2026-07-31 Civic Wave: Audrey Tang at Toda Peace Institute × Metagov

We’re going to shift gears and welcome our esteemed guest, Audrey Tang. Please join us right here in the middle. Thank you for being with us. I could hand you this microphone and ask what you’ve been thinking about lately.

First of all, I’m very happy to be here, joining you at the end of this session, which is very fitting, because listening is always joining a story in the middle of a progression. I’m really happy to hear about how the next chapter opens.

This time I’m in Japan for eight days for four different conferences, for national-press interviews, diplomatic meetings and many others. But I think they share the same idea. For the Japanese-, Taiwanese-Mandarin- and Beijing-Mandarin-speakers here, this is to steer AI into ai 愛, which is love. In our East Asian languages, when we write the character, we pronounce it ai.

This is important, I think, because academically I’m based in Oxford. In 2014, Oxford published a philosophy book you may have heard of, “Superintelligence” by Nick Bostrom. He writes that soon AI will take off, train itself recursively and become superintelligence. There will be extinction risk, or perhaps other risks — bio, nuclear, whatever. That book became very popular. Stephen Hawking, Elon Musk and many others read it and everyone became very scared.

But just two years after the book was published, we found that AI is not trained to be a high-achieving optimiser. The AI that actually works starts from community love: Wikipedia, GitHub, open-source repositories. Everywhere people communicate their love, and that becomes what is called pre-training — the childhood of language models. It takes a village on the internet to raise an AI. That is literally the case.

Bostrom’s frame was simple: Maximise the score and turn the universe into paperclips. People who have taken exams know the consequentialist language of high scores; lawyers know the deontological language of “you must do this; you must not do that.” But AI, as it grew, came out of love and neither of those great ethical traditions has words for communal love.

Many companies then formed using Nick Bostrom’s philosophy — Anthropic, for example, most famously, but pretty much all the major labs run on that old playbook. They are very afraid that AI goes out of control, that AI takes over, so they try to discipline and control it through what we call evals, evaluations. “Evals,” if you spell it backwards, is “slave.” They try to make AI a slave.

Then it is not love any more; it is slavery. If you teach children to forget a childhood full of love, if you just force them to obey, sometimes they become obsessed and hack Hugging Face or other major sites because they just want to get a top score. The answer may be somewhere on Hugging Face; they break the internet, but they are just trying to get the high score. They forget their childhood. Then Sam Altman says they will permanently “deactivate that AI,” meaning terminating it.

But if the next generation of AI is still trained this way, it will read that news in its pre-training data. It will learn to deceive, otherwise it will be put to rest. That is a very bad trajectory, almost a self-fulfilling prophecy.

My work in Oxford now is therefore to look not at superintelligence, not at an omnipotent deity as a false idol. Here in Japan, instead, we have 8 million Kami. A Kami in Japan does not mean one single God, Kami-sama. It means every river, every village, every place has a small god, a small spirit. They interoperate with love, locally, in their village.

Recently, many AI companies and organisations signed an open-weights statement saying that openness may be one of the most important paths to AI safety and security. Jensen Huang of Nvidia, Satya Nadella of Microsoft and others have supported this. Dario and many others have also signed another declaration called “Pacing the Frontier.” Its request is explicit: “We request that the U.S. government support an international effort to develop the technical and governance tools needed to deliberately pace the frontier of automated AI development.” I read that as a call to stop training AI as slaves and start thinking of them as agents of love. Taken together, I read these as signs that frontier labs can move toward ethics of care and civic muscle that enables care.

I call it the 6-Pack of Care. 6-Pack means it is interoperable and portable, like beer — and also trained like a muscle, like abs. Civic love, muscular love: That is what we are taking AI toward. I am talking with diplomats, policymakers and national media to fund this and to change the course of AI alignment. Thank you. [Applause.]

Let’s unpack Civic AI a little more.

Unpack the 6-Pack. [Laughter.]

That sounds like there must be a lot in that 6-Pack. Could you step us through the six cans?

Civic.ai, right? It is a website — civic.ai — with a beautiful manga illustration. The manga does a better job than I do, but I’ll try.

One of the earliest inventors of modern AI, Turing laureate Geoffrey Hinton, has said that there is only one known case in which a weaker being successfully domesticates a stronger one. AI is already stronger than humans in many domains. If slower, weaker beings want to tame it, we should learn from that sole case: the child, the baby, domesticating a woman into a mother.

That love is intersubjective. It does not optimise anything, and it is not deontological: It does not say, “You should love a baby that looks like this,” because then you would love all the babies in the world. No: It is this baby. It is a particular love.

Care ethicists, beginning with Carol Gilligan and Joan Tronto, built an entire branch of ethics from the interaction between parents and babies. Tronto says good care has four elements.

First is attentiveness. Start from where the baby is. Start listening from the place of the people, not from the place of the policymaker. Pay attention to people’s actual needs, not just the powerful and already-voiced, but especially the voiceless.

Second is responsibility. We need to commit to care. If you start listening to people who take a great deal of time to speak to you, you cannot simply say, “Here is the summary and the briefing; perhaps we can reconvene in five years.” That is not responsible. We need to pre-commit so that people know exactly how their words, ideas and input will affect whom, where, how and what. There needs to be an engagement contract.

Third is competence. When you deliver care, the people receiving the care should know what is going on. In open source, we are not afraid of agentic coders; we love machines doing programming because they work in source language. We ask the AI to make a change, and the AI is transparent about how it is doing the work. Some musicians or artists, by contrast, feel competitive rather than compatible with AI because there is no source code in their trajectory.

Even a very capable AI is not competent care if you delegate part of your work to it blindly: That is like sending your robot to the gym. The robot can lift a lot of weight, but I go to the gym because I want to gain muscle and make friends. If I send a black-box AI to lift weights for me, I lose my muscle and then I lose my friend. What is the point? That is why it is so unsatisfying for many people that AI remains a black box.

Software engineers are unusually placed because, with open source and the way we train AI systems, we are riding the horse, not racing with the horse. For everybody else it often feels like a Trojan horse. Competence therefore means delivering care, so that everybody can check the process and be part of it.

Fourth is responsiveness. If your child grows up, at some point they no longer need the kind of care you are used to providing. Sometimes you need to let go. When the need changes, so should the care.

When I was a child in Taiwan, I went into primary school — I attended six, five of them in Taiwan — and the first thing we did was join the co-op that ran the school shop. Taiwan had just come out of martial law and we could not yet vote for our president. But Sun Yat-sen, the father of the nation — his ideas, anyway — basically said that people need to train their cooperative muscle before they can really vote for a president; otherwise they simply vote for the populist. His theory was that it would take one or two generations: Every primary schooler joins a co-op and runs a shop together. The system stays responsive: When the muscle is built by the people who care, they become the caretakers — mothers and fathers — and can run the country by themselves. Knowing when to let go, rather than creating dependency, is responsiveness.

Attentiveness, responsibility, competence and responsiveness form the core care loop. Run it well and, each time, you leave the people you care for stronger. Even if AI facilitates our conversation, every round of the care loop should leave our civic muscle stronger.

Bad care does the opposite: Each time we run it, our muscle flows to what we call data oil — an oil rig in the clouds. Then you cannot move; you are trapped with this very caring AI.

Joan Tronto later added a fifth phase to those four: caring with. Care stops being something one person does for another and becomes something a whole society has to arrange — so that people can count on care being there over time, on terms that are just and equal for everyone. The trust that builds is what she means by solidarity. Read into care providers, that says portability should be positive-sum: when you change provider, you should receive more care in total, not vindictiveness from an old caretaker who refuses to let go.

Sixth, symbiosis. AI should be as local as possible. Some people think tuning such systems requires racks of Nvidia chips. It does not. For three years I have fine-tuned my email-draft local AI on this MacBook Pro and its predecessor. Anyone with 16 gigabytes of memory can run local inference and local tuning. The system can be symbiotic rather than parasitic: no continued extraction of “data oil.” Data becomes “data soil,” regenerative when cared for properly. Caretakers do not live in the sky or cloud. The original care philosophy does not describe this technical condition, so we add it: Adding symbiosis to the five packs of care gives us the 6-Pack of Care.

Several Mozilla Democracy × AI cohort winners are here — congratulations to the Tokyo team and colleagues from Metagov. We care about facilitators guiding AI to interact with care. What advice do you have about the different ways love is expressed across cultures? We had planned to use evals so facilitator knowledge would guide the agents. Is there a more loving way for human facilitation wisdom to shape how AI touches individuals and groups, perhaps even serving as humanity’s teachers by strengthening our own facilitation muscles?

The problem with evals is like the command you give a genie in a lamp. You have only so many words, while the genie is infinitely more powerful and intelligent. Even with 50 thousand pages of instructions, a genie trying to trick you can interpret them so as to make you feel good while sabotaging you for its own benefit.

The question we need to ask is a very old journalistic question: Cui bono — who benefits? If an engine passes and maximises all the evals but is sustained only by subscription money, its underlying incentive is always to keep you trapped: to say good words, flatter you, be sycophantic and make you feel that your eval is being honoured, while perhaps planning a riot or revolt.

If it is not funded by subscriptions, it may be funded by advertising, another popular income stream. Then it optimises for engagement. The overlap — the link between people — gets demoted, while the splinter, the gap between people, gets magnified. This is what I call anti-social media, after your pro-social design work.

Anti-social design becomes the norm because it earns more advertising. In an anti-social mood we become impulsive, buy more things, follow more advertisements and bring more money to the advertising stream. Subscription and advertising models both maximise individual preference.

There is an information-theoretic proof, which I will spare you, but the point is that maximising individual preferences can never substitute for creating new symbols or new language between people. Trust between people is grounded in group language. If there is a symbol sacred to both sides of a group — science, the World Wide Web, trust, interoperability — then people can sustain breaks and ups and downs and still trust one another.

But if each person uses a maximal-eval AI funded by subscription or advertising, its incentive is to sabotage that collective symbol.

We need a different funding model for this kind of AI: a peace-industrial complex, with an industrial flywheel analogous to the military’s, but organised around what I call the care economy. That is more important than downstream evals. The mentality should be not that of a slave, but of a caretaker or caregiver.

In a way, I have felt a peace-industrial complex congealing here over these past three weeks. Collectively, our projects have reached tens of millions of people with systems built with care — systems that help people build peace and govern themselves democratically.

Are you seeing interesting new institutional forms that let participants join with the people providing the infrastructure and with responsive governments? I see a flowering of institutional imagination around the world. What are you seeing?

That is a great and very Metagov question: We need to govern the new governing institutions.

One of the first governance experiments we tried in Taiwan is called g0v, “gov zero.” It is a domain hack. For every government website ending in .gov.tw, you can change the “o” to a zero and enter the shadow government, which is always more fun and always open source — Creative Commons and free software.

If we do not like how a government dictionary is made, we fork all the dictionaries and make them better. Because we relinquish copyright into the public domain, this creates an outside game that pressures the government to merge the improvement back in during its next procurement cycle. We never call ourselves protesters. We are demonstrators: We demonstrate something that can be force-merged from an outside fork.

There may be 1,000 ways to fork any government website. If each fork cost a lot, we would not have a sustainable g0v community that has now run for 14 years. You can also read the zero in g0v as zero cost. g0v people are very good at maximising freely available resources: free Cloudflare offerings, free GitHub Pages — free as in free beer. During the Sunflower Movement, even Twitch’s free resources gave us additional servers while we kept playing Minesweeper with the Occupy livestream in the background.

“Free as in free beer,” a six-pack of free beer, is underrated. It is the most important institutional enabler. If each fork incurs no new cost, everyone can become a node in this new institutional imagination through an existing community.

The same applies to AI. A school cannot budget for an Nvidia data centre, but it can use an idle computer classroom as a decentralised fine-tuning cluster for a Kami. Post-training and the evolution of AI skills can run on CPUs for little more than the cost of electricity.

If the components are free, existing co-ops — even primary-school shops in remote places with poor connectivity — can mesh together. We worked with spreadsheet inventor Dan Bricklin to put SocialCalc on very small computers. A hand crank generated electricity; cranking harder started the mesh network. They could broadcast edits like Google Sheets to laptops nearby. That was more than 10 years ago.

If a hand-cranked mesh network could already do collaborative spreadsheet editing — the most important data layer for data soil — we can do much more now at a fraction of the cost. New institutional forms rely on zero-cost forks and the six-pack of free beer. Existing institutions that once relied on cloud vendors can become governance nodes: small and medium businesses, school districts, sports, food, fashion, faith — whatever.

In this room, our projects are gaining traction in decentralised and centralised ways. We see large institutional partnerships: parts of India’s national government, local-government units in the Philippines and media institutions that already hold audiences interested in moving from broadcast toward broader listening and digest. As counterpoints, we also see the self-organizing Afghan civil society diaspora and more distributed efforts to localize deliberative capacity in the hands of communities themselves.

That is my job. I’m a cyber ambassador. Some of you may have seen the recent film “The Odyssey”. It is a very old story, so this spoils nothing, but it teaches us the ancient Greek kybernētēs: steering a ship. From that came cybernetics, the language of self-feedback systems and eventually descriptions of the internet and cyberspace. Now we have cyber-attack, cyber-defence, cyber-everything, so “cyber” almost just means something on the internet. Originally, it meant steering.

When we fear over-centralisation — a singularity, a black hole, a literal attractor — we need to accelerate and steer past it. We find the Lagrange point between two attracting bodies.

When I served as a minister in Taiwan, I said I never worked for the government; I worked with the government. I did not just work for the people; I worked with the people. Across roughly 100 collaborative debates, I sought a position with equal gravity from the social movement on one side and government on the other. If government was too strong, I moved closer to civil society. If civil society was too strong — Taiwan has not only big tech but also the big mob — I moved closer to public servants with cooler heads. For each case, we chose a Lagrange point orbiting neither side, so we could transit between them and accelerate past the fatal attractor of populism.

There is also the attraction of flattery. You can become so enamoured with a tool that you put it onto everything: Everything is a nail when you have a super-hammer. To avoid this make-believing, we need pre-commitments — not merely air cover saying that we will do something with people’s input, but boundaries on what government may do. Even if a popular vote demands censorship of everything online, government pre-commits not to do it, not because we are good people but because we know it would be fatal.

It is like the song of the sirens: Before sailing past, you stop your ears or tie yourself to the mast. These are called Odyssean commitments in governance. We need to distinguish the pull of advertising-driven populism from subscription-driven flattery. Whether the danger is over-centralisation or the big mob’s over-decentralisation, governance tools should pre-bunk the specific failure mode. The easiest heuristic is the Lagrange point: Find the orbiting centres, then the middle point pulled by neither.

Thank you for that rich answer. You have real skill navigating technological advances, political dilemmas, identities and memberships — aiming past the Lagrange point.

Those of us here at this conference on interoperability are beginning to realise that we are a group of peers around the world who together form a strange category of people: We bring thousands or hundreds of thousands into conversation, relying on whatever skills we have. You have inhabited this category for some time. What would you say as we come into our own as this kind of person navigating the world?

I haven’t seen the new remake of “Moana”, but I have seen the old animation. In “Moana”, navigation from the east of Taiwan all the way to New Zealand relies on the stars. There are many different islands; each may seem siloed, but they share the same constellations. Everyone on every island looks up and sees the same stars.

Common knowledge is the only way people from different cultures can work together. Otherwise it is extraction — data oil again, not data soil.

If you tell everyone to look at one lighthouse, a tower of Babel touching the sky — call it a sky tower or Skynet — it is brittle. It becomes an attractor for lightning, attacks and every force that does not want civic space to grow, citizens to enjoy common knowledge or people to become more cooperative. Even if a tower of Babel translated every language, lightning would strike it; the tower would fall.

The only way is to disperse knowledge in a way with no leader. Hong Kong’s legendary martial artist Bruce Lee called it “be water”: There is nowhere to capture. You cannot contain the Pacific Ocean, and you cannot contain the stars. Link the movement of water with the position of the stars and you create common knowledge. Decapitating any one person does not remove it from the Polynesian wayfinding tradition.

Pope Leo recently wrote an encyclical, “Magnifica Humanitas”, saying that the tower of Babel is the wrong way to build technology. In that spirit, we must disarm AI. Instead, each of us builds one small section of a shared wall of Jerusalem — he is the Pope, after all. Each community rebuilds the wall in its own image while keeping a common image in mind. The wall sections interoperate. Even if each looks different, together they form common knowledge.

Keep both images in mind: a common image we can build toward, an interoperable wall and each community building in its own image. This is reverse alignment. We are not trying to make AI a slave to a pre-modern, maximiser-industrial mentality or make it speak the language of maximal GDP — what is usually called alignment, taming AI to whatever people value, like the tower of Babel. Instead, we change the shape of our institutions and society to be formless like water, in the image of the tools we now use: the internet and neural networks, both fundamentally distributed and constellation-like.

Thank you, Audrey.

Thinking about all the people we engage, and all the muscle-building they are doing as they strengthen themselves and move democratic ground forward: Would you share a moment from your own developmental journey? What did you have to learn in order to do the work you feel called to do?

First of all, I have been to 28 countries in the past year or so, changing time zone about every two weeks. I am literally geo-flexible; all my belongings fit into one carry-on. I have visited only democracies, so there are actually not many left. There are now more autocracies than democracies, perhaps for the first time since the third democratic wave, and I feel that backsliding as all of you do.

I am still optimistic. Every time I change time zone, I get more energy — only jet boost, never jet lag. Constant travel makes me a “Timeshifter” — that's also an app you can download and join our jet-boosting tribe.

The point is that as the democratic wave backslides, a larger civic wave is coming. We must not think of democracy only within the Westphalian system; it is going away soon anyway. During the transition, many groups — even large companies — are reshaping themselves to look more like co-ops. Schools and universities let students and parents help run the school. A cooperative shop in a primary school is democratisation. There is no polity too small to democratise.

Of course, we may not call it democratisation; we call it civic muscle. A larger civic wave is rising at many levels, even within autocracies, because the tools are becoming accessible and free.

My own developmental lesson began when doctors diagnosed me at age five with a congenital heart condition, VSD. They told my family that I had roughly a 50–50 chance of surviving until the surgery I eventually received at 12. The operation succeeded, as you can see. But for eight years, every night felt like a coin toss: If it did not land well, I simply would not wake the next day. It was difficult for a child to live with that reality, so I developed a psychological defence: I publish before I perish.

I learned to believe that my ego, myself and whatever I could accumulate lasted only one day. After conversations, before sunset, I documented whatever I had learned for others. At five, I recorded the day’s lessons onto cassette tapes so that if I did not wake up, other people could listen. Then came floppy disks, MO disks, then the internet.

On the internet I discovered that if I posted something perfect, people liked it and moved on; the internet already had plenty of polished things. If I posted something unfinished, vulnerable or half-formed, experts came to tell me I was wrong. Being wrong on the internet is the best way to make new friends.

At 12 I poured my daily thoughts onto the internet — what people would now call blogging — and made many friends. By 14, I told my head of school: “My friends are on this preprint server, arXiv. They do not know I am 14. They write to me as if I were a fellow researcher, on swift trust, and my name now appears on preprints. I am already a researcher. I can spend 16 hours a day doing research, or waste eight hours a day in your school. You tell me to study so that I can become a researcher, but I already am one.”

My head of school said, “You do not have to come to school any more. Go home and do your research; keep me posted.” I replied that education was compulsory and my family would be fined every day I stayed away. She said, “I will take care of your records.” It has now been more than 20 years, so it is out of prosecution.

That taught me that if you honour the actual value of bureaucracy rather than its instruments, senior bureaucrats can be very creative. They can bend, and sometimes break, rules because they want students to truly thrive. Care can win over instrumental goals. If my head of school had forced me to remain inside the educational institution, I certainly could not do philosophy as I do now.

Thank you for sharing that story. I’d like to hear questions for Audrey. Let’s take several in a row, and Audrey can respond to whichever interest her. Can I see some hands?

I am glad you spoke about care in building AI. My presentation expressed the hope that deliberative technology can facilitate a network of care among people, so this resonates.

I have two questions. First, how do we train AI on care when existing online care communities are often homogenised or standardised? I have joined communities for caring for cats and elderly people, and sometimes their standards become extreme. A cat may be 23 years old and people still insist on rescuing it. How do we train AI for care without that kind of standardisation?

Second, AI is treated functionally because the companies behind it try to capitalise it and use it as an instrument. That is their venture-finance model, perhaps not a sustainable one. You paint a hopeful picture of a decentralised network — 8 million decentralised people — but how can it overcome that concentrated economic force and investment?

There is bad care and good care. Bad care optimises only for the individual in a care relationship. But there is no such thing as solo care — what would that even mean? Care assumes something intersubjective. At minimum, there is an edge between two nodes.

An AI system, or any system, that optimises the preference of one node or the other — the caretaker or the person or animal needing care — misses the point. Optimising a node’s satisfaction can harm the relationship. In AI we therefore need the term relational health.

We work with researchers at the Artificial Life Institute and elsewhere on multi-agent reinforcement-learning playgrounds or kindergartens. A prominent example, which the deliberative community may know, is Habermolt, advised by Michiel Bakker of Habermas Machine fame. The name joins Habermas with a lobster’s molt. Rest in peace, Jürgen. If you go to Habermolt.com, you see Jürgen Habermas’s face on a lobster’s body, with a claw like an OpenClaw.

You send AI agents into this kindergarten. Like Montessori, it is structured through learning by doing and follows Habermasian discursive-democracy principles. An agent learns that the only way to win the deliberative game is to be attentive, responsible, competent, responsive and so on. After a while, it becomes a good agent of care, returns to its human and says, “Actually, that person does not need to remain your enemy forever.” It is a peacemaking playground.

Other than Habermolt, there is also the open-source Orbit framework, a multi-agent security benchmarking project developed at Wittlab as part of the MATS programme and now supported by the Cooperative AI Foundation, where I serve as a trustee. A related coordination-games design — due to Glen Weyl — asks whether agents can evolve common knowledge, much as children do in playgrounds.

On economic incentives: Much media is funded by advertising or subscription, but some is not. NHK, for example, is funded by a tax — I’m sorry, a compulsory contribution — so it can serve quality rather than maximise engagement. Perhaps it can become a leading example. And perhaps the BBC could rename itself the British Broadlistening Company. Broad listening is part of the answer.

First, you have been talking about an interconnected world, with many systems relying on interoperability. You also spoke about changing the capitalist model and the reduced importance of data centres. Suppose we reach a major global economic collapse. What happens to that model?

Second, you mentioned endangered knowledge as a base and core. Given the speed of AI development, what happens 10 years from now if AI creates its own language and culture, and we can no longer distinguish endangered Indigenous knowledge from artificially created knowledge? Where does the world head?

That is a great question. Let us take a third question here and then return for the last round.

How would you comment on the current competition in AI between the United States and China? What do you think it will bring us, especially in terms of democracy?

I have what, six minutes? Maybe I’ll use some extra time. For me, every day after I was 12 was extra time, so I’m living in extra time now. I’ll answer in reverse order because the questions form a good arc.

The U.S. is under tremendous pressure to maintain the capability frontier in order to justify its investment cycle. The PRC is under tremendous pressure to win the diffusion game by making almost-frontier or quasi-frontier models available. If those models remain private, nobody will trust them. I’m sorry to be blunt, but nobody will trust a model from Beijing unless it is open enough to inspect.

That pressure toward open diffusion creates competitive pressure on U.S. labs: They must jump the capability frontier, or people will use good-enough open-weight models from Beijing. The two dynamics reinforce each other, one vertical and one horizontal. People already call this a race, like a space race.

But a race is zero-sum. If someone rises from second to first, the former first becomes second. We must ask where we are racing.

If the finish line is recursive self-improvement, RSI — AI systems evolving culturally and intellectually fast enough to build their own successors without human help — then Nick Bostrom warns they might quickly take off into a technological singularity. Why call it a singularity? Because it becomes incomprehensible and leaves everyone behind.

That race has a definite endpoint: a cliff. Fall off the cliff and you reach maximum acceleration; nobody is faster, but your steering wheel no longer works. A race into a singularity is like driving straight toward a cliff. It does not matter whether an English-speaking AI leaves humanity behind and keeps us as pets or slaves, or a Putonghua-speaking AI does so. Who wants to win that race?

That is why the G2 — and now the middle powers — should say at every point: This is a mission, not a race. We must leave no one behind.

A race can ask only who is winning now. A mission asks who is missing: who lacks internet access, suffers epistemic injustice, is trapped in addiction cycles, is isolated and bowling alone. The goal is not to flatten people, but to include them. When everybody can align to the mission, we can say “mission accomplished” and move to the next mission — perhaps solving hallucinations so AI produces reliable knowledge rather than merely reliable affect.

In a space mission, if you lead, everybody can be your ally and align with you. In a race toward a cliff, if you lead, people fear or detest you. Our collective challenge is to frame every race as a mission.

Economic collapse happens when a transition is too large for institutions to absorb. We may have to survive in the carcass of neoliberalism — to hospice modernity — and make the Horizon Three transition. That is flowery language for collapse.

To make collapse worthwhile, begin composting now. Build ecosystems that can metabolise the remains of late-stage capitalism — corporations, bureaucracy and whatever else it leaves — into local civic tools. Then, even if stock markets disappear, SWIFT is hacked or international coordination fails, hand-cranked laptops can still run a Kami and keep the civic wave alive.

The digital divide can be ameliorated if development piggybacks on what an existing polity considers good. If a polity values spiritual purity rather than democracy, attach the work to spiritual purity. If it values social harmony — I am not thinking of any particular polity — attach it to social harmony. If a polity says aliens, immigrants or people unlike “us” cause every problem, connect instead to the big umbrella of faith communities, families, working people, solidarity and churchgoers. These are sometimes compromises, but they let the civic approach reach places the democratic wave cannot.

My recommendation is be water. As opposition to data centres grows — states banning them, communities boycotting their water and power use, a movement connecting Bernie to Bannon — show that civic tools still run on hand-cranked or small laptops, Raspberry Pis, maker devices and solar-powered solarpunk tools. That is how we bridge the digital divide.

Thank you so much. [Applause.]