attitudes about AI by age

In the New York Times, Michelle Goldberg writes that college students are jeering at tech oligarchs who give commencement speeches about the benefits of AI. A Wall Street Journal article begins, “The only thing growing faster than the artificial-intelligence industry may be Americans’ negative feelings about it—as former Google Chief Executive Eric Schmidt saw on Friday” when he was booed at University of Arizona.

I had been wondering about this topic. The students I know best tend to be highly critical of AI, but presumably their generation holds varied opinions.

The Quinnipiac Poll asked Americans their attitudes about AI, and here I show those broken down by age.

Older people are the most likely to say that they are not excited at all by AI. Millennials (now at least 30 years old) are the most likely to say they are very excited, although that it is true for only 12% of them. Among Gen Z (under 30), the most common response is “not so excited,” and only 4 percent of them are very excited.

When asked whether AI will do more good or harm to people’s own day-to-day life, most Americans say “more harm,” and that is true of 55% of Gen-Z. The youngest generation is the most likely to say that AI will harm education (68% think it will harm education and 29% that it will help).

Just over 40% of each generation is somewhat concerned about AI overall, with no age differences. A bit more than half of Gen Z are very concerned, but rates of concern are higher among Millennials, who are quite polarized on the topic.

Without access to the raw data, I can’t see how age, education, gender, ideology, race, and personal experience with AI relate to opinions about AI. However, respondents who have more education and income are generally more favorable to AI in this poll. Those patterns hint that current college students may be more sanguine about AI than their contemporaries who are not going to college.

people as clusters of attention

Attention is endangered. It is what Silicon Valley has learned to capture and commoditize. It is what LLMs pretend to offer by speaking in the first-person singular, often in a sycophantic voice. It is what my iPhone takes from me. It is what Donald Trump constantly demands.

To understand why our attention should be valuable to us, we need a satisfactory theory of it. We should not depend on the idea that we have a private, inner self that creates or determines its own attention and owns it like a plot of property. Yet our attention does not belong to Google and Meta or to Donald Trump, and we are worse off when they determine it. Here is an effort at an explanation.

1. The belief in a willing self

It feels as if we decide to do certain things. The reason they occur is that we will them. Other things happen to us, or just happen. For instance, I stand up because I decide to do so, but I fall down because someone pushes me or the leg of my chair breaks.

What am I? I am the thing that wills my own actions.

Sometimes we hear that this theory is “Western” or “modern,” but classical Indian Buddhist thinkers–who disagreed with the theory–nevertheless argued that all sentient beings believe it until they achieve enlightenment. The intended reader of a classical Buddhist text was neither Western nor modern yet believed in a self that willed its own actions. Classical Buddhist authors defined themselves as opponents of other Asian authors who explicitly endorsed this theory, including foundational Hindu texts.

I presume that most or all people believe in a willing self because it makes sense of experience. We are so constituted that we feel that we decide and choose some things, while other things happen to us.

This theory also supports significant and appropriate moral distinctions. We hold ourselves and other people accountable for choices, not for accidents. And just as we value and care for our self–which we credit with making choices–so we value and care for other selves.

When we begin by believing in our own willing selves, we naturally pose questions about other wills. Presumably, other human beings are just like us; to assume otherwise is solipsistic and maybe even psychopathic. But from there, the answers become trickier. Do other animals have selves, and if so, which animals? (My dog seems to, but it’s hard to believe that a bacterium does.) Can a group of human beings or a human institution have a will? How about a computer?

2. Drawbacks of the theory

The theory of a willing self has advantages but also limitations that many people recognize, in principle, even as our experiences keep convincing us that it is true.

For one thing, we have no direct knowledge of the self. It can seem like a magical exception in a universe otherwise determined by the causes that are known to science.

The theory of a self implies a sharp distinction between choices and accidents, even though many–possibly all–intentional behavior seems to be a mix of both. I assume that I have freely decided to stand up, but that behavior resulted from a series of neurological events that were affected, in part, by other people and objects.

Although the theory suggests a binary, the world seems to be shaded in grey. My dog Luca has a similar psychology to mine but not completely the same; a lizard is like Luca but also different from him; and an ant is further along the same continuum. A crowd of humans can have a kind of will, but not exactly like mine. A Large Language Model (LLM) exhibits will-like behavior but isn’t a person.

Finally, the notion of a freely choosing self violates important moral intuitions. It is incompatible with Moral Luck, the idea that we can be better or worse as a result of things that happen without our choosing them. For example, I didn’t choose to be an American citizen led by President Trump, but I am. It is wrong to distance myself from that fact on the ground that I didn’t will it. The theory can also encourage us to care too much about our own selves and to regard our freedom and survival as paramount while making us too judgmental about other people. In Buddhism, an enlightened person has shed the belief in itself.

But it is also problematic to deny the existence of selves in such a way that it no longer seems to matter whether we and other people have agency–or even whether we or they survive. A person is a thing of inestimable value even it’s not quite right to understand it as a self that has a will. And a dog is a being of great value even if it’s not on a par with a human person. Somehow, it must make sense to complain when a person’s private space has been violated.

3. Attention, not self

Here is an alternative. I am inspired by Jonardon Ganeri’s book Attention, Not Self (Oxford 2017), which is primarily an interpretation of Buddhaghosa’s The Path of Purification (written around 450 CE) and other works by this classical Theravada thinker, who (in turn) claimed to be faithfully interpreting the words of the Buddha as recorded in the Pali Canon. Indeed, Buddhaghosa claims that his whole Path of Purification, which is 853 pages long in the English translation by Bhikkhu Nanamoli, is a commentary on the second stanza of Linked Discourses 1.23 (which I have loosely translated here.)

It would be a thorny matter to decide whether I am interpreting Ganeri reasonably well, whether he offers an accurate reading of Buddhaghosa, whether Buddhaghosa is a reliable interpreter of the Pali Canon, and whether the Canon reflects the ideas of the actual Buddha. Instead, I will simply sketch a view that I’ve formed while reading Ganeri.

We can begin with attention. Although this word does not have a self-evident meaning, we use it successfully. Even a toddler can understand the phrase “Pay attention!” When I say my dog’s name, he attends to me, and when he barks, he wants to get my attention. In other words, Luca and I can play language-games involving attention even if he couldn’t learn the word. In this sense, “attention” is much more tractable than “consciousness.”

In its most general sense, attention is some kind of ordering of experience by an organism. An ant can attend to a leaf.

Ganeri argues that our attention has two general aspects: it functions like a window or aperture that removes most of what we could notice so that we are less distracted; and it directs or faces us toward certain phenomena within the window so that we can more deeply understand those things. When I stare at a tree, I am ignoring other objects in my peripheral vision and I am thinking about the tree. “I have reconstructed Pali Buddhist theory as consisting in the claim that the role of attention in experience consists in an exclusion-guided placing together with a directing towards, where there is no incompatibility between them” (Ganeri 117).

This is a general account of attention, at least for human beings. Ganeri further argues that “attention is disunified;” it comes in many forms.

Among the varieties of attention are focal and placed attention, retained attention, reflective attention, attention through language to the world beyond one’s horizons, attention to one’s own mind, attention to the minds of others through their poise and posture, and attention to one’s life in total. These varieties of attention are, as we will see, put to work to explain perception, memory, mindfulness, testimony, introspection, and empathy (Generi, 221).

Each person’s attention is differentiated from others’. For example, only I can remember my own past experiences, which is a particular way of attending. You can learn about my past and possibly even know facts about my past that I don’t know, but I alone can attend to my past as a memory. Likewise, only I can focus on my future as my own, which I do when I plan. I can attend to you in the way that we call empathy, which you cannot offer to yourself.

If you and I are sitting in a lecture, I may be paying attention while your mind is wondering (or vice-versa, of course). If there is a sudden loud noise, such as a thunderclap, both of us may have our attentions captured or “grabbed,” but this may feel different to each of us because I experienced an interrupted lecture while you experienced an interrupted daydream. Compare William James:

for what we hear when the thunder crashes is not thunder pure, but thunder-breaking-upon-silence-and-contrasting-with-it. Our feeling of the same objective thunder, coming in this way, is quite different from what it would be were the thunder a continuation of previous thunder. The thunder itself we believe to abolish and exclude the silence; but the feeling of the thunder is also a feeling of the silence as just gone; and it would be difficult to find in the actual concrete consciousness of man a feeling so limited to the present as not to have an inkling of anything that went before. (James, The Principles of Psychology, 1890, vol. 1, Chapter 9, p. 103.)

There is such a thing as voluntary or intended attention. We can tell by the fact that such attention requires effort. Maybe I am forcing myself to pay attention to the lecture while you are allowing yourself be distracted by someone else in the room, by a feeling of hunger, or by a memory.

James argues that “the question of free-will is insoluble on strictly psychologic grounds” yet there is a clear difference between trying to attend to something and doing so because we failed to try or because something else compelled our attention. The difference matters morally:

The question of fact in the free-will controversy is thus extremely simple. It relates solely to the amount of effort of attention or consent which we can at any time put forth. Are the duration and intensity of this effort fixed functions of the object, or are they not? Now, as I just said, it seems as if the effort were an independent variable, as if we might exert more or less of it in any given case. When a man has let his thoughts go for days and weeks until at last they culminate in some particularly dirty or cowardly or cruel act, it is hard to persuade him, in the midst of his remorse, that he might not have reined them in…. But, on the other hand, there is the certainty that all his effortless volitions are resultants of interests and associations whose strength and sequence are mechanically determined by the structure of that physical mass, his brain; and the general continuity of things and the monistic conception of the world may lead one irresistibly to postulate that a little fact like effort can form no real exception to the overwhelming reign of deterministic law (James, vol; 2, chap 35, p. 497).

Ganeri posits that “Attention is the active organization of experience and action into centred arenas, and Buddhist anatta [the doctrine of no-self] is the claim that there is no room for something real at the centre doing or observing the ordering” (p. 26).

4. Consequences and applications

This theory has the advantage of explaining why each person’s attention is different from others’ without positing a self behind the curtain. It allows us to care whether a given person, including me or you, remains alive and free. A person is a unique cluster or concentration of attention that can attend to its past and future in a unique way. The world will be less when it is gone.

Yet there is also a continuum of qualities and degrees of attention, so that I am very similar to Luca and yet not completely like him. My attention while I write this post is not the same as your attention while you read it, but they connect to each other via the text and our shared experiences. When I am gone, some of what I attended to will be forgotten and some will still receive attention.

Most examples of attention have many causes, some of which can be located mostly inside the organism and others beyond it. There are no sharp boundaries between self and other or between freedom and necessity, but there is a difference between an intense, effortful, deliberated, and concentrated experience of attention versus a complete accident, such as a thunderclap that interrupts a lecture. There is also a difference between reading a novel or listening to a friend and being directed by an algorithm.

Moral responsibility waxes to the degree that we do–or could–expend effort on our own attention. Thus we can be blamed for focusing on bad things or for failing to attend to our responsibilities.

I think we can blame a dog for failing to attend, although much less censoriously than we would blame an adult human being; and we can blame an institution, like the Supreme Court, although we should excuse a dissenting minority.

Ganeri’s theory (to the extent that I have captured it here) is perennial, developed in dialogue with authors who lived in Asia more than 1,500 years ago. It is a theory about human beings, or perhaps about all sentient creatures. But it also feels timely and urgent because human attention is so badly threatened now.

I am currently on vacation in Penzance, Cornwall. I asked Google Gemini’s LLM whether it could summarize a long text for me, and it replied:

I would love to! Please go ahead and upload or paste the text.

Since I’m in Penzance, I’m ready to dive right into your document and pull out the key points, actionable items, or core arguments so you can get the information you need at a glance.

What would you like me to focus on?

Gemini is here in Penzance? That is just creepy. Nevertheless, I uploaded the poem from the Pali Canon that had absorbed Buddaghosa for 853 pages. Gemini “focused on it” and cheerfully gave me a summary in four bullet points. All that was lost was any possible advantage of my attending to that text.

You might think the same of this blog post. if you have read this far, you have devoted some time to my essay, whereas you could instead have read a bit of Ganeri’s book, or the 5th-century Buddhist classic that he interprets, or the original Pali Canon. The fact that I attended to my writing whereas Gemini automatically generated its summary does not make my text better for you.

Indeed, it would be better to read a classic than my blog, but it is also true that we have limited attention and cannot contemplate everything. Summaries are not intrinsically bad, so long as they allow us to focus seriously on other things. Even Gemini’s four-point summary of a poem attributed to the Buddha could enrich a person’s attention if that person then turned to other works.

If we are clusters or concentrations of attention, then each of us has the opportunity to improve their own cluster. What makes attention better is a matter for discussion, but I would nominate complexity, depth, distinctiveness, and service to other people as criteria that we can strive for. A technical tool, such as an LLM or an iPhone, can help, but it can surely erode each of those values if we are not vigilant about it.


See also: The Tangle (a translation of 1.23); AI as Satanic; what should we pay attention to?

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.

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.)

can AI solve “wicked problems”?

I’ve been reading predictions that artificial intelligence will wipe out swaths of jobs–see Josh Tyrangiel in The Atlantic or Jan Tegze. Meanwhile, this week, I’m teaching Rittel & Webber (1973), the classic article that coined the phrase “wicked problems.” I started to wonder whether AI can ever resolve wicked problems. If not, the best way to find an interesting job in the near future may be to specialize in wicked problems. (Take my public policy course!)

According to Rittel & Webber, wicked problems have the following features:

  1. They have no definitive formulation.
  2. There is no stopping rule, no way to declare that the issue is done.
  3. Choices are not true or false, but good or bad.
  4. There is no way to test the chosen solution (immediate or ultimate).
  5. It is impossible, or unethical, to experiment.
  6. There is no list of all possible solutions.
  7. Since each problem is unique, inductive reasoning can’t work.
  8. Each problem is a symptom of another one.
  9. You can choose the explanations, and they affect your proposals.
  10. You have no “No right to be wrong.” (You are affecting other people, not just yourself. And the results are irreversible.)

Rittel and Webber argue that those features of wicked problems deflate the 20th-century ideal of a “planning system” that could be automated:

Many now have an image of how an idealized planning system would function. It is being seen as an on-going, cybernetic process of governance, incorporating systematic procedures for continuously searching out goals; identifying problems; forecasting uncontrollable contextual changes; inventing alternative strategies, tactics, and time-sequenced actions; stimulating alternative and plausible action sets and their consequences; evaluating alternatively forecasted outcomes; statistically monitoring those conditions of the publics and of systems that are judged to be germane; feeding back information to the simulation and decision channels so that errors can be corrected–all in a simultaneously functioning governing process. That set of steps is familiar to all of us, for it comprises what is by now the modern-classical mode planning. And yet we all know that such a planning system is unattainable, even as we seek more closely to approximate it. It is even questionable whether such a planning system is desirable (p. 159)

Here they describe planning systems that would have been very labor-intensive in 1973, but many people today imagine that this is how AI works, or will work.

why are problems wicked?

Some of the 10 reasons that some problems are “wicked,” according to Rittel & Webber, relate to the difficulty of generating knowledge. Policy problems involve specific things that have many features or aspects and that relate to many other specific things. For example, a given school system has a vast and unique set of characteristics and is connected by causes and effects to other systems and parts of society. These qualities make a school system difficult to study in conventional, scientific ways. However, could a massive LLM resolve that problem by modeling a wide swath of the society?

Another reason that problems are wicked is that they involve moral choices. In a policy debate, the question is not what would happen if we did something but what should happen. When I asked ChatGPT whether AI will be able to resolve wicked problems, it told me no, because wicked problems “are value-laden.” It added, “AI can optimize for values, but it cannot choose them in a legitimate way. Deciding whose values count, how to weigh them, and when to revise them is a normative, political act, not a computational one.”

Claude was less explicit about this point but emphasized that “stakeholders can’t even agree on what the problem actually is.” Therefore, an AI agent cannot supply a definitive answer.

A third source of the difficulty of wicked problems involves responsibility and legitimacy. In their responses to my question, both ChatGPT and Claude implied that AI models should not resolve wicked problems because they don’t have the right or the standing to do so.

what’s our underlying theory of decision-making?

Here are three rival views of how people decide value questions:

First, perhaps we are creatures who happen to want some things and abhor other things. We experience policies and their outcomes with pleasure, pain, or other emotions. It is better for us to get what we want–because of our feelings. Since an AI agent doesn’t feel anything, it can’t really want anything; and if it says it does, we shouldn’t care. Since we disagree about what we want, we must decide collectively and not offload the decision onto a computer.

Some problems with this view: People may want very bad things–should their preferences count? If we just happen to want various things, is there any better way to make decisions than to maximize as many subjective preferences as possible? Couldn’t a computer do that? But would the world be better if we did maximize subjective preferences?

In any case, you are not going to find a job making value-judgments. Today, lots of people are paid to make decisions, but only because they are assumed to know things. Nobody will pay for preferences. Life works the other way around: you have to pay to get your preferences satisfied.

Second, perhaps value questions have right and wrong answers. A candidate for the right answer would be utilitarianism: maximize the total amount of welfare. Maybe this rule needs constraints, or we should use a different rule. Regardless, it would be possible for a computer to calculate what is best for us. In fact, a machine can be less biased than humans.

Some problems with this view: We haven’t resolved the debate about which algorithm-like method should be used to decide what is right. Furthermore, I and others doubt that good moral reasoning is algorithmic. For one thing, it appears to be “holistic” in the specific sense that the unit of assessment is a whole object (such as a school or a market), not separate variables.

Third, perhaps all moral opinions are strictly subjective, including the opinion that we should maximize the satisfaction of everyone’s subjective opinions. Then it doesn’t matter what we do. We could outsource decisions to a computer, or just roll a die.

The problem with this view: It certainly does matter what we do. If not, we might as well pack it in.

AI as a social institution

I am still tentatively using the following model. AI is not like a human brain; it is like a social institution. For instance, medicine aggregates vast amounts of information and huge numbers of decisions and generates findings and advice. A labor market similarly processes a vast number of preferences and decisions and yields wages and employment rates. These are familiar examples of entities that are much larger than any human being–and they can feel impersonal or even cruel–but they are composed of human inputs, rules, and some hardware.

Another interesting example: integrated assessment models (IAMs) for predicting the global impact of carbon emissions and the costs and benefits of proposed remedies. These models have developed collaboratively and cumulatively for half a century. They take in thousands of peer-reviewed findings about specific processes (deforestation in Brazil, tax credits in Germany) and integrate them mathematically. No human being can understand even a tiny proportion of the data, methods, and instruments that generate the IAMs as a whole. But an IAM is a human product.

A large language model (LLM) is similar. At a first approximation, it is a machine that takes in lots of human generated text, processes it according to rules, and generates new text. Just the same could be said of science or law. This description actually understates the involvement of humans, because we do not merely produce the text that the LLM processes to generate output. We also conceive the idea of an LLM, write the software, build the hardware, construct the data centers, manage the power plants, pour the cement, and otherwise work to make the LLM.

If this is the case, then a given AI agent is not fundamentally different from a given social institution, such as a scientific discipline, a market, a body of law, or a democracy. Like these other institutions, it can address complexity, uncertainty, and disagreements about values. We will be able to ask it for answers to wicked problems. If current LLMs like ChatGPT and Claude refuse to provide such answers, it is because their authors have chosen–so far–to tell them not to.

However, AI’s rules are different from those in law, democracy, or science. I am biased to think that its rules are worse, although that could be contested. The threat is that AI will start to generate answers to wicked problems, and we will accept its answers because our own responses are not definitively better and because it responds instantly at low cost. But then we will lose not only the vast array of jobs that involve decision-making but also the intrinsic value of being decision-makers.


Source: Rittel, Horst WJ, and Melvin M. Webber. “Dilemmas in a general theory of planning.” Policy sciences 4.2 (1973): 155-169. See also: the human coordination involved in AIthe difference between human and artificial intelligence: relationships; the age of cybernetics; choosing models that illuminate issues–on the logic of abduction in the social sciences and policy

Reading Arendt in Palo Alto

During a recent week at Stanford, I reread selections from Hannah Arendt’s On Revolution (ON) and The Human Condition (HC) to prepare for upcoming seminar sessions. My somewhat grim thoughts were evidently informed by the national news. I share them here without casting aspersions on my gracious Stanford hosts, who bear no responsibility for what I describe and are working on solutions.

I can imagine telling Arendt that Silicon Valley has become the capital of a certain kind of power, explaining how it reaches through Elon Musk to control the US government and the US military and through Musk and Mark Zuckerberg to dominate the global public sphere. I imagine showing her Sand Hill Road, the completely prosaic—although nicely landscaped—suburban highway where venture capitalists meet in undistinguished office parks to decide the flow of billions. This is Arendt’s nightmare.

For her, there should be a public domain in which diverse people convene for the “speech-making and decision-taking, the oratory and the business, the thinking and the persuading, and the actual doing” that constitutes politics (OR 24).

Politics enables a particular kind of equality: the equal standing to debate and influence collective decisions. Politics also enables a specific kind of freedom, because a person who decides with others what to do together is neither a boss nor a subordinate but a free actor.

Politics allows us to be–and to be recognized–as genuine individuals, having our own perspectives on topics that also matter to others (HC 41). And politics defeats death because it is where we concern ourselves with making a common world that can outlast us. “It is what we have in common not only with those who live with us, but also with those who were here before and with those who will come after us” (HC 55).

Politics excludes force against fellow citizens. “To be political, to live in a polis, meant that everything was decided through words and persuasion and not through force and violence” (HC 26). Speech is not persuasive unless the recipient is free to accept or reject it, and force destroys that freedom. By the same token, force prevents the one who uses it from being genuinely persuasive, which is a sign of rationality.

Musk’s DOGE efforts are clear examples of force. But I also think about when Zuckerberg decided to try to improve the schools of Newark, NJ. He had derived his vast wealth from developing a platform on which people live their private lives in the view of algorithms that nudge them to buy goods. He allocated some of this wealth to a reform project in Newark, discovered that people were ungrateful and that his plan didn’t work, and retreated in a huff because he didn’t receive the praise or impact that he expected to buy.

From Arendt’s perspective, each teenager in Newark was exactly Zuckerberg’s equal, worthy to look him in the eye and say what they they should do together. This would constitute what she calls “action.” However, Zuckerberg showed himself incapable of such equality and therefore devoid of genuine freedom.

Musk, Zuckerberg, and other tech billionaires understand themselves as deservedly powerful and receive adulation from millions. But, says Arendt, “The popular belief in ‘strong men’ … is either sheer superstition … or is a conscious despair of all action, political and non-political, coupled with the utopian hope that it may be possible to treat men as one treats other ‘material'” (HC 188).

There is no public space on Sand Hill Road. Palo Alto has a city hall, but it is not where Silicon Valley is governed. And the laborers “who with their bodies minister to the [bodily] needs of life” (Aristotle) are carefully hidden away (HC 72).

Arendt describes how economic activity has eclipsed politics in modern times. Descriptions of private life in the form of lyric poetry and novels have flourished–today, thousands of fine novels are available on the Kindle store–a development “coinciding with a no less striking decline of all the more public arts, especially architecture” (HC 39). In her day, corporations still built quite impressive urban headquarters, like Rockefeller Center, which continued the tradition of the Medici Palace or a Rothschild estate. But Sand Hill Road is a perfect example of wealth refusing to create anything of public value. Unless you are invited to a meeting there, you just drive by.

Arendt acknowledges that people need private property to afford political participation and to develop individual perspectives. We each need a dwelling and objects (such as, perhaps, books or mementos) that are protected from outsiders: “a tangible. worldly place of one’s own” (HC 70). But we do not need wealth. Arendt decries the “present emergence everywhere of actually or potentially very wealthy societies which at the same time are essentially propertyless, because the wealth of any single individual consists of his share in the annual income of society as a whole” (HC 61). For example, to own a great deal of stock is not to have property (the basis of individuality) but to be part of a mass society that renders your behavior statistically predictable, like a natural phenomenon (HC43). All those Teslas that cruise silently around Palo Alto are metaphors for wealth that is not truly private property.

Much of the wealth of Silicon Valley comes from digital media through which we live our private lives in the view of algorithms that assess us statistically and influence our behavior. For Arendt, “A life spent entirely in public, in the presence of others, becomes, as we would say, shallow” (HC 71). She is against socialist and communist efforts to expropriate property, but she also believes that privacy can be invaded by society in other ways (HC72). She expresses this concern vaguely, but nothing epitomizes it better than a corporate social media platform that becomes the space for ostensibly private life.

Artificial Intelligence represents the latest wave of innovation in Silicon Valley, producing software that appears to speak in the first-person singular but actually aggregates billions of people’s previous thought. Arendt’s words are eerie: “Without the accompaniment of speech .., action would not only lose its revelatory power, but, and by the same token, it would lose its subject; not acting men but performing robots would achieve what, humanly speaking, would be incomprehensible” (HC 178).

The result is a kind of death: “A life without speech and without action … is literally dead to the world; it has ceased to be a human life because it is no longer lived among men” (HC 176).


See also: Arendt, freedom, Trump (2017); the design choice to make ChatGPT sound like a human; Victorians warn us about AI; “Complaint,” by Hannah Arendt etc.

Victorians warn us about AI

In the fictional dialogue entitled Impressions of Theophrastus Such (first edition, 1879), George Eliot’s first-person narrator envisions the development of machines that can think, affect the physical world, and reproduce themselves. Humans suffer as a result, devolving into passivity and ultimately becoming extinct:

Under such uncomfortable circumstances our race will have diminished with the diminishing call on their energies, and by the time that the self-repairing and reproducing machines arise, all but a few of the rare inventors, calculators, and speculators will have become pale, pulpy, and cretinous from fatty or other degeneration, and behold around them a scanty hydrocephalous offspring. As to the breed of the ingenious and intellectual, their nervous systems will at last have been overwrought in following the molecular revelations of the immensely more powerful unconscious race, and they will naturally, as the less energetic combinations of movement, subside like the flame of a candle in the sunlight. Thus the feebler race, whose corporeal adjustments happened to be accompanied with a maniacal consciousness which imagined itself moving its mover, will have vanished, as all less adapted existences do before the fittest—i.e., the existence composed of the most persistent groups of movements and the most capable of incorporating new groups in harmonious relation. Who—if our consciousness is, as I have been given to understand, a mere stumbling of our organisms on their way to unconscious perfection—who shall say that those fittest existences will not be found along the track of what we call inorganic combinations, which will carry on the most elaborate processes as mutely and painlessly as we are now told that the minerals are metamorphosing themselves continually in the dark laboratory of the earth’s crust? Thus this planet may be filled with beings who will be blind and deaf as the inmost rock, yet will execute changes as delicate and complicated as those of human language and all the intricate web of what we call its effects, without sensitive impression, without sensitive impulse: there may be, let us say, mute orations, mute rhapsodies, mute discussions, and no consciousness there even to enjoy the silence.

In On Liberty (1859), John Stuart Mill had not forecast such a future as explicitly as Eliot would do, but he used it as a thought-experiment to demonstrate that the point of life is to develop one’s own capacities, not to accomplish any practical ends. A life in which important matters are handled by other minds–or by machines–is a life devoid of value:

He who lets the world, or his own portion of it, choose his plan of life for him, has no need of any other faculty than the ape-like one of imitation. He who chooses his plan for himself, employs all his faculties. He must use observation to see, reasoning and judgment to foresee, activity to gather materials for decision, discrimination to decide, and when he has decided, firmness and self-control to hold to his deliberate decision. And these qualities he requires and exercises exactly in proportion as the part of his conduct which he determines according to his own judgment and feelings is a large one. It is possible that he might be guided in some good path, and kept out of harm’s way, without any of these things. But what will be his comparative worth as a human being? It really is of importance, not only what men do, but also what manner of men they are that do it. Among the works of man, which human life is rightly employed in perfecting and beautifying, the first in importance surely is man himself. Supposing it were possible to get houses built, corn grown, battles fought, causes tried, and even churches erected and prayers said, by machinery—by automatons in human form—it would be a considerable loss to exchange for these automatons even the men and women who at present inhabit the more civilised parts of the world, and who assuredly are but starved specimens of what nature can and will produce. Human nature is not a machine to be built after a model, and set to do exactly the work prescribed for it, but a tree, which requires to grow and develop itself on all sides, according to the tendency of the inward forces which make it a living thing.

The possibility that AI will render us extinct remains speculative, 150 years after Eliot posited it. But there is an urgent, present threat that AI tools will “guide” us along “some good path” and thereby block “the free development of individuality,” which “is one of the leading essentials of well-being.”

See also: the difference between human and artificial intelligence: relationships; artificial intelligence and problems of collective action; what I would advise students about ChatGPT; the human coordination involved in AI; the design choice to make ChatGPT sound like a human etc. I owe the reference to Eliot to Harry Law.

New publication: The Limits of Representativeness in Citizens’ Assemblies

New article published in the inaugural issue of the Journal of Sortition. In The Limits of Representativeness in Citizens’ Assemblies: A Critical Analysis of Democratic Minipublics Paolo Spada and I explores key questions about representation in citizens’ assemblies, building on ideas from a blog post we publised two years ago. Refined through discussions with scholars and practitioners – particularly in the Deliberative Democracy Digest – it examines the challenges of representativeness and proposes constructive paths forward.

We explore ways to enhance these democratic innovations by:

  • Integrating multiple minipublics to address inclusion failures.
  • Leveraging emerging technologies, like AI-supported mediation, to scale deliberation.
  • Shifting the focus of legitimacy from unattainable claims of representativeness to fostering inclusion and preventing domination by organized minorities.

By reframing these approaches, we hope to contribute to ongoing efforts to make citizens’ assemblies more inclusive, effective, and impactful for democratic governance.

Printed copies of this inaugural issue are available free upon request here.

Unwritten 2025

In a discussion with a government official last week, she made a point that stuck with me: “Every time we discuss AI readiness,” she said, “someone tells us to wait, or to get something else done before trying it. But waiting is a decision that may cost us in the future.”

She’s right. The technology sector has mastered the art of sophisticated hand-wringing. In AI discussions, over and over again, the same cautionary refrain echoes: “We don’t know where this technology is going.” It sounds thoughtful. It feels responsible. But increasingly, I’m convinced it’s neither.

Consider how differently we approached other transformative technologies. When my colleagues and I started experimentation with mobile phones, Internet, and voice recognition over two decades ago for participatory processes, we didn’t have a crystal ball. We couldn’t have predicted cryptocurrency, TikTok, or the weaponization of social media. What we did have was a vision of the democracy we wanted to build, one where technology served citizens, not the other way around.

The results of those who have been purposefully designing technology for the public good are far from perfect, but they are revealing. While social media algorithms were amplifying political divisions in the US and Myanmar, in Taiwan technology was used for large scale consensus building. While Cambridge Analytica was mining personal data, Estonian citizens were using secure digital IDs to access public services and to conveniently vote from their homes. The difference isn’t technological sophistication – it is purpose and values.

I see the same pattern repeating with AI. In India, OpenNyAI (‘Open AI for Justice’) isn’t waiting for perfect models to explore how AI can improve access to justice. In Africa, Viamo isn’t waiting for universal internet access to leverage AI, delivering vital information to citizens through simple mobile phones without internet.

This isn’t an argument for reckless adoption – ensuring that the best guardrails available are in place must be a constant pursuit. But there’s a world of difference between thoughtful experimentation and perpetual hesitation. When we say “we don’t know where this technology is going,” we’re often abdicating our responsibility to shape its direction. It’s a comfortable excuse that mainly serves those who benefit from the status quo. That is reckless.

The future of AI isn’t a set destination we discover with time. The question isn’t whether we can predict it perfectly, but whether we’re willing to shape it at all.

Being wrong is part of the job. 

Waiting for perfect clarity is a luxury we can’t afford. But that shouldn’t mean falling prey to solutionism. This week alone, I came across one pitch promising to solve wealth inequality with blockchain-powered AI (whatever that means) and another claiming to democratize healthcare with an empathy-enhanced chatbot. Technology won’t bend the arc of history on its own – that’s still on us. 

But we can choose to stay curious, to keep questioning our assumptions, and to build technology that leaves room for human judgment, trial, and error. The future isn’t written in binary. It’s written in the messy, imperfect choices we will all make while navigating uncertainty.

Agents for the few, queues for the many – or agents for all? Closing the public services divide by regulating for AI’s opportunities.

(co-authored with Luke Jordan, originally posted on Reboot Democracy Blog)

Inequality in accessing public services is prevalent worldwide. In the UK, “priority fees” for services like passport issuance or Schengen visas allow the affluent to expedite the process. In Brazil, the middle-class hires “despachantes” – intermediaries who navigate bureaucratic hurdles on their behalf. Add technology to the mix, and you get businesses like South Africa’s WeQ4U, which help the privileged sidestep the vehicle licensing queues that others endure daily. An African exception? Hardly. In the U.S., landlords use paid online services to expedite rental property licensing, while travelers pay annual fees for faster airport security screening.

If AI development continues and public sector services fail to evolve, inequalities in access will only grow.  AI agents – capable of handling tasks like forms filling and queries – have the potential to transform access to public services. But rather than embracing this potential, the public sector risks turning a blind eye – or worse, banning these tools outright – leaving those without resources even further behind.

The result? The private sector will have to navigate the gaps, finding ways to make AI agents work with rigid public systems. Often, this will mean operating in a legal grey zone, where the agents neither confirm nor deny they are software, masquerading as applicants themselves. Accountants routinely log into government tax portals using their clients’ credentials, acting as digital proxies without any formal delegation system. If human intermediaries are already “impersonating” their clients in government systems, it’s easy to envision AI agents seamlessly stepping into this role, automatically handling documentation and responses while operating under the same informal arrangements.

The high costs of developing reliable AI agents and the legal risks of operating in regulatory grey zones will require them to earn high returns, keep these tools firmly in the hands of the wealthier – replicating the same inequalities that define access to today’s analogue services. 

For those who can afford AI agents, life will become far more convenient. Their agents will handle everything from tax filings to medical appointments and permit applications. Meanwhile, the majority will remain stuck in endless queues, their time undervalued and wasted by outdated bureaucratic processes. Both groups, however, will lose faith in the public sector: the affluent will see it as archaic, while the underserved will face worsening service as the system fails to adapt.

The question is no longer whether AI agents will transform public services. They will. The partners of Y Combinator recently advised startup founders to “find the most boring, repetitive administrative work you can and automate it”. There is little work more boring and repetitive than public service management. The real question is whether this transformation will widen the existing divide or help bridge it. 

Banning AI agents outright is a mistake. Such an approach would amount to an admission of defeat, and entrenching inequalities by design. Instead, policymakers must take bold steps to ensure equitable access to AI agents in public services. Three measures could lay the groundwork:

  1. Establish an “AI Opportunities Agency”: This agency would focus on equitable uses of AI agents to alleviate bureaucratic burdens. Its mandate would be to harness AI’s potential to improve services while reducing inequality, rather than exacerbating it. This would be the analogue of the “AI Safety Agency”, itself also a necessary body. 
  2. Develop an “Agent Power of Attorney” framework: This framework would allow users to explicitly agree that agents on an approved list could sign digitally for them for a specified list of services. Such a digital power of attorney could improve on existing forms of legal representation by being more widely accessible, and having clearer and simpler means of delegating for specific scopes.
  3. Create a competitive ecosystem for AI agents: Governments could enable an open competition in which the state provides an option but holds no monopoly. Companies that provided agents which qualified for an approved list could be compensated by a publicly paid fixed fee tied to successful completions of service applications. That would create strong incentives for companies to compete to deliver higher and higher success rates for a wider and wider audience.

A public option for such agents should also be available from the beginning. If not, capture will likely result and be very difficult to reverse later. For example, the IRS’s Direct File, launched in 2024 to provide free tax filing for lower-income taxpayers, only emerged after years of resistance from tax preparation firms that had long blocked such efforts – and it continues to face strong pushback from these same firms.

One significant risk with our approach is that the approval process for AI agents could become outdated and inefficient, resulting in a roster of poorly functioning tools – a common fate in government, where approval processes often turn into bureaucratic roadblocks that stifle innovation rather than enable it.

In such a scenario, the affluent would inevitably turn to off-list agents provided by more agile startups, while ordinary citizens would view the initiative as yet another example of government mismanaging new technology. Conversely, an overly open approval process could allow bad actors to infiltrate the system, compromising digital signatures and eroding public trust in the framework.

These risks are real, but the status quo does nothing to address them. If anything, it leaves the door wide open for unregulated, exploitative actors to flood the market with potentially harmful solutions. Bad actors are already on the horizon, and their services will emerge whether governments act or not.

However, we are not starting from scratch when it comes to regulating such systems. The experience of open banking provides valuable lessons. In many countries, it is now standard practice for a curated list of authorized companies to request and receive permission to manage users’ financial accounts. This model of governance, which balances security and innovation, could serve as a blueprint for managing digital agents in public services. After all, granting permission for an agent to apply for a driver’s license or file a tax return involves similar risks to those we’ve already learned to manage in the financial sector.

The path ahead requires careful balance. We must embrace the efficiency gains of AI agents while ensuring these gains are democratically distributed. This means moving beyond the simple dichotomy of adoption versus rejection, toward a nuanced approach that considers how these tools can serve all citizens.

The alternative – a world of agents for the few, and queues for the many – would represent not just a failure of policy, but a betrayal of the fundamental promise of public services in a democratic society.