educating for democratic resilience

On Friday, I’ll be speaking on a panel about “Educating for Democratic Resilience” at a conference on Democratic Resilience at Boston College’s Clough Center. Here are some notes:

All my work is based on the theory that democracy is more resilient when many people belong to self-governing, autonomous associations. That was Alexis de Tocqueville’s insight, subsequently developed in different ways by John Dewey, Hannah Arendt, and others, and tested in recent decades by illustrious social scientists like Elinor Ostrom (who won the Nobel Prize for her work on this topic) and Robert Putnam.

I believe in it–not as an article of faith, but as a useful model. It does not always turn out to be right empirically. Democracies depend on many things, not simply on associations; and not all associations support democracy. But the model often explains phenomena that we observe in the world. More importantly, it often generates practical insights that we can use to act. Basically, by strengthening associations, we can improve the condition of our democracy. This is one of the main levers that we can pull.

Here is just one example of an empirical finding. The most recent available American National Election Study (2020) asked several items about civic participation, including this one: “During the past 12 months, have you worked with other people to deal with some issue facing your community?” That is a measure of Tocquevillian civic participation.

The ANES also asked several items relevant to the resilience of democracy. For instance, it asked whether “Much of what people hear in schools and the media are lies designed to keep people from learning the real truth about those in power.”

When controlling for education, age, gender, race, and ideology, respondents who participated in groups were much less likely to hold a hostile view of media and schools. Conservatives were more likely to be hostile, but when I included ideology in the model along with civic participation, ideology was no longer significant. In other words, irrespective of ideology, people who work with others to address local issues are more likely to trust schools and media.

From my perspective (which is contestable, obviously) approving of Donald Trump is a problematic sign. A person may vote for him for various reasons, but appreciating him as a leader suggests a lack of support for democracy. In the 2020 ANES, people who worked with others on community issues strongly disliked Trump. As expected, conservatives were more likely than liberals to approve of Trump. However, once I controlled for participation in local groups, conservatives felt no differently from liberals about Trump.

These are selected statistics, which should never be persuasive on their own. However, they illustrate the common patterns that are central to the work of Ostrom, Putnam, and others.

What does this model mean for education?

To form and sustain groups requires practical know-how. Traditionally, the most common way to obtain such knowledge was by growing up around successful organizations, but such experience has become rare as civil society has shrunk. Although US schools still teach American history and civics (with a focus on government), they do not regularly teach how to manage effective groups. Meanwhile, changes in the economy and media have created new challenges for voluntary associations, so that traditional know-how may no longer suffice.

The weakness of associational life has been evident recently. For example, when Donald Trump was elected president in 2016, informal groups popped up almost everywhere. They often attracted people who had never been involved in politics before. Stereotyped in the media as suburban white women, these citizens were informally named “The Resistance.” About a half-million of them attended the Women’s March in Washington on Jan. 21, 2017, with another 5 million marching in their home communities.

But the Resistance proved evanescent because the nascent groups mainly encouraged their members to support famous, large national organizations. About 350,000 people donated to the ACLU in just one weekend during Trump’s first month as president. People also shared and encouraged each other to follow news from national outlets, and digital subscriptions for The New York Times and The Washington Post tripled under Trump. A bit later on, many people gave money and time to Democratic Party candidates.

But this generosity and energy did not build local associations. Most–although not all–of the nascent groups faded away. As Erica Chenoweth and Zoe Marks observed, many committed and skilled activists emerged, yet we have “no established, organizational infrastructure that can facilitate sustained collective action across a multiracial, multiclass constituency.”

I believe one reason is that too few Americans understand the nuts-and-bolts of associations. An initial meeting often draws many concerned people, who may use the time to express strong feelings and to share ideas, some of which are excellent. But nothing concrete is decided. The second meeting draws some of the same people along with many newbies and recapitulates the first discussion. By the third meeting, most people are too frustrated to continue. The group needed an agenda, a committee of accountable, volunteer leaders, a decision-making process, and a budget.

The resistance in Minneapolis this winter may suggest a more optimistic story. In January, The Atlantic’s Robert F. Worth reported that local groups there had trained 65,000 residents in nonviolent civil resistance since the previous month. That is evidence of impressive organizational muscle.

Thomas Friedman recently celebrated the Minneapolis movement as “neighboring.” He quoted a local business executive who described its decentralized and participatory structure. “There were hundreds of leaders of this movement,” he said, “and I don’t know a single one of their names.” Geneva Cole argues that Minnesota groups that launched or expanded in the wake of George Floyd’s murder in 2020 have become strong enough–both as organizations and as a network– that they were able to accomplish disciplined mass action this winter.

That is a promising sign, but in general, associations remain too weak, and too few Americans have the necessary practical skills. Schools and colleges should devote more attention to the nuts-and-bolts of effective groups.

I would also love to teach older adults how to do those things, but it’s unrealistic to offer civic education to millions of older people. An alternative is to make processes easier so that groups can focus on the substance of their work.

After all, associations depend on documents: recruitment emails, agendas, minutes, job descriptions, and budgets, among many others. Say what you will about Large Language Models (LLMs), but they can quickly draft documents. I’ve launched an experimental helpdesk that uses an LLM that is trained on specific documents about voluntary organizations and instructed to respond to queries in specific ways.

The target user of this helpdesk is a newcomer to civic life who is highly concerned about a current problem: I call these people “Alarmed Complete Newbies” or ACNs. The helpdesk encourages them to find and join groups that already exist. If users needs to launch a new group or help to strengthen an existing one, the AI nudges them to request useful documents, which it then drafts for them.

The goals of this project are, first, to enable people to make progress together even if they never learned how to manage voluntary groups; and, second, to learn principles and skills from this experience.

The helpdesk is just an experiment, not a panacea. The underlying idea is that democracy and local communities will be stronger once more groups of concerned people gell into effective organizations that collect time and money from their own members, use their own resources to build their own capacity, make collective decisions, and act effectively. People must learn how to do those things. That is what I mean by “education for democratic resilience.”

making this site feel alive

Web designers use the phrase “social proof” for visible evidence that people are visiting and using a website. Without signs of life, a website looks about as inviting and reliable as an urban street without any pedestrians.

For almost two decades, my main social proof came from Facebook. I had installed a counter that showed how many times people shared any post or “liked” or commented on anyone else’s share. The maximum number of engagements for any single post reached almost 6,000 immediately after the 2016 election, but every post showed at least a few. Although I could not directly see most of the Facebook activity (which occurred on strangers’ pages), I think these statistics were accurate.

Early this month, Facebook stopped sharing engagement statistics. I have switched off the counts, which would look like zeroes even though people are still sharing my posts on Facebook. People often comment there or on LinkedIn, but those discussions are invisible here.

One suggestion would be to move to Substack, or at least cross-post there. But I have been blogging for so long (since January 2003) that I have seen various platforms come and go–and some have gone bad. I’m glad that I have kept plugging along on my own website.

I’ve added a line of code that shows the total number of visitors to this homepage since 2023 (currently: 280,441). It appears on the right-hand navigation bar. I’m hoping that it indicates some life without just looking braggy. I could present visitor data for each post, but that would require more complicated coding, and I’m not sure that it would add anything. Suggestions are welcome!

(This is also an opportunity to remind you that you can subscribe to get a free weekly email with my recent posts. The subscription link is at the bottom-right of this page.)

Jurgen Habermas (1929-2026)

Jürgen Habermas died on Saturday. His death has been the occasion for several substantial and interesting obituaries. So far, I prefer Gal Beckerman’s in the New York Times.

I took a seminar on Habermas in 1988, when I was a college junior. Georgia Warnke was the professor, and I have kept her useful packet of readings to this day. Habermas crystallized my early thinking about politics and philosophy and has remained a pillar for me ever since. I discuss him in most of my books, with the most general and extensive presentation in chapter 5 of What Should We Do? A Theory of Civic Life (2022) The title of that book basically captures Habermas in a phrase. I have also recorded a 29-minute introductory lecture on him.

It is misleading to treat Habermas as a proponent of rational, civil discourse. (See “Habermas with a Whiff of Tear Gas,” 2018). I suspect that more Americans have read Iris Marion Young’s critique of Habermas (“Activist Challenges to Deliberative Democracy, Political Theory, 2011) than have read Habermas itself. The late and lamented Iris Young caricatured him in that article. If Habermas wanted everyone to talk calmly all the time, then why did he conclude his two-volume magnum opus, The Theory of Communicative Action, with a celebration of disruptive social movements?

Habermas lived so long and became famous so early that his public role is itself an interesting phenomenon. Apparently, Ronald Dworkin remarked that even Habermas’ fame is famous, and it is worth asking why someone who wrote such thorny theory occupied the position of (arguably) the most influential German thinker for half a century.

I took a whole semester course on Habermas–in English, on the other side of the Atlantic–when he still had 38 years ahead of him. That is an indication of his stature. But it does not mean that he shaped the course of history, or even of scholarship.

In Postwar, Tony Judt discusses “the demise of the continental intellectual.” On May 31, 2003, Habermas plus Jacques Derrida, Umberto Eco, Richard Rorty, and several other leading thinkers published coordinated essays against the Iraq War in distinguished European newspapers. The result “passed virtually unnoticed. It was not reported as news, nor was it quoted by sympathizers. No-one implored the authors to take up their pens and lead the way forward. … The whole project sputtered out. One hundred years after the Dreyfus Affair, fifty years after the apotheosis of Jean-Paul Sartre, Europe’s leading intellectuals had thrown a petition–and no one came” (pp. 785-7).

I am not quoting Judt today to cast aspersions on Habermas, whose work was deep and broad. I suspect that changes in media and communications have reduced the influence of serious intellectuals. Besides, Habermas may never have wanted to be the new Jean-Paul Sartre. Elsewhere, I have discussed how Michel Foucault (born just three years before Habermas) deliberately shunned the role of the “universal intellectual”; and perhaps we are better off without such people. By all accounts, Habermas welcomed criticism and learned from a wide range of responses. He modeled what he advocated: listening and learning from others. I think his work will long outlive him.

See also: introducing Habermas; saving Habermas from the deliberative democrats; Habermas with a Whiff of Tear Gas: Nonviolent Campaigns and Deliberation in an Era of Authoritarianism; Matthew G. Specter, Habermas: An Intellectual Biography, and many other posts.

Prisoner’s Dilemma in the Gulf

Although many people are using principles of game theory to analyze the Trump/Iran war and to predict the next steps, I haven’t come across an explicit model. Any model drastically oversimplifies reality but also serves to clarify assumptions.

The model that I present is essentially a Prisoner’s Dilemma. For each side, it is better to continue deadly offensive operations than to cease, regardless of what the other side does. Therefore, the model predicts that the war will continue (bottom-right box) even though both sides would be somewhat better off with a mutual ceasefire (top-left). That’s how a Prisoner’s Dilemma works.

The model presumes that both sides have the capacity to continue offensive operations–that the US won’t run critically low on munitions and Iran will retain drones, missiles, mines, and possibly sleeper cells abroad. To the extent that the US and Israel have a plan, it is to destroy Iran’s military assets so that Iran cannot choose to continue to bomb or lay mines. I cannot assess whether this is possible, but it seems doubtful. The recent reduction in the tempo of Iranian strikes may simply reflect a strategy of operating for a longer period.

The model is symmetrical, which is misleading. The Iranian leader, Mojtaba Khamenei, has already lost his father, wife, daughter, son-in-law, and 14-month-old granddaughter in a strike and could be killed himself. More than 1,000 Iranians (and probably many more) have died so far. Donald Trump is much safer, as are American citizens–presumably. On the other hand, Trump’s political fortunes are sensitive to exactly what happens in the war, whereas Khamenei and his team are trying to survive. For them, a difference in the length of the conflict or the number of casualties may be immaterial.

Another way that the model simplifies is by reducing the whole war to two parties. Israel is not shown. Nor are other major countries, such as Russia, China, Saudi Arabia, and others. Also, the two sides are shown as if they were unitary, but there are internal conflicts on both sides. In fact, each leader may care most about the struggle with his own domestic opponents. However, to some extent, that dynamic is captured by the outcomes shown in the model. For example, each side benefits domestically from being able to claim victory credibly, and each side loses domestically if it cannot.

See also: making our models explicit; Brag, Cave and Crow: a contribution to game theory

How do we know whether fish are happy? How do we know whether we are? (Zen, Aristotelian, and Taoist discussions)

When you watch fish swimming around in very cold water, they look fine. Human beings have a protein, TRPM8, that reacts to cold and affects our nervous system, causing discomfort or even pain when the temperature goes down. But fish do not have any TRPM8 (Yong p. 138). Thus we can infer that fish do not sense cold in the way we do.

This does not mean that we know what cold is really like, while fish do not. Nor does it mean that our pain is nothing real, as if we can make it go away by disbelieving it. Nor does it mean that we know what it feels like to be a fish. But we can perceive a difference between species.

Long before anyone knew about proteins, the behavioral difference between us and fish was obvious enough that it served as an example for several thinkers who asked whether experiences like pleasure and suffering are subjective. More deeply, they asked what happiness is.

Japanese Zen Buddhism uses the term kyogai. Often translated as “consciousness,” it literally means “boundary” or “bounded place,” deriving originally from the Sanskrit word visayah, in the sense of a pasture that has a boundary. The Buddhist Abbot Mumon Yamada (1900-1988) taught:

This thing called kyogai is an individual thing. …. Only another fish can understand the kyogai of a fish. In this cold weather, perhaps you are feeling sorry for the fish, poor thing, for it has to live in the freezing water. But don’t make the mistake of thinking it would be better off if you put it in warm water; that would kill it. You are a human and there is no way you can understand the kyogai of a fish.

I think the upshot here is humility: if things seem and feel very different to creatures that have different senses, we cannot really know how things are. We should be compassionate, but that is harder than it may at first appear because it requires knowing what another feels. It would not be compassionate to move carp to a warmer pond. Our humility must temper even our compassion.

Aristotle wants to distinguish wisdom, which is knowledge of objective truths, from practical wisdom or phronesis, which allows us to act well. For example, “straight” (using the term from geometry) always means the same thing. The line that takes the shortest distance between two points is straight, regardless of whether any creature sees it as such–or sees it at all. In fact, a line would be straight even if there were no sentient creatures. Hence geometry is a part of wisdom.

However, says Aristotle, different things are healthy and good for people and for fish, and human phronesis involves doing the healthy thing for us, not for them. The “lower animals” also have practical wisdom because they also know what to do. If we try to convince ourselves that our phronesis is wisdom because we are higher than fish, we are foolish because there are things far more divine than we are (NE 1143a).

The upshot, for Aristotle, is that each creature has its own nature, and the proper definition of happiness is acting according to that nature. This means that a fish is happy if it swims around in the cold, not because that behavior feels good to it, but because happiness is accordance with nature. One distinguishing feature of human beings is that we can also know wisdom, or glimpses of it, by studying things higher than ourselves. Thus, for Aristotle, observing the behavior of fish does not really encourage humility. It directs us to identify our proper nature and its place in the cosmos as a whole.

Now here is a passage from Zhuangzi:

Zhuangzi and Huìzi wandered along the bridge over the Hao river. Zhuangzi said, ‘The minnows swim about so freely and easily. This is the happiness of fish’.

Huìzi said, ‘You’re not a fish. How do you know the happiness of fish?

Zhuangzi said, ‘You’re not me. How do you know I don’t know the happiness of fish?’

Huìzi said, ‘I’m not you, so indeed I don’t know about you. You’re indeed not a fish, so that completes the case for your not knowing the happiness of fish’.

Zhuangzi said, ‘Let’s go back to where we started. When you said, “How do you know the happiness of fish”, you asked me about it already knowing that I knew it. I knew it over the Hao river’. (17/87–91)

I have virtually no knowledge of Taoism or its context, so it is risky for me to venture an interpretation. But I think the idea here is that neither of the men in the story can know the other, let alone the fish, and therefore all knowledge (including of one’s self) is illusory. However, Zhuangzi was right in the first place. “This” was the happiness of fish. He could not know its content or how happiness would feel to a fish, only that because fish were being fish, they were happy.


Ed Yong, An Immense World: How Animal Senses Reveal the Hidden Realms Around Us (Penguin Random House, 2022); Yamada as cited in Victor Sogen Hori, “Koan and Kensho in the Rinzai Zen Curriculum,” in The Koan: Texts and Contexts in Zen Buddhism (2000); Zhuangzi. The Complete Writings, translated by Chris Fraser (Oxford World’s Classics, p. 200). I translated Aristotle from the 1894 Clarendon edition on https://scaife.perseus.org/, but I have paraphrased here because the literal text is thorny. See also: some basics; Verdant mountains usually walk

AI as Satanic

“Now there was a day when the sons of God came to present themselves before the LORD, and Satan came also among them. And the LORD said unto Satan, Whence comest thou?

Then Satan answered the LORD, and said, From going to and fro in the earth, and from walking up and down in it” (Job 1:6)

Iain McGilchrist quoted this verse in a keynote that I just heard him deliver at a conference at Duke. McGilchrist ranged from neuroscience to theology in a long and rich talk. His premises were scientific, metaphysical, moral, and political, and I wouldn’t endorse them all. But his description of artificial intelligence as satanic is worth serious consideration on its own.

For me (although perhaps not for McGilchrist), Satan is a metaphor. But we need metaphors or models to make sense of phenomena like AI, and Satan provides a valuable alternative to some other metaphors, such as AI as a tool, a machine, a mind, a person, or a social organization.

The Satanic metaphor draws our attention to temptation, which is Satan’s favorite trick. It presents AI as not new but instead as an appearance of things that have been walking to and fro all along, such as greed and power-lust. It explains why AI might seem like a god to some (for instance, Silicon Valley tech-bros), since Satan is known to appear as a false savior. Large language models also speak to us as if they were people, talking sycophantically in the first-person singular, much as Satan does. (“Then Satan answered the LORD, and said, Doth Job fear God for nought?”) Finally, the metaphor poses the classic question of whether AI is an active force or rather a manifestation of human freedom.

See also: Reading Arendt in Palo Alto; the design choice to make ChatGPT sound like a human, etc.

Don’t Call them Underdogs

I wrote a review of a new PBS documentary about urban debate leagues for Education Next. It was published today, and it begins:

You may have seen a movie in which teenagers experience grave injustice and then enter a prestigious competition where they prove to the world that they are smart. The competition might be the AP math exam (Stand and Deliver, 1988), the National Spelling Bee (Akeelah and the Bee, 2006), robotics (Spare Parts, 2015), or chess (Queen of Katwe, 2016), to name just a few.

Typically, one charismatic adult believes in the kids, inspires them to confront their doubts and society’s stereotypes, and leads them—through setbacks—to an exciting victory that demonstrates their dignity and character as well as their skills.

Immutable, a new documentary film produced by Found Object and available for streaming at PBS on March 6, is much better …

the USA at 250: constitutional crisis

Last night, I was part of The United States at 250: A Tufts Faculty Panel. In a full room of students, Tufts historians and political scientists with various specialities addressed the question: “Where are we as a nation and what’s next?”

I offered the following argument. I have derived it from other people’s scholarship, and I am not sure it is true, but I think Americans should consider it.

We’re marking a 250th anniversary because 1776 began the period that concluded with our Constitution. However, the Constitution is now in a deep crisis. We may now be coming to the end of a 250-year period. The reasons are not named “Donald J. Trump.” These are three deeper reasons.

First, presidential republics have a fatal flaw, and none except the US–and arguably, France–has survived for a long period (Linz 1990). Whenever opposing parties control the legislature and executive, they are motivated to battle at the cost of the republic.

For most of our first two centuries, we did not have regular impasses, because the Democrats were divided into two major blocs, resulting in at least three effective parties in Congress; and most presidents could build a working majority. However, when conservative Democrats defected to the GOP, the two parties polarized. Since 1990, it has been possible to govern in the ways envisioned by the Constitution only when the same party has controlled both elected branches (6 periods of 14 total years). During the other 24 years since 1990, presidents have tried to rule by executive order and Congress has tried to undermine the current administration. We have moved ever closer to complete constitutional breakdown.

Second, the Constitution enacts three branches of government: the executive, legislative, and judiciary. Since at least 1932, we have actually had another branch: the administrative and regulatory agencies, staffed by about about 2.2 million federal employees who are understood to be insulated from politics. They follow rules, norms, and principles of their own that are not mentioned in the Constitution–for example, scientifically measuring the costs and benefits of proposed policies and publishing drafts of policies for public review and comment. Perhaps we have also had a fifth branch, the national security apparatus.

We muddled through for decades by pretending that the agencies were part of the executive branch while the White House usually deferred to them. Under a 1984 Supreme Court decision, Chevron, the courts also generally deferred to agencies’ decisions. Meanwhile, Congress intentionally gave agencies broad scope. The regulatory state was largely independent from the other branches.

However, in 2024, the Court repealed Chevron with the Loper decision, allowing courts to review agency decisions. And Donald Trump has fired and replaced many civil servants and members of so-called independent agencies for openly political reasons.

Libertarians argue that we shouldn’t have had a massive federal government in the first place. And populists of right and left argue that an elected president should be able to determine policies. A left populist may celebrate the opportunity for a Democratic president to reshape the agencies at will now that they have lost their independence. I think, however, that every country with an advanced economy has built an elaborate and quasi-independent regulatory apparatus that applies science and managerial acumen to generate benefits that voters want. We may not have that anymore.

Third, Congress no longer legislates, in the sense of passing or reforming substantive statutes. In 1965 alone, Congress passed at least 10 landmark bills that established agencies or dramatically altered national policies. As recently at the 1980s, Congress sometimes legislated by substantially cutting regulation. But Congress has arguably passed no major laws in this whole century so far.

For example, Congress has never passed legislation explicitly about the climate. Federal regulatory agencies have used 1970s Clean Air Act (written before Congress was really aware of climate change) to try to regulate carbon. Likewise, federal financial laws were passed before cryptocurrency; and the Telecommunications Act of 1996 still governs despite some minor new developments, such as social media and smartphones.

In sum, we can’t handle frequent periods of divided government; our massive regulatory state lacks a constitutional basis; and the branch in which “all legislative power” is “vested” no longer legislates.

It is possible that we will keep driving ahead, frequently bumping into the Constitution’s guardrails but somehow staying on the road for decades.

Or we could see substantial reforms–major constitutional amendments or new voting laws that change the basic structure. (For instance, proportional representation would transform Congress–for better or worse–and could be accomplished by law.) I sometimes wonder whether our incompetent and blatantly authoritarian president is a blessing, alerting people to the need for reform without successfully consolidating power.

Or we could see a collapse. The typical final act of a presidential republic is a soft dictatorship. That’s why this topic is important to discuss on our 250th.


Prophetic works include Juan J. Linz, “The Perils of Presidentialism.” Journal of democracy 1.1 (1990): 51-69 and Theodore Lowi, The End of Liberalism (1969). See also: rule of law means more than obeying laws: a richer vision to guide post-Trump reconstruction; on the Deep State, the administrative state, and the civil service; the Constitution is crumbling; etc.

What Counts As Success? Assessing The Impact Of Civics In Higher Ed

On February 18, the Alliance for Civics in the Academy hosted a webinar on “What Counts as Success? Assessing the Impact of Civics in Higher Ed” with Trygve Throntveit, Rachel Wahl, Joseph Kahne, and me.

We discussed some of the advantages of developing reliable and consistent measurements of civic education, particularly the opportunity to learn from data and the need to be accountable. We also discussed some drawbacks and risks, including Campbell’s Law (a remark by Donald T. Campbell): “The more any quantitative social indicator is used for social decision-making, the more subject it will be to corruption pressures and the more apt it will be to distort and corrupt the social processes it is intended to monitor.”

We asked ourselves who should use assessments, and for what purposes. For example, it is a different matter for a college professor to get feedback from the students in a course or for a university to measure student outcomes. I thought the conversation was both intellectually serious and relevant to practice.

Panelists:

  • Rachel Wahl: Associate Professor in the Social Foundations Program, Department of Educational Leadership, Foundations, and Policy at the School of Education and Human Development at the University of Virginia
  • Joseph Kahne: Ted and Jo Dutton Presidential Professor for Education Policy and Politics and Director of the Civic Engagement Research Group at the University of California, Riverside.
  • Trygve Throntveit: PhD, Research Professor in Higher Education and Associate Director of the Center for Economic and Civic Learning (CECL) at Ball State University.

I was the moderator. The video is here:

AI as the road to socialism?

Just under 40% of occupations jobs in the USA may be replaced by AI if it proves to be as powerful as some think it will be.* As a thought-experiment (not as a prediction), imagine that 40% of current workers, or about 60 million Americans, are no longer employed because AI does their former work. However, their former employers are still producing the same goods and services. These firms are therefore far more profitable.

The profits flow to shareholders. Individuals are already taxed now, but with tens of millions of new people out of work, there would be more political will to raise taxes. Therefore, imagine that a set of competing tech. firms have become responsible for a substantial portion of the whole economy and are heavily taxed. The proceeds flow back out of the government in the form of cash payments, perhaps a Universal Basic Income (UBI). Recipients are able to pay for the goods and services that machines now heavily produce. Meanwhile, jobs that are not automated are relatively well paid, because the UBI enables individuals not to work unless they want to.

Silicon Valley ideologues like Sam Altman tend to envision a UBI on the scale of $1,500/month. Today’s white collar workers earn a median income of about $5,000/month. Therefore, the kind of UBI that Altman imagines would result in a massive loss of income for millions of people, which would have cascading effects. All the former office-workers who now live in nice houses and buy costly services would have to give those up, causing additional unemployment and declining demand for the products produced by the tech. companies.

However, the public might demand a UBI more like $5,000/month. Then half of today’s white collar workers would be worse off, but half would be richer–and none would have to work.

Looking a little more deeply, we might notice that AI tools are not simply machines. They process text and ideas that human beings create. Therefore, we could see this whole system as deeply socialistic. Billions of people’s mental output would be processed by relatively few AI models that produce generally similar output. These tools would generate profits that would be distributed equitably to the people. Most individuals would receive $5,000/month, neither more or less. Since they wouldn’t have to work, they could spend their time as they wish. And–via electoral politics–the people could regulate the AI companies.

It all sounds like Karl Marx’s early utopian vision:

In communist society, where nobody has one exclusive sphere of activity but each can become accomplished in any branch he wishes, society regulates the general production and thus makes it possible for me to do one thing today and another tomorrow, to hunt in the morning, fish in the afternoon, rear cattle in the evening, criticise after dinner, just as I have a mind, without ever becoming hunter, fisherman, herdsman or critic. (The German Ideology, 1845)

Problems:

  1. The transition to this imaginary equilibrium might be chaotic, violent, and destructive– perhaps to such a degree that we wouldn’t make it through.
  2. Modern people tend to derive dignity and purpose from work. Perhaps this is a contingent fact about today’s society. In the future, maybe we will be happy fishing in the afternoon and writing criticism after dinner. Or perhaps we will be deeply depressed without jobs. To make matters worse, would we really spend our time writing or playing music or even fishing, if machines can do all those things better? This is not a problem that confronted Marx, because in his day, machines automated tasks that people would not do voluntarily.
  3. It’s easy to posit that the people can tax and regulate AI companies through the device of a democratically elected government, but millions of people’s interests and values do not automatically turn onto one public will. Interest groups have agendas and power. At large scales, democracy is complicated, messy, factional, and very easily corrupted. In this case, the AI companies and investors would be political players.
  4. It could be that not only AI companies but also the models themselves become players that have interests. Sentient, self-interested AI is the source of much current anxiety. I am not sure what to make of that concern, but it surely adds a layer of risk.
  5. I have discussed the USA alone, but how would this look for people in a country without competitive AI companies? US citizens might demand that Silicon Valley provide them with a UBI, but it’s implausible that US citizens would demand a global UBI. And how would people in Africa or Latin America gain leverage have over US policy?
  6. For the people to govern the “means of production” (to use the Marxist term), they must understand it. Industrial workers have understood industrial machines, so they can run factories. None of us understand Large Language Models, not even the developers who design them. Can we, therefore, govern them? (Having said that, we also do not fully understand the human brain, yet people have governed people.)
  7. Even if democracy works well, the public will not really control AI. So far, I have suggested that AI is like a machine that can be regulated by people through their government. But AI also shapes our knowledge, values, and understandings of ourselves in ways that are controlled either by the designers and owners of the platforms, or by the machines, or–perhaps–by no one at all. Evegeny Morozov writes:

Now imagine a future in which a [public] Investment Board, under pressure to avoid bias and misinformation, mandates that AI systems be fair according to agreed metrics, respect privacy, minimize energy use, and promote well-being. Call this woke AI by democratic mandate–an infrastructure whose outputs are correct, diverse, and balanced. Yet it still feels like it was designed over our heads.

Morozov suggests a different path. Instead of allowing corporate AI to grow and then trying to regulate it and capture its value, develop non-corporate AI:

A city government might maintain open models trained on public documents and local knowledge, integrated into schools, clinics, and housing offices under rules set by residents. A network of artists and archivists might build models specialized in endangered languages and regional cultures, fine?tuned to materials their communities actually care about. 

The point is not that these examples are the answer, but that a socialism worthy of AI would institutionalize the capacity to try such arrangements, inhabit them, and modify or abandon them—and at scale, with real resources. This kind of socialism would treat AI as plastic enough to accommodate uses, values, and social forms that emerge only as it is deployed. It would see AI less as an object to govern (or govern with) and more as a field of collective discovery and self-transformation. 

I should say that I am not a socialist, partly because available socialist theories have not persuaded me, and partly because I am also drawn to liberal ideals of individual rights, privacy, and negative liberties. However, “socialism” is a broad and protean term, and socialist thought may offer resources to envision better futures. Confronting the massive threat–and opportunity–of AI, we should use any intellectual resources we can get our hands on.


*I have aggregated the categories of office and administrative support; sales and related; management; healthcare support; architecture and engineering; life, physical, and social science; and legal from the Bureau of Labor Statistics. I omitted education (5.8% of all jobs) on the–probably vain–hope that my own occupation won’t also be automated. If that happens, raise the estimate of obsolete jobs to 45%.

See also: can AI solve “wicked problems”?; Reading Arendt in Palo Alto; the human coordination involved in AI (etc.)