is weighing similar legislation.

These developments have made AI giants realize that without the capability to implement and respond to safety policies, the entire industry risks facing sweeping regulation.

At the Paris AI Summit in February, I co-launched the ROOST Foundation with Meta's Chief AI Scientist Yann LeCun and former Google CEO Eric Schmidt. The foundation is committed to working with OpenAI and other major players to develop openly licensed safety reasoning models.

We leverage production systems that run daily—such as ChatGPT and Sora—which not only possess advanced semantic understanding but are also adept at handling various attempts to exploit loopholes and jailbreaks. After eight months of development, the release of this open model signals that AI content moderation can move from a "black box" approach toward transparency.

Using the model starts with inputting a safety policy (covering local laws, organizational rules, and socio-cultural norms), followed by the content to be classified. The model then produces a complete reasoning trace: it determines whether the content complies with the policy and explains its reasoning process—without OpenAI imposing any preset stance.

Notably, it can adapt to local contexts. In Thailand, for instance, insulting the monarch is illegal, whereas foreign nationals in other countries are free to discuss the topic.

When the reasoning process can be publicly audited, not only is the frequency of misclassification reduced, but the boundaries of safety are no longer set unilaterally by Silicon Valley. Instead, communities around the world can adjust safety policies to fit their own contexts at any time.

Because any organization can directly adopt, modify, and deploy its own safety system, this also addresses a long-standing challenge for small and mid-sized platforms: relying solely on human moderation is expensive, often leaving minors exposed to risk or allowing illegal content to circulate unchecked.

This model eliminates those dilemmas as excuses. When perceived risks are converted into publicly verifiable safety mechanisms, the industry can avoid being dragged down by a few non-compliant actors—and sidestep extreme measures such as an outright ban on minors using AI.

All of this suggests that content safety is shifting away from the past "arms races" and closed systems toward transparency and collaboration. A more diverse and open AI future is accelerating its arrival.

(Interview and Compilation by Yu-Tang You. License: CC BY 4.0)

In late September, a U.S. presidential executive order set the final terms for TikTok's American operations: its parent company, ByteDance, must divest, and control must pass to a U.S.-led investment group spearheaded by Oracle and Dell. This "Divest-or-Ban" deal has finally been settled.

Foreign-led services are ubiquitous, so why is the U.S. government taking such a hard line on TikTok? The answer lies in its powerful user engagement and its opaque algorithmic "black box."

In recent years, TikTok has consistently held a U.S. market share of over 25%. Its secret is an algorithm that precisely captures user interests from their behavior, then continually pushes highly relevant content. In this model, people receive an endless, personalized stream of information without needing to actively build social connections or follow accounts, creating an addictive and immersive experience.

However, this platform-monopolized "content curation power" also gives TikTok a formidable ability to shape public opinion and collective perception. In the long term, this could intensify social division and polarization; in the short term, it creates a significant risk of opinion manipulation. For instance, if tensions in the Taiwan Strait were to escalate, the platform could flood feeds with content suggesting "a majority of Taiwanese favor surrender," thereby steering public discourse.

Concerned by this influence, U.S. lawmakers reached a rare bipartisan consensus that TikTok is effectively controlled by the Chinese Communist Party and therefore constitutes a national security risk, which ultimately drove this forced divestiture.

Going forward, TikTok's U.S. data will be managed by Oracle. Updates and adjustments to its algorithm will be reviewed by a board of directors composed primarily of Americans, blocking foreign interference at its source.

Notably, this controversy has also clarified an important legal principle: protecting free speech does not conflict with regulating the algorithmic amplification of that speech.

First, anyone—including accounts that praise the CCP—can still express themselves freely on TikTok. That right is protected. The core issue this ban targets is the amplification mechanism: when the platform’s algorithm *proactively* and *at scale* pushes specific, unsolicited content to users who have not subscribed to it.

However, these platform power structures are not unbreakable. The rise of generative AI presents a new opportunity for users to reclaim their "content curation power." Platforms like Bluesky and X are already exploring such applications. In the future, you will be able to give a platform direct commands—"I want to see content like A, but less of B"—and it will instantly generate a new feed tailored to you.

When content recommendation is no longer captive to a single, closed black box but can instead offer a truly personalized model for every user, the market will open up to more diverse options. At that point, the monopoly that short-form video holds on our attention could very well be broken.

(Interview and Compilation by Yu-Tang You. License: CC BY 4.0)