Audrey Tang

So the idea is to keep exercising it time after time — not to solve it analytically from the get-go.

Initially, in Engaged California with Amy, we thought we should start with that question, by asking teenagers: “What would be the acceptable, pro-social use of social media? We allow those, and then we ban the rest.” And then we talked to their parents, in tables of 10 with random sampling, and the governor and his first partner could pre-commit to whatever the children and the parents could agree on, and that could be the direction.

And we got it all planned out. But on the day of launch, we did not launch, because the week before, the wildfire happened. So Amy had to steer the entire platform from talking about social media and teenagers, to talk about how to recover and mitigate and prevent the future wildfires — which she did heroically and very successfully. But I think the original impulse was exactly right.

Definitely. Yeah. When I’m preparing the next book, at civic.ai, I was struck by this notion of the labs of democracy — laboratories of democracies, as I just said — because it says that democracy is a technology, but it’s a social technology. We can all experiment on it in the 50 labs, and it can produce new inventions. In fact, the town hall, the jury of your peers — many of these things are inventions in America. So what we did in Taiwan was take those ideas and run them at the scale of the internet, and show that they still work. So what you just said — learning from Taiwan — is just really America taking back its own tradition, and now with better tools.

Thank you. Live long and prosper.

Good local time, everyone. Very happy to be here.

I would say that the singularity is definitely nearer. Pretty much all of us realize that Plurality is actually here.

The first few lines of that job description have conclusively been realized. Let me quickly recite them:

When we see the Internet of Things, let's make it an Internet of Beings.

That's the agentic web.

When we see virtual reality, let's make it a shared reality. When we see machine learning, let's make it collaborative learning. When we see user experience, let's make it human experience. And when we hear the singularity is near, let's remember the Plurality is here.

I would say we are done with four out of five. On the fifth line, I would now add: we the people are truly the superintelligence.

You and your audience may have heard the Oxford idea of a singleton , discussed by Nick Bostrom and colleagues at the Future of Humanity Institute. Bostrom's 2014 book Superintelligence describes a technological singularity: if we train an AI system apart from humanity, it may develop goals—things it wants to keep optimizing—that differ from the human community. It could then train a more capable system to realize those goals, which could train an even more capable system, and very quickly lead to what is called a vertical takeoff: a point at which the system can unilaterally optimize a metric—paperclips, or whatever—to the detriment of humanity.

To me, Plurality means the opposite. Instead of one single optimizing agent, we start in the middle of things. We look at communities and see our differences without erasing them. We use those differences as fuel to turn polarization into co-creation. We train bounded systems that are good at bridging across communities, without trying to optimize an abstract metric. They seek to sustain the relational health between our communities.

That's Plurality: from conflict to co-creation.

Around the time Bostrom's book was published, I was involved in the LessWrong community. I contributed to discussions of an idea called coherent blended volition , or CBV—a Ben Goertzel variation on Eliezer Yudkowsky's idea of coherent extrapolated volition, or CEV.

In the original CEV, our digital twins deliberate and extrapolate toward the best possible outcome. In blended volition, every step returns to the community so that people themselves deliberate.

The difference is like sending your digital double to the gym with your gym card. It gets very strong, but you lose your muscles and your friends. CBV is like working with an exoskeleton and scaffolding: at the end of the day, you have exercised your own muscles, and you become wiser together rather than merely hoping that your digital double becomes wiser.

In Taiwan, we used Polis through vTaiwan to realize coherent blended volition around ride-sharing. Uber partners and taxi drivers had preferences; passengers had preferences; people in rural places needed service. We put these perspectives together rather than averaging them or extrapolating a single ideal preference.

People could see one another not as enemies in a polarized way, but as people who could endorse statements that found uncommon ground. We put the resulting ideas into law, and the ride-sharing issue was resolved in Taiwan within a few short months. That was my first implementation of the LessWrong idea of coherent blended volition.

Like many people, I encountered ideas about superintelligence, acceleration and takeoff through science fiction. I'm an avid science-fiction reader, and I would include Harry Potter and the Methods of Rationality as science fiction even though it has a fantasy setting.

It was almost osmosis. Around the turn of the century, I was involved in the cypherpunk movement, where people used mathematics to protect fundamental freedoms—privacy, freedom of association online and so on. That overlapped somewhat with what we would now call the effective-altruist community. From there came the forums, the science-fiction discussions, the Sequences, and so on.

Working with the Siri team was my first experience with a proprietary AI pipeline. I learned that many people at Apple care deeply about accessibility—whether a grandmother in Taipei can interact with Siri correctly and in a culturally sensitive way. It's not only about the technology; it's also about the aesthetic of “it just works.” There should be no setup. It should simply work.

But caring deeply is not enough if the system does not convey the same freedom to fork as free software and open-source software. Even if the people designing Siri care, they have no way to close the loop with the people who use it.

People become like individual humans plucked out of their lives and placed into a beautifully constructed AI loop—like hamsters on a wheel. The wheel rotates quickly and smoothly, but there is no way to steer Siri. It is a walled garden. It goes where the Siri team wants it to go.

There is no fork that a grandmother can ask someone to maintain. The most anyone outside Apple can do is complain loudly. Sometimes Apple fixes the problem; sometimes it introduces a regression. But there is no satisfactory way to close the loop.

Not even only for customization. People who speak a dialect or have a regional accent may have a hard time being recognized, and there is no way to fork the system.

With free software, a fork often turns into a merge if it is widely adopted. The system gets better and better; people build on each other's work like Lego blocks. But it is very difficult to fork Siri. If you have a HomePod and want to add language recognition, it is almost impossible.

Carefully.

First, a clarification: the single-digit figure was in 2014, not 2016. I had already begun working with the Cabinet in 2014 as a reverse mentor—a young adviser to ministers, usually people over 35. Taiwan had a system in which people under 35 could advise Cabinet ministers. I was 33.

The system was prompted by the single-digit approval rating and huge polarization. Taiwan was very connected to the Internet, so we were at the front row of what we now call antisocial media. Recommendation engines stopped putting common followers into the same feed and began pushing individual posts that maximized dunking. That single change in the recommendation algorithm polarized society.

People peacefully occupied our parliament for three weeks in protest against a trade deal with Beijing that would have allowed Huawei and ZTE to invest in Taiwan's newspapers, telecommunications and cybersecurity. I helped with broadcasting so that the occupation could remain peaceful.

But instead of only protesting, we ran a demonstration. Half a million people on the street, and many more online, converged in tables of 10. Each person talked with nine others. The conversations were facilitated, broadcast, and transcribed. Every day we could say: here is the newly discovered uncommon ground, and here are the remaining issues we have not yet agreed on. We should work on those today.

After three weeks, we converged on a coherent set of demands, which the speaker of the parliament adopted. It was one of the few Occupy movements that got somewhere—a safe landing, not simply a safe takeoff.

That holding pattern, that facilitation, was the first national-scale application of technology we had used online for consensus-making, deployed in people's lived experience. Once it became a peak experience people could refer to, there was no excuse not to use it for controversial issues nationally.

President Tsai Ing-wen said in her 2016 inauguration speech: “Before, democracy used to be a showdown between opposing values. From this point onward, democracy is to become a conversation between many diverse values.”

We ran more than a hundred collaboration meetings over the following years. I was no longer a young reverse mentor—I had turned 35—so I was brought into the Cabinet as a minister.

By 2020, trust was back above 70 percent, even at the height of the pandemic. Taiwan lost only seven people to COVID-19 that year, in part because a high-trust fabric enabled rapid visualization of mask supplies, contact tracing without sacrificing privacy, and other innovative measures.

In a sense, we invited ourselves in. I literally took a 450-meter Ethernet cable into the occupied parliament to livestream it. It was direct action; nobody invited us.

We also saw that it was possible to reach agreement by changing the political question. Instead of asking, “Do you support this party or that party?” or “Should we vote yes or no?”, we ask: What should we all be doing together?

Before that, people had only proxy mechanisms for political sense-making: polls, whose topics are determined by a proprietary system, and elections every four years, where each person uploads a few bits. None of these has enough resolution to help people come together around bridging statements.

One of our main contributions was to improve democracy's bandwidth. On issues such as Uber, Airbnb and Bitcoin, people could set an agenda by uploading a statement, then upvote and downvote one another's statements. Through facilitation, we could find surprising common ground and make it common knowledge.

Broadcasting technology existed before the Internet. Broad listening developed later. We managed to create a symmetry of attention between broad listening and broadcasting.

Democracy is entirely technology—not only digital technology, but a social technology for coming to terms and making decisions together.

Open-space technology, for example, lets people at unconferences and Foo Camps set their own agendas, vote with their feet, and so on. That is social technology, even if it can be replicated digitally.

Seeing democracy as technology is an unlock. It lets us see democracy as a set of interlocking systems that can be improved: increase the bandwidth here, reduce the latency there, close the loop sooner. Each component can be upgraded in the same way we upgrade semiconductor chips.

If we limit the discussion to digital technology, we put ourselves in a narrow lane—for example, improving tax-filing efficiency. That is useful, but it does not change democracy's bit rate.

We have already seen many implementations in the U.S. I advise California state government's Engaged California platform, which used bridging technology to consult people in Eaton and Palisades after the Los Angeles fires about wildfire mitigation and prevention.

A second consultation involved state employees and how to introduce AI systems and digital transformation while keeping judgment with people: technology on tap, not on top . The state employees offered thoughtful ideas. It is not a chainsaw; it is a chain reaction.

A third round asked Californians whose jobs are affected by AI about apprenticeship, belonging, care and dignity. Across ideological differences, people found a common vision: AI should serve communities rather than pluck people out of their lives and put them into an optimization loop.