Things have been happening since the New York Times published a comment piece by Alison Gopnik, James Evans and myself on the relationship between AI and social science last Sunday. Our piece talks about how the AI labs play an increasingly large role in shaping academic research.

In the interim, we’ve seen what happens when this relationship turns predatory. Not that OpenAI describes it in quite that way. When OpenAI’s Sébastien Bubeck offered help, collaboration and credit to an outside mathematician whose approach to solving a major problem he had gazumped by onboarding millions of dollars of compute over a few days, he says that he framed his offer in terms of “working together” to make “something good.” When the mathematician threatened to go public, Bubeck reportedly asked “[w]hy would you ruin your career?,” but he was most certainly asking in sorrow rather than anger. When Tom Hagen conveys the offer that you can’t refuse to the movie producer in Godfather I, he is, after all, presenting that rare treasure which is the friendship of Vito Corleone. It’s tragic and unfortunate for all concerned that his offer is rudely declined, necessitating the decapitation of a prized racehorse. With just a little more mutual understanding, all that unpleasantness could have been avoided.

Nor is that all that is happening. Media discussion about AI has rapidly shifted from ‘excitable’ to ‘febrile,’1 following revelations of agentic AI driven cybersecurity problems, an Anthropic employee quitting because of existential risk concerns and other stuff too. Max Read has an extremely helpful summary here. Much of the popular debate (and some of the AI debate too) has been driven more by misleading metaphors than by actual understanding of the technology and what happened, let alone what its actual consequences for society might be.

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All this unsurprisingly reinforces my belief that Alison, James and myself are right. We need a public interest oriented version of the social sciences and we need it now. But it is worth explaining why.

As we have already argued in a piece we wrote with Cosma Shalizi for Science, the best way to think about AI is as another social, political, cultural and economic shock in the series of shocks that have been underway over the long industrial revolution. The particular version of this argument laid out in the NYT rests heavily on the arguments of the late Charles Tilly, who was both a sociologist and political scientist, and arguably a historian as well. Tilly pointed out that the social sciences in their modern form were by-products of the upheavals of the nineteenth century industrial revolution and the desire to address and remedy them.

We would be better off if we could rely on actual social science, rather than the kind of bong-hit P(d00m)ing in much of this week’s media commentary to understand the upheavals that are happening around us.

Equally, although we don’t talk about it in the NYT article, Tilly scomplained that the social sciences were blinkered by the defunct anxieties of the period when they had originated. They needed, in his argument, to think much more systematically and ambitiously about big historical processes. That seems apposite as we wander amidst the miasma-producing fever swamp of ‘feel the AGI.’

The challenge is to figure out (a) how to fund the social sciences so that they don’t depend on AI labs, and (b) how to reorient the social sciences so that they approach AI in actually useful ways. We talk a lot about the first in the NYT piece: I summarize it below. We don’t talk about the second, which is what I am going to end up talking about here (and it is perfectly possible that Alison and James disagree with some or all of what I say - any errors are mine and mine alone).

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The New York Times article explains how massive shifts in social science funding may damage our ability to understand the consequences of AI. We start from the contention that the social sciences’ original job was to map out the large scale disruptions of the industrial revolution, enabling collective response.

The fear that machines were wrecking society created political demand for reliable information on what was happening on the ground and how people were living. In Britain, where the Industrial Revolution began, the journalist Henry Mayhew calculated that hundreds of Londoners made their living gathering bones, rags and cigar ends from the street, as well as dog excrement for tanneries. … Social science research arose to study these issues in a more rigorous fashion. Economists studied markets, political scientists studied politics and the state, sociologists studied changing society, and psychologists studied minds. Though these investigations had flaws and were sometimes biased, together they rebuilt our public understanding of what was necessary and possible.

Here, we are partly riffing on Tilly who explains in his book, Big Structures, Large Processes and Huge Comparisons that “Out of … nineteenth-century reflections on capitalism, national states, and the consequences of their growth grew the disciplines of social science as we know them.”

The social sciences should now be gearing themselves up to deal with a new set of radical technological disruptions. However, they are poorly situated to do that.

The Trump administration has slashed federal funding for the social and behavioral sciences, while private funding for certain A.I.-related work has skyrocketed. The result is that many social scientists have left academia and traditional research institutions for A.I. firms that are effectively building out their own social science programs — which threaten to favor the ambitions of A.I.’s creators over the public’s interests. To navigate the shock waves of A.I., we must restore social science research as an enterprise that starts from the public interest.

At exactly the same time as the Trump administration is zeroing out National Science Foundation funding for political science, economics and sociology, the two dominant AI “labs” - OpenAI and Anthropic - are promising enormous amounts of money to fund social scientists interested in working on the transition to an AI economy, and how best to manage the disruption.

We suggest that this is not going to work out well. Even if you believe that the AI labs are doing this with the public interest in mind (there are publicly minded people working in them), their understanding of the general interest is highly idiosyncratic. Specifically, as we note, “They are more interested in smoothing the A.I. transition than questioning it.” I personally think you could go a bit further (I don’t know if James or Alison agree). I suspect that some of the labs’ willingness to put money into the social sciences stems from the realization of senior people that the practical build out of AI is highly unpopular in the U.S and their urgent desire to figure out how to sugarcoat the pill so that it will get swallowed.

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What we don’t talk much about in the article is social science’s relationship to AI itself. Here, a different aspect of Tilly’s argument can help. Even if the social sciences manage to get the funding they need, they may end up approaching the problems that they are supposed to study in unhelpful ways. When Tilly says that the social sciences are the products of nineteenth century traumas, he also suggests this makes them worse suited to deal with more recent problems. In his account, they remain trapped behind the bars of nineteenth century cultural neuroses.

The beginning of Big Structures is simply fantastic. I’m going to quote it in extenso, not just to convey its insights, but because social scientists just don’t write like that any more.

We bear the nineteenth century like an incubus. Inspect the map of almost any American city. Notice the telltale marks: rail lines slicing one section from another; a speculator’s grid, with its numbered rectilinear streets and avenues repeating themselves to the horizon; clustered residential areas, once serving as suburban middle-class refuges from the city but now absorbed into the urban mass. Stroll through and see it up close. Notice the characteristic artifacts: department store, office building, warehouse, factory, chimney, boiler, electric pole, street mixing people with machines. For all the twentieth century’s new technologies and stylistic experiments, the apparatus of everyday life still bears strong markings of the nineteenth century .

The same is true for many of our ideas and institutional arrangements. … In these waning years of the twentieth century, the nineteenth century also keeps its hold on many ideas about social organization. In the analysis of social change, we cling loyally to ideas built up by nineteenth-century intellectuals. Intellectuals formed those ideas in their astonished reaction to what they saw going on around them: unprecedented concentrations of population, production, capital, coercive force, and organizational power. They formed ideas treating increasing differentiation as the master process of social change, ideas of societies as coherent but delicate structures vulnerable to imbalances between differentiation and integration, and other ideas connected to them. The nineteenth-century incubus weighs us down. I hope this little book will serve as a lever to lift some of the burden. It addresses one big question: How can we improve our understanding of the large-scale structures and processes that were transforming the world of the nineteenth century and those that are transforming our world today?

If you’re like me (in fairness: you probably aren’t and shouldn’t be), this is a thrilling call to arms, which still resonates as the twenty-first century begins to wax. The call resonates more because we are obliged to think about large scale changes, whether we want to or not. New structures and new processes are emerging, and we are poorly situated to understand, let alone guide them.

Tilly, writing in 1984, suggests that the social sciences ought to be better suited to think about such structures and processes, but that they often ,tend to on “timeless, placeless models of social organization and social change,” which are often reanimated by the dream of a return to order in lieu of “historically grounded analyses,” which recognize the mess and disorder of actually functioning human societies.

What does Tilly want such historical analysis to do? First, he argues that it should not get stuck at the level of the nation state. There are many important relationships that cross national boundaries. Indeed, Tilly argues we should replace talk of “society” and “societies,” which smack of some imaginary lost age of cohesion, with “multiple social relationships,” which vary over space and time. Nor should it be methodogically individualist: individuals are important, but social relations involve two or more of them. We ought treat the notion that history passes through abstracted “stages” of development with the contempt that it merits, discarding it as well as nostrums about modernization, fears of inevitable social atomization, and the belief that there is an innate distinction between legitimate and illegitimate violence. Instead, we should focus on the larger processes that produce enduring patterns: world systems that may in turn produce “networks of coercion,” some of which cluster into states; processes of proletarianization, urbanization, capital accumulation, state-making and bureaucratization, which vary and combine with each other. We should compare across different situations, but not get hung up on the “classical logic” of searching for precisely comparable cases. Our methodology should fit “like a sweatshirt and not like a straitjacket.”

Tilly was opinionated: his general methodological recommendations for the social sciences bear a remarkable resemblance to his own theoretical predilections. You don’t have to buy into all of his arguments to find the book valuable, though it would be really interesting to think about how his ideas might work today. We are able to do a lot more to study networks and flows than we were in the 1980s, when Tilly wrote. Equally, the social sciences now lay a much greater emphasis on identifying causal relationships, which tends to push away from the kinds of big secular shifts that he suggests we ought focus on.

Even if Tilly’s methodological hobbyhorses aren’t yours, I think that two elements remain important. First - that social scientists should not get caught up in simplifying narratives. In particular, they ought ignore both the soothing suggestions from modernizers that our history is an ineluctable path from past chaos to future order, and their intellectual adversaries, who hark back to some imagined past era of organic harmony before the steam engine, electricity and flush toilets ruined everything. Past, present, future - all are far messier and murkier than our abstractions suggest, with lots of different processes happening at once.

Second, that social scientists ought pay a lot of attention to struggle and power. People disagree and fight - the variable processes that drive change are not smooth and technocratic, but the consequence of their collective struggles with and against each other. And some have much greater power than others to shape their fates and the fates of those around them.

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This gets us back to the question of how the social sciences ought best study AI. The lesson from Tilly - and from others too - is that a good research agenda ought emphasize complex processes and power. That would be very different from the public debates of the last week (and indeed, the dominant debates of the last couple of years). In his summary, Max distinguishes between “doomers” who believe AI is powerful and will kill us all, accelerationists who believe AI is powerful and will liberate us all, and skeptics who don’t think that AI is powerful. There isn’t much room in this three cornered debate for what seems to me to be the best starting point for the social sciences: a recognition that AI is powerful (and will reshape power relations among humans) but that the consequences will flow messily through multiple complex processes that we really need to start mapping and understanding.

There ought to be room, and there is at least one programmatic article that points in the direction of building it. That is a piece that Alondra Nelson wrote for Daedalus earlier this year.

Alondra does not start from Tilly’s work (indeed she doesn’t mention it), but she ends up in a roughly similar place. She argues that we need to focus on big secular changes as Weber did (Tilly too praises Weber for studying processes in an unromantic way). She says that we absolutely need to focus on power:

Weber’s focus on rationalization left crucial social dimensions unexamined. At the same moment, W. E. B. Du Bois insisted that technological change could not be understood apart from relations of power, particularly those structured by race and empire.

We ought understand not just how technology shapes who has bargaining power but what seems socially possible. Specifically:

Efficiency and productivity became unquestioned ends, narrowing thought and desire and foreclosing the imagination of alternatives. This dynamic is legible in today’s AI systems. Recommendation engines steer attention and desire, curating environments that make certain possibilities appear natural while obscuring others.

This then, seems to me to provide the other part of the argument. Alison, James and I argue that the social sciences need funding to map the consequences of AI. Alondra sketches out a map for how sociology, economics, political science, and science and technology studies should address the ways in which AI itself is becoming a structuring force, shaping a multiplicity of processes.

What is needed is not simply a catalog of [AI’s] effects across sectors, but integrated accounts of how algorithmic systems are transforming the fundamental coordinates of social existence: the nature of work and economic value, the character of public life and political authority, the production and circulation of knowledge, and the very boundaries of human agency. Such frameworks will grasp artificial intelligence not as a discrete technology but as an infrastructural layer that increasingly mediates how institutions operate, how individuals are seen and sorted, and how collective futures are imagined and foreclosed.

AI, from this perspective, is best understood as a social technology:

At a high level, a consistent finding emerges from this research: AI is not an external force acting upon society from outside but an internal phenomenon constituted through social processes, institutional choices, market dynamics, and political struggles. Its effects are neither uniform nor preordained. They vary with context, design, and the distribution of power that determines how these technologies are built, deployed, and contested.

Cosma and I might additionally point to the opportunity to bring computer science and the social sciences together to understand this, building on the work of Herbert Simon.

That, then, is why I think we don’t just need well funded and politically and economically independent social sciences to understand the consequences of AI, but also to reorient the social sciences so that they are again well suited to deal with big structures and large processes. We are in a world where it is urgently important to understand change at scale, since it is just such change that we are going through.2 The social sciences need both solid funding, and a lot of internal work to reorient themselves towards the questions that matter. I’ll be writing more on this, both on my own and with others.

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When I wrote about how terribly wrong Noah Smith was about Daron Acemoglu and Simon Johnson, I didn’t know that I was volunteering for service in the Acemoglu Wars. The Economist had not yet published its piece,“The world’s most influential economist is oddly unconvincing. Daron Acemoglu is revered. Why?” This rather remarkable article cites Noah’s criticisms in support of its main thesis, as well as comments by Tyler Cowen,1 and has very much become its own Thing.

I have other stuff that I would have much more fun writing about (specifically: the interesting connections between the movie Backrooms and Susanna Clarke’s novel, Piranesi). Still, there is something useful to be extracted from the more recent imbroglio, which I wanted to write about in my original post, but didn’t have space. So then, once more into the breach …

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I’m not going to talk about the various ways in which the anonymous Economist writer’s piece on Acemoglu mistook debates among economists. Others have done that already, and spats over econometrics do not play to my strengths. I do want to ask why the Economist’s writer chose to wade into this particular morass, since it is clear that spats over econometrics did not play to their strengths either.

The article was written, to question, as the anonymous writer puts it, “whether [Acemoglu’s] reputation at the very apex of the economics profession—and the intellectual influence that goes with it—is justified by his scholarship.” As the writer themselves suggests, this is relevant because of what Acemoglu is saying about AI.

These academic disputes could end up mattering a great deal. Mr Acemoglu may not think AI is about to turbocharge productivity, but he does worry about its effects on democracy and jobs. His new book focuses on its potential social harm rather than its potential benefits. His proposal to limit that harm, “pro-worker AI” that would augment the value of human labour rather than replacing it, sounds terrific—but also obvious.

Mr Acemoglu’s influence in the AI debate is clear in a recent statement, signed by dozens of prominent economists, which argues that “we must act now” to “steer AI in a direction that complements humans and benefits society”. But who are “we”? And who is to decide what sort of AI does or does not complement humans? Even economists who signed the petition say they are not entirely sure. So great is Mr Acemoglu’s stardom that it can sometimes blind the critical faculties.

The writer (who I imagine does not represent the views of all Economist writers) prefers a different approach to Acemoglu: Let Technological Progress Rip.

The book plays down a more compelling claim: that technological progress in itself has made the average person many times richer than in pre-industrial times.

This ties together with what Noah has said already, and with a particular ideology that is propagated by a highly influential subset of Silicon Valley entrepreneurs and economic commentators. Marc Andreessen lays out the case for this ideology, in strikingly lively terms, in his “Techno-Optimist Manifesto”:

We are being lied to. We are told that technology takes our jobs, reduces our wages, increases inequality, threatens our health, ruins the environment, degrades our society, corrupts our children, impairs our humanity, threatens our future, and is ever on the verge of ruining everything.

Truth. Our civilization was built on technology. … Technology is the glory of human ambition and achievement, the spearhead of progress, and the realization of our potential. For hundreds of years, we properly glorified this – until recently.

Andreessen’s manifesto was its own declaration of war:

We believe any deceleration of AI will cost lives. Deaths that were preventable by the AI that was prevented from existing is a form of murder.

and the effort to discredit Acemoglu seems to me to be one offensive in that broader conflict. On the one side is a gallant alliance of CEOs, venture capitalists, econbloggers and journalists-without-bylines,2 fighting the good fight for AI and technological progress. On the other is a dismal conglomeration of oddly unconvincing economists, shabby intellectuals, smelly left-activists and other such human detritus, purporting to “steer” technology and send us all stumbling down the road to serfdom.

Or so it seems. The problem is that “technological progress” is not the undifferentiated force that Andreessen, the Economist writer and others take it to be. The question is not whether or not it ought to be steered, but which course will be taken and who ought be entrusted with doing the steering.

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Why was this unimpressive article written? Put more spicily, how exactly does it contribute to the broader war effort? The answer lies in the disconnect between the mood-music of the economics profession and what its theories about technological innovation can actually tell us. Economists, by and large, are in favor of market freedoms as a path towards greater human prosperity and happiness. After all, well established economic theories say that one can’t improve on markets as a way of connecting means to wants under reasonable seeming technical conditions.

Unfortunately, however, technological innovation doesn’t fit readily into the standard framework. Assumptions about e.g market actors having complete information about the world sit awkwardly with the fact that innovation necessarily involves discovering things that were not previously known.

This hasn’t stopped economists from trying to understand how key aspects of innovation can be reconciled with standard economic theories, and they have done a lot of very interesting thinking. I’m not going to summarize the debates leading from Robert Solow’s work to Paul Romer’s ideas about endogenous growth and beyond. Acemoglu’s own work on directed technical change explains how different paths of economic development may be biased in the direction of different “factors of production”: most importantly, labor or capital. But I think that it is fair to say that while substantial progress has been made discussing the consequences of technological change, explaining the trajectories that are discovered is still a hard problem, and liable to remain a hard problem, because it involves processes of discovery that are both inherently social, and unpredictable in ways that fit badly with the useful simplifications that economists prefer. The result is that folk theories flood in to fill the unfortunate intellectual gap between what many economists want to be true and what they can show to be plausible.

Noah, in his response to Acemoglu, Johnson and myself, seems to believe that technological innovation is what happens when a brilliant billionaire and an undifferentiated mass of consumers love each other very, very much. That is why we need to keep brain-geniuses like Elon Musk as closely attached to the side of the angels as we can manage.

General Motors and Ford have struggled and failed for decades to make commercially viable electric cars, but only Elon Musk was able to do it. NASA struggled with spiraling costs and declining launch volumes for decades, but Elon Musk was able to create a company that out-launched China and NASA put together. Now at a time when America and its old-line companies are struggling to rebuild the semiconductor industry, the only man who seems able to build the requisite fabs is…Elon Musk, once again.

Elon Musk, I believe, is not a good man. He is a cruel and selfish man. I don’t believe he murdered millions of people when he dismantled USAID, but I believe it was still a terrible and immoral thing to do. And he has supported some of the worst politicians in America and elsewhere. And yet, he is an effective man — almost preternaturally so. In many domains, his individual entrepreneurial and organizational abilities are the only thing that has kept China from steamrolling the U.S. in key technological sectors.⁷

What do you do with a man like that? The only thing that makes sense is to utilize his talents as best you can, and do your best to prevent him from subverting the national interest along the way. The same goes for the rest of the techbro class. Only in a few eras do we decide to worship titans of industry like Steve Jobs. But in every era, we need them.

Shorter Noah: love ‘em or hate ‘em, without techbros, we would be unable to innovate or get things done and China would devour us.

This theory of the world is unsurprisingly popular with the “techbro class.” And it probably has some explanatory power. As Quinn Slobodian and Ben Tarnoff say in their otherwise unsympathetic book, Musk did in fact get important things done at SpaceX. But Great Man Theory is, in the end, notably difficult to test. If we were able to run the last twenty years of tech development 100,000 times, randomly substituting different qualified people into key roles, perhaps we would indeed discover that Elon Musk and his brethren made an unique and indispensable causal contribution to the progress we have made. But perhaps we would not. What we do know is that the actual Elon Musk that we have to live with on this here timeline was flagrantly incompetent in government, contrary to Noah’s previous predictions.

Neither economic theory or econometrics tells us then, whether it is best to have technological development directed by a self-selecting elite of bold striving entrepreneurs, or whether some other social and economic arrangement might work better. Claims about the indispensability of Elon Musk et al. stem more from the sensibility and conventional wisdom of some economists (by no means all) than well-established theory, let alone testable empirics.

What we do know from economics, is that trajectories of technological development are often associated with increasing returns. In other words, when you choose a path, it may become self-reinforcing.

That has implications for economic efficiency. What if the trajectory that you choose turns out to be less efficient than some other possible trajectory? Perhaps we will be stuck. You can claim (and some libertarian economists have claimed), that far seeing capital markets will be able to guide us from the wrong trajectory to the right one, but that may be problematic given even moderate uncertainty. What if the trajectory systematically favors one class of actors (say: those whose advantages stem from their possession of capital) over another set of actors (say: those whose advantages stem from their labour power)? One group may end up on the wrong side of the economic bargaining relationship for a very long period of time. Finally, innovation does not happen in a vacuum, but in the context of institutions, which can systematically affect winners and losers. Those institutions are themselves path dependent. And that brings us back to the anonymous scribe for the Economist, who writes:

Nations prosper when institutions are good, and stagnate when they are bad. True. But what, exactly, is an institution? Rules, norms, enforcement, culture—everything, really. Where do institutions come from? “Critical junctures” and “institutional drift”

I don’t know whether the sarcasm in the last sentence stems from sincere ignorance or deliberate obtuseness. But “critical junctures” play a crucial role not just in pathways of institutional development but in technological development too. A critical juncture is the short period when one path is selected rather than another, leading to one set of self-reinforcing increasing returns phenomena being chosen, and another being left to wither amidst the tares.

Will the path that is chosen be the one that maximizes overall economic welfare, however understood? Will this path turn out to systematically advantage one group of actors at the expense of others? If you think that technological innovation is an increasing returns phenomenon (my sense is that this is not a hugely controversial claim among economists, though you absolutely could challenge it from other perspectives), then you absolutely want to pay attention to critical junctures. These are the moments at which the one trajectory rather than the other gets picked. Dismissing such conjunctures in a sneering aside is to ignore the argument that the other side is making rather than to address and refute it.

And that, in turn, brings us back to the politics. The Economist writer says that Acemoglu is wrong to propose that AI be “steered,” since “technological progress in itself has made the average person many times richer than in pre-industrial times,” helping themselves to two invisible assumptions: that AI development will not be steered if it is left unregulated, and that unregulated technological progress trumps regulated technological progress.

The first assumption is visibly untrue. AI’s development is being steered, right now, by a small self-selecting group of business leaders and investors. Do their interests reflect the general good? Perhaps you can make a case for that - but you need to make that case.

Has the technological progress that has historically made people richer than in pre-industrial times been untrammeled by institutions and regulation? Obviously it has not. You can certainly argue that it would have been better, or faster, had it not been hindered by bureaucrats well-meaning or malign, and that claims about e.g. the vital role of the entrepreneurial state are stuff and nonsense, but you would have to make that case, rather than just filling in the argumentative gap with grandiloquent self-confidence.

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As the title to this post suggests, I believe that the sudden outburst of hostility to Acemoglu has everything to do with the historical moment that we are now in. The article’s own conclusions suggest that it might not have been written if AI regulation was not a live political issue, if we were not, in Acemoglu’s terms, at a critical juncture where we will make one choice, rather than another. AI is surging, and there is ferocious debate about its consequences. On the one side, those like Andreessen, who argue that regulation and such are the “enemies” (his word) of technological progress, and need to be defeated. On the other, a variety of people who believe that AI ought be regulated (even if they disagree about how).

I don’t have any profound objection to people claiming that unregulated markets are the necessary best path towards AI that will benefit everyone. I think that they are wrong, on the basis of past historical experience, but I want them to make the best case that they can, since there’s obvious value to market-led entrepreneurialism. But, and I stress this, I want them to make the best case that they can. Simply assuming they are right, and pretending that the counterarguments do not exist, is both intellectually shoddy and unhelpful. It is also personally vexing. I really would prefer to be writing about topics where I don’t yet know what I think, rather than being stuck doing garbage pick-up yet again.

The actual choice is not between some undifferentiated path to “technological progress” and its wicked, envious or misguided opponents. The choice is between different paths to progress, and the live question is which groups or structures are best suited to identify better paths, and avoid worse ones, and (much more ambitious; not the topic of this post) to figure out mechanisms of self-correction when things go screwy. It is neither brilliant nor original to say that any such groups, structures and mechanisms will involve some mix of markets, bureaucracy, democracy, and perhaps other social technologies of discovery, coordination and self-correction too, rather than the sole predominance of the one or the other. But figuring out better mixes and avoiding worse ones is really fucking hard. It would be nice to see more of the pro-market people acknowledging this and engaging accordingly.

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[image from Christian Wiedeger, Unsplash]

Noah Smith has responded to my criticisms of his attempted fisking of Acemoglu and Johnson with his own counter-criticisms. Unsurprisingly, I don’t think his response is convincing. Before we get to the main course, though, two amuses-bouches; one mistake of mine, and one of his.

Noah correctly points out that a tweet that I screenshotted at the top of my third post, where Noah celebrated being screenshotted by Elon Musk, was meant sarcastically rather than sincerely. It may be useful to provide some background to how I made this mistake.

In early 2025, I wrote to Noah expressing my unhappiness with some of his writing about Elon Musk. I felt that Noah was soft-pedalling criticism of Musk’s politics (the “real Elon” discussion here) and I disagreed sharply with Noah’s suggestion that we were about to see Musk’s organizational genius made manifest in the DOGE project. It seemed to me that Musk had sketchy far right sympathies. Furthermore, far from being an all-round mastermind, I believed that Musk didn’t have a clue how government worked and was about to precipitate an unmitigated fucking disaster.

Some of the frustrations expressed in that post came from my side of this conversation, but I didn’t want to quote from it. I searched on Google to find what I thought was a representative tweet that could serve in lieu. I was wrong. I should clarify, in case anyone wonders, that I’ve told Noah in advance that I’d summarize my side of the conversation to explain the origin story without quoting his responses, and he hasn’t objected. Old history, but if he wants to provide his side, or indeed publish the whole damn conversation, he has my blessing.

Now for Noah’s mistake - in his original post, he complained about the “the patent absurdity of [Henry] citing Robert Reich as an authority on [the] opinions” of “techno-industrialists.” I quickly emailed Noah to point out that I was not quoting Robert Reich, former labor secretary, but Rob Reich, current Stanford political theory professor. Noah has since updated to acknowledge his mistake, describing Rob as “a Stanford scholar who makes a living criticizing philanthropic activities by rich people.”

This dismissive description seems to me to be curmudgeonly. Even if Noah disagrees with Rob, it is fair to say that Rob, as former “Marc and Laura Andreessen Faculty Co-director” at Stanford’s Center on Philanthropy and Civil Society, has a tolerable acquaintance with the Silicon Valley elite. Nor does it accurately reflect the range of what Rob thinks or does - I wonder (perhaps mistakenly - happy to be corrected) whether Noah went with what he could find with his own quick Google search.

I’m highlighting this, because Rob’s actual experience with “key people in Silicon Valley” as recounted in his jointly written book, System Error, provides some very useful context.

Several years ago, Rob received an invitation to a small dinner. Founders, venture capitalists, researchers at a secretive tech lab, and two professors assembled in the private dining room of a four-star hotel in Silicon Valley. The host—one of the most prominent names in technology - thanked everyone for coming and reminded us of the topic we’d been invited to discuss: “What if a new state were created to maximize science and tech progress powered by commercial models—what would that run like? Utopia? Dystopia? Ultimately human evolution propellant?”

A researcher at the secretive lab leapt into the conversation. Not a hypothetical question, he said. We have already spec’d this out! Your first thought is that we should find an island and build there, but it turns out to be difficult to optimize for scientific discovery on islands. Creating infrastructure is difficult. You have to find a plot of land elsewhere, and all the desirable plots of land are already occupied. So the first problem you confront is: What do you do about the natives? We decided that the best approach is to just pay them to leave.

The conversation progressed, with enthusiasm around the table for the establishment of a small nation-state dedicated to the maximal progress of science and technology. Rob raised his hand to speak. “I’m just wondering, would this state be a democracy? What’s the governance structure here?” The response was quick: “Democracy? No. To optimize for science, we need a beneficent technocrat in charge. Democracy is too slow and it holds science back.”

Noah has persistently framed this debate as one between the stalwarts of technological progress, and carpers and begrudgers like myself, who are driven by our envy for our superiors in actually getting stuff done. In the past, he has described this in lively terms as “the traditional class resentment of the shabby educated elite for the wealthy titans of industry.”

As per Rob’s dinner, I (and Acemoglu and Johnson too, if I understand them right) start from an altogether different position. We ask whether the most crucial decisions over how we organize society, politics and markets should be taken by a small and self-selecting elite who want to optimize for their preferred path of technological development, or whether instead these decisions should be shaped through democratic means.

If Noah thinks that my arguments are driven by resentment, I believe that his claims stem from a Candide-like naivete that renders him unable really to see power relations in the modern economy, or even assimilate the arguments that other people are making about these power relations. People on the left regularly complain about the cynicism of those who view the world in terms of market efficiency. Such criticisms dramatically underestimate the force and importance of true faith. I don’t have any doubts of Noah’s sincerity (I find much of Noah’s writing frustrating, but very much like the human being behind it). Still, I believe that Noah is sincerely wrong. Since his beliefs are shared by many other people, and are highly influential in policy debate, I think it’s useful to explain how they are wrong, where they come from, and how they lead to an utterly misleading understanding of the political economy of tech.

The below doesn’t properly address Noah’s claims about the need to cherish genius entrepreneurs such as Elon Musk to hold them back from embracing the dark side of the force. That is in part because I’ve already said much of what I would want to say on that topic, here and elsewhere. But it is mostly because this post is already too long without it [if you wish to walk no farther with me, reader, I do not blame you. It is no easy road].

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I found reading Noah’s response essay to be a quite disorienting experience. His personal copy of Acemoglu and Johnson’s book seems both to be unaccountably missing a lot of material that was in mine, and to have added a fair amount of material that I can’t find in my own. So too for Noah’s description of what I myself have argued.

Take, for example, Noah on Acemoglu and Johnson’s account of surveillance. Noah acknowledges that he hasn’t thought much about surveillance, but takes me to task for suggesting that Power and Progress has lots to say about it. Noah says, on the contrary, that the book seems to have had only one three page section on surveillance, which exclusively talks about it in the context of monitoring workers.

As far as I can tell, they never once address Henry’s core concern, which is that digital surveillance will lead to broad political disempowerment. So I only feel a little bad about not mentioning that in my review.

I hate to make Noah feel bad, but one out of eleven chapters is devoted to just this topic. In the authors’ own words.

In this chapter we argue that the pernicious effects of digital technologies and AI on politics and social discourse were not inevitable and resulted from the specific way in which these technologies were developed. Once these digital tools started being used primarily for massive data collection and processing, they became potent tools in the hands of both governments and companies interested in surveillance and manipulation. As people became more disempowered, top-down control intensified in both autocratic and democratic countries

That Noah somehow didn’t absorb what this chapter says - and is trying to score points off me on the basis of his incomprehension - suggests that there is something badly wrong with how he read the book.

Arguments sometimes appear as mysteriously as they disappear. Noah appears to have discovered big sweeping claims that I can’t find myself. Noah says:

when Acemoglu and Johnson talk about the “power” that techno-industrialists wield, what they actually mean is persuasion. Here’s what I wrote in my book review: “It seems like Acemoglu and Johnson, confronting the question of why regular people bought iPhones and signed up for Facebook and shopped on Amazon and started talking to ChatGPT, simply decided that techbros must have tricked them into it.”

I’ve looked through the book again and don’t see even a hint of such a claim anywhere. If Noah can point to specific source material for this claim, he absolutely should!

I read Acemoglu and Johnson’s actual argument as addressing general trajectories of technological development, and whether these should be set (in broad outline) solely by a small group of tech entrepreneurs, or instead should be subject to democratic control. Acemoglu and Johnson do not claim, or try to claim, that particular products are popular only because of techbro trickery. Instead, they suggest that popular products regularly come with strings attached, which users might reasonably not be particularly enthused about.

For example, when Acemoglu and Johnson talk about search engines, they explain how Page and Brin’s PageRank algorithm was an “important breakthrough” in making the vast amounts of information on the Internet accessible to ordinary people. Their objection is not to the technology, but the particular trajectory of commercial surveillance associated with it. This is not just a different argument but a fundamentally different kind of argument than the one that Noah says they are making.

Noah says in his response to me that “Power and Progress was a strong contender for the worst nonfiction book I’ve ever read all the way through.” Based on this and other evidence (discussed in the previous essays) I fear that the book that Noah read was not the one that Acemoglu and Johnson actually wrote, but the very different one that he largely composed for himself in his own head. It is terribly easy to excoriate a book for its bad arguments when you yourself have inserted them into the text.

To be clear: I do not believe for a moment that Noah is being dishonest. Vera Tobin’s fantastic book, The Element of Surprise, has a lot to say about “the curse of knowledge” in the context of detective fiction and related genres. When we believe that we know what is going on in a book, our preconceptions bar us from seeing what is actually being said. Again, I think that Noah genuinely believes that he understands what Acemoglu and Johnson are trying to do and say, even while he is weaving his own fantastical catalogue of intellectual crimes. When Noah suggests elsewhere that the reason that people like Bill Janeway disagree with him about the book is that they haven’t read the book with sufficient care, while he, in contrast, has read Power and Progress in “excruciating detail,” I’ve no doubt that he completely believes what he is saying. When you have convinced yourself incorrectly that you know what a book says, it takes a lot of hard work and serious thinking to unravel your misconceptions.

I don’t think that Noah has put in that work either on Power and Progress or on the other critics, such as, for example, myself. It would be invidious for me to comment on his suggestion that I am one example of a broader class of embittered intellectuals, whose opposition to the tech elite is driven by resentment.

a fairly large portion of the left-leaning intellectual class in America has become fairly-to-very resentful of the American tech industry, and has spent an enormous amount of intellectual effort on criticizing that industry. Both Power and Progress itself, and Henry’s defense of the book, channel those ideas and appeal to that sentiment … The core conceit of anti-tech thought is that a class of wealthy industrialists — the “techbros”, if you will — is hurting society by building and investing in technologies that have major negative side effects, and that the solution is to bring these industries under greater democratic control. … Henry’s perceptions of this debate are too clouded by class enmity toward the techno-industrialists and toward anyone who seems to make excuses or carry water for them

Noah believes that these jealousies have led me to make mistakes: those, I can address.

Henry believes — and claims, many times, in his two posts — that I think techbros managed to persuade Americans to embrace Big Tech simply by making good arguments in a free, equal, open, unfettered Marketplace of Ideas. In fact, I do not believe this. I believe that Americans embraced Big Tech — and tolerated the vast fortunes of techno-industrialists — because they liked the tech itself.

Again, this isn’t actually what I say. I wasn’t writing about Noah’s grand theory of Why People Love Tech (which had not then been unveiled in its full glory), but trying to unravel Noah’s criticisms of Acemoglu and Robinson’s theory of power, which were in large part about the marketplace of ideas. E.g. when Noah talks about how:

the power of these entrepreneurs is nothing more than their ability to talk in an “unrestricted marketplace of ideas” with “freedom of the press and speech." Such an understanding of power is “useless and even counterproductive” since it assumes that “people just hearing arguments and deciding that those arguments are right means power has been exercised.” Power becomes a “residual” explanation: “[t]he only way you get there is by just assuming that all outcomes are due to power and then … applying the label power to whatever you think produces the outcomes.”

If that summarization of Noah’s own words is misleading, I would genuinely be interested in knowing, and would be very happy to correct.

Finally, when I describe Noah’s own influence as a blogger as a kind of power, Noah accuses me of circular reasoning.

What bothers me about this definition of “power” — and what should bother you — is that it’s tautological. Why does Noah have power? Because people like his arguments! And why do people like his arguments? Because he has power! The problem with that definition isn’t that it’s empirically hard to falsify; it’s that it’s unfalsifiable.

My suggestion here draws on the broader claims about power in that post, which have been complemented in two others. Noah largely ignores the discussion across all three posts of why various theories of power are not, in fact, unfalsifiable. Ash, Chen and Naidu’s NBER paper, discussed in all three as well as Acemoglu and Johnson’s book, looks to measure the power of ideas in sophisticated ways. If Noah has problems with their identification strategy, then he should explain what they are. The resulting debate would certainly be entertaining, and might even be informative. Two other posts provide a list of mechanisms through which Acemoglu and Johnson say that power-as-influence works, which Noah completely ignored in his original critiques, wrongly suggesting that their single argument was that power depended on luck and eloquence. All these mechanisms could be turned into falsifiable arguments (some more readily empirically testable than others). Noah does accept in his new response that one of these mechanisms (who has money?) can be a source of power, but that is pretty well it.

On the particulars of Noah’s complaint: I am not able to say how much of Noah’s success depends on his native talent, any more than I able to say how much of my own (quite different) success does. I have no information on the alternative universes in which we are talentless hacks so that structural factors alone are at work. What I can say, based on what we know from social science is that much cultural success is stochastic (the experimental evidence suggests that talent only moderately predicts success in culture markets), and that path dependence shapes chances of success (both of us were around and active at a moment of phase change in online culture, so that the Polya urns spoke in our favor). Also, personal connections help: anecdotally, the statistics would suggest that a very large amount of my readership is an unintended by-product of one particular relationship.

Similarly, it is difficult to disentangle the ways in which our greater or lesser eloquence makes us more or less persuasive, from the fact that we are highly connected nodes in the relevant networks. Some people pay attention to what we say because they like our arguments. Some people pay attention because they think that we are influential. It is a fool’s quest to try to separate the two out, but there is excellent reason from cognitive science to believe that when we try, we are systematically likely to overestimate our own individual awesomeness, and to underestimate the external power asymmetries that magnify our voices.

And - jumping ahead to the next section - the capacity to deploy social resources to shape others’ choices via social influence, is indeed a form of power. Ernest Gellner, in his classic liberal/sociological history of the human species Plough, Sword and Book (see Brad DeLong for broad outline), talks about how pre-modern societies were ruled by coalitions of the ‘sword’ (those who could deploy physical coercion) and ‘book’ (those who could shape collective knowledge and beliefs). Those politics are far more complex in modern societies where coercion is often less openly deployed, and where literacy is close-to-universal, but sword and book are still the most crucial channels of power. If anything, I think that the book has gained very substantially in relevance, as more of our social structures have come to rely on coordination rather than violence.

So in short, I don’t think that Noah’s criticisms of Acemoglu and Johnson, or for that matter of me, are either accurate or good. There are remarkable incongruities between what Noah thinks he has read, and what he has in fact read. There’s a lot more that I could say here, but I don’t want to drive away more readers with prolixity than I have to.

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So why does Noah have such a hard time seeing what is on the page in front of him? Based on his most recent post, I think that Noah is afflicted with a serious case of what Tom Slee calls MarketThink, in his unjustly neglected book, No-One Makes You Shop at Wal-Mart.

Noah argues that Acemoglu and Robinson’s entire book is an exercise in cope. They construct an elaborate, sweeping and tautological account of power because they can’t handle the truth about Big Tech:

They tried to argue that any persuasion, not just purchased persuasion, is power. Why did they try to claim that? Because on some level, I think they know that Americans embraced Big Tech for a lot more reasons than simply because billboards and Super Bowl ads told them to. They realize — or at least, they strongly suspect — that the techno-optimist arguments of the 1990s, the 2000s, and the early 2010s carried real, inherent persuasive force that had little to do with the money in Bill Gates’ or Steve Jobs’ bank accounts.

In Noah’s book, techno-optimist arguments carry “real, inherent, persuasive force” because they stem from genuine consumer satisfaction. Acemoglu and Johnson’s theory in contrast:

seems sort of like a definition intentionally designed to delegitimate consumer choice — to say that people bought iPhones and signed up for Facebook and shopped on Amazon and talked to ChatGPT because someone wielded power over them, rather than simply because they wanted to. And it seems like a definition designed to explain away the broad popularity of big tech companies as something nefarious and illegitimate.

Again, none of this helps explain what Acemoglu and Johnson are saying, but it is absolutely helpful in explaining Noah’s own thinking. He is riffing on the notion of consumer sovereignty: that consumers know what they want, and can get it thanks to free markets. That people buy iPhones tells us that they like iPhones. That they sign up for Facebook tells us that they like Facebook. That they shop on Amazon tells us that they like to shop on Amazon. And so on. That, then, transfers into people’s broader attitude to Big Tech. We know that Big Tech is popular, because people like to buy the products of Big Tech.

Noah himself is doing a tautology here. He assumes that we know what people want because they buy it, and we know that they buy it because they want it. I suspect (but don’t know) that Noah doesn’t realize that he himself is engaged in tautology, because it’s so fundamental to the way that he thinks. In fairness, there are a lot of people who think that way.

As Tom says:

MarketThink is a way of interpreting the world. The success of a company proves that customers like it. … Once you adopt the MarketThink worldview, there is no longer a rationale for collective approaches to social and economic problems.

The last sentence is a bit strong as a description of Noah’s beliefs about Big Tech, but it goes in the right direction. That, I think, is why Noah has difficulty processing Acemoglu and Johnson’s (and my) alternative perspective. He is committed to an understanding of markets that makes it genuinely difficult to understand words written by people who do not share this perspective.

Equally, as you might imagine given that Acemoglu and Johnson share a Nobel Prize in economics (to Noah’s mild dismay), you can be an economist without fully embracing MarketThink. Pretty well all economists recognize (rightly) that markets have great advantages, but not all believe that those advantages are universal. And economics itself provides methodological toolkits for understanding many of the ways in which MarketThink can lead you astray.

MarketThink has many things in its favor. It is immediately intuitive. Quite often, it is more or less right. Markets are a miraculous social creation, and can often be wonderful. But if all you have is MarketThink, you are going to miss out on many of the squirrelly complications of real life markets.

Tom’s book provides a very accessible tour of these complications, as once recognized by Alex Tabarrok a fervently libertarian economist and MarketThink exponent extraordinaire. Alex once wrote in the opening paragraphs of a review (he has since expunged those paragraphs for some unexplained reason, leaving only his criticisms)

Slee’s book is the best of the anti-market books: it is well written, serious, and knowledgeable about economics. In fact, I regard Slee’s book as an excellent primer on asymmetric information, free riding, externalities, herding, coordination problems and identity - Economics 301 for all those budding young Ezra Klein’s [sic] of the world who think that Economics 101 isn’t quite right.

Tom begins the book by arguing that the success of a company does not necessarily mean that customers like its consequences. He starts with a toy example of Wal-Mart coming to town, creating a big box store in the suburbs. If I like cheap prices as well as other things, then this will make me happy. I may enjoy the social interactions of the high street with all of its little shops, but I can keep that, while also now economizing on my shopping bill by doing my bulk buying at Wal-Mart. But if everyone else does what I do, then the high street is likely to dwindle away, or even disappear.

This is, of course, a toy finding based on posited preferences. Perhaps people generally prefer to shop at Wal-Mart or Amazon, regardless of its consequences for the broader economy. No doubt many do! Equally, Polly Mackenzie in the Financial Times last week:

The new UK government’s cut in business rates for pubs and music venues is, by my count, the 12th tweak to the much-loathed property tax in a decade. When politicians start fiddling like this, it’s usually a sign that the problem is bigger than they want to admit. That’s certainly true when it comes to our high streets, which seem to matter more to people when voting than when shopping online.

Libertarians might say we should give up: if people wanted high streets, they would use them. The more interesting approach is to wonder why there is such a disconnect between people’s choices and their political demands.

Walk down almost any high street in England and it becomes obvious. The boarded-up pub, bank or bakery, the market hall or community centre that became a bar, then a waffle house and then closed. Tens of thousands of buildings across the country sit derelict or vacant and no economist’s lecture about “revealed preference” offers consolation for this not-even-managed decline.

If I care about both prices and sociable interactions across small stores, and shop at Wal-Mart like everyone else, I may end up with lower utility, even though all I have done is to exercise my free choice in the open market. My individual choice conducts, combined with the choices of others, to a collectively unfortunate result - in technical language, individual choices may result in collectively sub-optimal equilibria. Game theory, which studies strategic interactions, is rife with such findings. The assumption that people shop on Amazon because they are happy with the overall outcomes is not always right. Such claims are, much of the time, a reasonable first approximation. Equally, as Tom says:

Unfortunately, much political and business discussion today has adopted the MarketThink line that pretty much everything is like a free market, pretty much all the time. … But economies and (even more so) other aspects of society are made of intricately connected, constantly interacting choices. Many of our choices, the outcomes of those choices, and how we feel about those outcomes all depend on the choices of others, whose choices likewise depend on ours. Claiming that the free market basically decides how such a world works is not merely a simplification, it is simplistic.

Tom’s book goes on to discuss other aspects of this problem. Networks bollocks up a lot of standard efficiency arguments, because the value of a network to any one individual depends on how many other people are in the network. This then, creates some obvious power asymmetries. If you don’t like how Twitter/X is treating your data these days, but your livelihood depends on contact with other people who use the service, you may be stuck. You may be stuck even if all those other people hate it too - coordination costs make it hard to defect.

On a much bigger level, the same is true of banks or countries that might want to depart SWIFT or the dollar clearing system because the US is liable to weaponize it against them. It is very costly for them to defect because everyone else relies on these fundamental infrastructures of international exchange. The results for global power relations - as Abe Newman and I have discussed extensively - are dramatic.

This is not a general argument against markets or other such forms of economic exchange. Tom quotes Amartya Sen:

To be generically against markets would be almost as odd as being generically against conversations between people … The freedom to exchange words, or goods, or gifts does not need defensive justification.

What it is is an argument that one very often (and perhaps even usually) cannot separate exchange from power relations. Tom again:

The core of the problem with the market is not the practice of exchange, but the opportunities it gives for manipulation by those with the resources to do so: to turn exchange into exploitation.

Twitter/X - and Meta - and in different ways Amazon, Google, OpenAI, Anthropic, and all the other big platforms - systematically take advantage of such asymmetries. To observe this is not to express resentment but to recognize reality. I can both love Apple products (which I do) and be aware that Apple has used its dominance of high value aspects of the mobile phone market to create an App Store that rakes off rents at scale. Inside every hungry entrepreneur, there is a bloated monopolist struggling to get out.

As Alex notes, Tom’s effort to build these ideas into a general account of power relations is one of the weaker aspects of the book. But there are other ways to use game theory to think about power relations. As Jack Knight argues, mixed motive coordination games, in which players have good reason to coordinate, but different interests in which equilibrium to coordinate on, provide an valuable simple model of power relations. Actors’ breakdown values - the payoffs they get if they don’t coordinate - are a straightforward index of bargaining strength. Those who are more indifferent about whether a deal is reached are better able to hold out for equilibria that they prefer.

Over time, as identifiably different classes of actors with different bargaining strength interact, informal institutions can emerge that concretize these power asymmetries. There is a small, valuable literature in economics and social epistemology that uses evolutionary games to model the consequences. Danielle Allen, Cosma Shalizi and I have a paper in the works that looks to figure out how this works at the level of social-institutional trajectories, combining compartment models and evolutionary dynamics into a single mathematical framework (which is all Cosma’s).

The TLDR of all of this is that exchange - including decentralized exchange - regularly involves some combination of (a) collective advantage, and (b) asymmetric power relations. Much of political economy then, involves actors trying to make sure that the coordination solutions that are reached advantage them rather than their rivals. None of these arguments are unfamiliar to even vaguely literate economists. But popularizing economists tend very often to throw away these complications, in favor of crude MarketThink.

It appears to me that Noah is engaging in just this kind of popularization. He starts from MarketThink, while Acemoglu and Johnson start from the belief that tech development is substantially about strategic interactions, in which some actors are trying to gain advantage over others, and in which path dependence is rife. Despite repeated invitations from me (in all fairness to Noah: more harangues than invitations), Noah has systematically declined to engage with Acemoglu and Johnson’s actual arguments, instead coming up with the arguments that he would have preferred them to have made, and refuting the latter entirely to his own satisfaction.

Acemoglu and Johnson do not even hint that consumers are being tricked into liking tech products. Instead, their framework suggests that consumers are regularly at the wrong end of the bargaining interaction in coordination situations. If they want Google Search, they have little choice to accept the surveillance economy that goes along with it. Coordinating against power is extremely hard. When Noah asks, rhetorically, “What can I say or do that would alter this brute fact — that humanity has once again chosen to give their lives over to the machine?,” he needs to realize this is not a choice the affected humans necessarily had much control over. Men (and women) make technological history, but not in the circumstances of their choosing.

Noah is not only at liberty to disagree with my arguments, but is actively encouraged to. This, however, would require him to stop fighting with what he imagines people have written, and engage with what they actually say. He can’t just help himself to the assumption that market outcomes in tech faithfully reflect consumer preferences without actually talking about these markets actually work. A disagreement that engaged with the evidence on tech markets would be genuinely clarifying - we do not want voting on technological choices all the way down, and figuring out the practical balance between democratic control and entrepreneurial energy is a real and important endeavor.

Equally, engaging with these realities would involve, in my estimation at least, addressing the ubiquity of network effects, extensive evidence of accelerating tech enshittification, the transformation of corporate control structures into princely domains, and the fact that many of the tech elite who he wants to defend are perfectly frank about their desire to escape the confines of market competition. It may only be my envious resentment of Silicon Valley elites that makes me think all this! Noah should feel encouraged to provide actual factual information that demonstrates that I am wrong.

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Of course, Noah is not the only proponent of MarketThink. There is a lot of it about, especially in Silicon Valley, which helps, I think, to explain why many of Big Tech’s ambitions are increasingly unpopular with ordinary people. If you can remember Rob Reich’s anecdote a few thousand words ago, it is practically a parable of how AI companies and infrastructure providers are being walloped by opposition to data centers. Rob describes how founders, funders and lab people regarded democracy as an obstacle, and wanted to just pay people to go away. That is exactly what data center builders are doing now, and it is not turning out to be a universal winning strategy.

Jasmine Sun has been doing the work of actually going out to talk to people who oppose data centers, and finding out what they think. She talked to Ezra Klein about what happens when AI companies pay to try to make the opposition go away. It doesn’t work:

one big gap I noticed between Silicon Valley and the folks in these communities I was talking to is: Silicon Valley does tend to think that money solves all problems — that if you just make the check bigger, everything is going to be OK.

And I think people have a sense for: I’m being bribed. This corporation is not offering me a free lunch or whatever. There is going to be something that I’m losing here.

And, in fact, sometimes the fact that the data center deals were bigger, or the amount of political spending was bigger, actually just makes people more suspicious. In the Abdul El-Sayed Senate race in Michigan, his No. 1 hit on opponent Haley Stevens is how much money she is getting from AIPAC, from DTE, from Big Pharma or whatever.

And so I think we’re in a political environment where making the numbers bigger and the amounts of money bigger makes people more suspicious, not less.

People - at least the people she talked to - are more interested in having a say than in getting money.

The phrase that you hear a lot from A.I. critics is: Why is this being shoved down our throats? Or with ChatGPT, it’s not that people are saying there is literally no use for ChatGPT, it’s people are saying: Why are you forcing me, at my job, to use A.I. to do something worse when I could do it better?

And so I think that a lot of the public backlash to A.I. that has arisen over the past six months is not explained by people thinking that the technology has no use at all. It’s not explained by their being worried about specific technical properties of large language models that might lead to rogue A.I. or misalignment or whatever, which are the safetyist arguments.

It’s A.I. as sort of an avatar for a small group of Silicon Valley billionaires’ ability to impose their vision of the world onto everybody else without their consent.

And I think that’s also what I hear echoed in these data center debates. It’s not just: It’s going to use this much water or that much water. I frankly think that even if there was no misinformation about water use, people would be just as angry about the data centers.

I do agree with Noah on one thing: that Big Tech’s current approach is in serious political trouble. But, as Jasmine suggests in her own newsletter, it is important to take the people who oppose the current model of Big Tech seriously even if you disagree with them, rather than dismissively suggesting they are driven by irrational grudges. I don’t think that Noah - or the people who he aligns with - are really doing that yet. That may be a mistake.

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According to various sources, John Crowley has died. Even if you don’t know who John Crowley was, you quite likely have been indirectly affected by his work. If you read at all in the space where science fiction and fantasy meet literature, you’ll almost certainly have read someone who knew his books, and very likely loved them. Ursula Le Guin described Little, Big as “a book that all by itself calls for a redefinition of fantasy.” Susanna Clarke said in her long ago Crooked Timber seminar that she too had read Little, Big, and I think that it shows. Her most recent book, Piranesi, draws on Borges’ Library of Babel, but I suspect that it was influenced too by the memory palaces that Crowley built, on foundations laid by Dame Frances Yates. As James Hynes described Little, Big in the Boston Review.

it is a long, gorgeously written, picaresque family saga, in the last fifty pages of which all the major characters, with one heartbreaking exception, turn into fairies. I’ve read Little, Big four times now, and wept shamelessly each time over those last, extraordinary fifty pages, and over the years have purchased and given away fifteen copies of it (when I could find it–it is inconsistently in print). When “You’ll love this” isn’t recommendation enough, I have proceeded to claim (as I’m claiming here) that Little, Big is an Important American Novel that bears comparison to such works as One Hundred Years of Solitude and Nabokov’s Ada.

Me too, on the tears, the giveaways, and the importance. Every time I see a copy anywhere, I buy it, later to press upon some unsuspecting person who seems a likely prospect.

I could keep going. Kim Stanley Robinson admires Crowley’s non-f/sf book about Roosevelt’s dream of industrial America, Four Freedoms. Francis Spufford’s Nonesuch owes nearly as much to Crowley as it does to Narnia, and at one point practically quotes the thesis of Crowley’s extraordinary long story, “Great Work of Time.”

I don’t mourn Crowley because of his influence on others, but because of how his writing shaped me. I mention his work often in my own writing, but have never talked about it in its own right. What Crowley wrote is too deeply and intimately entwined with my sense of my own self for me to to be able to view it from the outside.

When I think about Little, Big, I remember being twenty-one, walking up the hill from work to my family’s house in a small town in Tipperary in Ireland. I had bounced off the book the first time I read it, but for some reason felt pulled back to try again, and read and re-read it, falling in love with it as I have never fallen in love with a book before or since. Walking up the road on a temperate summer afternoon, thinking about the book and how it folded back into the world and itself, felt like a quiet explosion of joy in my chest.

When I think about Aegypt, it is a memory from a year earlier, taking a long train ride from New York to Toronto to visit friends, and rounding a bend to see what was obviously the original for Butterman’s castle. Decades later, when I read the short story “This Is Our Town,” collected in the small Totalitopia volume, it seemed to me a version of my own childhood, transplanted to a small American town; an ordinary Catholic boyhood, whose suffusions of the supernatural were painstakingly captured with the apparatus of the fantasy genre.

I got to know Crowley very slightly via email, as he wrote his final book, Flint and Mirror, which was set in Ireland before and during Hugh O’Neill’s rebellion. It was a bitter war. My and Maria’s ancestor Turloch Breasalach was a contender against Hugh for the O’Neillship. Our family tree records that out of twelve sons that Turloch Breasalach had alive at the beginning of the Elizabethan wars, only three were surviving at the end of it. Crowley wanted to use the book to talk about colonialism. The brutal war and its aftermath set some of the parameters for what Britain did later in other parts of the world. John Dee, who appeared in Flint and Mirror as well as several other Crowley novels, was not just a wizard but an imperial propagandist.

There’s a single word in Flint and Mirror that I helped shape; a tiny return for the way in which Crowley’s work shaped me. The wave that sinks a ship in the last part of the book is the third wave, because in Donegal folklore, the third wave was the fairy wave that you had to beware of. I passed that on to Crowley from a story that my and Maria’s grandaunt Maire MacNeill translated from Irish, “The Knife Against the Wave.” Since Crowley loved reading fairytales as well as writing them, here it is. I will miss his writing very much.

The Knife Against the Wave

Long ago a fine able handsome man named Séimín Rua was living at Baile Mór. He and his crew went out fishing with lines to Cúl Fastó an Chladaigh Bháin, a fishing ground in Donegal Bay.

The day changed and the sea rose and it seemed they would be wrecked. Séimín was in the stern of the boat and saw a monstrous wave coming at them. He pulled off one of his shoes and threw it out against the danger. Before long he saw a second wave coming at them: he pulled off his other shoe and threw it out and the sea calmed a little but this was only a short respite. It was not long until the third wave of the sea rose at them and each one of the seven thought that this one surely would drown them. There was a big bait-knife stuck in behind the stern-seat. Séimín caught hold of it and threw it against the danger. No sooner had he done so than the sea fell and became as smooth as a board.

They came home then drenched to the skin and when they reached the shore their families were there waiting for them for they thought by the way the day had changed and the sea had risen they would never see them again. No wonder there was great rejoicing for them. When they had divided whatever fish they had caught and moored the boat they all went home.

Well and good. When darkness fell Séimín was sitting beside the fire, his soles to the warmth, all the neighbours in and he telling them about the hard day they had had. With that, there was a bang on the door. Someone went to open it and who was standing on the threshold but a rider on a white horse! The rider asked if Séimín was within and was told he was. The rider called from the door:

“Séimín,” he said, “I would be very thankful if you would come with me to such a place to pull out the knife you cast into my sister’s heart today!”

Séimín recognized immediately what kind he was. “I will not go out the door on such a journey unless you promise me that neither you nor any of your kind will ever harm me or anyone belonging to me.” “I promise you that,” said the rider, “and I promise you as well that you will be left back home safe and sound before daybreak tomorrow.”

Séimín went with the rider and he neither heard nor saw anything until the white horse came to shore on the Red Strand at Mullaghmore in Connacht. The horse continued on to a crag at the top of the strand where a door opened into the hill and the horse entered. Before long they reached the finest castle Séimín had ever seen. The rider tethered the horse and led Séimín inside. They went upstairs and he was taken into a room where a girl was lying. The bait-knife he had thrown against the dangerous wave was stuck in under her heart and she was screaming with pain.

“Come here,” she said, “and pull this knife out of my heart!”

“I will do that willingly,” said Séimín, “if you promise me you will never harm me or anyone belonging to me.”

“I promise you that,” she said.

Séimín drew out the knife and she was healed. “Why did you try to drown us today?” asked Séimín. “Because I am in love with you and would like to have you for myself.”

“And you were going to drown the whole crew because of me!” “I was,” she said. “I would do anything in the world to get you for myself.”

“Well, you won’t get me,” said Séimín. “I must be on my way home.”

The rider of the white horse was standing at the door waiting for Séimín who mounted behind him, and the horse never stopped until he was left at the door of his own house. The rider bade him farewell and Séimín saw him no more from that day until the day of his death.

Dan Wang and I have written the Weekend Essay for the Financial Times, “Does Silicon Valley dream of Philip K Dick?” (update - non-paywalled links have been used up - so you will now need a sub to read this) I think it came out well. Nut graf, as they say in journalism:

Silicon Valley likes to dress its ambitions in the glistening garb of science fiction from the 1950s, when authors such as Isaac Asimov and Arthur C Clarke spun tales of humanity spreading through the stars. But what kind of future are we really hurtling towards? Increasingly, it is looking much more like the one anticipated by the drugged-up 1960s oeuvre of Philip K Dick, filled with deranged billionaires, machines that claim consciousness, hallucinated worlds and political breakdown. Musk and his brethren seem more like escapees from this universe than the Asimovian super-geniuses they imagine themselves to be. It is Dick’s far weirder sensibilities that offer the better guide to Silicon Valley today.

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I’ve never before had so much artistic intelligence poured into framing a piece that I co-wrote or wrote. If I could, I’d hold a parade and medal-giving ceremony for whoever it was in the FT’s art department that found a photo hinting that Elon Musk’s SpaceX IPO took place in the Blade Runner extended universe. Indeed, the point of our essay is that the SpaceX launch did happen in some version of that universe, since that is the universe we live in.

Philip K. Dick wrote most of his great work in the period between the mid 1960s to the early 1980s, and he captured something very important about our current condition. Borges says in his essay, “Kafka and His Precursors,” that “[t]he fact is that each writer creates his precursors. His work modifies our conception of the past, as it will modify the future.” Once you read Kafka, you stumble across Kafkaesque moments in the past writing of those who themselves never read Kafka. For Dick, you don’t see those moments in other writers. You find them in the world all around you.

In many senses, Philip K. Dick was not a good writer. His prose was at best workmanlike. If you’re looking for a good novel in the traditional sense, he wrote maybe one? The Man in the High Castle has characters, plot, and enough space to properly let them play out. But (and there are many examples of this), you can be a great writer, revealing possibilities that others haven’t seen, without always being a good one. Dick’s best novels - I would name in addition to TMITHC, Martian Time-Slip, Ubik, The Three Stigmata of Palmer Eldritch - capture something that is both important and terrifying about the world. As Dick says in his essay, “How to Build a Universe That Doesn’t Fall Apart Two Days Later”:

The two basic topics which fascinate me are "What is reality?" and "What constitutes the authentic human being?" Over the twenty-seven years in which I have published novels and stories I have investigated these two interrelated topics over and over again.

But none of his universes were ever real in the first place. Nor, for that matter, is the one that we seem to inhabit at the moment. Again, from his essay:

So I ask, in my writing, What is real? Because unceasingly we are bombarded with pseudo-realities manufactured by very sophisticated people using very sophisticated electronic mechanisms. I do not distrust their motives; I distrust their power. They have a lot of it. And it is an astonishing power: that of creating whole universes, universes of the mind. I ought to know. I do the same thing. It is my job to create universes, as the basis of one novel after another. And I have to build them in such a way that they do not fall apart two days later. Or at least that is what my editors hope. However, I will reveal a secret to you:I like to build universes which do fall apart. I like to see them come unglued, and I like to see how the characters in the novels cope with this problem.

It is Elon Musk’s power to create universes, in which, provided that we provide him full scope for his ambitions, he will bring us to the stars. It is Peter Thiel’s power to impose his morbid fantasies about the Antichrist on the rest of us. It is Mark Zuckerberg’s, to create vast, self-replicating social worlds that seem to be emptied of any of the elements that make society feel meaningful. So too Sam Altman, who promises like Dick’s alien-infected business tycoon, Palmer Eldritch, to fulfil our every wish if we only grant him the control that he needs.

The reason that you read Dick with a terrified sense of recognition is that the shoddy pseudo-realities that he describes in his novels and short stories map onto the stories that we read in the news every day. All of these grand visions hide truths that are messier, more sordid and more contingent. It helps, of course, that we seem to be recapitulating the elements of his era that his fiction emphasizes: the cold war paranoia of the 1950s; the bitter political fights of the 1960s; Richard Nixon’s dreamworld of the 1970s. It helps too that the science fiction genre that he wrote in and criticized has become part of today’s dominating culture.

In the 1960s, Dick was already depicting the failures of that science fiction future: the consensual hallucination of cyberspace; an overheated world; Martian colonies that no-one wants to live in; machine intelligence that is ubiquitous and uncertainly alive; technology that never works as it should, while trying to squeeze every penny out of us that it possibly can. I read yesterday that BMW was running obligatory commercials for the new Spiderman movie on the digital dashboards of its car. If the ad had only been for the forthcoming Blade Runner 2099 miniseries, Ourobouros would have swallowed its own tail.

Dan and I are both “Dickheads” - passionate aficionados of Dick’s writing. I’ve written before about their resonance for today, though Dan is the one who has deeper knowledge of Dick’s fiction (and better jokes about it too). As we note in the piece, riffing on Ursula Le Guin, the great thing about Dick’s failed universes, indifferently crafted by incompetent demiurges, is that there are seeds of hope amidst the generalized paranoia. Reality, as he famously said, is that which, when you stop believing in it, doesn’t go away. Read rightly, Dick’s work suggests that we can get back to bedrock. Do read our essay. But read Dick’s own work too.

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The last couple of weeks have been quiet thanks to other writing with other people, some of which is about to come out. In the meantime, this article reminded me of one of Alan Moore’s greatest and least-well-known creations. Abelard Snazz, “the man with the two-storey brain,” offers “to handle complex problems with even more complicated solutions.”

Someone at the Economist is following in Snazz’s footsteps. The problem: AI is “breaking the British state” by making it too easy for citizens to complain.

Citizens frustrated by the poor deal they get from the authorities are turning to artificial intelligence to file objections and appeals, and claim their dues. The resulting deluge of complaints, and demands, will overwhelm bureaucracies built for the age of the post and the telephone. Too little is being done to prevent the state from drowning.

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The worry isn’t that the AI is bad, but that it threatens to become too good.

AI is superb at dealing with bureaucracy. It can digest small print, ferret out loopholes and draft appeal letters in seconds. Autonomous AI agents will accelerate the trend, filing tax complaints with minimal intervention. Unlike humans faced with officialdom, AI agents will never lose their temper—or the will to live. Officials complain about AI “slop” and hallucinations, but as the technology improves they will face the opposite problem: demands as well-crafted as a first-class lawyer’s.

This is structurally similar to the problems of the planet Twopp, where the state was overwhelmed by street crime, until it called in Abelard Snazz’s specialized consultancy services. Substitute in ‘thieving criminals’ for ‘aggrieved citizens’ and the analogy is nearly perfect.

It only took a few moments of cogitation for Snazz to come up with a solution.

Similarly, the Economist:

stop creating entitlements that are ripe for AI-fuelled claims. … prune the mass of procedural rights … The most important task is to act faster and more radically on remaking the state with AI. That means not just speeding up the old cycle of applications, decisions and appeals, but replacing it altogether. … Rather than just handling locals’ objections, a new AI-driven planning system could decide for itself whether a housing development meets a zoning code. Rather than processing benefits claims, it could devise personalised welfare interventions.

As the citizens of Twopp discover, automating the state turns out to have its own problems. Then, as now, robots really love proceduralism.

Abelard Snazz, of course has a solution to his previous solution.

The resulting system of robot police versus robot robbers expands and expands and expands, sucking in innocent bystanders and making ordinary life impossible.

So too for other efforts to counter the bureaucratic problems of AI with more AI. A couple of years ago, the New York Times described how this was already happening in US health insurance.

The list of treatments that needed pre-approval from insurers seemed ever broadening, and the denials seemed ever rising. … For a growing number of doctors, A.I. chatbots — which can draft letters to insurers in seconds — are opening up a new front in the battle to approve costly claims, accomplishing in minutes what years of advocacy and attempts at health care reform have not. … As doctors use A.I. to get faster at writing prior-authorization letters, Dr. Wachter said he had “tremendous confidence” that the insurance companies would use A.I. to get better at denying them. “You have automatic conflict,” said Dr. Wachter, who wrote a book about digital technology in medicine. “Their A.I. will deny our A.I., and we’ll go back and forth.”

The Economist’s leader writer waxes lyrical about the burgeoning of the AI state as rights and entitlements are pruned away, but has little to say about how citizens are likely to respond. Like other such proposals (the author seems to be riffing on the notion that the UK can e.g. solve its planning problems by making it harder for people to object), this tacitly assumes that citizens will not find other attack surfaces. As per Dan Davies, this is a heroic assumption.

Abelard Snazz, in fairness, proposes a more radical solution to his solution to his solution.

The consequences are not entirely satisfactory.

Beneath Alan Moore’s jokes, there lurks a very useful general point. Abelard Snazz believes that the solution is always more robots, even when robot solutionism is obviously the root cause of the problem. So too for the Economist, arguing that the solution to the problems caused by AI is more AI, with a healthy dollop of rights-pruning on the side. As a general matter, trying to solve a systemic problem by doubling down risks creating out-of-control feedback loops.

If you look around, you will see similar approaches to problem solving everywhere in AI. Frontier models are threatening to devastate cybersecurity? I see frontier models deployed for defense as well as offense. Ha, ha, ha! It’s brilliant. Ha, ha, ha.

While you can fill in the ‘you’re a genius, master’ yourself, as you like, such ideas may not always be wrong in the particular. Abelard Snazz; bureaucrats versus complainants; offensive versus defensive cybersecurity; these are all examples of technology driven arms races. Once an arms race has begun, strategic uncertainty means that it may be difficult to prevent escalation, even if it ends up making everyone worse off.

Equally - and this is where the Economist article misses the mark - you need to think about these dynamics as a system. How does the solution that you are proposing interact with the problem that it is intended to solve? Which other problems is it likely to give rise to? How will the solution be gamed? (and it will be gamed - AI bureaucracies will absolutely have their own politics). And so on. If you don’t think in this way, you’re liable to Abelard Snazz it up, providing ever more complicated solutions for already-complex problems, many of which you have created yourself thanks to your past problem solving efforts.

Next up, another ‘how science fiction explains the world we’re in’ special, likely on Monday.

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I didn’t expect to have more uncomplimentary things to say about the unfortunate intellectual relationship between (a few) economists and (a few) Silicon Valley billionaires right away. But Noah Smith republished his review of Daron Acemoglu and Simon Johnson’s Power and Progress this morning as part of a feud with Acemoglu and Johnson over whether we ought be trying to “steer” the development of AI.

A “timely repost” he says. Not so much. When Noah first put up his review, I responded, pointing out some of the things it got very badly wrong about the book it purported to describe. Then, he and Brad DeLong did one of their very occasional podcasts. Noah did not so much respond to any of the particulars as double down on his (again: obviously wrong) claims about the book without actually responding to any of the things I had said. I responded to that too.

Now, Noah is republishing the original piece with the note:

So anyway, here’s that review for you to enjoy. Hopefully it gets a few people to think a little harder about the AI-related ideas that Acemoglu has been promoting throughout the econ world, and why it would be a big mistake to make those ideas the default position of the economics profession on the AI issue.

I don’t for a moment think that Noah is a wicked person, but if there are economists who “need to think a little harder about … AI-related ideas,” he absolutely ought to be first in the queue. When Noah wrote his original review back in February 2024, it was to ridicule the notion that we ought worry about the power of Silicon Valley billionaires. That was an obviously bad argument then, but it is now much worse, given everything that has happened since.

So here’s my own “timely repost”: an edited and very slightly updated remix of relevant bits from the two very long pieces that I wrote in response to his earlier review and the podcast he did with Brad doubling down on his position. The TLDR of a very long post is that Silicon Valley billionaires do, actually, have power to shape political debate and get people to fawn over them, that Acemoglu and Johnson’s book is very valuable on this very important topic, that economists actually should adopt some form of their view as a default position, and that Noah himself should not be digging himself deeper, but trying to dig himself out, by admitting how badly he got things wrong back then. I don’t hold out much hope that he will: the even shorter TLDR is the screenshot at the top of this newsletter. Perhaps I will be happily surprised.

I don’t much like having to do this - I genuinely like Noah as a person - but I am startled and dismayed to see him resurrect claims that he should never have made in the first place. He has a lot of readers, and his failure to think hard and honestly about what he got wrong does them a great disservice. Repost below the fold.

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******

Before letting rip, Noah says nice things about Acemoglu and Johnson, and I’ll do the same here for him. There are many on the left who detest Noah, but I know him to be a genuinely decent person. What I do think (and I’ve said more or less this to him in person - my views won’t come as news) is that Noah represents a style of economics that has an overly Panglossian view of power, economics and progress.

Many (but not all by any means!) economists just don’t like to think about power that does not involve visible coercion at gunpoint. They are especially resistant to the notion that power and ideas might be intertwined. Instead, being economists, they prefer to believe that ideas are intertwined with markets. Specifically:

By gesturing toward this imaginary intellectual marketplace, in which everyone definitionally competes on an equal footing, they can wave away all counterclaims. Neither impertinent criticisms from non-economists, nor heretical anti-market arguments from fellow initiates can even scratch the surface of their magnificent assurance. Such counterclaims effectively become invisible.

That might seem like a rather strong hypothesis. But it is the only plausible theory I can come up with as to why Noah apparently can’t see the words and sentences printed on the pages that he read. There is a dizzyingly vast gulf between what Noah believes Acemoglu and Johnson are saying about power and what they actually say.

Noah says that Acemoglu and Johnson’s

entire chapter on power and persuasion left me bewildered. I do not understand why we should put accidental success in a nonviolent marketplace of ideas in the same conceptual category as chattel slavery and feudalism. [all Noah’s italics]

He suggests that Acemoglu and Johnson’s argument is both impossibly capacious and intellectually incoherent:

it’s clearly true that technology can be used to benefit average people or to hurt them. But how does society choose how to use technologies? Acemoglu and Johnson’s answer is “power”, from which they get the title of their book. But what is power? Here, in Chapter 3, Acemoglu and Johnson deploy a definition that veers into the tautological: “Power is about the ability of an individual or group to achieve explicit or implicit objectives. If two people want the same loaf of bread, power determines who will get it.”

Using this definition, how could we ever conclude that power wasn’t the reason for an observed outcome? Two people want a loaf of bread, and one of them gets it; we know this was due to “power”, because “power” is defined by who gets a loaf of bread. This kind of definition is semantically valid, but empirically useless; if you define “power” such that it simply means “whatever caused an outcome to happen”, you haven’t isolated causality, you have simply given it a new name.

Acemoglu and Johnson have a reason for employing a definition this infinitely broad; it allows them to include persuasion and compulsion in a single category of “power”.

In practice, according to Noah, Acemoglu and Johnson base their entire theory of power on the eloquence of techbro poasting. Specifically:

Acemoglu and Johnson believe that entrepreneurs … select from a menu of technologies, … to replace people or … that complements people’s labor … often they’ll choose the wrong thing. And not only will they choose the wrong thing, but they will use their quote-unquote power … persuading people [by] writing articles and … PR, … [and] their mighty power of blog posts … tweeting to … convince the world to allow them to invent the human replacing thing

Such an understanding of power is “useless and even counterproductive” since it assumes that “people just hearing arguments and deciding that those arguments are right means power has been exercised.” Power becomes a “residual” explanation: “[t]he only way you get there is by just assuming that all outcomes are due to power and then … applying the label power to whatever you think produces the outcomes.”

It isn’t unreasonable for Noah to want to know how to identify power when he sees it. Nor is it unreasonable for him to note that social scientists find it hard to prove that power inequalities caused this or that outcome, when the power dynamic doesn’t come out of the barrel of a smoking gun.

However, the fact that causation is really hard to isolate for certain social phenomena does not, ipso facto, mean that they lack causal force.

What Acemoglu and Robinson are saying is something quite different than what Noah depicts them as saying. For sure, they acknowledge that persuasion has some stochasticity. But they stress that it is not a series of haphazard accidents. Instead, under their argument, there are some kinds of people who are systematically more likely to succeed in getting their views listened to than other kinds of people. This asymmetry can reasonably be considered to be an asymmetry of power.

Noah claims Acemoglu and Johnson respond to the question “why are the tech bros so persuasive?” with a simple answer: “luck.” He says that he has found the killer quote:

They admit that quite a bit of the process of persuasion is random and declare that you are enormously lucky if you get the right idea, just the right ring to it at just the right time. … I do not understand why we should put accidental success in a non violent marketplace of ideas in the same conceptual category as chattel slavery and feudalism.

Let’s look at what Acemoglu and Johnson actually say! They do indeed argue that “all else equal, an idea is more likely to spread if it is simple, is backed by a nice story, and has a ring of truth to it.” But the problem for Noah’s interpretation is that they spend the next fifteen pages or so explaining at great length why all else is not equal, beginning with the very next sentence after the one that Noah quotes.

Acemoglu and Johnson argue that the success of ideas has some stochasticity - it is not a simple product of brute power. That is the burden of the sentence that Noah singles out as their core claim, and of similar provisos scattered throughout. But they certainly do not think that “success” is merely “accidental.”

For starters, Acemoglu and Johnson claim that the success of ideas depends on the “social status” of their originators and transmitters. Ideas that come from “respected cheerleaders” are more likely to spread. Social status is associated with success - we are hardwired to admire and imitate those who seem to be successful. But “societies and their rules” also shape social status and charisma: women and minorities have had enormous difficulty in having their voices heard, even when they have great ideas! More generally:

A huge amount of creativity, charisma, and hard work is no guarantee that an academic or entrepreneur will come up with an impactful idea. Prevailing beliefs and the attitudes of powerful individuals and organizations determine which ideas will appear compelling, rather than wacky or so ahead of their time as to be safely ignored.

Social networks also create power imbalances. Some people are very well connected. Others, not so much. Acemoglu and Johnson say that:

when it came to the power of big banks, it was not only ideas and stories. Bank executives and board members belonged to social networks that had enormous economic power and propagated these ideas. The big-finance-is-good idea was being repeated by economists and lawmakers, who were eager to provide theories and supportive evidence.

Furthermore:

It is not just the self-confidence and the social networks that powerful people have for propagating their ideas. It is also whether your voice is amplified by existing organizations and institutions, and whether you have the authority to counter objections.

Specifically this allows for important forms of agenda control:

The rules of the political system determine who is fully represented and who has political power, and thus who will be at the table. If you are the king or the president, in many political systems you will have ample influence on the agenda—sometimes you can even directly dictate it. Likewise, economic institutions influence who has the resources and the economic networks to mobilize support and, when necessary, pay politicians and journalists.

In short: theirs is not a simple theory of tweeting eloquence and accidental success! Nor, for that matter, is it an argument that starts from a “marketplace of ideas.” As Acemoglu and Johnson explicitly note:

the marketplace for ideas is an imperfect frame for technology choices, which are at the heart of this book. To many people, the word market implies a level playing field in which different ideas try to outcompete each other primarily on their merits. This is not how it happens most of the time.

To summarize: Noah’s Theory of Acemoglu and Johnson is as follows.

• Techbros and entrepreneurs write blogposts and tweets in a free and thriving marketplace of ideas. Through luck and eloquence, the techbros persuade people that their arguments are right. This Is Somehow Power.

Acemoglu and Johnson’s Theory of Acemoglu and Johnson is just a little bit different.

• Success in ideas is in part stochastic. Luck does play a role. But so do systematic inequalities. Specifically:

  • People who are perceived as more successful have higher social status and are more likely to have influence.

  • People who do not belong to disempowered minorities have higher social status and are more likely to have influence.

  • People who have dense connections to powerful economic networks are more likely to have influence.

  • People who have the ears of politicians are more likely to have influence.

  • People whose voice is amplified by existing institutions and organizations are more likely to have influence.

  • People who have substantial financial resources to sway journalists and experts are more likely to have influence.

• This ain’t no level playing field market of ideas! Political, financial and social asymmetries have substantial consequences for who has influence and who has not.

Such inequalities of influence help explain something else that perplexes Noah: why Acemoglu and Johnson “explicitly try to rehabilitate the original Luddites,” and argue that these Luddites “were right to worry about knitting frames decimating their livelihoods.” From Power and Progress:

The Luddites themselves seem to have understood not just what the machines of the age meant for them but also that this was a choice about how to use technology and for whose benefit. In the words of a Glasgow weaver, “The theorists in political economy attach more importance to the aggregate accumulation of wealth and power than to the manner of its diffusion, or its effects on the interior of society.”

The Glasgow weaver still has a point, some two centuries later. Many “theorists in political economy” still attach importance to the collective accumulation of wealth and power, without inquiring closely how it is distributed, or what second order consequences it may have for society. Acemoglu and Johnson are, of course, themselves political economists, but they look to push back against some of the main tendencies in the field.

There are two parts to the Glasgow weaver’s complaint. One is a distributional concern: in their words, economists usually pay more attention to aggregate social benefits than how those benefits are diffused to particular groups in society. They all too readily leap from a broad utilitarian-philosophical claim - that we ought maximize on some notion of the general social benefit - to the empirical assumption that if we maximize aggregate benefits at time t1 we can expect that the distributional problems will sort themselves out in some proximate future period, t2. More crudely put: they assume that if you let ‘progress’ rip right now, the particulars of who gets what will sort themselves out in some broadly acceptable way in the future.

According to Noah (in the podcast), Acemoglu and Robinson’s argument is that “entrepreneurs … select from a menu of technologies, … to replace people or … that complements people’s labor … often they’ll choose the wrong thing.” Under this interpretation, Acemoglu and Johnson are wrong, because they believe that entrepreneurs have the foresight to envision what their technologies will do to labor in the future. Noah is then moved to launch into an extended fantasia on how “after combing through” the book, he has failed to find a “scrap of evidence” for the proposition that entrepreneurs and technologists can foresee the consequences for labor of the technologies that they invent, and how various tech folk and entrepreneurs are terrible at predicting the future.

Again (I regret that I have to keep saying this), this is not what Acemoglu and Johnson say. They do not claim that entrepreneurs make a single choice over technologies that they might get right or wrong, leading to the one path of development or to the other. They do not worry that tech people are prophetic geniuses, fiendishly choosing the worst possible human future so as to maximize their personal advantage. Instead, they fear that the bros’ unseeing ignorance leads to over-reach, but that their choices may nonetheless become self-reinforcing because of the mutually reinforcing intersection of influence, technological development and political power. Again, it is worth reading their actual written words:

Those enriched by new technologies, or whose prestige and voice are magnified, become more powerful. Technological choices are themselves defined by dominant visions and tend to reinforce the power and status of those whose vision is shaping technology’s trajectory. This self-reinforcing dynamic is a type of vicious circle. Students of history and political economy have highlighted such dynamics, documenting the pathways that make the rich politically more influential and how this additional political power enables them to become richer. The same is true of the new vision oligarchy that has come to dominate the future of modern technology.

In other words, economic inequality tends on average to compound into political inequality, and inequality of social influence, in a kind of feedback loop. And even the feedback has feedbacks! Just as social influence is magnified by economic clout, so too social influence can translate into economic and political benefits. Hence:

persuasion power generates strong self-reinforcing dynamics: the more people listen to you, the more status you gain and the more successful you become economically and politically. You are thus enabled to propagate your ideas more forcefully, amplifying your power to persuade and further boost your economic and political resources. This feedback is even more important when it comes to technology choices. The technological landscape not only determines who prospers and who languishes, but it also critically influences who holds social power. Those enriched by new technologies, or whose prestige and voice are magnified, become more powerful. Technological choices are themselves defined by dominant visions and tend to reinforce the power and status of those whose vision is shaping technology’s trajectory.

There are long standing battles about feedback processes within the profession of economics. Many economists - in particular right-leaning economists - Do Not Like increasing returns processes because they mess up a lot of the economic standard arguments for market efficiencies. But at this point, I think that most reasonable economists accept that feedback helps explain how some trajectories of technological progress get locked in, and why some get locked out. So Acemoglu and Johnson take that one step further, arguing that technological trajectories are shaped by political and social feedback in ways that will magnify the influence of elites, absent strong countervailing forces.

If (a) there are different technological trajectories, (b) these trajectories have long term distributional implications (they lock in economic patterns of who gets what), and (c) this in turn affects the distribution of political and social power, then you can’t assume that these problems will sort themselves out in some fair and equitable fashion in the long run. They will not: instead, whatever solutions are created in the future will reflect inequities that are being generated today.

The second part of the weaver’s complaint is a social concern - that maximizing on aggregate wealth and power may have adverse effects for society, and may hurt some groups particularly badly. This of course, fades into the distributional problem, but it has some broader consequences too. Again, there is an unfortunate tendency in the mainstream of economics towards a callous calculus of the ultimate good, in which the travails of those who suffer through economic adjustments are regarded as sad, in some abstract and generalized way, but a necessary cost of building a better world that will manifest itself at some hazy point in the future. At the extreme, this can turn into statements like Nassau Senior’s infamous quip that the Irish famine “would not kill more than a million people, and that would scarcely be enough to do much good.”

Acemoglu and Johnson are not responding to sentiments as vile as those of Nassau Senior. But I suspect that they are familiar with the attitude of much of their profession to workplace innovations that seem to improve ‘efficiency’ at the expense of making workers’ lives worse. For example, algorithms including machine learning algorithms, are having more immediate consequences within the U.S. workplace, as illustrated by this Bloomberg article on Amazon’s work practices. Rather than making the customer wait, Flex drivers ensure the packages are delivered the same day. They also handle a large number of same-day grocery deliveries from Amazon’s Whole Foods Market chain. Flex drivers helped keep Amazon humming during the pandemic and were only too happy to earn about $25 an hour shuttling packages after their Uber and Lyft gigs dried up.

But the efficiencies come at a stark human cost:

the moment they sign on, Flex drivers discover algorithms are monitoring their every move. Did they get to the delivery station when they said they would? Did they complete their route in the prescribed window? Did they leave a package in full view of porch pirates instead of hidden behind a planter as requested? Amazon algorithms scan the gusher of incoming data for performance patterns and decide which drivers get more routes and which are deactivated. Human feedback is rare.

… former Amazon managers … say the largely automated system is insufficiently attuned to the real-world challenges drivers face every day. Amazon knew delegating work to machines would lead to mistakes and damaging headlines, these former managers said, but decided it was cheaper to trust the algorithms than pay people to investigate mistaken firings so long as the drivers could be replaced easily. …

Inside Amazon, the Flex program is considered a great success, whose benefits far outweigh the collateral damage, said a former engineer who helped design the system. “Executives knew this was gonna shit the bed,” this person said. “That’s actually how they put it in meetings. The only question was how much poo we wanted there to be.”

From the perspective of management, algorithmic bedshitting is wonderfully efficient! It costs less, and is much less of a pain in the ass. And if drivers have to piss into bottles in their vans because they fear being punished by the algorithms, then so be it. This is a cost that they pay, not investors, nor managers, nor customers.

Whether Noah cares to acknowledge it or not, the perspective of people like Amazon Flex drivers is less influential on decision making than the perspective of certain other groups. As Acemoglu and Johnson mention in their bibliographical essay, there is some very solid empirical research - and by economists too! - explaining situations in which power-as-social-influence has been very consequential indeed! Specifically, Elliott Ash, Daniel Chen and Suresh Naidu FOIA-ed the hell out of George Mason University, and combined the results with other data. They wanted to discover what happened when right wing foundations paid for judges to attend seminars in nice resorts, with great food and drink, where they listened to Milton Friedman and his mates explaining The Virtues of Free Markets, the Evils of Government Regulators, the Benefits of Chicago School Antitrust Doctrine, and the Healthy Incentives Provided by Harsh Criminal Sentences.

The evidence shows, unsurprisingly, that the judges were influenced! Not only were there measurable long term consequences for the judges’ decisions after they were wined and dined but there were secondary consequences, via social influence osmosis, for other judges whom the first set of judges worked with.

Noah would like to believe that we live in a thriving “marketplace of ideas.” Ash et al. focus in contrast on ideas of the marketplace, how rich people have shaped them, and how in turn, they are reshaping U.S. society. Again: this ain’t no residual account. There’s actual econometrics! These findings were published by the National Bureau of Economic Research, an organization that Noah is possibly familiar with. Oh happy marketplace of ideas, where the Scaife Foundation and Amazon Flex workers alike can try their luck, and hope for accidental success in influencing federal judges!

So how do we create a situation in which other voices than those of accidentally eloquent billionaires like Elon Musk can have some influence on debates over tech policy? Succinctly, Acemoglu and Johnson want MOAR equality and MOAR democracy. The first borrows from J.K. Galbraith’s notion of countervailing power:

We need to reshape the future by creating countervailing forces, particularly by ensuring that there is a diverse set of voices, interests, and perspectives as a counterweight to the dominant vision.

The second derives from a more recent body of findings about problem solving and social cognition, which I and various co-authors have also drawn from.

There is also another reason for democratic success: cacophonous voices may be the greatest strength of democracy. When it is hard for a single viewpoint to dominate political and social choices, there are more likely to be opposing forces and perspectives that undercut selfish visions imposed on people, regardless of whether they want them or benefit from them.

and

The democratic advantage may not be just the aggregation of separate views, but rather the encouraging of diverse perspectives to engage with and counterbalance each other. The strength of democracy is thus in the deliberation among different viewpoints, as well as in the disagreements that this often generates. Hence … a major implication of our approach is that diversity is not a “nice to have” feature; its presence is necessary to counteract and contain the overconfident visions of elites. Such diversity is also the essence of democracy’s strength.

When Acemoglu and Johnson talk about “steering,” they are suggesting that it is better not to leave crucially important decisions to the techbros, but to have them decided democratically through processes that allow ordinary people to have some countervailing power against elites. The downside of democracy is that it is slow, conflictual, and a massive pain in the arse. The upside is that it allows people with different interests to defend their interests, and to collectively draw on diverse understandings of the world for a more complex mapping of plausible future developments. And when you are making decisions that may set patterns in stone for very long periods of time, you really want those who are affected by these decisions to have a say.

If Acemoglu and Johnson are right, our children and grandchildren may not be dealing with technology-produced problems in circumstances of their own making. Instead, the trajectories that determine who makes the choices, who, indeed, defines what is a problem and what is not a problem, are being shaped today and tomorrow and tomorrow. When increasing returns intersect with political and social influence, it is perfectly plausible that the decisions we make right now could dramatically constrain the ability of people in the future to make decisions at all.

Moving away from democracy towards a fuller embrace of market awesome may radically undermine the capacity to exercise democratic choice later. Plumping for the new vision oligarchs, and continuing to support them over time, potentially vitiates collective choice through democracy in order to encourage unbound marketism, with a side-order of unbridled authoritarianism should folks like Musk, Thiel and their ilk get their way. That is what is at stake in Noah’s fight against Acemoglu and Johnson. And that is why both his criticisms and the blinkered perspective that they emerge from get the situation we are in so badly wrong.

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Two pieces last week had sharply differing takes on the politics of Silicon Valley billionaires.

Tyler Cowen:

Andrew Hall is on a roll … Excerpt: “For most of the past decade, anti-billionaire language was a niche product. … In the weeks after an inauguration that seated tech CEOs in the front row and the dizzying drama of Elon Musk’s ill-fated DOGE experiment, billionaire mentions in Democratic emails quadrupled, peaking above 20 percent of all emails sent and holding around 15 percent ever since. Anti-billionaire fundraising tactics are now a mainstay of Democratic messaging.”

Yes, billionaire derangement syndrome is now a thing.

Tim O’Reilly:

Elon Musk … now envisions SpaceX becoming “worth more than the rest of Earth” if it accomplishes its long-term goals. … Before anyone claimed that markets were efficient, political thinkers claimed that the self-interest of merchants would be a gentler master than the passions of princes. Albert Hirschman reconstructed this forgotten case in “The Passions and the Interests”. Montesquieu held that commerce makes manners gentle, le doux commerce. James Steuart thought economic complexity would restrain a prince more reliably than any constitution, since a ruler who wrecked the delicate machinery of trade would beggar his own kingdom. John Maynard Keynes put it plainly 150 years later: better that a man should tyrannise over his bank balance than over his fellow citizens.

… The self-interest of merchants did not tame the passions. It became infused with them, and turned the greatest of merchants back into princes carrying all the undisciplined appetites of old. They built a new and stranger tyranny: not the old corporate machine obedient to shareholders, but a machine that uses shareholder capitalism’s legal forms while escaping its restraints. SpaceX’s astronaut-in-chief is not maximising shareholder value. He is using the instruments of shareholder capitalism to raise enormous amounts of capital while freeing himself from any restraints from those who provide it, so that he can spend the proceeds on Mars, humanoid robots, artificial intelligence or whatever next satisfies his ambition.

Unsurprisingly, I think that the second of these is vastly more insightful than the first. But rather than comparing the pieces I want to hybridize them. Tyler’s riff on “Trump Derangement Syndrome”1 insinuates that you likely have a screw loose if you oppose the political influence of billionaires. But what if you turned this phrase against itself, and instead asked why some tech billionaires seem to be deranged?

Then, I think that you could weld it together with Tim’s basic argument to reach some plausible conclusions about how this happened. Under the wrong conditions, undisciplined princely appetites can transform into madness.

To be clear: this hybrid would be notably different to Tim’s argument, and likely actively objectionable to Tyler. I alone am to blame. But there is inadvertent support for some of its key elements from Peter Thiel, who is a surprisingly valuable chronicler of the conditions that give rise to billionaire derangement, as well as a living example of it (the Antichrist is among us!).2 Here, I’m drawing both on lectures that Thiel gave at Stanford, as summarized by his amanuensis Blake Masters, and the resulting co-authored book, Zero To One. These sources have a lot to say about the connections between entrepreneurship, kingship, and personal eccentricity.

The conclusion I draw is that the forces that Tim identifies, combined with specific aspects of the political economy of Silicon Valley, help explain the derangement of certain Silicon Valley billionaires and their epigones. Old style princes were notorious for their tendencies to deranged behavior, which came not just from their inbreeding, but their power, and the unwillingness and inability of others to contradict or check them. So too, modern princes.

Not too long ago, many people, including Tyler, hoped that the advance of classical liberalism would go hand-in-hand with the growing power of tech billionaires, advancing both causes at once. Now, politically influential tech billionaires have visibly lost contact both with reality and with anything that could plausibly be described as classical liberal values. See the screen shotted quote above. Rather than making snide asides about billionaire derangement syndrome, it might be time for such people to confront what actual billionaire derangement means for the ideological straddle that they have relied on for so long. That is even more so, in a world where markets as well as market-makers are being devoured by the passions.

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To summarize the particulars of my theory: Thiel’s lectures and book provide good, if incomplete evidence that the princely passions described by Hirschman didn’t disappear, but went underground. Commerce and power were fused into a new ideology of entrepreneurial virtù that became highly influential among the founder community in Silicon Valley. This combined with Silicon Valley’s (and popular culture’s) tendency to connect genius with eccentricity, not simply selecting people who seemed strange, but compounding their strangeness through self-reinforcing feedback loops. Finally, this all happened in an intimate social environment of founders and funders. Billionaires know each other and measure themselves and their success against those they consider peers, in a dense entangled clique that commingles high degrees of mutual influence with rivalries and jealousies.

Of course, all of these factors have benign as well as malign aspects. Entrepreneurial risk taking can be awesome; weird people are often more likely to be original; densely linked communities have many advantages. Furthermore, I would guess that none of these factors was sufficient on its own to precipitate the madness of princes that we see today. It is perfectly possible that they would have worked together in much more benign ways under different external circumstances. But we are in the world we’re in: one where the boundless appetites and irrationalities of a small number of billionaires seem increasingly incompatible with the need to maintain a stable civil society.

The victory of doux commerce was never complete. Economists like Schumpeter and Hayek deplored the encroachments of bureaucracy and treated disruptive entrepreneurialism as a kind of heroism. Schumpeter specifically argued that monopoly rents were needed to spur technological innovation.

These ideas about entrepreneurialism resonated with well connected Silicon Valley thinkers like Peter Thiel and Marc Andreessen (who regularly mentions Schumpeter as a source of ideas). Even more interesting, given Tim’s piece, is that they often went hand-in-hand with ideas about kingliness. Thiel’s Stanford lectures reportedly stressed the similarities between founders and monarchs.

A startup is basically structured as a monarchy. We don’t call it that, of course. That would seem weirdly outdated, and anything that’s not democracy makes people uncomfortable. But look at the org chart:

… Once a startup becomes a mature company, it may gravitate toward being more of a constitutional republic. There is a board that theoretically votes on behalf of all the shareholders. But in practice, even in those cases it ends up somewhere between constitutional republic and monarchy. Early on, it’s straight monarchy. Importantly, it isn’t an absolute dictatorship. No founder or CEO has absolute power. It’s more like the archaic feudal structure. People vest the top person with all sorts of power and ability, and then blame them if and when things go wrong.

This notion of the monarch as someone vested with “all sorts of power and ability” who gets the blame “if and when things go wrong” reflect Thiel’s Girardian hotchpotch of notions from The Golden Bough combined with management theory. For Thiel, kingship is a quasi-religious institution that combines the right to rule and the prospect of being sacrificed by the multitudes when things go bad. In three successive sections of Zero To One, “Where Kings Come From,” “American Royalty,” and “Return of the King,” Thiel and Masters emphasize the need both to cultivate a kingly myth and not allow it to dominate you.

Thiel also believes that you need not just to thwart socialist bureaucracy but to break out of the stifling confines of market discipline to get things done. The good stuff comes from convention breakers.

The usual narrative is that society should be organized to cater to and reward the people who play by the rules. Things should be as easy as possible for them. But perhaps we should focus more on the people who don’t play by the rules. Maybe they are, in some key way, the most important. Maybe we should let them off the hook.

Monopoly profits are what enable rule-breakers to achieve greatness and technological innovation, allowing them to escape the war of all against all that is market competition and establish more secure forms of dominion. From Zero To One

Americans mythologize competition and credit it with saving us from socialist bread lines. Actually, capitalism and competition are opposites. Capitalism is premised on the accumulation of capital, but under perfect competition all profits get competed away. The lesson for entrepreneurs is clear: if you want to create and capture lasting value, don’t build an undifferentiated commodity business.

… Monopolists lie to protect themselves. They know that bragging about their great monopoly invites being audited, scrutinized, and attacked. Since they very much want their monopoly profits to continue unmolested, they tend to do whatever they can to conceal their monopoly—usually by exaggerating the power of their (nonexistent) competition.

… In business, money is either an important thing or it is everything. Monopolists can afford to think about things other than making money; non-monopolists can’t. In perfect competition, a business is so focused on today’s margins that it can’t possibly plan for a long-term future. Only one thing can allow a business to transcend the daily brute struggle for survival: monopoly profits.

In short then, Thiel sees tech entrepreneurialism and kingship as intimately bound together: acting outside the rules to build the future that no-one else has the long term vision to grasp. “There have to be CEOs and founders. Those people are expected to wear the crown.” Monopoly is what enables you to do things that no-one else can do. Thiel is also a strong proponent of fending off bureaucracy and accountability as long as possible, so that founders can do founder stuff.

Of course, such ideas are not unique to Thiel or Musk. The “Silicon Valley Canon” is substantially made up of texts celebrating the willingness of great men to do great things, bringing together politicians, traditional tycoons and tech founders into a linear history of awesome overachievement by men who dared to do what no-one else would do.

Furthermore, as Thiel acknowledges, founders tend to be extreme outliers who get more extreme over time.

Thinking about founders … the dynamic might work like this. People start out being different. They are nurtured to develop their already somewhat extreme traits. Those traits become more important, and they learn to exaggerate them. Others perceive that inflated importance and exaggerate in turn. The founders thus end up being even more different than they were before. And we cycle and repeat.

Finally, Silicon Valley is highly cliquish. Both Zero to One and Thiel’s lectures depict the tech industry as a place where everyone who counts knows everyone else. Venture capitalists are closely interconnected. Founders and CEO's look at each other with a mixture of respect and jealousy.

Thiel describes how you want to build your company around a ‘mafia,’ and Silicon Valley is organized in ways that are not entirely unlike its Sicilian predecessor in its golden era. There is no central organization, but there are central cliques. You need to be connected to get things done. Ostentatious declarations of friendship and mutual interest go hand-in-hand with indirect threats, and the ever-present risk of betrayal. While the histories of the ‘Paypal Mafia’ do not reveal garrotings, shootouts and dissolutions of the bodies of opponents in acid baths (all part of the history of the Sicilian mafia - I once had to do a lot of secondary research), they are drenched with the sense that the Valley is ‘Our Thing.’ Figures like Peter Thiel and Elon Musk may possibly despise other, but also clearly recognize what they have in common that others don’t.

This is an environment in which ideas and modes of comportment may spread rapidly, both among true insiders, and those who aren’t inside but would dearly like to be. It has been quite entertaining, for example, to see Thiel’s theories of Girardian conformity spread among those who want to signal how hard they conform. And so too for other modes of relating to the world.

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In short then, Thiel - both as reported by Masters and in collaboration with him - suggests that Silicon Valley is a place where being a founder is tantamount to being a king. You have a greater chance of succeeding if you are weird, and if you succeed, your weirdness will likely feed upon itself in feedback loops of positive reinforcement. Finally, it is a closely interconnected social system where the key people are conjoined in a dense tangible clique. They observe each other all the time, and are observed by others for cues of what you need to do to become a made man.

Thiel is, of course, not the only force in Silicon Valley, but he, and other Paypal Mafia members, including Elon Musk, have helped to shape an environment in which we might expect (a) the kinds of kingly notions that Tim talks about to be popular; (b) for personal eccentricities to compound upon themselves into active derangement, and (c) for both kingly ambitions and derangement to spread among a broader group of both principals and observers. And this is what we appear to be seeing.

SpaceX perhaps marks a culmination of the kingly ideal, a moment in which, as Tim puts it, someone like Musk can “raise enormous amounts of capital while freeing himself from any restraints from those who provide it, so that he can spend the proceeds on Mars, humanoid robots, artificial intelligence or whatever next satisfies his ambition.” But it is also the moment in which someone who is visibly profoundly disturbed has briefly become the world’s first trillionaire. And it is one in which others want to copy him.

Bringing together Tim’s ideas with deranged billionaire syndrome helps clarify how the stripping of restraints can combine with circumstances to produce a more generalized frenzy. As Hirschman said, it was the disorderly passions that the classical liberals wanted to escape from. Those passions regularly led to chaos and to madness. And now they are back.

I’ll finish by noting that this is just one aspect of a greater change. Deranged billionaires like Musk and Thiel are both partly responsible for this transformation, and notable symptoms of it. Still, they are not the whole of it. The reason that the SpaceX IPO temporarily succeeded, even though the numbers make no sense whatsoever, is that investment markets too are ruled by vibes. Tyler’s co-blogger, Alex Tabarrok, devoted years of his life to making the case for prediction markets, arguing that they would distill market wisdom into a more general source of knowledge on a multitude of topics. Now they are here, but all too often it’s spirits, not wisdom, that they seem to be distilling. Even more so for crypto. Matt Levine makes a joke about “Bleebzorx Tokens” to explain the worthlessness of memecoins, and someone else invents Bleebzorx Tokens to make a tidy profit.

We live not just amidst the madness of princes, but the madness of crowds. It might be nice for classical liberals to start thinking - and talking - more systematically about what this means for their understanding of the world.

If I wanted to write a cute post today, it would be about the Balogun red card reversal as a synecdoche for Trump’s corrupting influence. And there is a way in which it provides a useful metaphor for something broader. But I’m going to be perverse and contrary, and leave that until the end. After all, the purpose of this newsletter is not to woo readers with up-to-the-minute takes. If we want to really understand corrupting influences, we can’t just look at Trump. We want to pay attention to the current Supreme Court majority too.

You can make the argument that the Roberts Court is a corrupt institution if you want to. Instead, I want to put the motivations of the individual justices, Roberts included, aside, and make the importantly different claim that it is a corrupting institution. Specifically, the last two decades of Supreme Court jurisprudence have tended systematically to corrupt American democracy across a variety of related issues: campaign finance (including last week’s decision), presidential immunity, and, of course, the prosecution of bread-and-butter political corruption.

To understand this, you need to think more systematically about the relationship between democracy and corruption. The best way of understanding this relationship that I know of is laid out in an academic article that was published the year before John Roberts was named Chief Justice, Mark Warren’s “What Does Corruption Mean in a Democracy?" Here goes.

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Mark taught me political theory when I was doing my Ph.D. (I still remember, and regularly quote, his off-the-cuff dictum that Weber’s essay, “Politics as a Vocation,” concealed “diamonds of Nietzschean brilliance in a great bowl of sociological porridge”). His article on corruption starts from the insight that our current understanding of corruption is both too expansive and too limited.

To understand corruption properly, we shouldn’t think of it as an individual level phenomenon. Classical thinkers, like Machiavelli, understood corruption as a condition that could afflict governments and indeed societies. But if we value democracy as the best way to order our affairs, we should understand corruption as not a moral phenomenon but a political one, which involves the corruption of democratic processes.

Specifically, we should understand corruption as involving what Mark calls “duplicitous exclusion.” The most fundamental norm of democracy is “empowered inclusion of those affected in collective decisions and actions,” so that “every individual potentially affected by a decision should have an equal opportunity to influence the decision.” This implies equal power: people should be provided with “individual participation in collective decisions, such as the right to vote.” People should also have equal voice: “equal chances to influence public judgment, actualized in rights and effective opportunities to speak and to be heard in those deliberative processes that define the agendas, choices, and public framing of issues.”

What that suggests is that “the norm violated by corruption is that of inclusion in collective decisions and actions of all affected.” Corruption is not just exclusion, but duplicitous exclusion.

Corruption does not imply normative disagreement fought out in public, but rather a corrosion of public norms by those who profess them. Corruption involves hypocrisy. For an elite (or group or individual) to be corrupt in the democratic sense, it must both profess and violate the democratic norm of inclusion. Thus, corrupt exclusion is distinguished by duplicity, a characteristic that implies not just the possibility of condemnation, but also the possibility of immanent critique: the corrupt can be called to account by their own standards.

It furthermore involves some benefit that is shared by insiders at the expense of some or many outsiders. Such duplicitous exclusion directly corrupts democracy, even when it is facially harmless.

it is the fact of exclusion and the duplicity of justification that corrupts democracy, whether or not specific harms can be identified. What suffers [from] corruption are the political processes and institutions that would, ideally, expose and limit potential harms by including those affected in making collective decisions and organizing collective actions. Importantly, because a democratic conception of corruption focuses on processes, it also highlights institutionalized corruption. Duplicitous corruption can become routine within institutions, usually in the form of dual cultures, one of which pays lip service to the norms of democratic inclusion, and the other which facilitates and justifies corrupt exchanges.

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Mark’s article describes how courts and judges can become democratically corrupt. Again, I want to put that to one side.1 Instead, I think it is useful to ask whether the Supreme Court, as its majority is currently constituted, is democratically corrupting. The answer, under Mark’s definition, is an unqualified yes. And what is useful about Mark’s approach is that it shows how apparently quite different lines of Supreme Court jurisprudence are mutually reinforcing. Across various dimensions, they systematically corrupt the democratic process.

Consider, for example, the line of jurisprudence that has culminated in last week’s decision to overturn limits on the amounts that political parties can spend in direct coordination with candidates. The practical consequence of this is to undermine limits on campaign contributions. In his opinion for the majority, Kavanaugh claims that:

under the Supreme Court’s current campaign finance cases, the only rationale for campaign-finance restrictions is to prevent “quid pro quo” corruption – that is, “contributions in exchange for official action.” … there are other measures in place to prevent such circumvention that do not restrict speech in the same way that the coordinated-expenditure limits do, Kavanaugh emphasized: the base limits on contributions, federal laws that treat “earmarked” contributions as contributions to a candidate, and federal disclosure laws. … most states do not impose these kinds of coordinated-expenditure limits in their elections, but “‘no evidence of corruption’ via circumvention ‘has materialized.’ … Kagan argued in her dissenting opinion that the court’s ruling “ushers in the same opportunities for quid pro quo corruption that the contribution limits were meant to check.”

Properly understanding the relationship between democracy and corruption means that we should not just be worried about direct “quid pro quo” corruption. Allowing those who can afford it to make very large contributions undermines people’s equal opportunity to participate in collective decision making as well as their collective voice. It means that very rich people and organizations will have much greater say in politics than poor, middle class, or even moderately rich ones. Already, 300 billionaires and their close family members were responsible for nearly 20% of campaign contributions in 2024.

Consider too, the 2024 decision that US presidents enjoy “absolute immunity for actions that relate to “core” or “exclusive” presidential powers, [and] at least presumptive immunity for all other “official acts.” This decision has clearly empowered the current president to enrich himself in a staggeringly corrupt fashion, without any plausible means of judicial redress. But that enabling of individual corruption directly contributes to the corruption of democracy too. As Mark describes it:

Consistent with the modern conception, corruption consists in activities by public officials that depart from the norms, laws, and expectations of their offices, from which they realize gains. Government as the trustee and executor of collective purposes is corrupted. Democracy is directly harmed when people lack a collective agent they can trust to execute collective decisions because they are effectively disempowered. Indirect harms to democracy also result: corruption not only leads to inefficient and ineffective government but also produces an atmosphere that is arbitrary, permeated by differential treatment. Under these circumstances, individuals lack the securities necessary for association, pressure, voice, and other modes of citizen participation that underwrite democracy. An atmosphere in which even everyday acts of democratic participation require heroism can induce or reinforce passivity and distrust among citizens. [my italics]

In other words, the presidential immunity decision does not just make it easier for Trump to enrich himself and his cronies. It enables him to remake government into a means for exercising arbitrary power, punishing individuals, non-profit entities and corporations who oppose him, or who simply have things that he wants and refuse to hand them over. That in turn undermines the conditions for democratic association and voice.

The Roberts Court’s long term campaign to defang traditional legal protections against day-to-day corruption enables democratic corruption too. Take, for example, the landmark decision in McDonnell v. United States:

In 2014, Robert McDonnell was found guilty of eleven counts of public corruption for accepting gifts and loans from Jonnie Williams, a Virginia businessman and CEO of Star Scientific. McDonnell and his wife, owing over $80,000 in credit card debt, needed help to alleviate this debt and turned to Williams. Williams promised to alleviate this debt and in return, McDonnell helped Williams get FDA approval for his dietary supplement, Anatabloc, by attempting to persuade Virginia universities to do the necessary research so that Williams’s company would not have to pay for it. As a result of this arrangement, McDonnell and his wife received over $175,000 in loans and gifts from Williams, while McDonnell facilitated meetings between Williams and state agency officials.

The federal bribery statute makes it a crime “for a public official to receive or accept anything of value in exchange for being influenced in the performance of any official act … The Supreme Court rejected the prosecution’s argument that McDonnell’s actions constituted bribery under the federal bribery statute, concluding that McDonnell’s actions did not meet the definition of “official acts.” … Justice Roberts wrote that only formal government actions, such as filing a lawsuit or making an administrative determination, could be considered “official acts.” He went on to say that political courtesies such as “arranging meetings or urging underlings to consider a matter” generally do not constitute official acts, even if the public official receives gifts or money in exchange. The Court conceded that, although McDonnell’s conduct is “distasteful,” the Court characterized this behavior as a type of “condoned politicking,”

Again, this decision very clearly fosters democratic corruption. It actively encourages the kinds of corrupt exchange that Mark describes, in which democratic equality counts for far less than willingness to pay off politicians’ credit card debts. By relentlessly narrowing the scope for prosecution of corrupt acts, the Supreme Court is increasing the perception and the actuality of democratic corruption in American politics today.

Finally, Court decisions that nominally have nothing to do with individual corruption can be democratically corrupting. This applies to the Court’s gutting of voting provisions aimed at ensuring racial equality, which have culminated in a system where the Court is prepared to bless gerrymandering more or less explicitly, so long as it is done for partisan reasons, rather than to provide racial minorities with voice. This has, predictably, led to a gerrymandering arms race, which undermines basic political equality, by devaluing the votes of Democrats in red states and Republicans in blue ones. This, indeed, may be the most democratically corrupting line of jurisprudence that the Roberts Court has been responsible for.

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I don’t believe that Chief Justice John Roberts, or most of his colleagues are personally corrupt, in the sense that they are willing to take bribes [if you disagree, that’s fine, but I’ll ask you not to make a thing of it in comments, since that would miss the actual argument of this post]. However, it seems to me to be indisputable that their jurisprudence has and is doing a great deal to corrupt American democracy.

Mark Warren’s account of democratic corruption is valuable because it explains why various aspects of this jurisprudence have had this joint consequence. Across an apparently unrelated range of issues - campaign finance, executive immunity, political corruption and gerrymandering - the Roberts Court’s decisions have persistently corrupted the workings of democracy, so as to undermine equality in decision making and voice in favor of processes that are both duplicitous and exclusive. Whether this is deliberate, or the consequence of induced myopia, or some combination, is secondary. To adapt the rhyme: One cannot hope to bribe or twist/ (thank God!) the U.S. chief jurist. But seeing what the man will do/ unbribed there’s no occasion to.

There are bits of Mark’s approach that could be updated. Two decades later, it turns out that democratic corruption can be blatant rather than hypocritical. But again, its great value is in bringing the whole together.

And this, at last, brings us to Trump’s efforts to reverse Balogun’s red card. Trump himself has drawn the comparison to democracy in his characteristically perverse way.

President Trump said the U.S. team’s match against Belgium at the World Cup on Monday night will be more fair after his intervention because both teams will have a full slate of players available, and invoked his baseless claims that the 2020 election was stolen. “And we’re going to have a full team, and Belgium is going to have a full team,” he said. “And you know what? If they beat us, then they can be really proud. The other way, if they beat us, we’ll say it was — I’d say— it was rigged, just like the election was rigged in 2020.

Just as the harm of democratic corruption is not individual self-enrichment but the hurt to democracy as a whole, the damage that the corruption of the red card rules will do is to soccer as a whole, weakening confidence in the sport, and corrupting enjoyment of it, regardless of who wins. So too, we should pay less attention to the specific consequences of corruption, instead focusing on the general systemic harms that it does to democracy, undermining a system that should be based on equality with one where clandestine connections and political influence don’t just matter at the edges, but corrode the basic processes that are supposed to provide some minimal guarantee of fairness.

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Paul Krugman gives my and Abe Newman’s book, Underground Empire, a shoutout in today’s newsletter. That reminds me that I’ve been meaning to provide an update on what’s happening to US dollar power in the light of recent events.

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The short version. The Trump administration seems to be abandoning its long-existing weaponization of the dollar against Iran. It is also enthusiastically embracing cryptocurrencies that were already allowing adversaries like Iran to make and receive payments. But it continues to weaponize dollars against allies such as Europe. This is not resulting in direct pushback, but it is leading Europe to embrace alternative payment architectures that will make it less vulnerable to U.S. pressure in the future. The dollar is not being challenged head-on, but dollar based payment systems are becoming ever less attractive to allies, who worry both about dollar based coercion and the instability and crime risks of dollar-based crypto. That will not undermine the dollar on its own, but might open up possibilities if Other Stuff Begins To Happen.

A decades-long policy direction has been overturned

Last week, I briefly explained how Stuart Levey had radically increased pressure on foreign banks to comply with U.S. sanctions. As Abe and I say in our book, Levey’s main target was Iran. Previously, U.S. banks were already prevented from facilitating direct transactions involving Iran. As we describe it:

Countries that bought Iranian oil needed to pay for it. … Previous U.S. sanctions against Iran specifically allowed Iranian companies to conduct so-called U-turn transactions, transferring their money from one non-U.S. bank to another via the dollar clearing system. This exception acknowledged the central role of the U.S. dollar in global financial transactions, and the U.S. worry that other countries might stop using dollar clearing if it became politicized.

Levey, who was the US Treasury Undersecretary for Terrorism and Financial Intelligence began to reverse that, blocking Bank Saderat from using the U-turn in 2006, and gradually extending the ban so that all Iranian banks were blocked from using the U-turn by 2008. This was a very big part of what prompted Iran to come to the bargaining table on its nuclear program. Financial sanctions made it extraordinarily hard for Iran to get paid for oil. As one former US official described it, Iran was “forced to barter oil directly for ‘wheat and tea from India, rice from Uruguay, and zippers and bricks from China.’”

This is changing. The Department of Treasury has issued a general license, allowing the unblocking of “all transactions prohibited by the above-listed authorities that are ordinarily incident and necessary to the production, sale, delivery, or offloading of crude oil, petrochemical products, or petroleum products of Iranian origin” until August 21. In plain language, if you want to buy or sell Iranian oil using U.S. dollars, you are now welcome to do it. If you want to import Iranian oil into the United States, you now have the blessing of the U.S. Treasury. This goes far beyond reversing the U-turn ban, into normalizing Iranian oil transactions on global markets.

The license is a temporary measure - but it is an extraordinarily important one. It’s notable that the U.S. has not revoked it, even while it and Iran exchange physical attacks. If the license does get revoked, it will send a much stronger signal that the deal is dead than desultory exchanges of fire. If it does not, it signals an extraordinary climb-down by the U.S, and there may be more to come.

The U.S.-Iran memorandum reportedly talks about lifting all U.S. sanctions in the context of a full deal. That would require more than unilateral administration promises - some of the sanctions are written into law. But it is hard to see how the U.S. regains full financial authority, even if the Trump administration decides to do another U-turn on its U-turn on the U-turn. After all, there are now other ways in which Iran can get paid for its oil.

The dollar is being weakened by crypto

Iran has already been able to use cryptocurrency to circumvent the US dollar. A few days ago, the Wall Street Journal published a piece explaining how Iran’s Central Bank has been funneling large sums of money through a complex system of crypto transactions involving the crypto exchange, CoinEx.

Since 2019, wallets with an identifiable link to Iran have moved more than $3.84 billion through CoinEx, according to blockchain intelligence firm TRM Labs. Among its dealings, wallets hosted by the exchange have received hacked crypto acquired by Iran’s Central Bank and transacted directly with accounts that U.S. officials have since attributed to the country’s Revolutionary Guard, that analysis shows. Haipo Yang, a former Tencent engineer who runs one of the world’s largest bitcoin mining pools, launched CoinEx from Hong Kong in 2017. In text messages with The Wall Street Journal, Yang acknowledged that the exchange had been widely used by Iranians, but said it doesn’t have a relationship with the Iranian government.

Iran’s complex system for laundering money also relied on two stablecoins, Tether and DAI, the Binance crypto exchange, and a smart contract on the Ethereum blockchain that swapped Tether for a variety of crypto currencies.

Tether and Binance are prominent members of the rogue’s gallery of crypto: Zeke Faux’s great book, Number Go Up, memorably describes Tether as having a business model that is “practically quilted out of red flags,” while Binance had to pay $4.3 billion in penalties under the Biden Administration, and its CEO was convicted of money laundering and served time. Underground Empire quotes DAI’s founder, Rune Christensen, as arguing that DAO had been designed so that “it could never possibly become a tool of financial surveillance and control,” and explains how the notorious Tornado Cash smart contract was built to facilitate the mixing and obfuscation of crypto currencies.

Past U.S. administrations worried that crypto might undermine the U.S. dollar. The second Trump administration has instead embraced crypto. A few months ago, Dan Davies and I wrote a report for the British Council, describing how even before the Iran debacle, Trump administration policy was weakening the dollar. As we said:

The administration has partly abandoned efforts to extend its enforcement capacities to crypto. An internal memo sent by Deputy Attorney General Todd Blanche … instructed staff at the Department of Justice that it ‘will no longer target virtual currency exchanges, mixing and tumbling services, and offline wallets for the acts of their end users or unwitting violations of regulations’ …. Of course, targeting intermediaries for the acts of their end users is how money launderers are prosecuted; on its face, this appears to give these entities carte blanche.

While the Trump administration has sanctioned particular entities for facilitating Iranian transactions, its unwillingness to take general actions against crypto has encouraged the growth of a system that is resistant to traditional financial controls. Notably, the Trump administration pardoned Binance’s CEO, and Trump appears to view crypto as a handy means to enrich himself, his family, and people close to them.

The U.S. is still willing to use sanctions

Of course, the U.S. is still willing to weaponize the U.S. dollar - just not against Iran. Soon after the Trump administration came to power, a former U.S. sanctions official remarked to me that U.S. financial power was now more likely to be used against allies than adversaries. The U.S. has notoriously pursued financial sanctions against judges and International Criminal Court officials for their impertinence in prosecuting Israeli officials and authorizing investigations of CIA “secret sites” in which detainees were tortured. It also reportedly considered sanctioning the French judge who banned French politician Marine Le Pen from politics for five years for misuse of EU funds. Judges and officials who are sanctioned find themselves cut off from businesses that rely on U.S. financial infrastructure. For example, they can’t get Gmail accounts, use Microsoft Office, or use standard credit cards

European officials have so far been unwilling to push back directly, for fear of escalating the fight. As Lindsay Graham charmingly put it, “So to any ally, Canada, Britain, Germany, France, if you try to help the ICC, we're going to sanction you.” But they have become notably more enthusiastic about building alternative infrastructure that would help to insulate Europe against American financial coercion. This plays into international debates over payment systems, including crypto but not limited to it.

From the Trump administration’s perspective, stablecoins that are linked to the U.S. dollar make illicit transactions much easier, but also extend dollar dominance. From Europe’s perspective, stablecoin’s combination of dollar-domineering, criming and possible financial instability is increasingly unattractive in ways that bleed over into more conventional dollar based payment systems.

The European Union’s “digital euro” initiative recently got a go-ahead from the European Parliament, and is explicitly intended to provide an alternative payment system within Europe that wold push back against dollar-linked stablecoins like Tether. Dan and I quote Philip Lane, the European Central Bank’s chief economist, as explaining how stablecoin:

exposes Europe to risks of economic pressure and coercion and has implications for our strategic autonomy, limiting our ability to control critical aspects of our financial infrastructure. When we rely on international cards, apps or stablecoins, we effectively outsource our payment infrastructure.

As Lane suggests, the EU also increasingly views more traditional US linked payment systems, such as Visa and Mastercard as threats as well. And at least one ECB board member foresees the digital euro being attractive outside Europe:

While the digital euro would primarily be used in the euro area, it is worth considering its possible international use. The current draft legislation foresees an approach that respects the sovereignty of third countries, mitigates potential risks for them and offers them new opportunities. ... Moreover, the digital euro’s design includes multi-currency enabling features similar to those of [the ECB’s existing international interbank payments system]. In practice, this means that non-euro area countries could use the digital euro infrastructure to offer their own digital currencies, thus facilitating transactions across these currencies.

The dollar is not in huge trouble … yet

None of this means that the dollar is in great danger of immediate collapse. As Mark Blyth argues in a recent interview, there is a great deal of ruin in a dominant currency. It is hard for others to move away from dollar payments. It will be even harder for other powers to replace the dollar without radical shifts to their own internal economy. The power of the dollar rests not just on dollar based coercion, but also on its central role in transactions, and the need of countries to maintain dollar reserves.

Still, we are in for some interesting times. During the first Trump administration, Abe and I spoke to former officials who had previously worried that sanctions overreach might destabilize the international role of the dollar. They were revising their opinions, now that other countries had not fled the dollar, and more likely to think that there was little risk of blowback from US financal sanctions. It’s notable that the Biden administration was unable to come up with a coherent policy for limits on financial coercion. In the administration’s “2021 Sanctions Review’ ..., the 40-page draft … dwindled to eight pages and contained the earlier document’s most toothless recommendations.”

Now, in Trump’s second go-around, the coercive power of the dollar is being put to a much more extreme stress test. The result is that US allies are increasingly looking to build alternative payment rails, just as adversaries have done in the past. Almost certainly, this is not enough on its own to precipitate a crisis, but it is likely to leach away at dollar power at its edges, and might combine with other crises and U.S. actions (e.g. would the Trump administration extend unlimited swap lines to other countries in the event of a new financial crisis?) to precipitate a more rapid unraveling.

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Below, a post on the Trump-Anthropic fight over Fable, focusing on the generalities (which are more important than you might think) rather than the particulars, which I don’t know any better than anyone who has been following events. If you haven’t been following events, skip down to the first footnote for a brief outline of the facts that don’t seem to be in dispute.1

I’m less interested in the fight itself than the politics behind it. One group of commenters sees the blocking of Fable as an arbitrary exercise of power by an administration that is using export controls to punish Anthropic rather than cracking down on exports of AI chips to China. Another blames Anthropic, saying that the company pressed for regulation of what it itself has described as a dangerous technology, and is hypocritically complaining now that it has gotten what it was looking for.

These two perspectives loosely map onto two broader approaches to AI regulation which are currently struggling for dominance. The first is associated with the past Biden administration and Anthropic itself, and argues that geopolitics and US-China relations make it urgently necessary to regulate the diffusion of AI to make sure that America stays in control. The other is connected to the informal alliance between the Trump administration and the pro-AI, anti-DEI coalition in Silicon Valley, and argues that impeding the development of a transformative technology will carry massive geopolitical and human costs. Both assume that AI will likely have world-changing consequences, but disagree on what to do next.

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There is a third approach that better gets at the underlying problems. It is best articulated in a piece that Alondra Nelson wrote a few months back for Science. Alondra argued that it was a big mistake to think that the Trump administration wanted to deregulate AI. As she put it:

What distinguishes the current [Trump administration] approach is … its character: a greater reliance on executive discretion than on deliberative, public processes. … what it has advanced is not the absence of AI regulation but its rearrangement, often by caprice: intensive state intervention operating through industrial policy, trade restrictions, immigration controls, equity stakes in private firms (selected by the state), the redirection of research funding, and the strategic preemption of state authority.

Perhaps then, instead of shoehorning debate into stylized pro- and anti- regulation positions, we should ask what particular forms of regulation actually involve, and what degree of discretion is associated with them.

Specifically, given what has happened with Fable (and in earlier Trump administration moves against Anthropic), we should pay attention to the tools of national security policy as a mode of regulation. Export controls and related national security tools provide the administration with great discretion in regulating tech. This was true before Trump’s second term: the Biden administration proposed a vast system that would control who had access to AI across the entire planet, which would not be founded on treaties, or even unilateral legislation, but on export controls and executive agreements. It is even more true now.

In the past, this discretion has been deployed in pursuit of a vision of global American technological hegemony. Now, it is being used for very different purposes. What the Trump administration is doing to Anthropic is bad, in the same way that other Trumpian exercises of arbitrary regulatory authority to punish enemies have been bad. Equally, it is the past vision of national security via American power, which Anthropic openly embraces, that makes the Trump administration’s sweeping claims of authority over technology possible. Anthropic is trapped by the assumptions it shares with the administration. It cannot readily push back against arbitrary exercises of export control powers, without recognizing how this arbitrariness is built into the broader framework of national security.

********

Alondra’s article highlights the ambiguities of the word ‘regulation.’ People often think about regulation as narrowly involving governing through the predictable application of specific rules. As Alondra suggests, there are many means through which government regulates private sector behavior. My own personal list would also include informal connections, antitrust and other such legal interventions, spending decisions and a variety of other threats and inducements, whether public or private; perhaps that goes too far.

Under any broader understanding of regulation, the Trump administration is not opposed to regulating AI or other private sector activities. It is emphatically interested in controlling what private actors do and say. It just wants to move away from regulatory approaches that emphasize predictability and public or democratic accountability and towards approaches that maximize its flexibility and discretion. Such a shift (a) reflects a notional theory of the unitary executive, under which the president uniquely represents the interests of the American people, (b) allows the administration to undermine and punish actors whose politics they dislike, and (c) allows people in the administration or their friends to pursue private benefit at the public expense while minimizing the opportunity for outside pushback.

That differs in very important ways from how other modern administrations have viewed their role. But - and this is a big but - there are genuine continuities with existing institutional frameworks. Some policy areas are much more open to executive discretion than others. In particular, the zone where national security, economics and technological knowledge intersect has always allowed a great deal of governmental discretion, because such discretion was seen as necessary to the untrammeled pursuit of the national interest.

That applies, for example, to financial sanctions. Abe’s and my book, Underground Empire (obligatory plug: do go buy ) explains how the US employed strategic ambiguity to scare foreign banks and businesses into advance compliance with administration wishes. As Stuart Levey, who used to run the relevant bits of Treasury, described banks’ incentives:

Keeping a few customers that we have identified as terrorists or proliferators is not worth the risk of . . . a regulatory action that may impact on [sic] their ability to do business with the United States or the responsible international financial community.

A major US law firm said that US sanctions rules “at times seem almost purposefully confusing” so that “the strongest impact of these sanctions may be their mere existence rather than their exercise, as highly-regulated and risk-averse financial institutions steer well clear of the line.” The result, as economic sociologists Gregoire Mallard and Jin Sun have described it, is that banks have developed extraordinarily cumbersome systems of internal controls to satisfy U.S. demands for compliance.

Export controls have similarly become a cornerstone of US power. Kevin Wolf, who ran the export controls bureau at the US Department of Commerce, told us how the first Trump administration built up the so-called Foreign Direct Product Rule (FDPR), which would apply U.S. export controls to a much broader range of products made across the world, like financial sanctions. As he put it, “trillions of dollars of transactions, trillions with a ‘T’ ” were affected by a technical rule “that was buried in 9 point font in a footnote at the bottom of a 320 page Entity List.” Weirdly for Abe and me, our ideas were used by a senior official in the first Trump administration to justify this rule change.

Export controls are often specified in much greater technical detail than sanctions, sometimes allowing them to be gamed. Equally, crafting and applying them involves difficult judgment calls. As Mario Daniels and John Krige note in their recent history, Knowledge Regulation and National Security in Postwar America, “to assess how dangerous a technology, if exported, could be, is incredibly challenging.” The result is an incredibly detailed and complex body of regulation, which is based on tricky judgments both of how the technology might be used against the interests of the United States, and of the actors most likely to use it in these ways.

Daniels and Krige emphasize that export controls stem from America’s sweeping vision of national security, which posits that:

the control of intangible knowledge and know-how was the most important function of export controls. They target objects as carriers of “embodied” knowledge that can, for example, be extracted by means of reverse engineering or actively transferred in face-to-face interactions between an American donor and a foreign receiver. Moreover, these targets are assessed not in isolation, but in relation to the technological system in which they are, or will be incorporated. The overriding question is: “would this export help the foreign country to close a gap it cannot fill with its own knowledge?”

In a response, Karen Alter argues that it’s more complicated than this: some US officials recognize that it can be counterproductive to try too hard to control the spread of knowledge between the United States and other countries. But all these authors agree that export controls have the purpose of ensuring continued US technological leadership. Furthermore, Karen notes some agreement with Daniels and Krige:

The far reaches the US government asserts via the deemed export category alarmed the authors, and it will also surprise most readers because the rules apply to all US citizens and firms, regardless of where the sharing occurs. Sharing knowledge with graduate students or foreign colleagues in a US university or academic meeting; international research collaboration with foreign labs or a company subsidiary; working with new foreign owners to help them understand what a US company does; setting up a joint production or licensing arrangement in the US or abroad; even the non-commercial sharing of the ideas and expertise that budding entrepreneurs or researchers generate and own within their very person may be controlled, disallowed, and even criminalized.

To be clear: there are limits. Such controls over conversation are generally understood to be restricted to engineering and technical knowledge: mere social scientific knowledge and ordinary conversation aren’t covered. Daniels and Krige describe how the first Trump administration’s National Security Strategy:

continue[d] to stress the need for deregulation to stimulate exports. But it combines the use of export controls to deny sensitive technology to rivals with a raft of new measures to erect a protective wall around sharing emerging critical technologies and related knowledge with China, in particular.

When the Biden administration came into office, it built upon this approach, creating what then-National Security Advisor Jake Sullivan described in a speech as a “Small Yard, High Fence” strategy, under which China would be denied access to the high-end semiconductors needed to train powerful AI, and other “foundational technologies” as needed. The fence that was supposed to deny China access was mostly made out of export controls.

As Trump was preparing to come into power, the Biden administration proposed an extraordinary plan to use export controls on semiconductors and AI model ‘weights’ to control the development and use of AI across the planet. The entire world would be divided into three different zones - close friends and allies, which would have more or less unrestricted access to AI, competitors and enemies who would have none, and a large intermediary zone of countries with initially limited access, who could negotiate with the US for better arrangements.

Like many other exercises of US technological power, this plan depended on a complex mixture of detailed technocracy and high discretion. Biden administration officials hoped that extraterritorial export controls would allow the US to leverage the US head-start in AI, building an enduring system of global control over future development of technology, while providing officials with substantial discretion to cut specific deals with other countries, such as the Gulf States, which wanted more access to AI.

The Trump administration, however, declined to take up this detailed technocratic plan to impose a system on the rest of the world, in favor of its own vaguer but equally sweeping ambitions for AI “dominance.” It has no principled objection to sanctions and other tools of economic coercion, although officials express their contempt for the moralism that has inspired their use in past administrations. Instead, the current administration cares more about short-term deal-making and breaking, and raw exercises of power. It seems at least as interested in subduing its domestic enemies as in coercing adversaries abroad. Both are means towards the end of remaking a world so that the administration and its friends are in control, and others either have to supplicate them or suffer the consequences.

Hence, the administration is less interested in further cutting off China from AI, especially after China has demonstrated its ability to cut off the supply of rare earths that US manufacturers care about, than in figuring out deals. The Biden administration wanted to use AI to bind the Gulf States closer to the US, and further from China. The Trump administration has looked to cut them into the bargain, in return for general and particular concessions. Externally, AI is another means of leverage to exploit as the administration tries to make one-sided bargains. Internally, it would like to adopt AI to replace bureaucracy with all its human inconveniences. It worries about the AI labs as an alternative power base, and is especially concerned about Anthropic because of its connection to the past administration and loosely liberal politics.

*****

Thus, the Fable fight is part of a bigger struggle. Both of the two dominant approaches to regulating AI believe in continued American technological hegemony. Both tend to be bullish about the potential of AI to radically transform society. They disagree on whether it is better to build global power through painstaking technocracy or through deals based on self interest, and on what should happen on the home front.

As already mentioned, the Biden administration enthusiastically embraced the first Trump administration’s expansion of export controls to cover actors outside the US, and deployed it systematically to stifle China’s AI ambitions, through restrictions on high end semiconductors. As Graham Webster describes it, officials feared that authoritarian governments might use AI as a tool of widespread control. In part, they worried that whoever got first to ‘recursive self-improvement’ (AI that could make itself better in a feedback loop of self-reinforcing awesome), would be able to shape the destiny of the world.

The stakes were especially high, some believed, because a powerful AI system might be used to develop even more capable AI systems, compounding a country’s lead and leaving competitors not just incrementally behind, but wallowing in a bygone paradigm. If some of these fears sound vague or far out, that’s because they are. Policymakers, however, are forced to take action in the face of uncertainty, and here, the uncertainty itself was cause for alarm.

That led them to embrace export controls as a tool of global management, hoping that they could keep China behind for just long enough to make the necessary difference.

the US government found the FDPR irresistible. It would … eventually wield it to constrain high-powered computing in China. “Obviously we started using it like candy,” says Estevez. “Certainly threatening to use it if not actually using it.”

The Biden administration’s belated sweeping plan to remake the world, using export controls to subordinate other countries to America’s technological hegemony, was a culmination of this approach.

Now, the Trump administration is more interested in deals with side-payments than in constructing a global empire of technocracy. It wants instead to use national security tools to subordinate American companies. First, the US Department of Defense designated Anthropic as a supply chain risk - a tool that has hitherto been deployed against non-US companies - because it refused to revise a contract that prevented its models from being used for autonomous weapons or for domestic mass surveillance in the US. As Dean Ball (who was the White House AI advisor for the first part of this administration) described what happened:

Anthropic was the company most enthusiastic about these national security uses, and they came to an agreement with the Biden administration … the Trump administration agreed to those restrictions, as well. Then in the fall of 2025 … Emil Michael as under secretary of war for research and engineering…. comes to the conclusion that no, we cannot be bound by these usage restrictions. The objection is not so much to the substance of the restrictions but to the idea of usage restrictions in general. So that conflict actually began several months ago.

That is how and why the US government has gone after Fable. Supply chain risk designation and export controls authorities most certainly cover technologies like AI - indeed it is hard to imagine a technology that is more precisely a “carrier of embodied knowledge” than a frontier model - but they are not supposed to be used for domestic control. However, because national security authorities are quite open-ended, they provide the executive branch with a lot of discretion to go after domestic actors whom it doesn’t like. The result is that export controls and related measures are being used to shape domestic politics in Trump-friendly ways.

People clustered around the Biden administration-Anthropic axis argue that something has gone wrong when the Trump administration goes after Anthropic, while apparently slow-walking many of the Biden era’s measures against China. Under their understanding, the US is misusing these tools, neglecting actual national security threats in favor of notional ones.

Others such as Dean - who has just moved to a political/strategic position at OpenAI - do not fully embrace the administration’s approach but say that it is understandable, since they believe that AI is a fundamentally world changing technology.

Everything the U.S. government was communicating, in policy and in rhetoric, seemed to suggest “go ahead, release your model!” And yet common sense would dictate otherwise. Anthropic is still in the midst of a heated dispute with the Department of War about that agency’s decision to label Anthropic a supply-chain risk. Bitter disputes about policy and politics between the Administration and Anthropic remain unresolved, among them export controls, federal preemption, and the general reality that Anthropic supports Democratic candidates for office while Republicans occupy the seat of power.

Of course they needed to tread carefully. What the law says does not matter. What Administration officials argue on one day does not matter. Anthropic is a political enemy of this Administration, in part because they have explicitly chosen to make themselves one. …

The stark reality is that making superintelligence is a profoundly political act even in the healthiest of societies, to say nothing of the filthily political world we Americans currently inhabit. A model like Mythos goes beyond being a mere political act and implicates the sovereignty of the state itself. No company gets to shake the foundation of state sovereignty while staying blithely above the raw reality of politics.

It is unusual for a sincere classical liberal to defend an administration exploiting regulatory ambiguity to go after its “political enemy” on the grounds that “[w]hat the law says does not matter. “ The presumable justification is that AI is in that small class of technologies (e.g. nuclear weapons) that are so powerful that private companies, especially one that “supports Democratic candidates,” cannot be allowed control.

That seems a questionable way of thinking about the politics of AI, but it is startling that so many of the underlying premises are shared with the arguments of a blogpost that Amodei put up shortly before the launch of Fable. AI is set to transform national security.

Mythos Preview scrambled the global cybersecurity landscape. But its broader significance is that it proves beyond doubt that AI models are now tools of global and national strategic consequence.

And it needs to be regulated.

I believe the best analogy, at least at the current stage of the exponential, is to cars, airplanes, or drugs—powerful technologies essential to the modern economy, but capable of killing large numbers of people if designed or operated poorly. I therefore believe we should model AI regulation on agencies like the Federal Aviation Administration (FAA). Frontier AI models, like airplanes, should be required to go through technical testing and auditing, and their release should be blocked or reversed as a threat to public safety if they do not meet high standards of safety [emphasis in original]. I am grateful to see the Trump administration’s Executive Order move incrementally towards a greater role for government in AI, though Anthropic’s proposal recommends even further action.

To be clear: it would be completely unfair to claim that Amodei was begging to be export controlled - he was calling for the careful construction of ordinary bureaucratic institutions rather than for sweeping seat-of-the-pants national security decisions by the executive. But when Amodei writes that:

US export controls on frontier chips and SME to China have been a major contributor to the US’s overall lead in AI, and these policies need to be expanded, tightened, and coordinated with other likeminded states.

he seems to be suggesting that export controls and other national security authorities are absolutely fine for China. It’s when they are applied to Anthropic (or, more generously: American and likely allied companies that are not looking to cooperate with the adversary) that something has gone wrong.

And this gets to what I see as the heart of the matter. National security tools like export controls and supply chain risk designations are relatively unbounded. That makes them very attractive to the current administration which, as Alondra says, wants to regulate in ways that maximize its discretion. And these tools are likely to become more attractive soon. If the Republicans lose one or both houses of Congress in November, as seems likely, the administration will find some of the means that it is using harder to employ. The president’s foreign policy powers as commander-in-chief - and willingness of courts to defer to them - have always been substantial, and have grown over the last couple of decades. That suggests that we are likely to see the Trump administration using national security powers more, and possibly much more, after November, to pursue domestic goals and punish domestic enemies.

Such overreach can be fought - possibly successfully - in the courts. On the one hand, courts tend to defer to the president on many national security issues. On the other, of the Achilles’ heels of the whole economic security apparatus is that large parts of it are built upon authorities that have been stretched, and stretched, and stretched some more. The foreign actors who have been the typical targets have often not wanted to appeal, because they haven’t fancied their chances in US courts. That might change with plaintiffs like Anthropic (and in so doing, reshape what the US government can and can’t do). But courts tend to work slowly, especially on sensitive matters. Victories won through the legal system may come too late to make a real difference.

The use of such powers puts both sides of the existing debate in an awkward position. I believe that Dean (who I know slightly) is sincere in his arguments, but they depend both on his enthusiasm for unitary executive theory, and his belief that current AI is, or is about to become, so powerful that it’s going to sweep traditional forms of government away. I think that Dean is straightforwardly wrong on both - but even if he is not, does he really believe that the Trump administration will confine its use of these tools to Anthropic and other AI companies? The logic of his position that perceived enemies of the administration, including companies that support “Democratic candidates for office,” can place no faith in the law, goes in obviously problematic directions for classical liberalism, which has typically been understood to imply the need to defend a genuinely plural society against the incursions of the state.

The implications for the other side are more subtle, but quite as damaging. The standard approach - as Amodei’s own writings repeatedly make clear - is that you can have two great things at once. You can build up a thriving democracy at home, regulating AI carefully to avoid existential risk and support democratic institutions, and you can use your awesome national security powers abroad to stunt the technological development of authoritarian adversaries.

I think that this formulation was already in trouble, in a world where China is clearly capable of building up a large array of technological advantages regardless of US coercion. But it is now being undermined from inside too, as the second Trump administration is beginning to turn the national security state against domestic enemies.

What is now becoming obvious is that the Amodei and Biden administration position that you could have your cake and eat it too, depended on a semi-explicit consensus on what US national security was, and how sweeping powers with ambiguous constraints could or could not be used to pursue it. That consensus has now shattered, and it isn’t plausibly going to be rebuilt. Strengthening powers such as export controls without very careful design (and I am not sure that they can be so designed) will strengthen the present and future abilities of illiberal and antidemocratic officials to deploy these powers against domestic as well as foreign adversaries.

These and other related tradeoffs are likely to get sharper over time. The United States has a set of national security institutions that are purpose-built for a dominant power, but the United States itself is now clearly in relative decline. Its ability to use these institutions against foreign adversaries will dwindle. Its ability to deploy them against perceived domestic enemies will not shrink to nearly the same extent. We need to think more seriously about the problems that this will create, and indeed is already creating. I’m not seeing much of that thinking yet.2

Addendum: This, from Ganesh Sitaraman and Tim Meyer is extremely useful:

To date, AI companies have wanted an unregulated federal regime – and they have even pushed for a ban on any state regulation of AI. But the demand for regulation is like water: it has to go somewhere. Frontier technologies like advanced AI models raise too many policy considerations–to say nothing of the economic competition between technology firms both within and outside the United States–for policymakers to simply turn a blind eye.

In practice, the failure to regulate economically and politically significant developments means that we will get presidential regulation, an idiosyncratic and non-transparent or procedurally irregular system of regulation that will oscillate based on who is president, which company is at issue, and the particular issue. To the average American, it will seem much more like rule by fiat than the rule of law.

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The SpaceX IPO is today: here’s a paragraph from the rather remarkable SEC filing.

we have formed the most ambitious, vertically integrated innovation engine on (and off) Earth with unmatched capabilities to rapidly manufacture and launch space-based communications that connect the world, to harness the Sun to power a truth-seeking artificial intelligence that advances scientific discovery, and ultimately to build a base on the Moon and cities on other planets.

A co-author and myself will soon have more to say about the science fictional aspects, which most certainly don’t end with “cities on other planets.” This is almost certainly the first major IPO prospectus that requires a definition in its glossary for Kardashev II Civilization.

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But the document also serves as a prime example of what Quinn Slobodian and Ben Tarnoff call “Muskism” in their new book, Muskism: A Guide for the Perplexed. They suggest that just as we thought of classic mid-twentieth century producer capitalism as Fordism, we should think of the new approach to economic organization that Musk and others are trying to put together as Muskism, and start figuring out how it works.

Slobodian and Tarnoff explain how Muskism involves both a particular approach to organizing physical production, and exoticized human-machine feedback loops. The former is briefly identified in the chunk of SEC filing above, by the four words describing Musk’s actual engineering approach: “vertically integrated innovation engine.” The rest of the text exemplifies a particular example of the latter: propagating a mythology of the future that is in fact a promissory note that you can turn into financial and political capital today.

Both are important. The vertical integration is as much a geopolitical as an engineering strategy, and shapes the specific ideas that get propagated. Equally, if you focus just on the engineering, you’ll miss out on the crucial role of cybernetic feedback circuits in shaping both the political and the economic project. Gigafactories are infrastructure, but as Slobodian and Tarnoff provocatively say, “trolling is infrastructure” too.

So below is a piece that is not a real review, but my own response to the book and where the project might go next. The two sides of Slobodian and Tarnoff’s argument address topics that I have been fascinated with for years, but have never been able to join together: geopolitics and political economy on the one hand, and the consequences of technology for democratic politics on the other.

Hence, I focus on the bits of the book that are close to my own obsessions, disregarding all sorts of interesting pieces of information that don’t fit. The purpose is to pull out ideas about the broader political economy that we might be able to build by thinking more clearly about the incoherence of Muskism, rather than the aspects that seem to fit together. As Slobodian and Tarnoff discuss throughout, Muskism also involves the increasing integration of infrastructure businesses with the state. How do ideology, production and state hybridization fit together? What is most valuable about the book - for my selfish purposes - is that it pushes us to start asking these questions. Equally, just as you had to move beyond Henry Ford to think about Fordism, mapping out all of this will require us to ask what Muskism would look like without Musk.

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Much of the first half of Muskism is about Musk’s approach to physical production. Some of the story has been told before - lessons from Silicon Valley; fail, iterate, fail better, eventually succeed; the move away from waterfall approaches (plan everything from above) to agile development (keep on revising the relationship between goals and means). What is new and extremely interesting to me is the relationship that Slobodian and Tarnoff identify between Musk’s approach to vertical integration and geopolitics.

The standard story we tell about high tech manufacturing in the early 2000s is all about outsourcing. Abe and I use semiconductor manufacturing as our example in Underground Empire - the modern semiconductor industry starts from the belief that it makes sense to separate design from manufacture, which has led to a complex production network involving myriads of separate firms. Much the same is true for the manufacture of laptops, iPhones or many other complex products. All of these involve contractors and subcontractors, engaged in an intricate dance of production.

Musk’s companies have tried, as much as possible, to take just the opposite approach. As Slobodian and Tarnoff describe it:

Musk’s drive to reduce his reliance on external suppliers and to concentrate production as much as possible within the walls of the firm cut against the globalizing currents of the 2000s, which positioned the factory as a node within an international production network woven together through supply chains. Muskism, by contrast, envisions the factory as an enclave.

For example, Tesla’s “Gigafactories” were intended to liberate Tesla from reliance on external battery manufacturers. Musk’s insistence on control meant that his companies were better insulated from geopolitics than many competitors. Equally, it sometimes forced him to make awkward choices. Tesla built Gigafactories in Texas, Shanghai, and then Berlin, giving him footholds in each of the major power blocs.

But if from one perspective, Muskism is a series of islands of production in different geopolitical zones, from another it’s an enclave that was built inside the confines of the American state. SpaceX had its origins in an effort to break away from the existing, heavily regulated approach that NASA took to spaceflight, and to the domination of the ‘primes’ - the big defense companies that understood the byzantine contracting procedures of the US national security state, and could navigate them. Vigorous legal activism and friends in high places allowed SpaceX to get contracts, and work actively to reduce costs by remaking the production process. As Slobodian and Tarnoff accept, this did result in real efficiency gains, while increasing the power of the private sector.

Admittedly, the company conquered the market in large part by achieving a dramatic reduction in launch costs. … But this cheapness came with a cost: the state would eventually cede its sovereignty to such a degree that it was forced to buy it back in increments from a corporation.

SpaceX’s launch model led to the Starlink satellite network. That in turn became “indispensable” to modern militaries, obliging then Undersecretary of Defense Colin Kahl to beseech Musk personally to allow Starlink to be used by Ukraine.

But Muskism is more than the relentless pursuit of efficiency. As the SpaceX SEC filing suggests, it has a strong ideological element too. Slobodian and Tarnoff say that Musk has long “spun persuasive science fictions to win the confidence of his investors.”

“Like the Soviet state dangling the promise of a radiant future in front of its tired citizens,” the critic Phil Jones observed, “Musk’s success is sustained by predictions of a technological sublime that’s only ever another decade away.”

They describe how Musk was eaten by the attention economy even as he was trying to devour it. I’ve paid attention to Musk’s relationship with the attention economy over the last few years, but I still learned quite a lot from Slobodian and Tarnoff’s telling of it.

Dogecoin provided a kind of entry drug - a joke cryptocurrency whose only value was what people attributed to it. Musk’s half-mocking enthusiasm helped pump up its price. Memes became ever more central to Musk’s self-image - as did half-baked cybernetic theories of how the circulation of beliefs and information might provide a collective countermeasure against evil AI. And then Musk began worrying about cybernetic contagion (he was latching onto a much older idea1 ), theorizing that anything that upset his preferred hierarchies was an example of the “woke mind virus,” which threatened to destroy humanity.

When Musk bought Twitter and turned it into X, he had his engineers goose the algorithms to ensure that people were relentlessly exposed to the prophylactic of his own received truths. Next, there were various amateurish efforts to re-engineer the Grok model so that it would deviate from its purportedly woke training data.

The general consequence has been to create ever more deranged feedback loops between Musk and the information structures that he has created, as illustrated by this New York Times article from last year. I’ve recently been re-reading a lot of Philip K. Dick for reasons, and this bit from The Transmigration of Timothy Archer sticks out:

he simply recycles his own nutty thoughts forever, enjoying them even though, like transmitted information, they degenerate. They become, finally, noise. And the signal that is intellect fades out.

Just so, except that Musk doesn’t give the impression that he is particularly enjoying his recycled thoughts. His own form of degeneration is darker and its consequences more vicious, despite the rhetoric about humanity’s destiny amidst the stars.

These two quite different tendencies - engineering for optimality and social media-driven promises of a radiant future - came together in DOGE, which both purported to remake American government on the basis of private sector efficiencies, and was named after a meme. The first is described by Sam Hammond of the Foundation for American Innovation in his encomium to “DOGEmaxing”

I concluded that, if we were ever going to transition to a more libertarian form of government, it would be essential to first use information technology to improve and streamline core government services in a way that creates space for crowding-in privatized forms of governance. … just as Marc Andreessen said “software is eating the world,” perhaps software will eventually eat the state. And nearly a decade later, it seems like this strategy for disrupting bureaucracy is finally coming to fruition in the form of Elon Musk’s Department of Government Efficiency.

The second is what we actually ended up with - a chaotic process of lopping off core aspects of government functioning that often seemed to be driven by whichever pustule of idiocy and malevolence had most recently appeared on Musk’s social media feed. Slobodian and Tarnoff conclude the last substantial chapter by saying that you can read the brief, sordid history of DOGE either as a story of overreach, in which Musk’s celebrity was incapable of transforming the state, or as a symptom of a deeper structural story, in which Palantir and Musk are nonetheless succeeding in reshaping the state in their image.

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Slobodian and Tarnoff’s final chapter provides four stylized accounts of where Muskism might go, each based on an exaggerated form of some particular element in their account. Another approach might be to ask how different elements, such as production, ideology and state fusion interact with each other. My best guess is that Muskism is more fragile than it might seem, and that the most likely place to discover fragilities is the crude joins between these different aspects.

Musk’s vertically integrated production model has insulated his companies from some geopolitical risks, so that they have adapted more quickly to a world of mutually suspicious sovereigns than many of his competitors. Equally, it carries its own risks and contradictions. Having factories in China and the EU as well as the US gives Musk greater clout, but also exposes him to a variety of governments, each with its own interests, each jealous to some greater or lesser degree of the others.

This has not been as much of a problem as it might have been thanks to Musk’s fraught but continuing alliance with Trump. Other countries that might otherwise want to press Musk, or even exclude him from their markets have to worry that they will find themselves on Trump’s shitlist.

The current situation of the world - enough geopolitical risk that complex supply chains are not the miracle of efficiency that they once seemed to be, but not so much as to actively endanger Musk’s companies in other parts of the world - seems well calibrated to Musk’s interests. He wants enough risk to drive people to rely on his island fortresses, but not so much that he can’t build or maintain them. But this temporary balance probably isn’t politically stable. As geopolitical tension accelerates, it becomes harder for Musk to straddle horses, forcing him to double down on American imperial hegemony.

That creates its own risks. Obviously, other countries may view Musk’s infrastructures as dangerous, fearing that they may be used against them. They have few alternatives to Starlink right now, or to SpaceX’s industrialized launch capabilities. But they are trying to build them, and may be able to substitute for other aspects of Musk’s empire, including the AI that is the purported source of SpaceX’s profit model. No-one - not even in Trump’s government - wants to use Grok if they have an alternative.

So too perhaps at home. Governments - including the US government - are increasingly open to the idea of nationalizing essential parts of economic infrastructure. Manufacturing, space launch and, as J.S. Tan and Kathy Thelen have argued, modern AI all involve big, physically immovable investments that can rapidly become captive assets if government becomes unfriendly. There is a lot of talk about moving data centers to space. One of the informal arguments I’ve heard bruited is that space based platforms might be less vulnerable to terrestrial government aggression, building on a somewhat different set of science fiction mythologies (Robert A. Heinlein and others on LIBERTARIANS IN SPAACCE!) than the cosmic destiny tropes.

All this is held together by an ideology that seems to be at its peak right now. The SpaceX IPO marks the massive expansionary inflation of an entirely imaginary universe. As far as I can tell, no-one actually believes that SpaceX is going to capture its roughly-$23 trillion “total addressable market,” which as Matt Levine points out is “perhaps 20% of the world’s economic output and perhaps 40% of the revenue of the world’s corporations.” But there are lots of people who are willing to go along for the ride for the moment, as long as other people are willing as well. How long can that last? What will happen to SpaceX and Musk when it begins to fall apart? The Musk bet seems to be that he can make himself an essential enough part of the world’s global economic infrastructure by the time that the crash happens that it won’t matter. A lot of things will have to go right for this to be true.

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This then suggests a broader agenda for figuring out what is happening in the world right now. What is most valuable in the long term about Slobodian and Tarnoff’s perspective is what they really don’t have the space to discuss properly in a short book that is aimed at a broader audience. They are emphatic in arguing that Muskism is a phenomenon that is broader than Musk himself, but they don’t have much room to talk about the non-Musk aspects. Fordism too was about much more than the vagaries of Henry Ford, who, like Elon Musk, was a deeply weird and unpleasant dude, with vicious conspiratorial beliefs. Some aspects of Ford’s approach failed (his desire to control every last aspect of the private lives of his employees). Some spread and became generalized (as aspects of mass production). Some led to compromises such as the grudging accommodation with unions that Ford himself would have absolutely hated.

It’s hard to even begin to articulate this question on a day where on the one hand Musk is enjoying his stock market apotheosis, and on the other is doing everything he can to stir up race riots. But what would Muskism look like without Musk? I take Slobodian and Tarnoff’s ultimate lesson as being that we need to move beyond Musk’s personality, and start thinking about Muskism or Muskismus (I like the German word, which is why this post has the German translation’s cover) as a mode of organizing production, as a generic ideology, as a set of political bargains, as a form of state-business fusion, or some weird amalgam of all four. If Muskism is going to change the world as its backers hope, it is highly unlikely to be because SpaceX manages to corner the entire global economy. If it fails, as I personally suspect it will, it is going to be because of the underlying contradictions that are getting papered over.

To figure this out, we might begin with the other businesses that seem to exemplify Muskism. Obviously, this would include Palantir, and a bunch of the defense-tech businesses funded by Musk’s adversary Peter Thiel. Thielismus starts from many of the same premises as Muskism, but with a different clatter of tacked-on lunacies (less cosmism; more prophecies of the Antichrist). Are there businesses that adopt some of the aspects of Muskism that Slobodian and Tarnoff acknowledge as useful (SpaceX has transformed the launch business) without all of the negatives? It would be useful to know: the waterfall approach to project management has not been good e.g. for Europe’s defense sector.

We might also want to ask whether Muskism is inextricably intertwined with US hegemonic power. What happens to the Muskist project if US hegemony fails? What happens to US hegemony if Muskism fails? How does Muskism compare to e.g. the explosion of Chinese domestic manufacturing, which applies sometimes similar ideas in a very different political context? Is small-d democratic Muskism a contradiction in terms? If not, what would it look like? If so, what are the democratic alternatives that might press back against Muskism, and what opportunities and tradeoffs do they represent?

And so on. We are in a world where an enormous amount is up for grabs. Muskism, across its various dimensions, is a project for grabbing as much as possible before anyone else figures out how to push back. Slobodian and Tarnoff’s book is valuable both in identifying the contours of the problem, and setting out an agenda for a broader political economy of the world that is taking shape today, right around us, that might provide maps for action.

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No new post this week - I’ve got an unexpected short term writing deadline. In lieu of that, I’m republishing (with tiny updates/edits) one of the best short pieces of criticism that I think I’ve ever written, on William Gibson’s novel, The Peripheral.

I’ve been thinking of putting this up for a while, but was pushed into doing it now by Andrew Brown’s complaints about Gibson’s greatly inferior sequel, Agency. In retrospect, Agency is a book of the short period when many people believed that you could return to a timeline in which everyone could see that Hilary Clinton ought to have won, if it hadn’t been for social media manipulation. This watered down the bleak realism of The Peripheral, which better describes the future we have found ourselves in. Warning for those who haven’t read the book: there are plenty of spoilers.

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William Gibson’s The Peripheral time is the best novel of his late period, and in my view, the best novel that he’s ever written. It doesn’t have the shock value of Neuromancer (which blew my mind when I read it at the age of fifteen, in a small provincial town in Ireland). However, it’s a much better novel. The Sprawl books are all opaque and dazzling mirrorshades – the surfaces of high-gloss people reflecting the surfaces of high-gloss objects that reflect the surfaces of high-gloss people. The not-quite-science-fiction novels he was writing for a decade or two take the givens of the Sprawl books as a problem, engaging in a kind of archeology of objects and brand names, and how they reflect both the vast systems around us and our individual desires. I like them (they combine the intelligence of Don DeLillo with much of the warmth of Philip K. Dick), but I like his short book of essays, Distrust That Particular Flavor even better (it’s a book full of insights, which, like Borges’ version of Kafka, generates its own predecessors). The Peripheral returns to science fiction – but a science fiction that very clearly reflects present day concerns.

Gibson presents two timelines – one sort-of-nearish future, one several decades out again. They’re connected in some science fictional way that is never explained. The people in the further future are somehow able to access the past timeline (maybe the past timeline is a simulation so good that it’s effectively real; maybe it’s a parallel universe; nobody knows or seems to care particularly1). Physical contact between the two universes is impossible, but quite sophisticated forms of information can go back and forth, allowing people from the further future timeline to intervene in what used to be their past (as soon as they start intervening, the past starts to develop along a different path than the one that they know, becoming partly unpredictable).

What’s interesting is not the technology (which isn’t even a macguffin), but the uses that it’s put to. Gibson acknowledges the influence of Bruce Sterling and Lewis Shiner’s short story, “Mozart in Mirrorshades,” which depicts a future that uses alternative past timelines as a resource. I would guess that Paul McAuley’s criminally under-appreciated Cowboy Angels (in which an America on one timeline effectively colonizes and exploits other versions of itself in other timelines; our timeline is called the “Nixon sheaf”) is another significant influence. McAuley is thanked as an advance reader in Gibson’s acknowledgments. But Gibson doesn’t want to pursue the general questions of cultural appropriation that Sterling and Shiner’s story talks about, or the Cold War politics of McAuley’s book. He wants, I think, to talk about the relationship between the 99% and the 1%, using science fiction to turn the social relationships that Piketty and Saez talk about into a kind of ontology.

The farther future is one in which the 1% has won and become a global ruling class. A set of events called the “Jackpot” (a combination of environmental, technological and social failures) has led to most of humanity dying off. The results of the jackpot were rigged by the usual structural inequalities; those who were already rich and well connected were likely to survive; while those who weren’t so privileged mostly disappeared. This future is dominated by “klepts” (kleptocratic clans), the guilds of the City of London and the like, with the remnants of the state serving not as a restraint on the powerful but as an artifice for balancing between them (the politics is out of Engels via Poulantzas, with perhaps a touch of Wallerstein embedded in the pun in the book’s title). There may be ordinary people in this future, but we don’t see much of them; those we do see are the powerful and their higher servants (who have privileges, but only on the sufferance of those greater than they).

The nearer future timeline is set in a rural America where the real economy has collapsed, leaving people to survive making illicit drugs and working dead end jobs for homeland security (America’s comparative advantage turns out to be in meth, not music, coding and pizza-delivery). In this timeline, we don’t see the 1%, although they’re there in the background. Instead we see the kind of people who are about to be left behind and perish in the Jackpot.

Hence, the science fictional trick of The Peripheral, which is to turn the separation between the 1% and the 99% into a metaphor of physics (or, perhaps, information). The two literally live in different universes. They can perceive each other; they can act on each other to some degree (through proxies enabled by the exchange of information); they cannot physically touch each other. The rural America timeline is a curiosity owned by a minor member of a kleptocratic clan. A few people in it become significant by accident – one of them is operating a remote security drone in the further future timeline, and witnesses an important murder. A group of people who were peripheral, who were, indeed, toys, become important for a short period of time.

The result is a dark comedy, played with a very straight face. Two different factions start manipulating the entire world economy of the past timeline in order either to kill or to protect a tiny group of individuals in a small and depressed corner of rural America. One of these factions certainly seems nicer than the other (although that’s in doubt at some points in the narrative), but it’s not at all clear that its interventions will work out well in the longer run. At the very best, it’s acting like a Western aid organization in Somalia, trying to improve things a little, profoundly disrupting local economic and power relations simply by virtue of being there, and hoping that the goodies it brings will be used for socially beneficial purposes and not to enable ‘technicals’.

The novel finishes with something that plausibly resembles a happy ending for the individuals involved – complete disaster is averted, friendships are maintained across the barriers between the twin universes, and a few sympathetic poor people become rich and powerful. There are relationships, and one pregnancy. But the larger story is one in which nothing really changes. Perhaps the Jackpot (which is a lovely metaphor for the arbitrary-but-not-random rigged game through which people become or don’t become members of the elite) won’t be quite as painful, thanks to the intervention of benign overlords from the future. Even so, the one universe is still a toy of someone in the other. With less benign owners, things would be very different. The world depicted in The Peripheral is one where the best we can hope for is that our masters will be motivated by paternalism or benign neglect. That’s not an especially hopeful vision.

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Above is a video-format coda to the AI as Social Technology paper that Cosma and I wrote, drawing on work with other collaborators too. I spent last week at Oxford’s Blavatnik School, where I gave the Astor lecture on AI as Social Technologies. I didn’t realize that they were recording it, but it’s now up on YouTube. If you’ve read Cosma’s and my paper, you’ll have a good idea of what it says - it does include some very brief discussion of how our argument applies not simply bureaucracy but to democracy and markets too. When we wrote our original paper, we were planning to do all three, but then discovered that just doing bureaucracy on its own took more than 10,000 words, and since we had not been commissioned to write a short book …

Anyway, enjoy and of course feel free to share.

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Here’s another data point about the relationship between AI and organization. While rightwing politicians in other countries are fantasizing about DOGE-ing their civil service and replacing large swathes with AI, businesses are boasting about what they have already done.

Last Saturday, Matthew Prince, the CEO of Cloudflare wrote a self-congratulatory op-ed in the Wall Street Journal, suggesting his company was the only one in recorded business history to grow by 30% while laying off more than 20% of its employees. His message was that everyone needed to follow his example, by using AI to implement the True Wisdom of Revered Management Guru Peter Drucker.

It’s worth quoting Prince’s arguments at some length:

what we did is likely going to become the norm over the next year. This is a story about artificial intelligence, but executives and commentators are misunderstanding how it will disrupt business and who will be affected. To understand the issue, I went back to a book published in 1954, 20 years before I was born: Peter Drucker’s “The Practice of Management.” Drucker explores the different roles inside every business, which I would categorize as builders, sellers and measurers. Builders create products. Sellers sell those products. Measurers do everything else: internal audit, revenue recognition, finance, legal, compliance, middle management, operations and on and on. … builders aren’t going anywhere. … Sellers, too, are safe from extinction. … Measurers are also critical to a business, but different from the other two. …

Drucker argues that measuring business is important, but customers are earned through building and selling. The best businesses would maximize investment in those two functions. AI isn’t coming for builders or sellers, but it is coming for measurers. … AI won’t kill all jobs. But it will change every business. Ultimately, it will prove Drucker right. AI will allow us to better measure our organizations so the humans on our teams can focus on where they create and capture value: building and selling.

Matthew Prince is right about one big thing. You should absolutely read Peter Drucker’s The Practice of Management if you can. But while I presume that Prince is telling the truth about reading and re-reading Drucker, he doesn’t seem to have really understood what Drucker actually wrote.

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Prince’s Peter Drucker wrote a book about how building and selling are crucial, and new technologies such as AI can be used to get rid of great swathes of middle management that don’t do more than measure what everyone else is doing, all for the benefit of the boss. Drucker’s Peter Drucker wrote a book about how all management involves measurement and planning and action on the basis of those measurements. Those measurements aren’t supposed to help the boss decide who to promote and fire, but to enable the managers themselves to do better. Drucker’s Peter Drucker is much less interested in keeping the accountants happy, or, for that matter, maximizing profits, than in increasing the capacity for managers to exercise human judgment, something that can’t be automated.

As I see it, Prince has badly misread an author whose understanding of business (at least in the book he cites; I haven’t read everything by Drucker) provides a radically different alternative to the standard Silicon Valley CEO narrative. If you take Drucker seriously, instead of whittling him into a peg to hang your boasts on, you will discover that he offers a different, and potentially useful understanding of how AI ought be applied within the firm, and equally importantly, how it ought not.

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Peter Drucker was an engaging and interesting general purpose intellectual, even if he is rarely considered as such. He was more a theorist than an ordinary guru - a Harvard Business Review poll of business writers identified Drucker, James March and Herbert Simon as their favorite thinkers. That seems the right crowd to situate him in: all three were worldly philosophers in the broad sense, and their ideas about organization have a surprising amount in common.

Drucker was visibly influenced by Schumpeter, without Schumpeter’s arrant elitism, occasional sneaking regard for Fascism, or extraordinarily lively prose style; Drucker has some very good lines but is careful rather than exuberant. Schumpeter and Drucker also had quite different attitudes to the workings of the post-World War II economy. While Schumpeter deplored the strangulation of the hero-entrepreneur by management and bureaucracy, Drucker was prepared to make peace with the new dispensation, and celebrated managers as innovators. He used the term “manager” capaciously, not just to celebrate CEOs and their immediate underlings, nor even middle-managers, but workplace foremen, who necessarily exercised managerial capacities and judgment as part of their responsibilities. If Druckerism is in part Schumpeterianism-lite, it is Schumpeterianism-lite nearly all the way down, focusing on cultivating initiative and self-actualization all the way through the rank-and-file of the firm.

Drucker believed that good management required giving a remarkable degree of leeway to managers. Instead of distinguishing between builders, sellers and measurers, he identified “measurement” as one of the five essential activities that all managers need to engage in, together with setting objectives, organizing, motivating and communicating, and developing people. The manager:

establishes measuring yardsticks, and there are few factors as important to the performance of the organization and of every man in it. He sees to it that each man in the organization has measurements available to him which are focused on the performance of the whole organization and which at the same time focus on the work of the individual and help him do it. He analyzes performance, appraises it and interprets it.

The problem of measurement is one of setting goals for oneself and keeping track of whether you are reaching them, and of communication. Everyone in the business needs to have some sense of what the business as a whole needs, and how his or her own activities meet those needs. Equally, everyone needs to exercise their own “judgment” (a term that Drucker employs repeatedly) as to how they carry out their particular role. Measurements are primarily supposed to orient the manager rather than judge and condemn him (or her, if we are writing in the 2020s rather than 1950s) from outside.

reports and procedures should be the tool of the man who fills them out. They must never themselves become the measure of his performance.

Measurement should absolutely not be used as a means of top down control:

I have not talked of ‘control’ at all; I have talked of measurement. This was intentional. For ‘control’ is an ambiguous word. It means the ability to direct oneself and one’s work. It can also mean domination of one person by another. Objectives are the basis of ‘control’ in the first sense; but they must never become the basis of ‘control’ in the second, for this would defeat their purpose.

Where measurement becomes an instrument of control, it can have disastrous consequences.

In [an unnamed] company a control section audits every one of the managerial units … The results of the audits do not go, however, to the managers audited. They go only to the president, who then calls in the managers to confront them with the audits of their operations. … the nickname the company’s managers have given the control section: ‘the president’s Gestapo.’ Indeed more and more managers are now running their units not to obtain the best performance but to obtain the best showing on the control section audits.

The quote about the Gestapo illustrates a more general point. Drucker believes that the pathological versions of top-down control-by-company-president-or-CEO have much in common with political dictatorship. Management, properly understood, is a mode of human autonomy, a liberal activity in the strong sense of the word. Squashing it into mere implementation is accordingly tyrannical.

Fundamental to Henry Ford’s misrule was a systematic, deliberate and conscious attempt to run the billion dollar business without managers. [italics in original] … Henry Ford’s concept was not even unique in industry. It was widely held in the early years of the century. He shared it, for instance, with one of his most distinguished contemporaries: Lenin.” … Above all, it seemed to make possible industrialization without management, in which the ‘owner,’ represented by the political dictatorship, would control all business decisions while business itself would employ only technicians.

Drucker was suspicious, on similar grounds, of Taylorist efforts to automate business activities so as to separate out planning from doing.

Planning and doing are separate parts of the same job; they are not separate jobs

By suggesting otherwise, Taylorism verges on authoritarianism:

the divorce of planning and doing was also part of the elite philosophy that swept the Western world in the generation between Nietzsche and World War I - the philosophy that has produced such monster offspring in our time. Taylor belongs with Sorel, Lenin and Pareto. … power must be grounded in moral responsibility; anything else is tyranny and usurpation

The role of the manager is not to plan what others do, or merely to implement the plans of others, but to exercise human judgment within a moral setting to integrate social activity:

The manager has the task of creating a true whole that is larger than the sum of its parts … One analogy is the conductor of a symphony orchestra, through whose effort, vision and leadership individual instrumental parts that are so much noise by themselves become the living whole of music. But the conductor has the composer’s score; he is only interpreter. The manager is both composer and conductor. … The task of creating a genuine whole also requires that the manager in every one of his acts consider simultaneously the performance and results of the enterprise as a whole and the diverse activities needed to achieve synchronized performance

Again, Drucker emphasizes repeatedly that this vision of managerial capacities does not apply to the top of the hierarchy but to everyone whose work is not completely technical.

It is no exaggeration to say that Drucker views management as a specific mode of realizing human potential. This is sweeping, romantic and occasionally a little corny. It also makes Drucker an enormously attractive writer and thinker in ways that many of his epigones are not. He views action and responsibility as going together, stressing both that business has a broader social role (profit is no more than a necessary condition to stay in operation, and business must pay attention to the common good), and that the task of the manager is to achieve a human synthesis of individual autonomous decision making with some reasonable understanding of the wellbeing of the firm and society.

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Matthew Prince’s goals seem notably different. Quoting the Wall Street Journal op-ed again:

Tireless, independent, efficient and available, AI systems can now measure an organization with a level of objective detail and precision that was previously impossible even for the best employees. … as CEO, I’ve never had better tools to measure exactly how the business is performing, including identifying our rising stars. The vast majority of those we laid off last week were measurers. We cut middle managers across the organization because AI allows us to have more direct reports per manager while still measuring and mentoring our teams effectively. … With fewer people needed for measuring, we can now invest more in people in the areas that drive growth

If you squinted with great vigor, you could perhaps just about interpret this as liberating managers from the tyranny of bureaucratic minutiae and establishing the conditions of autonomy that Drucker emphasizes. It does, after all, mention “mentoring” in passing. But those who are less charitable, such as myself, might emphasize the rather more explicit “as CEO, I’ve never had better tools to measure exactly how the business is performing, including identifying our rising stars.” That might in turn lead them to turn to a different analogy.

Deceased Business Intellectual: In my book I described Henry Ford’s Corporation Without Managers as a cautionary tale. Tech Company CEO: At long last, we are creating the Corporation Without Managers from classic business text Don't Create The Corporation Without Managers.

Matthew, Prince of Cloudflare, is deploying AI to better descry his realm as a whole, and to create a more efficient hierarchy. AI will allow him to reward those who the indicators say are valuable, while firing those whom he believes to be superfluous.

Do note a trick that appears and disappears very quickly in the text. Prince literally classifies everyone in his company who is not either building or selling as “measuring.” While he acknowledges that really good human measurers can be valuable, and presumably isn’t firing all of them, he claims that AI can do most human measurers’ jobs more tirelessly, independently and efficiently. AI, then, doesn’t simply automate the carrying out of technical functions. It replaces managers who aren’t visibly building or selling stuff, doing their jobs better than human beings ever could.

None of this is in Drucker’s book, unless I’m missing something. Indeed, as far as I can see, Drucker believes the contrary. He insists that measurement fails its purpose if it is used as a top-down tool of assessment. He argues that the people who do not contribute visibly to the bottom line regularly play a crucial role, even if they sometimes can be mismanaged.

what the accountant lumps together as ‘overhead’ - the very term reeks of moral disapproval - contains the most productive resource, the managers, planners, designers, innovators. It may also, however, contain purely parasitical, if not destructive elements in the form of high-priced personnel needed only because of malorganization, poor spirit or confused objectives, that is, because of mismanagement.

And all of this stems from the notion that measurement is a chancy undertaking, and is especially chancy when it is separated from human judgment. Anticipating both Herbert Simon and a lot of the modern discussion of metrics, Drucker emphasizes that the “measurement used determines what one pays attention to. It makes things visible and tangible. The things included in the measurement become relevant; the things omitted are out of sight and out of mind.” While measurements are essential, they are highly imperfect. They can support managerial judgment but they cannot substitute for it.

to manage a business is to balance a variety of needs and goals. This requires judgment. … the attempt to replace judgment by formula is always irrational; all that can be done is to make judgment possible by narrowing its range and the available alternatives, giving it clear focus, a sound foundation in facts and reliable measurement of the effects and validities of actions and decisions. And this, by the very nature of business enterprise, requires multiple objectives

Such business judgment simply cannot be substituted by actually-existing AI. As Cosma and I have argued about other forms of organization (building on Ben Recht).

[optimization] simply does not provide any objective means of weighing the kinds of choices across non-commensurables that are essential to the bureaucratic process. Simon, Smithburg and Thompson (1958, 74) … note … “internal conflicts and contradictions among the ultimate objectives…” Such vexing choices regularly emerge in bureaucratic implementation and articulation, as well as in goal-setting at the top, making them fundamentally political problems, and this is just what optimization does not solve. As Recht (2023) bluntly says, “you can’t optimize a trade-off.” There is no consensus over how to optimize over multiple independent objectives in even moderately complex situations. Machine learning, with or without neural nets, does not offer magical solutions to this.

The obvious risk of identifying large groups of managers as mere “measurers,” and replacing them with AI, is that you later discover that they were doing a whole lot of irreplaceable tasks of judgment too, balancing across the different and complicated choices that Drucker, like Simon and his colleagues, identifies as crucial to managing. AI will certainly supplement measurement, and might usefully substitute for many aspects of it, if carefully implemented. However, it cannot reliably substitute for managerial judgment as Drucker describes it.

Perhaps Matthew Prince has just written a very sloppy op-ed; he and his immediate reports may possibly have correctly identified the tradeoffs in their hiring and firing decisions and calibrated them accordingly. Or perhaps not, in which case, he and his readers should consider Drucker’s pithy dictum that “It takes years to build a management team; but it can be destroyed in a short period of misrule.”

*******

Drucker’s actual philosophy of management provides an interesting alternative starting point for thinking about how AI ought be deployed in business. When Drucker wrote his book in the 1950s, both automation and “operations research” (optimization techniques which were an important ancestor of modern AI) were big deals. Drucker wanted managers to use information science to do their job better, but vehemently opposed claims that human judgment and human relationships could be magically automated away.

The Practice of Management expressed deep skepticism about “lurid ‘science fiction’ … about Automation,” invoking “visions of the “technocrat’s paradise, in which no human decision, no human responsibility, no human management is needed, and in which the push button run by its own ‘electronic brain’ produces and distributes abundant wealth.’ However valuable operations research was, it could not substitute for human management: “these are tools of information, and of information-processing, not of decision-making.” Failing to understand this would lead the manager, like “the Sorcerer’s Apprentice, [to] become the victim of his own bag of tricks.”

Back then too, there were grandiose claims about how This New Technology Will Fix Everything And Is Completely Inevitable, So Lie Back and Enjoy.

I have learned to be extremely sceptical of any prediction of imminent revolution or of sweeping changes in technology or business organization

and:

Now that we in the free world no longer accept the planners’ remedies as good for us, an attempt is being made to make us swallow the same nostrums under the pretext that they are inevitable.

Obviously, AI isn’t identical to early operations research or the Taylorist program to turn workers into optimizable machines. There are many things that modern AI can do more efficiently and reliably than past technologies, and indeed human beings. Equally, the differences between AI and past forms of automation are not nearly as marked as much of the current rhetoric might suggest.

Drucker suggests that

Procedures can work only where judgment is no longer required, that is, in the repetitive situation for whose handling the judgment has been already supplied and tested. Our civilization suffers from a superstitious belief in the magical effect of printed forms. And the superstition is most dangerous when it leads us into trying to handle the exceptional, non-routine situation by procedure.

Modern AI too is surrounded by superstitious beliefs about magical effects, and tends to fail when it encounters exceptional situations.

When street-level algorithms encounter a novel or marginal case, they execute their pre-trained classification boundary, potentially with erroneously high confidence.

The best bits of The Practice of Management lay out a moral vision that subordinates technology to a particular understanding of human wellbeing. When Drucker argues that automation ought replace repetitive drudge work, he suggests that this will lead to more management rather than less.

the new technology will not render managers superfluous or replace them by mere technicians. On the contrary, it will demand many more managers. It will greatly extend the management area; many people now considered rank-and-file will have to become capable of doing management work. The great majority of technicians will have to be able to understand what management is and to see and think managerially.

The bigger point is that management is a moral, as well as an economic activity. It involves people developing and exercising their independent capacities of judgment towards the broader goals of the organization and the society that they live in. Drucker’s perspective thus has more in common with Pope Leo’s new encyclical than with Matthew Prince’s effusions. The similarities are unsurprising: Drucker was both a Christian (his book talks in passing about “Our Lord’s Parable of the Talents”) and a Christian Democrat.

A plausible reading of Drucker’s argument is the following: Businesses should pursue automation up to the point, and only up to the point that it supports the capacities of both traditional managers and rank-and-file to exercise individual human judgment in their work. That is not only in the best interests of the business, properly considered, but of the living community of individuals who constitute it. “Extending the management area” is not just an expected outcome of automation done right. It is also an appropriate moral goal, allowing work to move away from drudgery and toward the development and use of human capacities. Where automation change business organization so that it departs from this goal it hurts both the people and the organization itself.

This is not the only possible reading of Drucker’s book in the age of AI, but it seems to me to be highly congruent with his thought. It also has obvious implications for how we think about AI in the workplace. My version of Drucker might say the following. The current Silicon Valley approach to AI is profoundly problematic. It reinforces hierarchy and subordination to the vision of the CEO, rather than enabling individual judgment. It grossly simplifies many of the complex and multifaceted activities of management into “measurement” processes that AI can purportedly replace. It subordinates the moral and developmental aspects of business activity to abstract measurements of efficiency. If someone were actually to write an op-ed that applied Drucker’s ideas to AI, and its associated business model, they would say very different things than Matthew Prince.

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Over the last few months, Cosma and I have been writing a new paper, “AI as Social Technology.” It’s now out, in the Knight First Amendment Institute’s series. I’m obviously terribly biased, but I think it’s a banger. Go read it.

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The piece has three main aims. First together with other things we have written, it pushes back against a generic strain of Singularity thinking, which deploys feral thought experiments to prophesy the near future. Instead, it argues that you ought draw on actual social science and history. They will tell you that things are more complicated and interesting than stylized accounts would have you believe, but that is because the future is likely to be a mess, in ways that both resemble and are dissimilar to the mess we have today.

Second, it aims to provide sa better way of thinking about the mess. In particular, it combines recent work on “coarse graining” with the ideas of Herbert Simon and Charles Lindblom, suggesting that these build common ground between computer science, the traditional social sciences and science and technology studies.

Finally, and most directly, it argues that one particular variety of dubious AI thinking helped fueled the rise of DOGE.

AGI-centric accounts depict bureaucracy as centralized coordination, the key problem being how lower layers fail to implement the priorities of the top … Speculative arguments about AGI helped inspire Elon Musk’s DOGE project, which sought to hack away great swathes of America’s administrative machinery. Much of DOGE’s work and aspirations involved the application of LLMs and AI ‘agents’ to accomplish a variety of open-ended tasks, often very badly. … We certainly can’t blame AGI speculation alone for DOGEmaxing and the Trump administration’s evisceration of the federal bureaucracy; there are many overlapping causes. However, if you believe that the duty of a bureaucracy is to implement the leader’s program, and AGI is nigh, it is not hard to conclude that the latter offers a providential way to accomplish the former.

There’s lots more in the paper, including plenty more detail on the intersection between AGI-speculation and DOGE thought, and argument how the consequences of AI for bureaucracy are going to be much messier than any of these stylized interpretations. But there are also things that we didn’t have time to talk about. Two are worth mentioning.

First, “effective accelerationism” is an important part of the intellectual mix that helped produce the AI-DOGE chimera. In particular, the neo-reactionary arguments of Nick Land have had real, and pernicious consequences. “DOGEMaxing” and similar notions owe an intellectual debt to Land’s ideas (the term “machinic” pops up a lot in argument), even if the people who make these arguments don’t necessarily agree with everything he says. Claims about how our world will be radically remade in the image of machine logic are nearly ubiquitous, whether the authors view the machine-god state as something to be feared, celebrated or both at once. Equally, as as we suggest in the quoted excerpt above, AI speculation is only one justification for the Trump administration’s efforts to disembowel the administrative state. I’d be startled to discover that e.g. Russell Vought has been reading technology-fueled neo-reactionary online manifestoes.

Second - and this is something I figured out belatedly when writing, is that there is a fundamental difference between the disastrous DOGE project and the apparently similar push by both center-right and left leaning people to create a more effective and responsive government bureaucracy. That has in fact been the source of considerable confusion. The two approaches differ crucially on the question of who should the government be responsive to?

DOGE/AI Thought starts from the premise that bureaucracy should be primarily (perhaps even exclusively) responsive to the people at the top. From this perspective, the problem that AI solves is a mixture of regular institutional inertia and specific “deep state” resistance. There are implied tradeoffs for some people who buy into this argument (others; the true idolators, may simply not care that much). Using AI to build the machinic Moloch means both empowering the Trump administration in its quest to build up American Greatness, and empowering the Chinese Communist Party in its quest to stamp out human individuality and freedom. What makes it easier for Trump to get rid of mid-level official saboteurs by buying in private sector AI and expertise, also makes it easier for Chinese Communist Party leaders to construct machine learning apparatchiks that perfectly implement its program. Under this analysis, perhaps you can hold China back by e.g. not exporting the fastest chips for AI training; perhaps not.

The effective government bureaucracy people, in contrast, are not in the business of making sure that Dear Leader’s commands get implemented as they ought. Instead, they are primarily interested in freeing bureaucrats to do things that are obviously the right things to do, rather than burying them beneath the concrete of top down mandates. The impulse, then, is to trust bureaucrats more, and give them the means and autonomy to respond to obvious needs. This involves creating better feedback loops between top and bottom, so that measures, tools and perhaps even goals are redefined as the problem becomes better understood. But it also means creating interfaces through which bureaucrats can engage more with the public, and respond better and more quickly to public demands, as well as helping them work sideways with others in the bureaucracy who have necessary skills and knowledge, without getting smothered in red tape. The general bet is not on better subjugating bureaucrats, but on making them more autonomous. This is more or less the opposite of DOGE, and its vision of AI is murkier.

Obviously, some reforms or technologies that might help the one vision might plausibly help the other. But one of the implications of Cosma’s and my arguments is that they have radically different understandings of what the state should be doing, and for whose purposes. I suspect - though this would require a lot more work to establish - that the differences between DOGEMaxing and the effective bureaucracy approach map quite well onto the differences between Soviet cybernetics with its programmatic ambitions to Plan Everything, and the management cybernetics of Stafford Beer and his colleagues. Like those, they are very different approaches that are lumped together because of some commonalities of terminology.

Anyway - these are side notes to a larger argument. If you are interested, do read the piece itself!

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[Battista Angelo del Moro, “The Corrupt Judge.” Courtesy of the Met]

Obviously this post is occasioned by the terrible Supreme Court decision earlier this week. It is also motivated by the string of other terrible decisions over the last few years, and by a long established amateur interest in the topic of the conservative legal movement. What follows are better described as notes toward an argument than a fully developed set of ideas, but that’s all I’ve got right now.

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The Conservative Legal Movement is Not Purely Political

There’s been a lot of scorn expressed on Bluesky and elsewhere at John Roberts’ apparent claim that Supreme Court justices are not “political actors.” If you read the original article, Roberts doesn’t quite say that, despite the AP’s spiced-up headline and somewhat misleading first line. What he actually says is:

“I think, at a very basic level, people think we’re making policy decisions, we’re saying we think this is how things should be, as opposed to what the law provides …I think they view us as purely political actors [my emphasis], which I don’t think is an accurate understanding of what we do.”

I haven’t been able to find a transcript of the original speech, and I imagine that Roberts didn’t elaborate further. I suspect that if he had so elaborated, he would have said very different things than what I am about to say below. Nonetheless, it is both true and important that the Supreme Court justices, and conservative legal scholars and movement activists are not purely political actors. They would be much less effective if they were. They have an ideology, originalism, which is clearly quite closely related to a set of policy priorities, but it doesn’t ineluctably lead on every possible occasion to the preferred policy outcomes.

If conservative legal thought were simple intellectual hackwork, it would be less legitimate, and hence much less suited to coalition building and getting results. Equally, treating it as a set of ideas floating far above politics in some empyrean of purely intellectual discourse would be even more wildly misleading [see further below]. The implication is that we can’t just treat the ideas as by-products of the politics. We have to understand how the ideas and the politics interact. And that leads us to the Federalist Society.

The Federalist Society is a Political Movement

If you want to understand how the conservative legal movement hangs together, you need to pay very close attention to the Federalist Society, a club of conservative lawyers. The Federalist Society is about ideas, as suggested by the title of Amanda Hollis-Brusky’s book on the organization, Ideas Have Consequences (a sardonic gloss on the title of a famous conservative book). It is also about distributing the spoils of patronage.

Hollis-Brusky - who has done the interviews - explains how the Federalist Society plays a central role in the promulgation of originalism, the doctrine that conservative justices have used to remake constitutional interpretation and jurisprudence in ways that are congenial to their ideology.

For example, as Federalist Society member Daniel Troy explained in our interview, “what the Federalist Society offers is an opportunity to interact with people who at least share your point of view about constitutional interpretation. . . people who have shared views about Originalism.” Similarly, Federalist Society member John Yoo emphasized in our interview that if he had to identify the single most important thing that the Federalist Society stood for, it would be “a commitment to Originalism.”

The Federalist Society does not simply develop originalist ideas and arguments . It also enforces the party line tacit combination of cheering section and ideological firing squad. Hollis-Brusky again:

Several conservative commentators when discussing Republican nominees John Paul Stevens, Harry Blackmun, Potter Stewart, David Souter, and even Anthony Kennedy in part attribute the leftward drift of these Justices to their desire for approval from the Washington, D.C., elite circles.

In helping to build a conservative and libertarian counter-elite around a shared belief in Originalism as the only valid mode of constitutional interpretation, the Federalist Society acts as a bulwark … holding members accountable for staying true to their principles. In the course of my interviews with key Federalist Society members, it became clear that they engage in this kind of feedback-loop with the Justices frequently—at Federalist Society conferences, at barbeques, through personal correspondence, and through scholarly publications.

As Federalist Society cofounder Steven Calabresi said in our interview, the growth of a conservative and libertarian counter-elite through the Federalist Society has “absolutely” helped keep Justices such as Scalia, Thomas, Roberts, and Alito in check: “I think it absolutely helps keep them in check. When one tries to think about what kinds of checks exist on officials as powerful as Supreme Court Justices I think the check of criticism by law schools, journalists, and conservative think tanks like the Federalist Society, criticism from those quarters is something that they notice. They may or may not be persuaded by it but I think they know it’s out there and I think it is something of a check on them.”

Hence, ideas are really important but they are ideas with a purpose, and an organizational structure behind them.

Still, if the Federalist Society were just about debating originalist ideas, it would be much less politically effective. The other, very important thing that the Federalist Society does is to distribute patronage. If you are a young aspiring lawyer and you want a political appointment in a Republican administration, or even more pertinently, a clerkship for a conservative judge, and all of the career benefits that flow from that, you really, really want to participate actively in the Federalist Society.

Thus, patronage politics (judges have great personal discretion in who they choose as clerks) plays a very important role in keeping the Federalist Society thriving and healthy. FedSoc’s liberal equivalent, the American Constitution Society, is relatively anemic, because it doesn’t have a similar stranglehold on who gets clerkships and who does not. It illustrates what FedSoc might look like if it were just about debate and nothing more.

In one sense then, you might think of the Federalist Society as a watered down version of what Tammany Hall might have looked like if it had taken all of the culturally offensive ceremonial nonsense about Grand Sachems and wigwams in deadly earnest. There is notably less riotous drinking, and far less overt corruption than in its nineteenth century equivalent, but FedSoc is in substantial part a machine for dispensing patronage to the right people. Just like nineteenth century Irish immigrants to New York, conservatives in law schools were denied access to the trough and had to organize themselves. They similarly came to dominate a system that once seemed to be rigged against them.

In another sense, the Federalist Society is indeed about ideas, but in a very different way to, say, an ordinary university department. Again, the Society’s mission is to ensure that ideas have consequences by shaping jurisprudence in specific ways. Debate happens only to the extent that it is compatible with this broader objective.

In conclusion then, you have to understand both aspects of the Federalist Society, and how they fit together, to see how it cements the conservative legal movement together. If it didn’t weld patronage politics to ideas, it would be much less attractive to young lawyers. If it didn’t weld ideas to patronage politics, it would be incapable of reshaping judicial discourse and constitutional politics around its conservative objectives.

The Conservative Legal Movement is Decadent

When it began, the conservative legal movement could see itself as an ideological vanguard. It was taking on a corrupt and complacent liberal legal academy that it saw, with some justification, as more interested in self-perpetuation than new ideas. Those days are long gone. Now, the conservative legal movement is the establishment, or at least a central element within it. Lawyers like Marty Lederman increasingly have to search for originalist justifications to achieve liberal ends [update: for qualifications see “Jon W” in comments].

Holding power - and seeking to maintain and extend it - have had predictably dismal consequences for ideas. Purportedly textualist Supreme Court justices come up with bizarre justifications that lack historical support, for decisions that they want to take. Historical constitutional interpretation becomes an exercise in cherry picking to justify new doctrines, rather than the sober moderating of judicial overreach that originalists purported to provide. Zealots of constraint, once they get into power, are keener to limit their opponents’ excesses than their own.

It is a problem when the concealing patina of ideas wears down. It is also a problem when patronage politics become so gross as to explode through the covering layer. The Roberts Court is not only presiding over the most spectacularly corrupt government in the history of US politics (itself a remarkable achievement). It is actively enabling it, both through its past jurisprudence and current decisions.

People rightly highlight decisions such as this week’s, as well as the grant of near complete immunity to presidents in the course of their official duties, the manipulation of the shadow docket to frustrate efforts to restrain the administration, and the efforts of DC circuit court justices to frustrate contempt proceedings against visibly contemptuous Trump officials. Other, less flashy lines of jurisprudence, such as the slow gutting of meaningful anti-corruption measures are likely to have equally pernicious consequences if control of the legislative and executive branches change hands.

I suspect that Roberts and his cronies do not like to think that they are associated with the more uncouth aspects of the political conservative movement. They are not only associated with it, but an active and vital element of the machine. It is increasingly difficult to maintain the pretense of separation. Once, like Burke in his early days, conservatives railed against official corruption. Now they are apologists for a regime whose decadent excesses resemble the more startling aspects of Marie Antoinette’s court.

Decadence opens up vulnerabilities.

To say that a system of pelf and ideology-laundering is in decay is not to say that it is in imminent danger of collapse. There is a great deal of ruin in a movement that has successfully seized power. Yet there are contradictions, weaknesses and fissures. Originalism is the theory that the Federalist Society knows what it wants, and deserves to get it good and hard. It’s now getting it good and hard, and some legal conservatives are reportedly unhappy.

Still, so long as the movement maintains its chokehold on patronage, it will be difficult to detach more than a few individuals. I’ve sometimes wondered what would happen to the Federalist Society if future reforms eliminated the power of individual judges to choose their own clerks, in favor of some form of panels or open competition. This would obviously discomfit many liberal and left leaning lawyers; they too have their own networks of benefit disbursal. But it is likely not nearly as essential a part of their organizing machinery.

It could be that the Federalist Society is sufficiently well established that it will continue to operate more or less as it does, or that it may integrate further into a conservative movement that is ever more closely organized around the division of spoils. It’s also possible - even likely - that it would lose a lot of the power that it has to attract and organize lawyers, unraveling the threads that weave patronage, cheerleading and disciplining lawyers into a single interconnected system of power. Perhaps in a few years we’ll have the opportunity to find out.

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I haven’t been writing as much here over the last several weeks, because of multiple other writing commitments. Some of these are about to start bearing fruit. Substack posts will get easier now that the semester is coming to a close, but in the meantime, here’s a short piece to tide you over, and an opportunity for those as wants to take it up.

One of the pieces that is forthcoming (co-authored with Cosma Shalizi) relates indirectly to the theme of this Elias Isquith post. Elias draws a comparison between how AI figures like Altman talk about the future, and the bizarre homespun philosophy of Anton Chigurh, the killer in the movie, No Country for Old Men.1

in both cases, we have men positing that they have a unique grasp of the nature of the universe and the direction of human history. And at the risk of stating the obvious: This is not a small claim!

In another era, in another culture, this hubristic assertion of having discerned the golden path to the inevitable future would be grounds for charges of heresy or blasphemy. The egomania here is astounding, no less so than that of a character who fancies himself Fate (and/or Death) incarnate.

And in both cases, we are confronted with false prophets who are telling us, albeit in different registers, that human superfluousness is either already true (Chigurh) or inevitable (Altman). In the face of such monstrous certainty, despair would be understandable.

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I think that it is worth drawing out a difference between Chigurh and Altman that is hinted at in Elias’s indirect Dune reference. Chigurh’s philosophy is a cockeyed version of determinism, suggesting that we don’t have real choices going forward. Altman is drawing on a much stranger set of notions, which suggests that our present is not determined by our past, or by chance, but by a radically constrained version of the future. A lot of AI discourse reverses the arrow of time, so that instead of a fixed past and an indefinite future, we face a definite future, which directly or indirectly shapes its own past.

This follows from standard arguments about the Singularity. People who subscribe to middling-to-stronger versions of Singularity thinking believe that we are about to hit a massive phase-change in human history, which will have a dichotomous outcome. Once strong AI hits, either the machines master us, or we master the machines. The result is to render present concerns largely irrelevant, except insofar as our collective decisions make us more likely to follow the one path or the other. The AI 2027 paper provides a fine example of the genre.

And it is a genre; a concatenation of LessWrong posts, academic and sort-of-academic articles, think-tank thought pieces, Substack posts and the like, which not only build on a similar set of tropes, but also (and here is the point I want to emphasize) adopt a narrative structure that interprets the present only in terms of some posited near term future of radical transformation. It’s notable how significant elements of the mythology of the Singularity - things that people in this space don’t usually fully believe, but that often indirectly affect their thinking - describes direct intervention by some future that rearranges the past so as to ensure that it comes into being. Here, most obviously, Roko’s Basilisk (more generally; when one considers the collective fate of humanity as an exercise in applied game theory, backward induction may lead you to some very strange conclusions).

This famous quote from Nick Land is even more on point:

what appears to humanity as the history of capitalism is an invasion from the future by an artificial intelligent space that must assemble itself entirely from its enemy’s resources.

The obvious rebuttal is no: we actually can’t see the future of AI. We can’t even engage in the limited kinds of hedged predictions that we can make when we model reasonably well understood phenomena. The question, “How technology will develop in the future?” is an open ended one.. If innovation were predictable in its consequences, like the tech tree in Civilization, we’d be in a very different kind of history altogether than the history we are in.

What we can see are the outcomes of thought experiments. Thought experiments can be very useful as a means of thinking more systematically about unknowns. However, one shouldn’t overestimate what you can do with them. In the end, thought experiments are nothing other than moderately disciplined guesswork. When we mistake them for reliable predictions of what lies ahead, and reshape our world around what they say, we’re liable to end up in a mess, unless we are improbably brilliant, lucky, or both.

But we are in a world where many people - including very important policy makers - see a particular strain of thought experiments as being determinative. I don’t think that these people are stupid or wicked, but I am frustrated with how their arguments are driving out other ways of thinking (that may finally be changing now that concerns about economic disruption are coming to the fore, but it is changing much more slowly than I would like). That makes it much harder to see the enormous variety of futures that might be possible, depending on the choices we make and their consequences, both predictable and stochastic. Those possible futures are being devoured by thought experiments which have gone feral, and have spread like an invasive species from their proper environment into the realm of general discourse. That stunts democratic debate and understanding of the choices we do and don’t have.

Some version of this complaint informs the opening parts of a jointly written paper with Cosma Shalizi that should be out soon. You can plausibly read this classic post of his as yet another version of this complaint, which was aimed at an earlier version of this discourse. If we see the Singularity as having happened in the past, we can better understand the ways in which it is contingent, rather than extrapolating the eschaton from growth charts.

Enough griping: here’s the positive opportunity. The Protopian Prize competition for short fiction that imagines a democratic future is going to open up tomorrow. There will be a $5,000 prize for the winning entry, and I’m going to be one of the judges. While I wasn’t even slightly involved in setting up the prize, and the bit that I am concerned with is not about AI, the competition will surely generate a variety of different possible futures, all of which will start from an understanding of how or whether we can collectively steer towards one direction or another. That kind of steering, is, after all, what democracy involves. I don’t have any further particulars beyond what is on the website, and absolutely don’t want to suggest that people should write in one or another vein (the variety of possibilities seems to be the point) but do encourage you to enter. As per Elias’s post, I would like fewer thought experiments about how we have no or very few choices to make, and more thinking about how our future is not constrained to one or the other narrowly defined path.

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Towards the end of his new book, The Irrational Decision, Ben Recht explains what he has set out to do.

Most books on technology either take the side that all technology is bad, or all technology is good. This isn’t one of those books. Such books focus too much on harms and not enough on limits. Limits are more empowering. Throughout the book, I’ve maintained that mathematical rationality is limited in what kinds of problems it is best placed to solve but has sweet spots that have yielded remarkable technological advances.

It may be that more books on technology escape the good-bad dichotomy than Ben allows. Even so, I haven’t read another book that is nearly as useful in explaining why and where the broad family of approaches that we (perhaps unfortunately) call AI work, and why and where they don’t. Ben (who is a mate) combines a deep understanding of the technologies with a grasp of the history and ability to write clearly and well about complicated things. I learned a lot from this book. Very likely, you will too.

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The good-bad dichotomy that Ben describes does indeed shape a whole lot of our current debate around “mathematical rationality” and AI. Regarding the first, Nate Silver’s book On The Edge argues for the kinds of Bayesian rationality that Silicon Valley people like to talk about. It praises the “River” of people who think about the world in terms of statistical probabilities, which you update whenever new information becomes available. As Ben suggests in a separate review essay with Leif Weatherby, the “River” wraps professional poker, rationalist thinking about AI, sports betting and crypto bro philosophizing together into a single package that appears sort-of-coherent, and even perhaps brilliant, if you don’t look at it too closely. As Ben suggests, rationalists of this persuasion tend to assume that “computers can make better decisions than humans,” and are often fervent cheerleaders for AI (Silver, in fairness to him, isn’t nearly as fervent as some others). Other books, like Emily Bender and Alex Hanna’s The AI Con, begin from just the opposite assumption: that most of what we call “AI” is hype. Bender and Hanna tell us that if we start poking around behind the grand spectacle and booming voice of “mathy math,” we will find the rather unimpressive wizard of machine learning, who is actually only capable of fancy spell-check, telling radiologists which parts of an image they might want to take a look at, and other such “well scoped” activities.

Neither AI Rationalism or AI-Con Thought is all that helpful in explaining the technologies we confront right now. The former tends to launch into fantasy, repeatedly demonstrating how starting from ridiculous premises allows you to reason your way to ridiculous results. The latter tends to curdle into denialism, claiming ever more loudly that disliked technologies are useless even as they find ever more uses. We ought to be much more worried about the claims of the triumphalists than the denialists, since they are far more influential. But to successfully deflate their claims, we need a more grounded perspective on what AI and related technologies are capable of than can be provided by the denialists.

The Irrational Decision provides strong reasons for skepticism about the grander aspirations of the rationalist project, while explaining why machine learning has remarkable uses in its appropriate domain. Those who are embroiled most closely with the rationalist project have a hard time understanding its limits because those limits shape their own world view. The one weird trick of rationalism is to recompose complex problems in terms that can readily be rationalized. When that is good, it is very, very good, but when it is bad, it is horrid. To understand this, it’s first necessary to understand where rationalism comes from.

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Much of the discussion of The Irrational Decision is historical. It reaches back to the 1940s and 1950s to figure out where rationalism actually comes from, providing a short history that is a little like what Erickson et al’s How Reason Almost Lost Its Mind might have been if it focused more on statistics and operations research than economics. Ben’s aim in all of this is to identify how ‘mathematical rationality’ came to be a relatively coherent set of ideas about how we might better organize society.

The story he tells is necessarily messy, but some important broad themes emerge, most importantly around the development of optimization theory. Linear programming makes it possible to find optimal ways to allocate resources within a limited budget so long as the constraints are linear (when they are not, all computational hell can break loose). Optimal control theory allows a control system to adjust optimally to its environments (again, under restrictive assumptions about the constraints). Game theory can postulate - and often even discover - optimal strategies to play against opponents in strategic situations. These toolkits overlap with others. A family of techniques, ranging from simulated annealing to the ancestral forms of the gradient descent/backpropagation that “deep learning” relies on, provides ways to discover superior local optima in more complex situations. Randomized clinical trials (RCTs) provided possible ways to discover whether a given intervention (a drug; a policy measure) worked or not.

All of these approaches suggest the superiority of technical forms of analysis over human judgments. RCTs apply protocols and statistical analysis to try to discover causal relationships (according to the standard story), or justify interventions (according to Ben’s). Other approaches involve the discovery of optimal solutions, given convenient mathematical assumptions and simplifications. Others still involve the discovery of local optima (that is: solutions that are better than others that are readily visible in their neighborhood), which may be better than those that ordinary humans could reach.

Rationalist approaches are very powerful in their domains of proper application, but you need some sense of what those domains are. Ben suggests that there is a “sweet spot” for many or most computational tools. For example, statistics is not useful for situations where a treatment always works (why would you need complicated tools of inference), or where outcomes are too variable and unpredictable, but for the messy zone between the two. When you hit the space where your tools have traction on reality despite their imperfections, you can accomplish extraordinary things. For example, in his own review of the book, Dan Davies talks about

the incredibly productive feedback loop between “optimisation algorithms are really demanding in terms of computer processing” and “optimisation algorithms are really useful for designing better and faster computers”.

As Ben describes it, designers were able to reduce the incredibly complex challenges of chip design into an optimizable task through making simplifying assumptions, about “standard cells” and combining them with simulated annealing algorithms that could discover optima that would otherwise not be easily visible. This, then, as per Dan, allowed faster chips to be developed, which in turn could run more powerful algorithms, and so on, in a loop.

But treating rationalism as a universal tool of discovery is problematic, especially given that these techniques are characteristically limited or start from implausible simplifying assumptions. Daphne Koller, one of the researchers who Ben describes, discovered some startlingly effective ways to reduce the complexity of poker so that it became more nearly “solvable.” But Koller eventually abandoned the study of game theory:

“Understanding the world around us is more important than understanding the optimal way to bluff,” she told me. In her experience, when she needed to model people in simulations of complex systems, modeling their decisions as random got her 90 percent of the way to a solution. How to best make decisions under wide-ranging uncertainty was far less cut-and-dried. For Koller, once you stepped away from the game board and had to make decisions in reality, understanding uncertainty and the myriad ways it could arise and impact plans was more important than strategy.

As it turned out, poker algorithms too generated feedback loops, not through simplifying chip design, but simplifying human beings (C. Thi Nguyen’s book, The Score provides a broader account of how this works). There is an important sense in which optimal poker theory was less successful in optimizing poker than in optimizing poker players, inspiring a style of play in which professionals “started memorizing expected value tables from poker solvers so that they could play ‘game theory optimal’ in big poker tournaments.” Perhaps that can be described as an improvement in human affairs. I’m not seeing it myself.

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Understanding mathematical rationalism helps us understand the strengths and limitations of AI. It isn’t just a form of rationalism, but the combined application of a variety of long established rationalist techniques - neural nets (which go back to the 1950s), statistical learning and backpropagation, made possible by more powerful computers and enormous amounts of readily available data. Claude Shannon’s methodology for modeling language, which is the intellectual basis of “large language models” is “an instance of statistical pattern recognition” or machine learning. And machine learning itself is no more and no less than a powerful statistical tool. I found this passage maybe the most clarifying explanation of what it does that I’ve ever read.

To frame the prototypical machine learning problem, I like to think about a hypothetical spreadsheet. Each row of the spreadsheet corresponds to some unit or example. But I don’t care what the units mean. I just know that I have a bunch of columns filled in with data. And I’m told one of the columns is special. I am about to get a load of new rows in the spreadsheet, but someone downstairs forgot to fill in the special column. Management has tasked me with writing a formula to fill in what should be there. For whatever reason, I don’t get to see these new rows and have to build the formula from the spreadsheet I have. The formula can use all sorts of spreadsheet operations: It can assign weights to different columns and add up the scores, it can use logical formulas based on whether certain columns exceed particular values, it can divide and multiply. … I’ll do an experiment. I’ll take the last row of my spreadsheet and pretend I don’t have the special column. I’ll write as many formulas as I can. … But why single out that last row? I can do something similar for every row! I’ll invent a set of plausible functions. I’ll evaluate how well they predict on the spreadsheet I have. I’ll choose the function that maximizes the accuracy. This is more or less the art of machine learning.

Guessing the missing rows of spreadsheets and optimizing turns out to have a lot of useful applications: not just language models, but protein folding, recognizing handwriting and a myriad of other applications. Equally, machine learning is just another form of optimization and/or prediction. Very large chunks of Silicon Valley’s current business model involve taking complex situations that don’t look like optimization or prediction problems, simplifying and redescribing them and then finding solutions.

Just like statistics, there is a “sweet spot” for machine learning. It is not useful for situations where you have a genuinely clean mathematical abstraction, which you can turn into running code. Nor is it useful for situations that are too messy or complicated to be predictable (it is, after all, an application of statistical technique). You want to use it in the intermediary situations where there isn’t an obvious neat solution, but where the clunky and computationally expensive techniques of machine learning can discover a useful approximation, even if you may not understand quite what it is based on or how it works.

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All this implies some important problems of evaluation. How can you tell where machine learning is a useful way to proceed? How can you tell which machine learning approach is the best one to apply for a given problem? And behind all this lurks the bigger question that we began with. How can you tell when machine learning techniques in general (or other rationalist shortcuts) are better or worse than ordinary human judgment?

The answer to the first is unfortunately indeterminate. As best as I understand Ben’s argument, the only real way to discover whether machine learning works for a given kind of problem is to come up with a working machine learning solution. There is no genuinely satisfactory ex ante way to distinguish between the problems that machine learning can solve for, and those that they can’t. Furthermore, as Ali Rahimi and Ben have noted elsewhere, AI practitioners rely more on “alchemy” than a deep understanding of why some approaches work and others don’t. More succinctly, XKCD:

As for how to tell which machine learning algorithms work better than others, computer scientists have come up with an approach commonly called the Common Task Framework (or variants thereon). Create a common dataset (canonically: photos of cats and dogs) and share all or (far more usually these days) some of it with different teams of researchers. Then come up with a common task that can be performed on the data and can evaluated in a fairly straightforward way (can the algorithm distinguish between cats and dogs). Then the different teams can come up with algorithms that work competitively against each other, which perhaps can be tested on data that has not been shared publicly, to ward against overfit and teaching to the test. The algorithm which works best (say; has the highest percentage accuracy in distinguishing between cats and dogs) is, ipso facto, the best algorithm for the task.

And this gets us to one of the major contributions of Ben’s book. A lot of people in AI claim that we can apply this framework to answer a very big question. Are AI algorithms generally superior to human beings at performing some set of cognitive tasks? There are a variety of common task framework tests that purport to do this, some with names that … beg questions. If you are hanging around the right (or wrong) places on the Internet, you will regularly read this or that excitable claim that humanity is doomed to be superseded because of the performance of AI on this or that test.

Ben suggests that such claims tend to make a fundamental error. He describes some famous results from the research of psychologist Paul Meehl on medical and other decisions, which suggested that “statistical prediction provided more accurate judgments about the future than clinical judgments” under certain conditions. But the conclusion that Ben comes to is not that this means that statistical prediction is generally better than expert judgment. Instead, it is better when there are clearly defined outcomes, good data, and clear reference cases that can be used for comparison. There are many situations in which this is not true, and cannot readily be made true.

If we use common task type approaches to measure success, we are loading the dice in favor of those tasks that can be described in terms of clear outcomes, and tested with good data, and loading them against those tasks that do not have such nice characteristics. Ben describes this even more pungently. Tasks that can be defined in those ways are definitionally the tasks that computer or other automated approaches will be able quickly to do better than human beings. Paradoxically:

If we can measure why humans might be able to outperform machines, then we can build machines to outperform people. On the other hand, if we can’t cleanly articulate a clean set of actions, outcomes, measurements, and metrics, then we can’t mechanize problem solving. It is this digitization, translating the world into the language of the computer, that is needed to automate.

The universe of tasks with clear goals, conditions and data is both the universe of tasks that are easily measured and the universe of tasks that computers and automated processes can carry out well. The one characteristic more or less predicts the other. This, then, is what makes it so hard for mathematical rationalists to see the limitations to their perspective. The tools and measures that they use to understand and solve problems could almost have been purpose crafted to confirm their broad intellectual biases by concealing the problems that their methods can’t easily solve.

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This helps us to situate the debate that is happening right now about AI. There are many AI enthusiasts, who believe that it can be applied to do pretty well any task that humans can do as well as the humans or better. Getting to this is just a matter of scaling and engineering, and is going to happen Real Soon Now. There are AI skeptics, who argue that its benefits are limited to a narrow range of well defined tasks, or even (I see the claim regularly, though it is rarely defended in any particularly sophisticated way) that the benefits are non-existent. These positions often map onto “AI good” and “AI bad,” along the lines that Ben suggests.

As per the quote at the beginning of this post, Ben doesn’t really engage with the question of whether AI is good or bad in any general sense. Instead, he proposes that it can carry out many tasks, including tasks that we might not anticipate right now, but that there are limits. AI, like mathematical rationality more generally, has a sweet spot: problems that are complicated enough that they can’t be solved by other computationally cheaper approaches, but that have enough regularities to be workable. Within that sweet spot, it can do extraordinary things. Outside the sweet spot, it may be redundant or completely useless. And there is an ambiguous zone in between, where it can do stuff but imperfectly.

It isn’t possible, except in very general terms, to define ex ante what the sweet spot is. Clever engineers are perpetually trying to expand it. Self-driving cars provide one example of a problem that has proved far harder to solve than engineers thought (as Ben puts it “we don't know how to articulate 'good driver' into a clean statistical outcome”), but they are brute-forcing the problem so that self-driving is far more plausible across different environments than it used to be. Equally, there are many, many edge cases. One way to deal with many of them might be to try to simplify them out of existence through e.g. having only self-driving cars without the unpredictabilities of idiosyncratic human drivers, or cyclists, or … or … or). Such simplification is a version of what management cyberneticists call ‘variety reduction.’

Equally, there are challenges that appear to be fundamentally resistant to mathematical rationality, including bureaucracy and politics:

societies are not computer chips. While I noted in chapter 2 that computer chips were often analogized as microscopic cities, chips were always designed to be hermetically sealed and perfectly controlled. This is what made them optimizable. Real societies, on the other hand, had people. While it’s convenient to model and view the population, its health, and its market flows as mathematical abstractions, these run into the limits of the messiness that people bring to bear.

In The Sciences of the Artificial, Herbert Simon makes a closely related argument:

When we come to the design of systems as complex as cities, or buildings, or economies, we must give up the aim of creating systems that will optimize some hypothesized utility function, and we must consider whether differences in style of the sort I have just been describing do not represent highly desirable variants in the design process rather than alternatives to be evaluated as “better” or “worse.” Variety, within the limits of satisfactory constraints, may be a desirable end in itself, among other reasons, because it permits us to attach value to the search as well as its outcome—to regard the design process as itself a valued activity for those who participate in it.

We have usually thought of city planning as a means whereby the planner’s creative activity could build a system that would satisfy the needs of a populace. Perhaps we should think of city planning as a valuable creative activity in which many members of a community can have the opportunity of participating—if we have wits to organize the process that way.

As per James Scott’s Seeing Like a State, the problems begin when technocrats begin to treat human beings and the complex societies they create as though they were simplified “standard cells” that can readily be re-arranged in more optimal patterns. Moreover, as Ben says elsewhere (Cosma and I quote this in our own forthcoming piece on AI and bureaucracy), political disagreement generally resists optimization. When you have incommensurable tradeoffs (even very simple ones: should you use money in your budget to pay for a playground to make parents happy or a fire station to make it less likely that businesses will burn down), you have moved decisively away from the kinds of problems that machine learning, or optimization more generally, can simplify in useful ways.

As soon as we can’t agree on a cost function, it’s not clear what our optimization machinery … buys us. Multi-objective optimization necessarily means there is a trade-off. And we can’t optimize a trade-off.

Barring the development of radically different approaches, there is no reason to believe that politics will come into the sweet spot. But many mathematical rationalists argue otherwise (e.g. this set of claims, which maybe deserve their own extended response). If you want to really understand the limits on AI, you owe it to yourself to read Ben’s book. There are many books on technology that are smart in some sense, but very few that are wise. This is one of those very rare exceptions.

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I’ve been thinking about this post for a few months, but haven’t wanted to write until I felt I had the argument right in my head. I think it gets at something useful, but it could very easily be misconstrued. It explains why the above (via Matthew Gertz this morning) plausibly describes how policy gets made in the Trump administration.1

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“Gooning Towards the Führer” is not a claim, either express or implied, that America is descending inexorably into Nazism. I obviously detest Donald Trump and what he is doing to America and the world, but I do not believe for a moment that he is akshually Adolf Hitler.

Instead, I think that some of the historical literature on the Nazi state (Ian Kershaw obviously; also Jane Caplan and bits from the late Detlev Peukert’s work on Weimar) is extremely helpful in thinking about how Trumpism works as a style of policy making. Kershaw compares Hitler’s chaotic mode of making and implementing decisions to Stalin’s more orderly bureaucratic approach. Both, obviously, were far more monstrous than Trump at his worst, but just as they had different styles of dictatorial rule, you could distinguish, say, between Trump’s approach to competitive authoritarianism and Viktor Orban’s.

There are also some very important differences in policy making style. Hitlerism was jerry-built on top of imperial Germany’s bureaucratic state, and could still rely on bureaucratic proceduralism to get things done. Trumpism, in contrast, leans very heavily on social media to coordinate policy across the regime. Propaganda and policy making are blurred so that one can’t tell where the one ends and the other begins. One of the aspects of Trumpism that is most difficult for people to wrap their heads around is the degree of overlap between the channels that the regime uses to communicate with the public, and the channels that the regime uses to communicate with itself.

Hence, Kershaw’s notion of “working towards the Führer” isn’t quite appropriate for understanding what is happening now. Trump’s underlings absolutely try to anticipate his desires and get his attention. But they do this by adopting a particular style, which is well suited to a small set of questions, and terribly suited to a much larger one.

This has consequences. First: ideas and arguments that aren’t readily translated into a particular language of visual memes have a very poor chance of making it through the process. Second: there are regular fuckups, as notions that make for great memes or Fox News talking points turn out to make for shitty and self-defeating policy actions. Third, problems that cannot be translated into the language of memes and Fox News hits don’t really exist for the Trump administration,until they cause complete breakdown, and sometimes not even then.

This, then, can all be described as “gooning towards the Führer.” More below.

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Ian Kershaw’s phrase, “working towards the Führer,” has escaped its original context, but it is absolutely worth going back and reading what he meant to argue with it. Kershaw was writing in the 1990s, right after the collapse of Soviet rule over Eastern Europe. People were making sloppy comparisons between Stalinism and Nazism as largely identical forms of “totalitarianism.” Kershaw wanted to argue that there was something different about the “gathering momentum of radicalization, [and] dynamic of destruction” of Nazi Germany. Stalin was a “man of the machine” who gathered his power from within the bureaucracy. In contrast, it was “hard to imagine” a “party leader and head of government less bureaucratically inclined” than Hitler. Kershaw quotes one of Hitler’s former adjutants:

Hitler normally appeared shortly before lunch, quickly read through Reich Press Chief Dietrich's press cuttings, and then went into lunch. … as even worse. There, he never left his room before 2.00 p.m. Then, he went to lunch. He spent most afternoons taking a walk, in the evening straight after dinner, there were films. He disliked the study of documents. I have sometimes secured decisions from him, even ones about important matters, without his ever asking to see the relevant files. He took the view that many things sorted themselves out on their own if one did not interfere.

This hands-off approach meant that Hitler, without formally intending it, “presided over an inexorable erosion of ‘rational forms of government.” The result was a system of rule that combined local islands of efficiency with a remarkable degree of general chaos.

Stalin had a system that survived him, but Hitler did not. Hitler’s organizing will was the system, such as it was, leading to a ceaseless dynamic of radicalization, based on “predatory character and improvised technique,” with no visible braking mechanism. Stalinism was based on bureaucratic technique, while Nazism was organized around Hitler’s personal charisma.

Most importantly, the Führer served as enabler: “Hitler's authority gave implicit backing and sanction to those whose actions, however inhumane, however radical, fell within the general and vague ideological remit of furthering the aims of the Führer.” This was how ‘working towards the Führer” actually functioned. Hitler himself rarely issued unambiguous commands, instead preferring to let arguments between his underlings sort themselves out, and communicating his preferences elliptically, when he communicated them at all. Kershaw takes the classic phrase from a speech by a provincial Nazi functionary:

Everyone who has the opportunity to observe it knows that the Führer can hardly dictate from above everything which he intends to realise sooner or later. On the contrary, up till now everyone with a post in the new Germany has worked best when he has, so to speak worked towards the Führer. Very often and in many spheres it has been the case - in previous years as well - that individuals have simply waited for orders and instructions. Unfortunately, the same will be true in the future; but in fact it is the duty of everybody to try to work towards the Führer along the lines he would wish. Anyone who makes mistakes will notice it soon enough. But anyone who really works towards the Führer along his lines and towards his goal will certainly both now and in the future one day have the form of the sudden legal confirmation of his work.

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There are obvious similarities between working towards the Führer and policy making in the second Trump administration. A boss who rolls in at lunchtime to read the press clippings has a lot in common with one who reportedly spends much of his day napping, playing golf, gossiping with friends and watching cable TV. There are also obvious differences. Again: Trumpism is not Nazism. Trump’s faults are volubility and incoherence rather than elliptical indifference (though he too, clearly does not like disciplining underlings).

There are important differences in the administrative problems faced by Trumpism and Nazism. Detlev Peukert argued in his book on Weimar that the Third Reich inherited a sympathetic (and largely effective) bureaucratic apparatus. The Nazis didn’t feel Trumpism’s need to strangle a “Deep State” that they saw as antithetical to their interests and goals. Bureaucrats were often prepared to go along with their program, and sometimes enthusiastic about it. In Government without Administration, Jane Caplan documents the incoherence and infighting inside the Nazi state, but the internal wars were usually waged through traditional bureaucratic communication channels such as memos.

There is still a lot of traditional bureaucratic policy making happening, even under Trump, but the key tools for coordinating top level policy aren’t formal bureaucratic documents. They are Signal messaging groups which we can’t (usually) see, and social media channels and cable/broadcast media, which we can.

The latter two are underestimated as a mode of policy coordination. There is a lot of discussion of the Trump administration’s intense relationship with social media. For example, this Politico article on how the administration tried to sell the Iran war:

A second senior White House official who is also closely involved in the video-making effort described it as a collegial, creative endeavor. “We’re over here just grinding away on banger memes, dude,” said the person, also granted anonymity to speak candidly. “There’s an entertainment factor to what we do. But ultimately, it boils down to the fact that no one has ever attempted to communicate with the American public this way before.”

But that is not all that is happening. “Banger memes” aren’t just being used to communicate with the public. They are being used to make and coordinate administration policy.

If you are trying to get Donald Trump’s attention, maybe you can just pick up the phone and get lucky. Even journalists sometimes manage this. But that isn’t going to work often, and may backfire badly if he doesn’t like what you are doing. So if you want to change public policy, you may instead want to do something that is highly meme-able; perhaps you actually meme it. That will be more likely to start doing the rounds, and maybe even attract the attention (and re-Truthing) of the Big Guy himself.

This is not “working towards” so much as “gooning towards.” For those who aren’t familiar with modern slang, ‘gooning’ is an Internet term of art for turning what Irish Catholics like myself once called the solitary vice into a highly sociable activity.

I am late middle aged and easily weirded out by what the young folk do these days - but there is a possible secondary meaning of gooning that is more deeply problematic. The Trump administration has become addicted to creating - and consuming - social media featuring masked and tattooed goons roughing up protestors, flexing biceps covered in politically sketchy tattoos, and dominating the libs. These days, Trumpism is all about getting off on a 24-7 flow of goon video content, which infects not just how the administration sells policy, but how it does it. The medium isn’t completely the message, but it absolutely shapes the kinds of messages that can, or cannot, be communicated through it.

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Putting the two pieces together, we live in an America whose influencers and officials are professionally obsessed with gooning towards the Führer. They desperately want Trump’s attention, so they can further their own aims and careers in an administration whose ordinary processes of policy discussion have broken down. The best way to get Trump’s attention - or just get ideas circulating - is through putting forward highly goonable proposals, or even directly gooning them up through creating memes, AI generated video, or highly misleading cable news hits of goons-crushing-protestors/immigrants/liberals.

Putting ICE agents on the line at airport TSA checkpoints, with or without masks? 1000% goonable! Who cares whether it will work or not. Gaming out whether Iran will block the strait of Hormuz before carrying out bombing strikes on its leadership? Boring!

Competitive gooning in a highly chaotic environment is a kind of factory for bad and self-defeating policies. If officials’ best way to advance their careers is by gooning it up for just one scatterbrained individual, they are regularly going to end up doing stupid things.

Take, for example, the harebrained idea to target Jerome Powell with subpoenas. As best as I can tell, Trump administration leaks that this did not originate from the White House are credible. The policy seems, instead to have come from Bill Pulte, a former memecoin influencer who got a mid-level administration position. Pulte discovered that he could get Trump’s attention by targeting his enemies with bogus investigations - but did not have the minimal political intelligence to understand that there are some enemies that You Do Not Go After, because they are too powerful and dangerous.

Such mistakes are likely to be endemic in a policy regime founded on competitive gooning. Furthermore, when officials come back with proposals for compromise, they will always risk being shot down by gooning rivals, or even by the Gooner-in-Chief. Compromises are not goonable unless they are done by the man himself, in which case they demonstrate Sublime Mastery of the Art of the Deal.

The more subtle consequence is that issues, problems and questions that are not goonable will become invisible. This isn’t always bad: some problems are sufficiently dull that the remaining bits and pieces of the federal government can continue to grind away at them in obscurity. But it is bad often enough.

To understand this, it is useful to consider traditional bureaucracy and goonability as alternative (and mostly incompatible) technologies of attention. Any political system faces the core problem of what it should pay attention to in an enormously complex world, and how it should pay attention to it. The US federal government, in all its creaking inefficiency, has many systems that are designed for just that purpose: to discover important problems, simplify them into abstractions that can be grasped, and to offer potential solutions. This is often dull and tedious work, but it is important. Much of it involves carving off the parts that don’t require top level attention to be dealt with in the middle realms of policy making or elsewhere.

The pathologies of Seeing Like a State - of failing to observe or understand problems that cannot be broken down into simple metrics and regularized categories - are very well documented. The state can’t easily coordinate its activities to deal with problems that cannot be expressed in terms that it understands. The pathologies of Seeing Like an Idiot are worse understood, because we haven’t had to think so hard about them. They don’t just involve ineptitude and blunders, but a nearly complete incapacity to see or talk about the much wider set of problems that can’t be expressed via goonposting. By and large, the abstractions of goonability carry sparser and less useful information than the abstractions of bureaucracy.

If you can’t goon it, it doesn’t really exist for the current Trump administration. There are some remarkably stupid people who occupy senior level positions in the administration. But I suspect that are many - perhaps quite a large number - who are not stupid in the same way. They can see the disasters that are coming for America and for themselves. But the breakdown of traditional policy making procedures, and the introduction of a new style of attentional politics make it extraordinarily hard - perhaps impossible - for them to mobilize or coordinate to do anything about it. Unfortunately, we are all being pulled along as well.

Hence I think that classic work on the Third Reich and the weirdnesses of modern Internet culture help us understand what is happening in the Trump administration right now. It is not that the Trump administration is trying to build death camps, let alone that it will succeed (its preferred authoritarian outcomes are more standard-issue), but that its general approach to policy coordination is similar to the one that produced the administrative chaos of Nazism. Similar but not identical: paying attention to the communicational inadequacies of a particular visual language of online memes and video clips helps to close the gap. What is happening right now then, is not working towards the Führer, but gooning towards him.

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