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 AI 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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A short post as an addendum to the previous, on an aspect of the fight between Anthropic and the Department of Defense that ought be clear but perhaps isn’t, because it doesn’t fit easily into the stated terms of disagreement. The important beef is not over whether Claude is going to become the overbrain for an army of T-1000s, marching in lockstep to advance America’s interests. It is over the bureaucratic uses of AI in war: both war fought abroad and war, perhaps, waged by the state against its own people.

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If you have any experience at all of the US Department of Defense, you will know that it is a labyrinthine bureaucracy, with its own complex interests. Paul Krugman asks sarcastically this morning whether US troops are supposed to flex their biceps at attacking drones. There is an important lesson behind this query. The “modern system” of war doesn’t depend on biceps, or even materiel, so much as it does on the complex organizational structures that allow assets to be deployed successfully in ways that reinforce each other. Logistics play an incredibly important role - if you don’t get stuff to roughly the right place at the right time, you are going to lose. A myriad of specific decisions taken by individuals need to cumulate properly. That all helps explain why the Pentagon has so much bureaucracy: even if it is inefficient in the specific; even if sometimes it is inefficient in the general, you can’t do without it.

That, in turn, helps explain why AI, including both general summarize-and-pull-information-together-and-generate systems like Claude, and more specialized systems for particular purposes are valuable to the Department of Defense. They potentially improve coordination. The “Management Singularity” is incredibly useful to large organizations that have a lot of information to manage. I was just on Jordan Schneider’s Chinatalk podcast (not up on Substack yet as far as I can see), talking with people who, unlike me, have direct experience of the US military. It’s hard to overestimate the advantages of LLM for carrying out tasks of organizational translation, such as semi-automating the stripping of sensitive sources-and-methods information from classified documents to be shared with allies.

Equally, there are things you ought worry about if these technologies are widely adopted. In actual war, you ought worry about target selection. See, for example, +972 Magazine’s account, based on disaffected sources from within the military, of how Israel used AI to decide whom to hit in Gaza.

During the early stages of the war, the army gave sweeping approval for officers to adopt Lavender’s kill lists, with no requirement to thoroughly check why the machine made those choices or to examine the raw intelligence data on which they were based. One source stated that human personnel often served only as a “rubber stamp” for the machine’s decisions, adding that, normally, they would personally devote only about “20 seconds” to each target before authorizing a bombing — just to make sure the Lavender-marked target is male. This was despite knowing that the system makes what are regarded as “errors” in approximately 10 percent of cases, and is known to occasionally mark individuals who have merely a loose connection to militant groups, or no connection at all.

AI classifiers - which is what Lavender clearly is - make a lot of sloppy mistakes. Slop means something very different when a system is designed to kill people based on incomprehensible and unreliable embeddings, than when it is designed to serve up ads.

So too for the deployment of AI to the home front, where parts of the US military could parse information on US citizens. This has direct consequences for the fight between Hegseth and Anthropic, which reportedly turned in large part on the circumstances under which Anthropic’s LLM could be used for “lawful surveillance of Americans.” So what kinds of surveillance are, in fact, lawful?

It is widely understood that e.g. the NSA is forbidden from deliberately conducting surveillance on US citizens, by Executive Order 12333. Past scandals (when the NSA e.g. was bugging the Reverend Martin Luther King) led to reforms in the 1970s and after. What is much less well known is that there are no strong legal controls that prevent the US military from purchasing ‘open source’ information that has been gathered by commercial providers.

There is an entire for-profit equivalent of the surveillance state that gathers data which it sells on to other businesses for targeted advertising and similar. And it doesn’t just sell on to other businesses. Government too - including some of the military parts of government - are reportedly enthusiastic customers.

This already presents dilemmas - government can potentially use this data to develop sophisticated profiles of US citizens with existing technologies. But LLMs can potentially greatly increase the abilities of bureaucracies to weave together different sources of data to provide a much more coherent picture of the individual and what they are doing. As Marion Fourcade and Kieran Healy describe it:

In the early days of the internet, being online brought certain freedoms. Not only was online anonymity or pseudonymity common, it was celebrated as a kind of liberation. Users embraced the opportunity to experiment with different versions of themselves. This multiplication of identities was a feature, not a bug. It also reflected the technical architecture of a less integrated internet, which gave participants what we might call ‘interstitial liberty’. This is the liberty granted us by the gaps between systems that will not or cannot efficiently talk to one another. It is a kind of negative freedom. If your gaming profile cannot easily be linked to your professional email or your forum discussions, you enjoy a form of privacy that depends less on explicit legal protections and more on the technical limitations of systems that are connected in principle but not integrated in practice. … Tools that recognise patterns, predict behaviours and detect anomalies can now work across previously separate domains.

These tools are still very imperfect, but that creates its own problems. Slop and error can be an integral part of the system. The dystopias we ought fear will be less like 1984 with its all seeing Big Brother, and more like Terry Gilliam’s Brazil, in which a bug caught in a teleprinter results in the wrong person being targeted and tormented.

This, it seems to me, captures the logic of the fight between Anthropic and the Department of Defense better than a lot of the commentary that I am reading. We should worry less about autonomous robots, and more about pseudo-autonomous systems embedded in bureaucracies that enable them to do things that they used not be able to do, but with a lot of slop.

I have taught a fair number of officers over my two decades in the Washington DC higher education nexus. I am very confident in their integrity and willingness to push back against the systematization of war crimes. I am, to put it more politely than I want to, less confident in the ethics and integrity of their current civilian leaders. You don’t need to agree with Dario Amodei on whether we are about to see the rapid deployment of “countries of geniuses in a datacenter” to worry about what an untrammeled Hegsethian wannabe-Department-of-War might try to do with this technology, or to believe that Anthropic did the right thing when it refused to cooperate (while wishing it had done more), or to hope that Amodei is right in speculating that this might spur some debate about where we have gotten to with these technologies, and where we might be heading.

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There’s lots that can and will be said about the battle between the Department of Defense and Anthropic. For those who haven’t been following, Anthropic says it won’t allow its models to be used for fully autonomous weapons or for mass domestic surveillance in the US.* The US Department of Defense (which currently identifies as a “Department of War”) insists that it will only contract with AI companies that allow “any lawful use” of their technologies, without reservations such as those that Anthropic has made. It has furthermore threatened that it will designate Anthropic as a “supply chain risk” and invoke the Defense Production Act to force Anthropic to create models without any safeguards for the US military.

It won’t surprise any of my readers that I don’t think that models should direct murderbots or power up the Panopticon. But I think that we also should pay attention to another, less immediate aspect of what is happening.

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One theory of the current era is that the interests of the Trump administration and a particular subsection of the Silicon Valley elite are fused so closely that the one cannot be distinguished from the other. Both sides after all are more interested in plunder than in liberal democracy. Another is that there are tensions - perhaps irreconcilable tensions - between them.

Obviously, Anthropic is not part of the pro-Trump Silicon Valley plunder squad. Still, if the Trump administration actually uses the Defense Production Act or similar measures against Anthropic, it’s going to mark a big shift in the political economy of state-private actor relations in the US which might endanger the looters more than they think. If Elon Musk or Alex Karp are at all capable of sober reflection,** they might realize that this change is likely not in their interests, regardless of whether the Trump administration loses or wins the next presidential election.

To understand this, you need to weave four skeins of thought together. First, that of Marion Fourcade and various of her co-authors, who have documented how private sector entities now dominate data gathering and supply, so that the state has become increasingly dependent on them. Second, that of Alondra Nelson, who explains how the Trump approach to AI is better understood as regulation based on arbitrary rule than deregulation. Third, Nikhil Kalyanpur, on how dangerous full oligarchy can be for oligarchs. And fourth, my own sort-of-addendum, which concerns what Democrats might do should they return to power.

Some side-notes: I know Marion, Alondra and Nik and will refer to them by their first names from here on in, since I would feel weird not so doing. Second - I thought through some of these issues together with the students in my class on AI and Democracy yesterday, since our week on AI regulation coincided with the sudden onset of the crazy. They deserve full credit should any of this be interesting or useful, and no blame if it is not.

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Marion’s ideas about data, the state and the private sector are expressed in half a dozen articles with various co-authors, and pulled together most succinctly in work that has yet to be published. So I’m not linking, but instead providing a likely imperfect summary of her and her co-authors’ contribution. For current purposes, the key thing to understand is that the game between the state and the private sector has changed. Once - and not too long ago, the state had much more and much higher quality data than private sector actors, and was much better capable of using it. Think e.g. about the work that the U.S. Census Bureau does, collecting data in tidy and well ordered (albeit sometimes problematic) categories and making it available. Now, the tables have turned. We live in a world where much much more, albeit much messier data is available, and the best tools for managing it and making it useful belong to the private sector rather than government statisticians.

This is one important aspect of a broader process of state transformation. The US government - like other governments - has increasingly contracted out many tasks that used to be core parts of state functioning to the private sector. This may or may not create greater efficiencies, depending (accounts vary). Undeniably, it hollows out state capacity and the capacity of the state to function independent of contractors. Governments find themselves increasingly incapable of carrying out even very basic functions without the help of private sector actors.

As government becomes more dependent, private sector actors become more powerful, whether or not they choose to exercise that power. Sometimes, the government may be able to shift responsibilities to actors it thinks more trustworthy: the Trump administration has done everything it can to shift certain government data functions towards Palantir, and away from Booz Allen, Accenture and other, more traditional contractors. Sometimes, it is nearly impossible to get rid of a contractor; their knowledge is so particular, and their systems so deeply integrated that they become effectively irreplaceable.

This is the dirty secret of the Trump administration push to both privatize government functions and increase executive authority at the same time. Extensive privatization of government functions and unitary executive theory are really hard to reconcile with each other, no matter how hard regime apologists try to claim that they go hand-in-hand. The more that the government relies on outside actors to carry out its core function, the more vulnerable it becomes to those private actors’ wants and desires, which may differ sharply from those of the administration itself.

That is likely a big part of the Trump administration’s discomfort with Anthropic. The push to discipline Anthropic comes from the need to reconcile its desire for top-down authority with its reliance on AI companies to carry through the large scale transformations that it wants to carry through. To reshape the public sector in its image, the Trump administration needs reliable private actors. But trying to discipline those actors may create its own difficulties

********

Unfortunately for the Trump administration, its proposal to ruthlessly crush Anthropic’s opposition carries its own problems. The immediate problems are legal: can the Trump administration actually deliver on its threats? Declaring Anthropic to be a supply chain risk would be a remarkably bold move: this is an instrument that was developed to target non-US (read: Chinese for the most part) firms that are directly loyal to another government. Courts might very plausibly take a skeptical view. The government is on somewhat firmer legal ground with the Defense Production Act, which allows the government to carry out very sweeping measures to press firms into service to provide products that the government wants, although Alan Rozenshtein at Lawfare suggests that the law could be interpreted in different ways, depending on what exactly the government wanted to do.

I want to ask a different question. What happens if the Trump administration can get away with this in the short term (courts move slowly, and are not good at dealing with facts on the ground) and perhaps in the long run too?

Here, it is useful to turn to Alondra’s analysis. She argues that it is a mistake to think of the Trump administration’s approach to AI policy as a kind of deregulation. Instead, she says, it is a kind of “hyper-regulation” that involves a “systematic preference for executive discretion over deliberative process.” In part this is done via “regulation through ownership;” acquiring stakes in businesses that provide a means for direct influence with little Congressional oversight. In part, this involves other forms of arbitrary caprice.

The Anthropic battle makes visible another kind of hyper-regulation: threatening enormous and potentially existential consequences for companies that don’t fully submit to the Trump administration’s demands. These threats are being made now because (a) Anthropic is visibly disaffected from the Trump administration’s political agenda, and (b) runs aspects of state functioning that are increasingly essential.

So one interesting question for the future is whether companies that fulfil condition (b) but not (a); that is, companies that are currently on board with the Trump administration’s ideology, but are essential to state functioning, ought worry? Should Alex Karp (Palantir) and Elon Musk (X/Space-X etc) be sweating? I think that the answer is yes: they absolutely should.

********

This is where Nik’s arguments come in. He suggests that Trump-friendly billionaires in America are making a “Faustian bargain” that is likely to bite them where it hurts. American oligarchy may not be a sustainable political equilibrium. As countries become more authoritarian, the options for billionaires become ever more tenuous and exigent. They find themselves increasingly subject to “billionaire discipline” and dependent on the whims of the state.

In essence, oligarchs risk vanishing—keeping your private income streams will turn you into a tycoon. Like Jack Ma, individuals that choose that path will live in fear of expropriation, intimidation, or regulatory suffocation. Or they could give up their independence, trying to turn themselves into kleptocrats, as we have seen Musk begin to do by relying more on government contracts for his income.

Even kleptocracy has its dangers. Kleptocrats who lose the regime’s favor are likely to find themselves expropriated, if they are lucky. If they are unlucky they may discover that plane accidents happen, people fall out of windows, and food poisoning can be rather more unpleasant than an uncomfortable 24 hours.

Obviously, American billionaires are still well insulated from the worst aspects of authoritarianism. But the enthusiasm of the Trump administration to threaten Anthropic with quasi-expropriation for not embracing its program 100%, suggests that they are not nearly as safe as they might like to be, and that this is especially true of billionaires like Musk and Karp, who run systems that connect to the vitals of government. Even the weird mixture of wannabe-dictatorship and democratic opposition that we are in presents them with real dangers. If Musk falls out with Trump again, what will happen to him? Just to the extent that he is increasingly reliant on government relationships, his business is increasingly at risk. It is not at all clear that it would survive.

The key point that Nik makes, as I read him, is that authoritarianism is not nearly as sweet a deal for well connected billionaires as they might like it to be. They swap the vagaries of democratic politics (the public may not like them) for the vagaries of court politics (people go in and out of favor all the time). And, as per Alondra, the latter is far less predictable than the former has been in the past. Billionaires could act against Biden without having to worry that they would be destroyed. They have much more serious worries about Trump, and the more that US democracy veers towards autocracy, the greater their worries should be. Russian oligarchs thought it was a great idea to help usher Putin into power. And indeed, some of them were right, sort of, so long as they were willing to knuckle when they were told to knuckle. For others, it turned out to be a rather unfortunate error.

There is a very obvious corollary to all of this. What if the Defense Production Act is invoked, but Trump and his cronies are weakened later this year, and kicked out of office in 2028? What possibilities might the Act offer, say, to President AOC?

Any post Trump administration is going to face the problem of disentangling the government and the political economy of the United States from the sleaze, corruption, self-dealing and autocratic measures that are piling up. Sweeping authorities to reshape the relationship between the state and private sector could make some aspects of that task much easier.

I think a lot about this conversation between Colin Kahl, then Biden’s Deputy Secretary for Defense, and Elon Musk, about Starlink access during the Ukraine war

The Pentagon would need to reach a contractual arrangement with SpaceX so that, at the very least, Musk “couldn’t wake up one morning and just decide, like, he didn’t want to do this anymore.” Kahl added, “It was kind of a way for us to lock in services across Ukraine. It could at least prevent Musk from turning off the switch altogether.”

Typically, such a negotiation would be handled by the Pentagon’s acquisitions department. But Musk had become more than just a vender like Boeing, Lockheed, or other defense-industry behemoths. On the phone with Musk from Paris, Kahl was deferential. According to unclassified talking points for the call, he thanked Musk for his efforts in Ukraine, acknowledged the steep costs he’d incurred, and pleaded for even a few weeks to devise a contract. “If you cut this off, it doesn’t end the war,” Kahl recalled telling Musk.

What would that conversation have looked like if the Defense Production Act had recently been deployed by a different administration to force a major tech provider to supply what the government wanted it to supply? What would negotiations between a Palantir that has become deeply embedded in the US government’s core systems and a putative Democratic administration in 2029 look like if the administration could just dictate terms? Perhaps Musk, Karp and their underlings have not thought through the implications, but they could be far-reaching.

To be absolutely clear: I do not want to imply that it would be a good thing to see the Defense Production Act invoked against Anthropic. I am deliberately focusing on just one set of relationships and possible implications under different scenarios, because I think they’re being overlooked in the debate right now. Even if you are in favor of empowering President AOC a few years down the line, you might want to think about the more immediate consequences. Nor do you need to believe in the case for strong or weak AGI to worry about how the Department of Defense might use these technologies. As Jack Shanahan suggests, you ought be more worried rather than less if you think these technologies are imperfect.

I am trying to weave Marion’s, Alondra’s and Nik’s work together towards the much more modest purpose of explaining why the tensions we are now seeing are not just about the specifics of the relationship between Anthropic and the Department of Defense. They highlight more general contradictions in the Trump coalition between executive-authority enthusiasts and Silicon Valley oligarch-wannabes. It may be possible to patch over these contradictions, but it will take some effort and skill, and the more that the Trump administration deploys arbitrary instruments of quasi-expropriation against tech companies, the more quickly the contradictions will widen. One path leads to Silicon Valley oligarchs becoming kleptocrats, increasingly vulnerable to the whims of the Commander in Thief. The other leaves them far more exposed to a future administration that can more forcefully chop away the roots of their power over government. Each would be its own kind of cage.

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* Anthropic seems to possibly be OK by implication with using its tech to facilitate US mass surveillance of other democracies? That raises some questions.

** I know, I know.

There is a reason I don’t accept paying subscribers for this newsletter (although I am grateful to those who offer to pay). I want to be free to use terrible headlines like the one I’ve used for this post, without feeling even slightly conflicted about it.

I spent three years as the editor of a political science blog that was hosted at the Washington Post. We spent a lot of time thinking about how to write catchy and seductive headlines that would get people to read posts about social science.* The common wisdom about how best to do this shifted, as different social media platforms grew or fell (we were pretty well out of the game by the time that social media stopped driving traffic entirely). Preferred headlines got shorter as the metrics started suggesting that they worked better: the newspaper imposed a character cap for headlines that we all tried to adhere to.

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One thing remained constant. You did not want to have any academic jargon whatsoever in your headline if you wanted to attract eyeballs. And you absolutely did not want to have a headline that juxtaposed two unfamiliar pieces of academic jargon, so that they could duke it out over which was better suited to drive punters away.

We knew how many readers each post got, and struggled to make the numbers as good as we could get them, while trying somehow to hold onto the truth that we weren’t, in the end of the day, primarily interested in the numbers game. It was what we had to do to justify our existence as a small ideas shop hitchhiking a ride on a much bigger publishing enterprise. Hence, when I started writing a newsletter, I decided from the beginning that it was going to be my newsletter. It would talk about the things I wanted to talk about in the ways I wanted to talk about them, without any profit model tugging me to juke the numbers by writing about the topic of the day or sanding away the weirdnesses of my writing style. I’m somewhat startled that so many people read it. My writing for it is deliberately idiosyncratic, discursive, even meandering.

Hence too, my unashamed love of C. Thi Nguyen’s new book, The Score. It is a book that is about metrics (like viewer numbers, though I don’t recall him citing those in particular) and how they define not simply our lives but our very selves, if we carelessly let them. It is a book about pizza. Also: weird yo-yo tricks and the zen-like states that accompany them. Also also: climbing, on which there is lots. Also also also: drunken cooking competitions. And that is just for starters. It is a book that absolutely ought not work, for the same structural reasons that bumblebees ought not be able to fly. The aerodynamics are all wrong. But good god, does it fly. The achieve of, the mastery of the thing! I would not have believed that a book about metrics could be a joyful and delightful book. The Score not only manages that extraordinarily difficult trick, but makes it look easy.

The Score, then, is about very many things, but it is not about electoral politics. This post explains why I think that its lessons travel to politics, in much the same way that Brian Eno’s ideas about music have nothing to do with democracy, except that they absolutely do.

In particular - and finally we begin to get to the meat of the post - electoral political strategy is increasingly driven by metrics and related simplifications. This has many advantages. It forces people to put their pet theories of what will work and what will not to the test, and makes it easier for everyone to coordinate around the same approach and message. But it has substantial drawbacks too, along the lines that Nguyen suggests. If everything gets reorganized around the metric, then all the important things that the metric hides are likely to rear their heads and devour you. Metrics are lossy abstractions of complex wholes. As Maxim Raginsky puts it, “abstraction hides a great deal of complexity from view, and this is both its main virtue and its primary peril.”

So first, I want to talk about a fight that is happening right now within the Democratic Party that conceals a more fundamental conflict about metrics and abstractions. Then, I’ll explain how the median voter theorem fits into this fight, and how it has become what Nguyen calls a “clarity trap.” Then, finally, I will talk about ways to maybe escape that trap and find something better.

*****

The electoral strategy of the US Democratic party mainstream is all about the main virtues of simple metrics. The perils that this ignores are the root cause of much unhappiness right now. Some of that spilled out into a forum in the Boston Review last week. Two prominent political scientists, Adam Bonica and Jake Grumbach, argued that the Democratic Party’s focus on milquetoast moderation was leading it astray. There were lively responses from those on the other side, including Matt Yglesias, who has argued that the Democrats need to moderate, and that left wing excesses explain Kamala Harris’s defeat.

This is one episode in an ongoing dispute with many rejoinders and counter-rejoinders about methodological particulars, “wins-above-replacement” metrics and the like. And it is not just an arid statistical squabble. Matt has written occasional spicy social media posts about how he is finally coming to understand the basic dishonesty of academic political science, while those on the other side, broadly considered, make their own mordant comments about the idiocy of pundits, and the economic incentives of pollsters who work regularly with the party.

While this fight seems superficially to be a back-and-forth over which are the best statistical instruments and abstractions to capture public opinion, it is bitterly fought because it maps onto a more directly political dispute over whether the Democratic party should turn to the left or to the center.

What I would like to see people paying real attention to is a third disagreement, which lurks beneath both the statistics and the politics. Should the Democratic party be sticking to tried-and-tested techniques (based on the standard metrics and assumptions) for attracting voters, or should it be trying new stuff out? At the moment, this fight very loosely maps onto the other two, because moderates are (a) the faction that would most visibly lose if the party starts experimenting with new ideas, and (b) are intellectually tied to a broader metric-driven approach that is shared across much of the business world, technology, and, perhaps weirdly depending on how you think about them, sports.** After Bezos gutted the Washington Post last week, he issued a statement defending the cuts that had been made on the theory that “the data tells us what is valuable and where to focus." That theory of what we ought pay attention to guides a lot of competitive activity these days.

But as Nguyen points out in the book, data often conceals as much as it tells us. As he describes the problem elsewhere, .

The basic methodology of data—as collected by real-world institutions obeying real-world forces of economy and scale—systematically leaves out certain kinds of information. Big datasets are not neutral and they are not all-encompassing. There are profound limitations on what large datasets can capture. … Data collection techniques must be repeatable across vast scales. They require standardized categories. Repeatability and standardization make data-based methods powerful, but that power has a price. It limits the kinds of information we can collect. … an overemphasis on data may mislead even the most well-intentioned of policymakers, who don’t realize that the demand to be “objective”—in this very specific and institutional sense—leads them to systematically ignore a crucial chunk of the world.

If you are already convinced that you understand how the world works based on a combination of the data and a few simplifying intuitions, you are going to be disinclined to experiment to find things out that you don’t already know. If you are the kind of cook who sticks mechanically to a particular recipe, you may produce good meals, but you are going to miss out on other, perhaps great meals that you could discover by messing around. If you live in a static world and don’t mess around, you are going to remain stuck at a possibly inferior local optimum, when there are much better possibilities out there that could be discovered by doing things that deviate from the ordinary (as Nguyen’s stories about the strange discoveries of drunken cooking illustrate; you can think about throwing ingredients together when you are half loaded as a kind of simulated annealing if you really have to).

And if you are in a rapidly changing environment, then things might be much, much worse. Your insistence on looking at the world through fixed metrics may make you incapable of seeing the transformations that are happening around you, but that get filtered out by the metrics that you think are important.

That, plausibly, is what is happening now with a largish chunk of the moderate wing of the Democratic party. I, myself, am a lefty, but from a broader political perspective, the problem with Democratic moderates right now is not that they are moderate. It is that they are defining their moderation in ways that depend on metrics and simplifying notions that (a) are increasingly out of sync with the environment, and (b) tend to preclude experimentation.

I think that the Democratic party would be in a significantly better place if the moderates became experimentalists. That is, it would be great if moderates moved decisively away from the position that the metrics tell us what we need to know, and treated them as valuable but limited tools of inquiry for grappling with a world that is vastly more complex than any metrics can plausibly capture, and started trying to explore those complexities in different ways. That is not least true because there are likely things that moderates can discover, if they are so inclined, that lefties (and conservatives) cannot.

I don’t think that the debates between the left and moderates would necessarily be any friendlier if they were disagreeing over the best ways to experiment to attract voters and create compelling policies, but I do think that they would be more likely to result in interesting and unexpected discoveries than what we have now: interminable disagreements over whether the statistically maybe-sort-of-measurable-if-you-squint-in-a-charitable-way rewards of moderation are a .5%, 1% or 2% greater share of the vote.

One way to get moderates moving in this direction might be to get them to move away from the “median voter theorem,” a particularly beautiful mathematical abstraction that has become what Nguyen calls a “clarity trap” for moderates: hence my incomprehensible seeming headline. But what is the median voter theorem, and why is it so weirdly politically important?

*****

The median voter theorem is, as the box says, a theorem. That is, it is a proof that if you start from certain mathematical assumptions, then certain conclusions follow. It is, furthermore, a really neat theorem. Its mathematical virtues do not explain why it has become politically important, but it is helpful to have at least a summarized version and simplified version of what the theorem actually says.

The median voter theorem starts in economics rather than politics, and its most broadly influential version is the one in Antony Downs’ book An Economic Theory of Democracy. It is in broad form a particular application of Harold Hotelling’s ideas about market convergence.

The bowdlerized version of Hotelling’s account goes as follows. Imagine two shopkeepers that want to maximize sales, but both have to locate their store on the same street. They know that customers are likely to frequent the shop that is closest to them. That means that the shop that is located just a little bit closer to the middle part of the street will get slightly more customers than the one that is further away. Like many influential economic models, this one has an equilibrium, a final state that rational economic actors will converge on. The rival shop owner will have an incentive to relocate their own store a little closer to the center, to grab back some customers. This then plausibly leads the first shop to move too. Eventually, the two shops will end up side by side, bang in the middle of the street. That is the equilibrium where there is no possible better move that an actor can make.

For the Median Voter Theorem, imagine a version of this model, where instead of shops you have political parties, instead of customers you have voters, and instead of a street, you have a single ideological dimension on which the parties and voters locate themselves. Again, you assume that voters vote for the party whose position on this one-dimensional continuum is closest to them. And much the same thing happens. Over time, parties too are likely to converge towards the central point, so that each is in its best possible position to maximize vote share. The Median Voter Theorem then makes a simple prediction. Rational parties, if they want to win elections, will necessarily pursue the median voter right at the center (according to one measure) of the political spectrum. Whoever attracts the median voter will win a majority. Hence, parties that want to win ought become centrists.

If you treat this as a mathematical theorem, you might want to think carefully about the many mathematical assumptions that lie behind this rather lovely result. Voters’ preferences need to be well behaved in certain ways (“single peaked”). More pertinently, it has to be possible to collapse the entire space of political issues into a single dimension of political contention. If there are two dimensions of political contention, things get a lot messier (although there are still some regularities). If there are three or more dimensions, then anything goes (social choice theory has various `chaos theorems’ that say that outcomes will be unpredictable). Finally, voters’ political preferences need to be fixed in advance. Parties cannot persuade voters under this model. They can only adapt themselves towards what the voters want. This is a static universe - the only dynamics are the ones that conduct voters and parties to the single predictable equilibrium.

If you forget the assumptions and treat the median voter theorem instead as a guide to party strategy, it is incredibly attractive to moderates. The median voter theorem entails that there is one weird trick to winning elections: always move towards the center. If you are a rational politician, you always need to asking yourself (and the polls), what the voter right at the center is looking for. If you give the median voter what they want, you will win. You don’t have to worry about voters on the left (if you are a Democrat) or the right (if you are a Republican). They will vote for you, because you are the party that is closest to them, even if they think that you are a centrist sellout. You should absolutely ignore what various identity groups are telling you about what to do - their advice is at best misleading and at worst treacherous and dangerous.

If you take this idea to its logical conclusion, you don’t even need a political party. All you need are accurate measures of public opinion, built on survey data that captures voters’ fixed preferences, and politicians who are willing to respond to it in the ways that their rational desire to win office dictates.

Of course, nobody - or nearly nobody - actually takes these ideas to their logical conclusion. But some moderates come a lot closer than you might imagine. I’ve seen one well-known Democratic ‘strategist’ (a term that covers a multitude of sins) describe the median voter theorem in terms that seem better suited to a recent, urgent personal religious revelation about the true nature of the world. They are young, so I am not naming them. And it is not hard to see why they feel so strongly. When you are inclined towards centrism, and find a simple, clarifying theory that tells you why your understanding of politics is not only right but inevitably right, it is difficult to resist. Add a panoply of surveys, and some other useful simplifications as auxiliary hypotheses and you are set up in business for life.

Furthermore, as per Raginsky’s dictum, the median voter theorem is a useful abstraction under many circumstances. It allows politicians to focus on strategic issues that are plausibly quite important. Pushing towards the center is indeed, quite often, an electorally useful strategy. The theorem, in its pop-culture form, furthermore serves as a valuable disciplining mechanism, to help prevent politicians from wishcasting the public that they would like to have into existence, instead of dealing with the public that they actually have. Finally, it helps build party discipline, getting a variety of disparate actors to concentrate on the same thing, rather than heading off in a million different and mutually contradictory directions.

Equally, as Raginsky suggests, these virtues can become perils. They can conceal a lot of dangerous complexity that you really ought be paying close attention to. Or as Nguyen might describe it, using a different but related vocabulary, they can make it far harder for you to see the world around you in all its glorious weirdness.

That is what Nguyen is getting at when he talks about “clarity traps.” When an idea - whether it be a weird conspiracy theory, a seductive social science result, or a beautiful graph - seems to simplify an altogether-too-complex world, it may give you the sensation of sudden understanding, of having the scales fall away from your eyes so that you suddenly arrive at the single true understanding of the world. That sense of epiphany (a word descending from the Greek word ‘to show’) is not always altogether a bad thing. Beautiful ideas are often beautiful because they do carve the world at its joints, or at least closer to the joints than we have hitherto been able to achieve. But this loveliness can betray you if you mistake the apparent clarity for the undoubtedly messier truth. As Nguyen puts it;

So here is a recipe for a seductive clarity trap:

First, build a belief system that offers a satisfyingly clear, coherent explanation of the world.

Second, make sure the belief system conceals any evidence of its own error.

The median voter theorem, and a set of closely associated ideas have become a clarity trap for Democratic moderates. They offer a simple, clear explanation of what the Democratic party always ought do. Moderate! Move to the center! Figure out what the median voter wants, and do just that! And they also offer a means of concealing errors. Whenever Democrats fail to win elections, it is definitionally due to their failure to observe this universally sound advice. Very obviously, they have been listening closely to the groups and failing to pay attention to rigorously conducted opinion surveys, which are the true and proper barometer of what the public wants.

There are other diagnoses that seem to me more plausible. Public opinion is not, actually fixed in the ways that the median voter theorem suggests, even if they are not nearly as protean as people on the left might want them to be. The more that political parties rely on metrics and other simplifications, the more they are likely to be blindsided by voters whose wants and ideas are not readily captured by simple measures.

Most radically (and this has implications for left-leaning political scientists too), we are living through times of upheaval in the underlying structures of democratic politics. In such times, all our instruments for identifying causal relationships will become less helpful, because some of the causal relationships we are most interested in are likely to be undergoing rapid ferment. Even the most accurate photographs of unpredictably moving targets may not be that useful for very long.

The deep problem of clarity traps such as the popularized version of the median voter theorem is a side effect of their attractiveness. Exactly because they are so attractive - they tell you that you are right goddamit, and there is objective proof of it - they are extremely difficult to escape.

*****

One way to conclude this essay might be to claim that moderates are wrong and that people to their left have the right of it. And there is a particular and temporary way in which this is true. In their essay, Bonica and Grumbach not only wallop the Median Voter Theorem, but say the below:

The reality is that electoral politics has entered an era of profound volatility, one when yesterday’s certainties become today’s mistakes. We are not making a general case for running to the left instead of to the center but for dispensing with outdated conventional wisdom. Instead, we favor experimentation and exploration. Embracing these requires expanding our sense of possibility and the range of our explorations, partly by paying close attention to what has worked in other countries that have faced democratic backsliding.

That seems to me to be absolutely on the money. But the lesson I personally take from it is not that moderates should abandon their values and priors. Those priors are likely to very often be quite useful. Many members of the public are likely more comfortable with moderate views than lefty ones. Some parts of the country are likely much more moderate than others. Perhaps (and on some issues probably) there are much easier national majorities to find based on moderate appeals than immoderate ones.

Instead, I think that moderates should put far less trust in their preferred simplifications and metrics. The median voter theorem is politically attractive to them because it reflects their political priors so perfectly, making it very hard to give up. Again: this also explains why it is liable to be a particularly vicious clarity trap. Who would not prefer to have the world clarified in ways that suggest that everyone who does not converge on their own preferred political philosophy is stupid, wicked or dishonest? It is in the nature of clarity traps that they continue to seem compelling even as they draw you ever closer to the brink of the abyss. It is really hard to escape this kind of trap, especially if you have built your identity around it. But it can be done, and doing it opens up new possibilities.

I think that there is a lot of room for moderates to start engaging in experimentation with different political approaches that are less reliant on opinion polls and median voter assumptions. Here, I particularly like a recent Daniel Schlozman essay that argues (as I read it - maybe wrongly) that it is the left that ought to start paying more attention to the kinds of institutionalization and partisan hardball that normie Democrats built up before, and ought start trying to build again under different conditions. A program to build up a normie Democratic party that was actually a real political party, connecting politicians to voters, would be a great start.

A second approach might be to look to the lessons of the evangelical movement. Ezra Klein got a lot of grief from people on the left for saying that there was something right about what Charlie Kirk was doing. My interpretation (again maybe wrong) of Ezra’s core intuition is that the Democrats too need to start evangelizing as Kirk did, by getting out and talking to people who they don’t agree with and don’t usually talk to. Again, there are a lot of ways in which some moderates are better positioned to experiment with doing that than people to the left (although left economics also maybe provides some useful starting points).

A third is that moderates should be stealing in quite different ways from the left than the ways that they are doing now. This gets back, in a weird, backhanded and not necessarily very intellectually coherent way, to something that my brief account of Nguyen’s book mostly misses out on. The Score a genuinely joyful book, about the delights and surprises of everyday life. It is striking that few people would deploy “joyful” as an adjective to describe the Democratic party’s way of thinking and communication. That of course reflects the real grief, anguish, anger and frustration of the world around us. But it also reflects bad habits of carping, begrudgery and misdirected spleen that plague all of us.

It would be great to try to break these habits, and I think that it is still, occasionally possible to find weird and surprising joy amidst it all. The most valuable transferrable lesson of Zohran Mamdani’s victory and early governing style may not be the talking points about “affordability.” Instead, it’s the capacity to discover delight in being outstaged by kids at press conferences, to admit fallibility, to experiment while admitting that things don’t always work, to build connections and to try to draw new constituencies in. I don’t see any fundamental reason why moderates shouldn’t be able to work with some version of that style of discovery and communication just as well as lefties, and I suspect that it would get them much further, and to more interesting and unexpected places, than doubling down on technocratic measures.

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* Or at least, start reading them. If you ever want to get depressed about the reading public, ask a newspaper executive how many people actually finish reading an article after clicking it.

** There is a great essay to be written about how Nguyen’s ideas about games intersect with “Moneyball.” This is not that essay.

[David Byrne playing “Life During Wartime” live, Washington DC, September 28, 2025. Author’s photo]

I did the Ezra Klein show last Friday, and it went up on the NYT website this morning. A whole lot has happened in the meantime. The way I think is through talking with other people, and a lot of thinking happened in the conversation. It wove together what happened in the world last week with what is happening in Minneapolis, in ways that I am still trying to work out. So here is a short interim report, written less as a polished essay than an attempt to pull these thoughts together.

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What became clearer to me, as Ezra and I talked, is the connection between the limits to US power in the world, and the limits to the Trump administration’s power inside the borders of America. We briefly mentioned a long-ago fight that I had with the late David Graeber, who advanced a theory of world politics in his book, Debt, that described the global economy as a tribute system, and emphasized the awesome power of the United States to terrify the rest of the world into submission. Back then, I disagreed with Graeber’s claims and Graeber took strong exception to my disagreement, provoking a very long response from me. The upshot of my argument was that the United States is incapable of pulling what I called the “Delian League Switcheroo.” Thucydides describes how 5th century BCE Athens transformed its alliance against the Persians, the Delian League, into a protection racket to squeeze allies and turn them into vassals. I argued back then that the US would find it very hard to do this at scale:

The US ability to intervene abroad is limited both by financial costs, and by difficulties in maintaining domestic political support. This suggests that the US power to intervene militarily abroad is far more qualified than Graeber thinks it is. The current world order can very reasonably be described as an empire. But it is not an empire of crude coercion where the US can call all the shots, based on its military capacity, or where other countries can expect military intervention if they e.g. stop denominating important stuff in dollars, or fail to pay their debts.

In fairness to Graeber, the Trump administration’s consistent policy over the last year appears to be to make his nightmare vision come true. The administration clearly does want to turn allies into vassals. But as we saw last week, it doesn’t have the power to do this. When it wants to intervene in one part of the world, it has to forego opportunities to intervene in others.

Ezra and I also discussed Thucydides’ Melian Dialogue - ‘the strong do what they will, while the weak suffer what they must’ - which Mark Carney quoted at the beginning of his speech. Trump-sycophants, catchfarts and sneaking regarders like Niall Ferguson like to cite this speech as evidence of the eternal verities of international power politics. But as Seva Gunitsky says here, Thucydides used it instead to illustrate Athenian folly. Athens’ hubris was clobbered by nemesis, when its expedition to Sicily failed, resulting in the destruction of hundreds of ships and the slaughter or enslavement of thousands of hoplites.

Domestic politics too was corrupted across the entire Greek city state system, as everyone took sides in a struggle between soi-disant democrats and soi-disant fans of oligarchy that fused warfare between city states with domestic factionalism :

Words had to change their ordinary meaning and to take that which was now given them. Reckless audacity came to be considered the courage of a loyal supporter; prudent hesitation, specious cowardice; moderation was held to be a cloak for unmanliness; ability to see all sides of a question incapacity to act on any. Frantic violence became the attribute of manliness; cautious plotting a justifiable means of self-defense. The advocate of extreme measures was always trustworthy; his opponent a man to be suspected. … even blood became a weaker tie than party, from the superior readiness of those united by the latter to dare everything without reserve; for such associations sought not the blessings derivable from established institutions but were formed by ambition to overthrow them; and the confidence of their members in each other rested less on any religious sanction than upon complicity in crime.

From Athens to America: does any of that sound at all familiar? Mothers and nurses become “domestic terrorists” when they are gunned down in the street. Hesitation to crush the “organized illegal insurgency” is a sign of weakness. Hatred of moderation, exaggerated manliness, extreme measures, destruction of institutions. I could keep going on, and on, and on, but I suspect that most readers know it already. The parallels are clear enough.

The Trump administration’s domestic hubris seems to be running aground, after the killing of Alex Pretti (which happened after the show was recorded). I don’t believe that the Trump administration is going to back down any more than it absolutely has to. Still, the limits to its power to escalate are becoming clearer. When confronted with large scale peaceful resistance, the administration finds that it is not nearly strong enough to overwhelm. The weak do not need to suffer so long as there are enough of them and they can get organized.

To summarize the joint claim of this week’s and last week’s posts, the Trump administration faces the same problems with its opponents at home as its soi-disant allies abroad. Its advantage is that it is much more powerful than any of them individually. Its disadvantage is that it is much too weak to crush them all at once. The administration’s powers of intervention are limited. When it tries to bully Europe into giving up Greenland, it discovers that it has to back down when it faces united opposition. ICE and CBP are perfectly capable of disrupting the life of a mid-sized American city. They are not capable of controlling it if the population pushes back.

That does not mean that the victory of the allies, or of protesters in Minnesota and around the country is assured. Collective action - whether among countries or people - is always hard, given differences of interest and belief. There is a lot that the administration can do, and will try to do, to pick off defectors. And both at home and abroad, there is a vacuum of leadership. If counteraction does not become institutionalized, it eventually becomes exhausted. Even so, there are possibilities to work with.

Finally, as Ezra emphasized in the conversation, visible moral degradation is a turn off for many people. I responded by drawing on the political science:

Susanne Lohmann, a political scientist, wrote this classic article on this. She argues that the Leipzig protesters seemed like normal people — good, decent people you would like to have as neighbors. The East German propaganda is that these are evil, weird freaks, that these are dissidents, they’re scruffy, they’re whatever. And it’s the fact that these look like normal, ordinary people that actually make this powerful.

So I think what we’re seeing in Minnesota is we’re seeing ordinary people. It’s very clear that the people who are organizing, the people who are pushing back are neighbors. They are people who seem like very straightforward, very ordinary Midwestern people, people who are part of the community. I think that the killing of Renee Good … She is not a domestic terrorist under any reasonable definition … this becomes more and more of a weakness.

Bringing the political science is in part a personal protection mechanism: I find it very hard to be impassive or coolly analytical about what is happening, and talking about the scholarship rather than the beatings and killings helps me get through. But there is an analytic point nonetheless, which has been made even more clearly by the events of the last few days. Cruelty can sometimes turn out to be a weakness rather than a strength.

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Corrected to fix a naming error and two errors about Greek history. Thanks to Brad DeLong and Neville Morley for spotting and pointing out.

This post tries to combine a conversation I had yesterday (more on that soon) with a piece from Adam Tooze this morning (behind paywall) and two other pieces from Adam and Paul Krugman over the last couple of days. We’re all trying to figure out what exactly happened at Davos, and I think that there is a very useful book that might help explain it. The book is Michael Chwe’s Rational Ritual: Culture, Coordination and Common Knowledge. It’s a game theoretic account of why ritual is important.*

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First, Adam and Paul. In his newest piece, Adam talks about the two alternative theories he had of what would happen at Davos this year. One guessed that it would be irrelevant; the other saw it as the place where capital might come together to coordinate against Trumpism.

These two particular theories map onto broader accounts about how Davos (a yearly meeting of very rich and very powerful people, with various hangers on and the odd academic or expert here or there for ornamentation) works. The ‘it doesn’t matter’ argument is closely compatible with the many journalistic and critical accounts of Davos that see it as an empty ceremony, in which people come together to boom out the shared collective wisdom at volume. The ‘capital might coordinate against Trump’ argument maps onto a different, but not entirely incompatible account of Davos as a place where the people with the money exercise their clout over politicians.

But as Adam says, neither is a good explanation of what happened this week.

The weight of Larry Fink and BlackRock added to the attraction of the WEF itself. It secured a truly remarkable turnout of capital of all kinds - asset managers, banks, hedge funds, PE, tech, industry from all over the world. … What was no less striking, however, was the collective public silence of this array in the face of the performance of the MAGA delegation. … The most plausible interpretation is not that this silence implies tacit approval, but rather fear of retaliation and victimization by the administration.

In his earlier post, Adam uses visceral language to describe what that feels like.

I remember the evening before, the stony-faced CEO warning me: “Be clear. Don’t be surprised. When he comes through the door … They will beat up on you. You will squeal. Then they will beat up on you again. You will hurt some more. They don’t mean to kill you. In the end you will settle on a spectrum of terms that they dictate. This is how it works. Time you understood it.”

Yet as he says, something unexpected happened. Adam explains this in terms of convening, staging and acting. You bring a lot of rich and powerful people together, you use media to build a stage that the outside world can see and is likely to pay attention to, and you call on politicians to be actors on that stage. That all creates an opportunity for politicians to go off script, as they did.

As Paul said separately:

Donald Trump and his team clearly went to Davos determined to demean and insult their hosts. It was, one might say, a novel approach to diplomacy: “You’re pathetic, your societies and economies are falling apart, now give us Greenland.” And it worked about as well as you’d expect. Trump may have imagined that the Europeans would cower in the face of his wrath. Instead, they humiliated him. He dropped his latest tariff threats in return for a “framework” that gave the United States essentially nothing it didn’t already have — and left behind a Europe that is finally united in resistance to his bullying.

You could see this shift happening in real time in the public statements of Trump administration officials. So what exactly happened, and how important is it?

This is where I find Chwe’s arguments to be extremely useful. I take two lessons from his book. First, that Davos fits very clearly into his definition of `ritual.’ Second, that rituals are important because they create common knowledge.

What we have seen at Davos over the last few days was an effort by the Trump administration to create new common knowledge in the world, an agreement that Trump was in charge, and that politics revolved around him. That effort has failed because of pushback from politicians, both Europeans who were furious at Trump, and Canada’s prime minister, Mark Carney who gave a quite extraordinary speech. However, the result is most certainly not a decisive victory for Europe, Canada, and the other forces allied with them. Instead, it is one significant moment in a longer story of struggle and contention.

Chwe argues that rituals are about creating coordinated expectations, and that this is why they are often an exercise in power. He quotes Clifford Geertz on ‘royal progresses’ - journeys undertaken by monarchs and their entourages through their countries and hinterlands, which are in large part about creating a shared understanding of who is in charge.

Royal progresses . . . locate the society’s center and affirm its connection with transcendent things by stamping a territory with ritual signs of dominance. . . . When kings journey around the countryside . . . they mark it, like some wolf or tiger spreading his scent through his territory, as almost physically part of them.

But Chwe qualifies Geertz’s evocative metaphor. He suggests that the exercise of power is less about the the royal progress awing the peasants, than the peasants realizing that other peasants are seeing the same thing, and being publicly awed by it. It is not the tiger’s musk, so much as the knowledge that everyone is smelling the musk at the same time that is important.

Our interpretation focuses exactly on publicity, the common knowledge that ceremonies create, with each onlooker seeing that everyone else is looking too. Progresses are mainly a technical means of increasing the total audience, because only so many people can stand in one place; common knowledge is extended because each onlooker knows that others in the path of the progress have seen or will see the same thing. That the monarch moves is hence not crucial; mass pilgrimages or receiving lines, in which the audience moves instead, form common knowledge also. Under our interpretation, widespread ritual signs of dominance do not by their omnipresence evoke transcendence but are rather more like saturation advertising: when I see the extent of a vast advertising campaign, I know that other people must see the advertisements too. This is quite different from the wolf analogy, if taken seriously: a lone animal knows to stay away from another’s area by smelling the scent at a given place; no one perceives or infers the entire scent trail (for that matter, scents keep away rivals, whereas progresses are for “domestic” consumption).

Rituals often take place in consecrated places. British kings are crowned in Westminster Abbey. They also often take place at a particular time of the year (see churches and organized religion, passim). So it is not at all a stretch to see the Davos meeting as a ritual that is held in the same overcrowded place at much the same time every year. Like many rituals, its boredom and its ceremony go hand in hand. For many years, Davos’s most obvious social purpose was to reinforce the consensus about globalization, in predictable ceremonial language. Its very dullness and lack of surprise was a side effect of its power.

That was then; this is now. I don’t think that it is at all implausible to see Trump’s planned descent on Davos this year as a version of a royal progress (see Stacie Goddard and Abe Newman on “neo-royalism”). Swooping into Davos, and making the world’s business and political elite bend their knees, would have created collective knowledge that there was a new political order, with Trump reigning above it all.

Business elites would be broken and cowed into submission, through the methods that Adam describes. The Europeans would be forced to recognize their place, having contempt heaped on them, while being obliged to show their gratitude for whatever scraps the monarch deigned to throw onto the floor beneath the table. The “Board of Peace” - an alarmingly vaguely defined organization whose main purpose seems to be to exact fealty and tribute to Trump - would emerge as a replacement for the multilateral arrangements that Trump wants to sweep away. And all this would be broadcast to the world. Adam’s combination of stage, convening and acting would provide a means to shape the collective understanding of a global audience that Trump was now in charge.

That, of course, is not what happened. First, the Europeans were finally pushed to the point where they pushed back. As Belgium’s prime minister put it, “Living as a happy vassal is one thing, existing as a miserable slave is another.”** It was clear that the Europeans were finally becoming willing to retaliate against Trump. That in turn had consequences for business.

As Adam suggests, businesses are unwilling to visibly step up to oppose Trump one on one. But businesses are not only individual participants in the ceremony. They are also members of a vast and depersonalized audience, via the anonymizing mechanism of the market, and, as Chwe suggests, it is the collective understanding of the audience that is most important. Just as the ouija board allows individuals to express their desires without being held accountable to them (thanks to the ‘ideomotor effect’) so too, the invisible hand of the market moves the planchette of stock prices in ways that no particular business can be held accountable for. When stock markets fall, even at the prospect of trade conflict between Europe and the United States, politicians pay attention. “Market fundamentals” (a loaded and problematic term) provided a very different understanding of the shared consensus than the one Trump sought to impose.

Second, Carney’s speech laid out an entirely different understanding of what was happening, and what had gone before. In his words:

Let me be direct: We are in the midst of a rupture, not a transition. Over the past two decades, a series of crises in finance, health, energy and geopolitics have laid bare the risks of extreme global integration. But more recently, great powers have begun using economic integration as weapons. Tariffs as leverage. Financial infrastructure as coercion. Supply chains as vulnerabilities to be exploited.

A family member joked with me that “it sounded like he was reading straight from Underground Empire,” Abe’s and my book (please go buy ! ) on how the integrated global world economy was weaponized. And that’s true, sort-of! Also, it is a rhetorically beautiful and well executed speech, in a way that politicians’ speeches rarely are (ask James Fallows, who knows political speech crafting from the inside). Its bluntness is the product of hard work and artifice.

But from Chwe’s more immediate perspective, what is more important than the vision of the past and future is where Carney said it and how he framed it. If you are planning a grand coronation ceremony, which is supposed to create collective knowledge that you are in charge, what happens when someone stands up to express their dissent in forceful terms?

The answer is that collective knowledge turns into disagreement. By giving the speech at Davos, Carney disrupted the performance of ritual, turning the Trumpian exercise in building common knowledge into a moment of conflict over whose narrative ought prevail. Chwe again, this time clarifying where he agrees and disagrees with James Scott.

A public declaration creates “political electricity” … But Scott’s main explanation is the same as ours, that public declarations create common knowledge: “It is only when this hidden transcript is openly declared that subordinates can fully recognize the full extent to which their claims, their dreams, their anger is shared by other subordinates.” When Ricardo Lagos accused General Pinochet of torture and assassination on live national television, he said “more or less what thousands of Chilean citizens had been thinking and saying in safer circumstances for fifteen years”; the openness and publicity, not the content, of his speech, made it a “political shock wave.” “In a curious way something that everyone knows at some level has only a shadowy existence until that moment when it steps boldly onto the stage”

This is why Carney’s speech was so remarkably efficacious. He wasn’t telling people anything that they didn’t know as individuals. He was, instead, turning that private knowledge into a putative collective understanding that countered the alternative collective understanding that Trump wanted to impose upon the world.

This is very much the way that dissidents think about politics, as Scott’s description of Lagos’s action suggests. And Carney very explicitly quotes Václav Havel to highlight the urgency and importance of disrupting the ritual.

In 1978, the Czech dissident Václav Havel, later president, wrote an essay called The Power of the Powerless. And in it, he asked a simple question: How did the communist system sustain itself?

And his answer began with a greengrocer. Every morning, this shopkeeper places a sign in his window: “Workers of the world, unite!” He doesn’t believe it. No one does. But he places the sign anyway to avoid trouble, to signal compliance, to get along. And because every shopkeeper on every street does the same, the system persists.

Not through violence alone, but through the participation of ordinary people in rituals they privately know to be false.

Havel called this “living within a lie.” The system’s power comes not from its truth but from everyone’s willingness to perform as if it were true. And its fragility comes from the same source: when even one person stops performing — when the greengrocer removes his sign — the illusion begins to crack.

So this, I think, provides a good integrated explanation of what happened at Davos; at least, it is the best that I can come up with. We should think about Davos as a site and moment of ceremony, in the terms that Chwe lays out, which cements common knowledge about who is in charge, and what the principles of rule are. That, in turn provided Trump with a possible opportunity to anoint himself as the central figure in a new vision of the West, in which, in Stacie and Abe’s terms,

a small clique [maintains] dominance in both material and symbolic goods. It thus rejects notions of sovereign equality and noninterference, and rests instead on the idea that a royalist clique is dominant, and will only recognize rival “great cliques” as peers; all others are unequal, and not due recognition.

The ceremony was disrupted by European threats of retaliation, which in turn led the market audience to express its unhappiness, and by Carney’s quite deliberate and self-conscious effort to crack the illusion of inevitability.

That does not mean that the Trump political project has been defeated. It is going to be very hard for Europe and Carney to build a viable counter-consensus. Already, Trump is looking to discipline Canada and seize back control of the narrative. What we have seen was a battle, not a war. But to appreciate the weapons that the battle was fought with, and understand the prize that was contended for, it is really helpful to emphasize the relationship between ritual and collective expectations. Chwe’s book is the clearest account of this relationship that I know of.

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* Non-rational choice sociologists may reasonably complain that they’ve been discussing this question for well over a century,. This is completely fair, but it is quite difficult for those who have not been initiated into the mysteries to read this work and understand why it is so important.

** “Happy vassal” has become a term of art in this debate.

My quick read on the incredible events of the last 24 hours is as follows. We don’t know what went into the apparent (and still ambiguous) pullback on the threat of invading Greenland. One plausible story is that the Trump administration did not realize that the Europeans were willing to come together and push back, and had to revise their expectations rapidly over the last couple of days.

I present: Bessent and Lutnick: a Farce in Four Acts.

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Act One: Europe Can’t Do Nothing to Stop Us!.

“Scott Bessent suggested the 27-strong group of nations’ slow decision-making would hamper its ability to put together a potent reaction or quickly wield the so-called anti-coercion instrument, its strongest trade measure. “I imagine they will form the dreaded European working group first, which seems to be their most forceful weapon,” Bessent told a small group of reporters in Davos, Switzerland, where he is attending the World Economic Forum.”

Act Two: Actually, Europe, We Don’t Want You to Escalate!

“What I’m urging everyone here to do is sit back, take a deep breath, and let things play out. As I said on April 2, the worst thing countries can do is escalate against the United States.” But he specified that “what President Trump is threatening on Greenland is very different than the other trade deals.” “So I would urge all countries to stick with their trade deals we have agreed on them,” Bessent added.

and

“Everyone, take a deep breath. Do not escalate, do not escalate. And President Trump has a strategy here.”

Act Three: Europe is Escalating But It Will All Work Out for America

Howard Lutnick, the US Commerce Secretary, projected calm, saying during a panel discussion that, “If we’re going to have a kerfuffle, so be it. But we know where it’s going to end. It’s going to end in a reasonable manner.”

Act Four: Exit, Stage Right, Pursued by Bears.

US commerce secretary Howard Lutnick was heckled at a World Economic Forum dinner in Davos, with European Central Bank president Christine Lagarde walking out during his speech. The gathering on Tuesday night descended into uproar after combative remarks from Lutnick, according to several people present, with widespread jeering amid appeals for calm from BlackRock’s Larry Fink, the host of the event and interim co-chair of the WEF. Lagarde was among the attendees who walked out during the speech, according to people familiar with the matter. This year’s gathering in the Alps has the theme: “A spirit of dialogue.”

But I also have a more structural read on Europe’s underlying strengths - and weaknesses - when escalation dynamics hit in the New York Times this morning. It focuses on the other side of Trump’s threats - economic coercion.

The fundamental message is that Europe needs to start thinking about political economy in a different way, drawing not on theories of market integration, but crisis bargaining from the nuclear era, if it wants to be able to push back against Trump (and China). I argue that:

The only way to maintain European independence is to escalate back. To do this well, Europe needs to incorporate ideas into its economic thinking that seem alien to a continent that prefers soft power to hard security strategies — deterrence, credible threats and escalation dominance.

Repeated submission has gotten Europe into a mess. To get out, Europe needs to commit to not back down.

Credible commitments and tripwires are the strategic concepts of Thomas Schelling, the Nobel-winning economist and national security thinker who died in 2016. Mr. Schelling’s ideas shaped America’s nuclear strategy in the Cold War. He saw proxy wars and threats of missile strikes as the brutal language in which the Soviet Union and the United States bargained with each other, each seeking political advantage while avoiding mutual nuclear annihilation.

The argument emphasizes how the anti-coercion instrument (see also Paul Krugman this morning) could be used to build leverage. It’s also worth reading a number of other people, starting from similar premises* to reach a variety of different conclusions.

There are two points that I wasn’t quite able to shoehorn into a NYT article that has word limits and is aimed at people who have almost certainly never heard of the anti-coercion instrument and may reasonably still not be sure why they ought care.

First, I suggest that the instrument can be thought of as a highly imperfect commitment mechanism but don’t explain why in any detail. The reasons have to do with the complications of EU decision making. How the instrument works is as follows (greatly simplified).

(1) The European Commission proposes an investigation into some other country that is apparently coercing the US. It can do this on the basis of a suggestion from the EU’s member states, or on its own initiative, but the politics suggest that it is unlikely to succeed unless it has substantial member state backing.

(2) It proposes a set of measures, which the member states can then vote up or down. To succeed, the measures need to get a ‘qualified majority’ (a weighted majority by number and population) of member states to agree to go forward. Member states can also come together to modify the proposals if they really don’t like them.

(3) The Commission then implements the measures until the coercing state gives in, or agrees to binding arbitration, or other less likely/relevant things happen.

The point is, then, that the process has some binding power over the member states once it begins. Individual member states cannot stop the measures from going through - they have to create a sufficiently large blocking minority (a minimum of four member states with sufficient population). That also means that an aggressive coercive power (say: the United States or China) can’t stymie action by getting a single member state (say: Hungary, which is close to Trump and also has a massive amount of Chinese inward investment) to refuse consent. Other instruments, such as economic sanctions, do require unanimous consent to go forward and are accordingly more difficult to deploy.

That is why I suggest that the anti-coercion mechanism is plausibly the best option to increase EU credibility if it wants to promise broad-scale retaliation against threats like Trump’s tariffs. Equally, it would be much more credible if it had greater binding force.

One of the most fundamental and useful points that Schelling makes is that there is often a stark trade-off between flexibility and control on the one hand, and ability to make credible commitments on the other. Making a credible commitment or credible threat (from a game theoretic perspective, they are much the same thing) involves binding yourself to do something in the future that might be painful or unpleasant, because it allows you to change other actors’ expectations about you today. I sign a contract to deliver a good, which stipulates that horrible things will happen if I fail to deliver, because this enables my customer to trust me enough to pay in advance. I station troops in West Berlin (Schelling’s example, which I use in the NYT piece) because they will die if the Soviets pour in, and this will oblige me to escalate and retaliate, perhaps to the point of precipitating war. The Soviets consider my credible threat, and decline to invade.

The implication, then, is that the European Union might be much more credible with the Bessents of the world if they could more readily bind themselves to take painful or difficult steps to counter aggression. However, the European Union’s member states often look at the problem differently. They are worried about delegating security power to the European Commission, for fear it will do something that hurts their economic interests or other national interests. The Schelling argument is that this increases their flexibility, but weakens their ability to demonstrate resolve against outside threats.

Other possible systems would lower flexibility but increase the Commission’s bargaining strength. If, for example, the member states had to reach a qualified majority to block the Commission from acting, it would greatly increase the risk that the Commission could ignore, say, the specific desires of Germany and its friends. But it would also correspondingly increase the credibility of the EU’s threat to retaliate against other countries that hurt it.

That suggests that the European Union might want to reconsider its priorities. The anti-coercion instrument is much less credible than it might be, because the member states worry that it will be used in ways that hurt their interests. If they want to increase their credibility ex ante in discouraging attacks, they are going to have to weaken their degree of ex post control, so that they are less tempted to back down when the going gets rough. That, in turn, lowers the chance that the going will get rough, because potential aggressors will see that the Europeans have bound themselves, and desist from attacking. This is one of Schelling’s most important arguments, and while it may seem counter-intuitive at first, it makes a lot of sense when you think about it.

This framework may be extremely dangerous if applied in stupid ways- one of the reasons that the US made such a disaster out of the Vietnam war is that it feared damaging its credibility if it withdrew. Still, the EU is some very considerable distance from even notionally being able to make such grievous mistakes.

The second point is that if we think about this in terms of escalation dominance, Europe has more options than it might initially seem to have. And this morning’s more conciliatory speech suggests that that Trump knows it.

Here, Paul Krugman has arguments from the economic side; I have ones from the strategic perspective. The first, which is mentioned in the NYT piece, is that the Greenland gambit is wildly unpopular among US citizens, and perhaps not enormously popular among Republican politicians either.

The most difficult point, which will likely arise again in other disputes, is that the ultimate doomsday weapon stems from America’s national security role as guarantor. The immediate risk is not that the US invades Europe, but that it withdraws support from Ukraine. That would be a disaster for Europe if it happened. Equally, it would be a disaster for the US on two mutually reinforcing fronts. It would precipitate a major crisis in transatlantic relations, causing likely economic crisis, as the stock market wobbles suggest. And it would mean that the US would suddenly lose its major hold over Europe, making Europe much more likely to start pulling out of the US technology stack, arming up even more quickly, and start using the actual economic leverage it has to hurt the US back.

This has implications for escalation dominance, which you ought think of in game theoretic terms. You look to the end-stage of the game, and see who wins and loses, then you reason backwards to see how this affects the ways in which actors ought behave (if they figure that they are going to ultimately lose if they play a belligerent strategy of always escalating, they will just not play that strategy at all).

If you are Europe, and you think of the end-stage as ‘the day that the US pulls out of Ukraine,’ then you may have strong incentives not to challenge the US, since it is going to be less hurt in the end than you are. If you are Europe, and you instead think of the end-stage as ‘the day after the day that the US pulls out of Ukraine, when Europe erupts and the US economy goes to hell,’ you may very plausibly revise your calculations, and be more willing to escalate.

It is clear from Trump’s speech that he backed down in part because of how markets were reacting to the Greenland dispute (even if he confused “Iceland” for “Greenland” when he was talking). Europeans should take note of this, and update their understanding of escalation dominance accordingly. Equally, if they want to be able to act strategically in a world that is much less friendly to them, they may need to sacrifice flexibility and member state control so as to enhance their credibility against outside threats. Other news today suggests the EU isn’t nearly there yet. It needs to get there, and soon.

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* It is a bit startling to see everyday commentary homing in on “escalation dominance” as a key concept.

Here are three things that are connected.

First, my sometime co-author James Evans dropped a banger a few days ago. James, Alison Gopnik, Cosma Shalizi and I were chatting via email about a lovely piece that Walter Frick wrote for Bloomberg a few days ago, which starts from our shared ideas about AI as a social and cultural technology. James mentioned in passing, as you do, that he and other co-authors had a forthcoming research article in Nature about how AI was changing science. The takeaway argument is that using AI (which they define as involving a variety of machine learning techniques) is great for scientists’ careers, but not so great for the broader scientific enterprise. That piece is now out.

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Second, by coincidence, there’s been a lot of conversation among political scientists about “academic slop” this week. Andy Hall, a political scientist at Stanford Business School, suggested that Claude Code would enable a single academic to write “thousands of empirical papers (especially survey experiments or LLM experiments) per year.” The very next day, he put his money where his mouth was, publishing an entire Claude Code replication of an earlier paper that he’d written, plus the prompts and other stuff, to Github. The result is a lot of nervous chatter about what the industrialization of social science might mean for academic publication and careers.

The third may seem at first to be the one of these things that is not like the others. It’s a piece I wrote myself a few weeks ago, which is mostly a repackaging of Cosma’s and Alison’s ideas, riffing on how sung Yugoslavian folk-tales from the 1930s do and don’t resemble the outputs of Large Language Models.* The upshot is that if you want to think of LLMs as a generative cultural technology, they are far from being the first such technologies that humans have come up with.

What I want to argue is that the third phenomenon is plausibly the glue that connects the second with the first. James and his co-authors suggest that older versions of AI are connected to collective pathologies in science. The Andy Hall Experiment is a specific micro-level instance of the way in which newer and different forms of generative AI present similar, and possibly much worse problems. But it is the Yugoslav folktales that join micro-level opportunities with macro-level pathologies. LLMs threaten to genre-fy the practice of science.

[Update: Kevin Munger has further arguments on the social sciences that start from a similar perspective to my own]

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James and his co-authors are interested in the natural sciences: physics, chemistry, medicine and the like, and how they work at scale. There is already a lot of worry among natural scientists about what is happening to their fields. In the news section of Nature I counted no less than three news pieces on the topic: “AI is saving time and money in research — but at what cost?,” “More than half of researchers now use AI for peer review — often against guidance,” and “‘I rarely get outside’: scientists ditch fieldwork in the age of AI.” The new piece is research rather than news, but it too suggests that there are reasons to be worried.

The article uses an early and customizable large language model called BERT to categorize over 40 million papers, identifying the ones that appear to have used AI/machine learning techniques (which have a wide variety of legitimate applications to data).

First, AI use seems to be really good for the careers of individual scientists. Scientists who use it are able to write a lot more papers, with less help from other human researchers. Those papers are more likely to be cited by others. Their authors are on average promoted more quickly. All these relationships are associational rather than causal, but they are both visible and important at scale.

The problem is that what is good for scientists may not be good for science as a whole. Papers that use AI are more likely to succeed, but apparently less likely to stretch boundaries. Evans and his co-authors deploy another bespoke AI model to measure how AI-aided papers shape knowledge production. They find that AI-enabled research tends to shrink scientific inquiry to a smaller set of more topical questions. Furthermore, the linkages between papers suggest that there is less vibrant horizontal exchange associated with AI. The authors conclude that:

These findings suggest that AI in science has become more concentrated around popular research topics that become “lonely crowds” with reduced interaction among papers, linking to more overlapping research and a contraction in knowledge extent and diversity across science.

So is this likely to become more of a problem as scholars use AI not just to interrogate data, but actually to carry out research, review other scientists’ papers and so on? To understand this it’s useful to step back a bit, and think about what science is supposed to be doing in the world.

The entire enterprise of scientific research is intended to produce and evaluate useful discoveries. Usefulness, of course, is subjective, and disputed, but few apart from the late Ted Kaczynski would condemn the entire enterprise wholesale. Unfortunately, while the delights and benefits of disinterested discovery are genuine, they are insufficient to keep the scientific enterprise going at scale. To do that, you need some set of social institutions that imperfectly reconciles individual self-centered incentives (I, a scientist want not just to find out about the world, but to have a great job and career, and the admiration of my colleagues) with the production of general scientific knowledge.

The gap between individual goals and collective benefits explains much of the workings of science. Publication pressures, peer review, competitive funding are all highly imperfect means to incentivize individuals to participate in the scientific enterprise, and to increase the chances that good work rises to the top.

So what happens when we add LLMs to the equation? To be clear, as best as I understand James and his colleagues’ research, it is not aimed at detecting LLM use but figuring out when researchers explicitly use AI tools e.g. for data analysis.

What LLMs plausibly do is to exacerbate already existing contradictions between individual incentives and collectively beneficial outcomes of interesting and creative research. This is where the Andy Hall Experiment provides a useful example. It provides a best-case scenario for the use of LLMs to enhance science: obviously, it is about the social sciences rather than the natural sciences, but I am pretty sure that the basics of datasets, packages and prompts carry over pretty well to a wide variety of fields.

The experiment is noteworthy in that there are collective as well as individual benefits to this kind of work. As Seva Gunitsky says, replication of existing results is (a) important, (b) unprestigious, and (c) a massive pain in the arse. Having Claude Code doing it instead usefully fills in some gaps in the existing scientific enterprise. Equally, the creation of an automated system that can churn out thousands of scientific papers sounds ominous. So too, the use of LLMs for peer review and a myriad other potential uses. But why should it be ominous? These, after all, are the kinds of industrialization and automation that have served us well in a myriad of other economic sectors. Are academics the equivalents of 19th century craftsmen, deploring the factories that are capable of turning out product at scale for putting them out of jobs?

I think academics’ worries are justified, but LLMS are probably not so much creating fundamentally new problems as exacerbating old ones. In particular, I suspect that LLMs are hastening the genre-fication of scientific research.

To understand this, it is useful to highlight three words in the Hall comments, which possibly gave rise to an instinctive shudder in some, though certainly not all of the political scientists reading this essay: “especially,” “survey” and “experiments.” My comments on survey experiments below are not only political science insider-baseball, but highly tendentious insider-baseball. All I can say in their defense is that they come from a place of genuine pain.

Survey experiments are opinion surveys in which you randomly assign respondents to different treatments (e.g. different question wordings, or different initial scenarios that may prime respondents to think about some topic in particular ways) to see if there are meaningful differences in their responses. They have become semi-ubiquitous in political science. There are a lot of publications that use this approach, but my sense is that much fewer of them are good, in the sense that they contribute in a genuinely significant way to collective knowledge of politics. They do, however, plausibly contribute to their authors’ chances of tenure and promotion.

Indeed, they are reasonable responses to the institutional incentives of the field of political science. Academics who want to land and keep good jobs want to get their work published in respectable journals. Editors and reviewers for such journals are often more inclined to reject than encourage submissions, because they get so many of them. Scholars, especially younger scholars, are desperate to figure out sure-fire ways of navigating the obstacles of a review process that seems purpose-designed to frustrate them.

Articles that employ survey experiments have a better chance of getting through this process than many other approaches. Political science is often a heavily lagging indicator of trends in economics, and economists have become much more interested in causal identification in the last couple of decades - isolating causal relationships to figure out what is actually causing what. Political scientists have followed suit, making it much harder for articles without a good story about causation to get published. The problem is that establishing causation is difficult, expensive and murky in the real world. Survey experiments do not tell you very much about the real world unless they are carefully done (some are!), but they do make it very easy to tell a story about causation (they are, after all, built around a treatment which may cause one response or another). The result is that low quality survey experiment articles have become the political science version of kudzu - an infestation of most-mediocre-output-that-is-potentially-publishable that threatens to take over the entire ecosystem.

So what does this have to do with AI? The way in which I would adapt Hall’s comment (this may or may not bear any resemblance to his own ideas) is that survey experiment articles have become a genre, for much the same reason that pop music generates genres. There too, myriads of desperate young people are trying to succeed in producing outputs that will prove acceptable to a fickle and inscrutable public. There too, when someone miraculously succeeds, everyone else will rush in to copy them. There too, successful methods tend to turn into replicable packages: specific beats, lengths, themes and vocals in the one; forms of presentation, methodologies, kinds of data and means of arguing for significance in the other.

And if there is one thing we know about LLMs, it is that they are machines for detecting and reproducing genres. Here, then, is where the Yugoslav folk singers described in Albert Lord’s The Singer of Tales get their due. LLMs are very much like the generative cultural systems that created these folk tales with their minor variations, processing textual material so that it hits the right linguistic beats, harks to the right tropes at the right times and so on.

There is a lot of speculation that LLMs are returning us to something like oral culture. There is rather less that engages in any very intelligent way with the particulars of how oral culture works. Oral culture, like LLMs, involves lossy abstractions that also serve as generative systems. It too produces myriads of variations on common themes, adapting them to particular prompts and circumstances. It too is indifferently geared for verbatim transmission of the work on which it has been trained. When Varshney describes Lord’s formulas as “heuristic solutions to constrained optimization problems that must be solved in real-time,” he is using language that Lord might perhaps have found peculiar (though also perhaps not; Jakobson was on his dissertation committee), but that Wolfe would readily have recognized.

Many academic articles too are “variations on common themes” that are adopted to particular prompts and circumstances. Is it any wonder that sophisticated LLMs like Claude Code are capable of replicating them en masse?

As I said back then:

many aspects of human work and culture involve broadly similar combination of templates and stereotypes to those employed by the singers of tales. I suspect that this helps explain the facility of LLMs in carrying out many programming tasks, since programming too involves figuring out how to apply a common formula to a particular problem. The poiesis of the programmer is closer to the heroic poiesis of the bard than we think. … And perhaps also for much of the practice of social science? Dani Rodrik has written that a great deal of the art of the economist consists in accumulating a large mental library of mathematical models, and building an intuitive grasp of which model one ought to use when.

I didn’t at all expect that this throwaway suggestion would become relevant so quickly!

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This, then, suggests a possible theory of what is happening. This is nowhere near a complete theory - there are plausibly lots and lots of specific micro-mechanisms jostling with each other to connect cause to effect. But I like it, perhaps only because it is my own.

Science is two things - a process of open-ended discovery and verification of those discoveries and an institutional system for employing the energies of scientists towards that process and compensating them. The two ought point in the same direction, and do, to some substantial degree. There is an awful lot of waste in the system, but it is impossible to eliminate some (the open-endedness is part of the point; apparently useless discoveries may cumulate into great things), and extremely difficult to eliminate others (many proposed cures are more damaging than the disease). There are always tensions, and there are disciplines and sub-disciplines (metascience; chunks of social epistemology) devoted to studying and perhaps partly remedying these tensions.

One way in which those tensions manifest is genre-fication. Interesting discovery is hard, unpredictable and often requires a lot of resources. Scientists across the hard, soft and social sciences would often prefer, as all humans would, to have a more predictable world in which they can land jobs. This gives rise, in turn, to tendencies towards genre-fication. When someone discovers a path through the kill-zone of peer-review, others will want to copy it, in they hope that they too will succeed in winning kudos and career success. This results in the creation of scientific genres - packets of techniques, methodological approaches and rhetorical claims that scientists adopt in the hope that they will prosper. And that opens up the way for technologies that are good at picking up on genre cues and replicating them.

People will reasonably disagree about the merits of specific scientific genres. As should be clear, I am skeptical about the merits of survey experiments in political science, but many of my colleagues may very reasonably disagree. And genre has value! Some coordination is necessary for science to work. But the overall effects of genre-fication are to winnow out some of the variety among scientists that produces unexpected discovery. That LLMs may create their very own new genre of social science articles that treat LLMs as a proxy for public opinion, generating outputs that become inputs? This only adds icing to the cake.

LLMs then, as they are currently employed by scientists, are likely to reduce diversity. Claude Code is plausibly still at the stage where it is good at doing replications, but not so great at assembling the package in ways that produce somewhat novel-seeming research. I suspect, as Hall does, that it is not far away from it. This will, however, rapidly accelerate the genre-fication of science.

LLMs are excellent at assembling outputs that match the requirements of particular templates - producing genre outputs. They are also very good at match-and-mixing genres. In contrast, they are remarkably poor at generating usefully novel outputs or recognizing novelty in the data they are trained on. Accordingly, the more that LLMs are employed in the ways that they are currently being employed, the more concentrated science will be on studying already-popular questions in already-popular ways, and the less well suited it will be to discovering the novel and unexpected. James and his colleagues’ findings identify an existing problem that may likely become much worse with the newer forms of generative AI that are rapidly reshaping science.

To be clear, this is not an inevitable consequence of the technology. To steal another analogy from pop music, Autotune has likely, on average, made pop music more bland, but it has also been used in weird and interesting ways to expand the range of things that you can do. The Nature article employs a basic LLM to make the scientific enterprise visible at scale in ways that would have been inconceivable fifteen years ago. But it is going to be hard to get to a place where the technology is better suited to serve the interests of science, rather than those interests of scientists that point away from discovery.

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* I do take credit - or blame - for the excursion into the Proustian science fiction of Gene Wolfe.

As I said when last I wrote a roundup post, in January 2025, this is a self-consciously idiosyncratic newsletter. I’m enormously grateful to the surprisingly large group of readers who have committed to pay for it, but don’t intend to take them up on their offer. As a matter of policy, I politely decline to engage in the kinds of reciprocity with other newsletters that can help build friendly relations, and I try to pay no systematic attention to eyeball counts. The reasons for these behaviors are largely self-centered. I want to write about what genuinely interests me, and to have a relationship with readers and other writers based on free exchange rather than implied obligation.

Hence, this too is a self-centered post! Rather than highlighting the pieces that got high readership or lots of feedback, I want to present the posts that from 2025 that I personally found useful to write. They build on the ideas of other people, but point towards a broader set of concerns that I am still trying to articulate in a semi-coherent way. So rather than dwelling on the bits that I liked about them, I will talk about how they contribute to what this newsletter is trying to do, how they fit into existing conversations, and where those conversations might go next.

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In order of publication date:

#1 - We’re Getting the Social Media Crisis Wrong

This was my first substantial post of 2025. It makes a simple claim, but one that I’m still trying to work out properly.

We tend to think about the informational crisis of democracy in individual terms, and to focus on individual solutions: training people e.g. to identify disinformation and misinformation. But this crisis is better thought of collectively. Rather than focusing on the frailties of individual citizens, we should be looking at the problems of democratic publics. Our publics are malformed in part because they build on and perpetuate incorrect understandings of what other citizens believe.

The implication is that we need to pay more systematic attention to the relationship between what might be called technologies of representation and publics. Publics do not magically manifest themselves in transparent ways - they are mediated through social and actual technologies such as voting, opinion polls, social media feeds, and, increasingly, soi-disant AI.

Here, I am riffing on the ideas of Hanna Pitkin (mediated through conversations with Nate Matias), Andy Perrin and Katherine McFarland and Kieran Healy and Marion Fourcade. Hahrie Han and I have written a piece on AI and Democratic Publics that begins to lay out a broader version of this argument. As Cosma Shalizi pointed out to me later, the newsletter does overemphasize the importance of individuals who are designing the algorithms that shape new publics: malformed publics are perfectly capable of building themselves without Elon Musk putting his thumb on the scale.

Actually doing something about all of this will require a much better understanding of how technologies of representation intersect with the ways that humans think. The post also builds on work that Hugo Mercier, Melissa Schwartzberg and I have done to sketch out an initial agenda for how to do this. Samuel Bagg has recently written an incredibly helpful overview of academic research and thinking about these questions that points in a broadly similar direction; see also his recent conversation with Dave Roberts.

#2 - Absolute Power Can Be a Terrible Weakness

I wrote this post on the train from DC to Baltimore, but was only able to do so quickly because I’d been thinking about it for years. It builds on the ideas of Russell Hardin, who was one of the great theorists of collective action, and also a scholar of David Hume (I strongly recommend his book on Hume).

Hardin has a valuable short essay on the relationship between power and social coordination. This, in turn, suggests a theory of the respective strengths and weaknesses of would-be tyrants and civil society in situations of democratic breakdown.

Hume proposes that tyrants too, depend on social power and influence. They want to create an impression of inevitability, in which everyone accepts that the tyrant is going to win, and have self-interested reasons to jump in on the side of the winning coalition. Civil society can coordinate against the tyrant, but coordination is really hard! The best way for civil society to coordinate is not to generate an expectation of inevitability, but a shared understanding that politics is in play, and that they can succeed in pressing back against incipient tyranny if they get out and do things together to push back.

I think that this was a useful essay in getting Hardin’s and Hume’s ideas out into the world. It also generated (with great editing and restructuring) a New York Times opinion piece that people have told me was helpful in generating a common understanding, although I think that would have come anyway, as people actually began to get out on the ground.

I’m not the right person to turn these loose notions into serious models, but I’m grateful to Filipe Campante for pointing to “global games” as the technique that might most plausibly allow you to do this. It might also be interesting and useful to turn this into an actual boardgame, perhaps like Root, which Thi Nguyen describes as a “completely asymmetric game” about political power struggle, “where each different position has totally different goals and totally different mechanisms.” That might help people understand the dynamics in a practical and concrete way, though again I’m not the right person to to do this.

What I can do, and hope to do more of, is to write about how Hume and other people who are usually treated as classical liberals, provide valuable lessons for the left, centrist liberals, and the actually-democratic right. I’m reading Laura K. Field’s book on intellectuals and Trump at the moment. One of the lessons I take from it is that the right’s unmooring from the ideas of people like Hume has had terrible and pernicious consequences. This has been especially visible in Silicon Valley, but has been important elsewhere. Loosely similar notions to Field’s led me to write this post on Ernest Gellner; further pieces on Gellner’s sometime-friend, sometime-antagonist Karl Popper, and on Gerald Gaus’s fascinating posthumous book on the open society and its complexities will be coming sooner or later.

#3- Brian Eno’s Theory of Democracy

This post comes at the problems of democracy from a different but complementary angle from the first two. It asks how we might think coherently about democracy as an adaptive system, and suggests that an essay by Brian Eno provides a very useful starting point, even if it doesn’t mention the word ‘democracy’ once.

The problem is as follows: that the kinds of democracy we have don’t seem to be working, either in representing people in satisfactory ways, or in responding to a world that is far more complex and threatening than existing institutions are geared to handle. Eno offers a set of design principles for music, which apply remarkably well to other forms of social organization too, including democracy. When you are in a complex environment, you want institutions that are capable of generating new experimental ways of doing things, and building on the variations that seem to work.

This is, as Eno explicitly says in the essay, a cybernetic understanding of design, along the lines proposed by Stafford Beer. Unsurprisingly, it fits well with Dan Davies’ ideas about why large scale organizations and modes of economic policy making are increasingly dysfunctional, which revive Beer Thought for the early-to-mid 21st century.

Dan describes how Allende, when asked what was the ultimate cybernetic control system for society, pointed back to ‘the people.’ Equally, as the social media crisis post suggests, Allende’s dictum begs the question of how ‘the people’ articulates its own views and perspectives. And so back to democratic publics. One way is to build entirely new approaches to democracy. Another is to take existing institutions - such as political parties - and ask how they can be made more adaptable and responsive in a changed attention economy. Something like this intuition pops up in this Ezra Klein and Chris Hayes conversation about Zohran Mamdani. Can you bring together an experimentalist approach to policy with a new kind of attention politics?

I’ll be writing more on this this year, both in academic form with Margaret Levi (we want to ask how democratic experimentalism and social science notions about the experimental method fit together), and in this newsletter. I think the fights between left and moderates in the Democratic Party obscure a more fundamental set of arguments about experimentalism, which I’d like to bring to the fore.

The fundamental motivation for writing about this is that people who talk about democracy are often not strong at understanding the problems of institutional design in a unpredictable world. That is not the body of ideas that they are trained to draw from or to contribute to. People who are actually trying to do stuff in the world are somewhat better, because they have to be, but not nearly as good as they might be if there was a more organized conversation. It would be great if we had a more explicit body of work, ideas and examples that people could draw on, and Eno’s pithy, lovely essay is one way to get people thinking in different ways than they usually do.

#4 - Understanding AI as a Social Technology

This is another effort to build coherent debate where it is lacking, riffing on the ideas of others (in particular, Alison Gopnik, James Evans and Cosma Shalizi). Earlier this year, we wrote an article arguing that we needed to think about AI (emphasizing LLMs, diffusion models and their cousins) as social and cultural technologies rather than agentic intelligence in the making. We felt that a lot of very important questions about social and cultural consequences were being left to one side, because they did not fit into arguments about When AGI Is Coming And What It Means.

This post provides a downpayment on what it might mean to think of AI as a social technology in particular. It suggests that AI is another social shock in the long series of shocks that are the Long Industrial Revolution. When we look at the Industrial Revolution, we tend to overemphasize the technologies themselves, and underestimate the social, economic, political and organizational changes that went along with them. That is likely a mistake.

The post is rather stronger on exhortatory statements about What We Must Do, Comrades, If We Are To Be Real Social Scientists than concrete proposals for how we ought do the things that ought be done. There will be a paper with Cosma Shalizi, sooner rather than later all going well, which is intended to provide a more concrete starting point for bridging the social sciences and computer science so that each can better understand the social consequences (and, indeed, nature) of AI. The paper will likely build on some version of the ideas of Herbert Simon, who managed somehow to make seminal contributions to AI, economics, administrative science and cognitive science, all at the one time. So more on this soon.

#5 - Large Language Models As The Tales That Are Sung

I happened upon the perfect illustration for this piece by accident. It’s a papercut by Hans-Christian Andersen, in which the themes of fairytales repeat and exfoliate, with tiny variations caused by the material and cutting. That aptly illustrates an argument about why LLMs more closely resemble cultural systems of production like fairytales than most technologists suppose. Albert Lord’s The Singer of Tales is the classic account of how such cultural systems of production work.

This certainly isn’t the article that brought in most readers, but it is the piece that I am happiest about having written last year. It helped me articulate things that I had wanted to say but couldn’t figure out how to. As with pretty well everything else I have written, it borrowed heavily from other people. The core intuitions about structure came from Cosma Shalizi and Leif Weatherby, filtered through the broader arguments about AI as a cultural technology that I’ve already mentioned. Others than Cosma and I have noticed the strong similarities between LLMs and the kinds of structures of story telling that Lord describes.

But there is also something deeper and more personal in there too. Gene Wolfe’s books have shaped me in ways I find difficult to describe; especially his Book of the New Sun. For years, I have been trying to articulate how his understanding of story (which is attentive to the problems of structure and predictability that LLMs raise) could help us think about new technologies. I’ve tried before and failed. This time, I feel that I’ve succeeded better, by putting Wolfe (a Catholic humanist who was also an engineer) in conversation with a kind of structuralism that doesn’t dismiss the importance of human intention.

I’d like more people to read Wolfe, and not just because I find current humanist debates about AI frustrating. He was one of the greats. I’d also like to see more AI enthusiasts pay attention to culture as generative structure, to broaden their theoretical vocabulary for understanding technologies like AI. I’ll try to write soon about this conversation between Tyler Cowen and Alison Gopnik, where Tyler, who is more interested in culture than most economists, nonetheless seems to me to radically underestimate its scope and importance, and hence to mistake what is Alison is saying about AI as a cultural technology.

These are the posts from 2025 that I found most useful for my own self-centered purposes. I write this newsletter to build a kind of nexus of conversation that draws together a small number of themes, ideas and people that I think are complementary and important. I’ve tried to explain how some of what I’ve written speaks to these themes, ideas and people, to pull them together and suggest ways in which the conversation might go from here. Thanks to all, and best wishes for 2026!

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Today’s post is brought to you by Donald Trump’s Truth Social comment on Rob Reiner. But first, a quick obligatory plug for my and Abraham Newman’s book, Underground Empire (a fantastic last minute gift for your loved one who wants to understand what in the name of god is happening to the world), and musings on Irish politicians, fairy forts and tattoos that will seem at first to be completely irrelevant. I think that they add up into a backhanded theory of why no-one ever describes Trump as classy,* and why this is both a strength and a weakness for him.

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First, the Irish politician and the fairy-fort. From way back in 2017.

Independent TD (HF - a TD is a member of the Irish ‘Dail’ or national parliament) Danny Healy-Rae has insisted a dip in a Kerry road which had been repaired before mysteriously reappearing is due to the presence of fairy forts.“There are numerous fairy forts in that area,” he said yesterday. “I know that they are linked. Anyone that tampered with them back over the years paid a high price and had bad luck.” Asked if he believed in fairies, the TD said the local belief – which he shared – was that “there was something in these places you shouldn’t touch”. These were “sacred places” and fairies were believed to inhabit them, he said. “I have a machine standing in the yard right now. And if someone told me to go out and knock a fairy fort or touch it, I would starve first,” said Mr Healy-Rae, who owns a plant hire company.

At first glance, this appears to be an absolutely mad thing for an astute politician to say in a modern country. And Ireland is a modern country. We save stories about fairies for casual experiments to discover what gullible American journalists will swallow, and occasional excursions into ‘Celtic mysticism.’ Danny Healy-Rae is a canny politician, as anyone who has spent time in Kerry will know. He spends a lot of time getting to know his constituents: he called in to my parents during the last election, even though he knew perfectly well that there were no votes to be had in that household. So why would Danny Healy-Rae want to say something that will surely lead a lot of people to laugh at him?

Second, tattoos. As the great sociologist, Diego Gambetta has observed, criminals in many Western societies have a striking enthusiasm for visible and offensive tattoos. He describes one prisoner who had “spit on my grave” tattooed on his forehead, and “I hate you Mum” on his left cheek. Why would criminals want to have tattoos that would prevent them from ever getting a regular job or being treated ordinarily by ordinary people?

Gambetta’s explanation is that such tattoos are examples of a particular style of strategic communication, which aims to win by dividing. As I will explain, the same is plausibly true both of Irish politicians warning about the dangers of fairy forts and Donald Trump’s entire political style. The problem is that politics isn’t just about communication, but about governing and making decisions that affect people’s lives.

As Gambetta argues, criminal tattoos can be understood in game theoretic terms as a costly signal. As I’ve written before, building on Gambetta’s arguments, trustworthiness is a big problem for criminals. As one wannabe criminal described the problem, in a delightfully vexed plea that I came across on the dark web when I was doing research on such matters:

I have been scammed more than twice now by assholes who say they’re legit when I say I want to purchase stolen credit cards. I want to do tons of business but I DO NOT want to be scammed. I wish there were people who were honest crooks. If anyone could help me out that would be awesome! I just want to buy one at first so I know the seller is legit and honest.

So how do you find honest crooks? Your strategy will vary, depending on which kind of honesty and reliability you are looking for. The scammer who will cheat you on credit cards may be different from the person who will turn you in to the cops.

As a very first step, if you want to find a genuine criminal to crime with, you might reasonably want to start with people who have offensive and visible tattoos. Precisely because they have taken a costly step that makes it unlikely they will reintegrate with ordinary society, they are going to be more reliable along some dimensions of trustworthiness (while possibly being wildly unreliable along others). That is Gambetta’s argument.

A whole class of signals aims to inform the truster that defection would be not so much unprofitable as impossible. This logic stresses the presence of constraints rather than benefits. If there are no ready-made constraints to display, there is still the option of designing some, of binding oneself in some way, of burning one’s bridges or tying one’s hands so that one’s partners know that one could not defect even if one wanted to. In terms of the basic trust game it amounts to persuading one’s partners that the option “cheat” just is not there, or is so infinitely costly thatit is not worth worrying about it.

Here, the logic is that of what game theorists call a ‘signaling game.’ Imagine (this is stylized) that you are trying to find the right people to do a criminal transaction with, and you know that there are two ‘types’ who you might encounter: narks who will turn you in, or ordinary decent criminals (ODCs) who will do business with you.

You might not be able to distinguish narks from ODCs on the basis of what they say - both types will swear blind that they are proper criminals. If you can’t distinguish the one from the other (which leads to what game theorists call a ‘pooling equilibrium’), you may decide that the transaction is just too risky. However, if it is much less expensive for ODCs (who are committed to the criminal life) to signal their type by irreversibly tattooing themselves than it is for narks, then the result may be a ‘separating equilibrium,’ in which you can easily distinguish ODCs from narks by their tats, and profitably do business with them.

Importantly, as per Gambetta’s argument, tattooing is an effective signal because it cuts off future options. After you get your tattoo, you are committed to staying as a denizen of the underworld, because you have cut off the option of reintegrating into the civilian economy.

This logic applies to politics too! Danny Healy-Rae’s publicly proclaimed belief in fairy forts can be understood as a costly signal to Kerry voters. Kerry is a rural and remote part of Ireland, which is often looked down upon by other Irish people. English people used to tell Irish jokes; Irish people used to tell much the same jokes as Kerry jokes.

When Healy-Rae professes the fairy faith in public, he knows that he is likely to be treated with scorn (as he was) by sophisticated Dublin commentators. But that is what he wants! He is making a costly signal, losing the respect of some so as to win the loyalty of others. He actively welcomes the contempt of the commentariat because this will secure his reputation in the eyes of rural voters; it is what makes his signal costly and effective. Healy-Rae’s constituents can trust that he will not go native in Dublin and come to look down on them, as other representatives might.

From this somewhat functionalist perspective, the fairy faith and tattoos are much the same thing. In both cases, the particulars of the signal are irrelevant. Nobody cares whether Danny Healy-Rae really believes in fairies, any more than they were interested in whether Gambetta’s tattooed prisoner had a profound and lasting hatred for his mum. Commitment, not content, is what matters.

So this gets us, in a very roundabout way (I’m Irish, and a commitment to lengthy and genial narrative indirection is my costly signaling device) to Donald Trump and Rob Reiner. I don’t think that I need to belabor the many ways in which Trump’s style of communication is the Danny Healy-Rae Fairy Fort Strategy played on a much grander scale. So much of Trump - his contempt for niceties; his love of burgers and delight in gaudy decorations; even his verbal incontinence - is a commitment to all the things that college educated elites and wannabes absolutely hate. And this contempt, as others have commented, generates a kind of self-perpetuating feedback loop that game theorists might characterize as type separation. The more that decadent elites like myself sneer, say, at Trump’s penchant for putting marble everywhere, the more straightforward it is for Trump to signal that he is on the side of all the people who don’t like decadent elites. Like a bizarro-world FDR, he welcomes our hatred.

Equally, there are serious drawbacks to Trump’s approach. Trump is visibly not a strategic thinker in the game theoretic sense of the word. He is incapable of modulating his signaling as circumstances suggest.

This likely marks an important difference between him and the likes of Danny Healy-Rae. Another of Healy-Rae’s communicative antics was to propose the introduction of a “drinking license.”

The Kerry TD said the closure of pubs in rural areas had “left the social fabric in smithereens” and the community trapped and isolated. He told Tánaiste [HF - deputy prime minister] Simon Coveney in the Dáil: “I’m asking you to provide a permit for the people who are only travelling on local rural class three roads so they can have their two pints and drive home on those roads. “If they stray beyond those roads then nail them, but give them a chance to live and give them a chance to try it. All work and no play makes Jack a dull boy.” But dismissing his call, the Tánaiste said Mr Healy-Rae seemed to be making the case that the way to keep pubs open is to allow people to drink and drive.

However, Healy-Rae almost certainly did not mean this as a serious policy proposal. It was total guff, purpose-designed to get a rise out of the likes of Simon Coveney. In the unlikely event that Healy-Rae ever became a member of the Irish government, he would not, actually, press for people in rural areas to be allowed to drink and drive home, because this would likely alienate many of his voters (drunk driving licenses would very quickly come to have visible downsides).

As president, Donald Trump would and has pressed for policies that are even more strategically short-sighted than drunk driving licenses. Unlike the canny Healy-Rae, there is no discernible difference between what Trump signals and who he is. There is a famous Mario Cuomo dictum that a politician should campaign in poetry and govern in prose. Donald Trump both campaigns and governs in the language of shitposts. His great tragedy is that this is all that he is capable of doing.

Trump is mostly uninterested in the content of policy; the signal and the dismay of his adversaries are what he cares about. That is great for sticking it to the man; not so great for moments where policy actually matters. And for many, and likely most people, policy does count when politicians are in government.

This finally gets us to Trump’s attack on the late Rob Reiner. It was mean-spirited, shitty, and almost certainly politically counterproductive. It didn’t obviously win Trump friends, and likely stirred up opposition, making it that little bit cheaper for unhappy members of his coalition to come out against him.

But such attacks are very much who Donald Trump is. He is the kind of person who will, almost inevitably, say things like that, even when it cuts against self-interest. He is incapable of being classy in public - of showing generosity to those who oppose him or who he feels have injured him. That can be a great advantage in winning over voters who feel screwed over by the prevailing compromises of politics, and don’t want someone who will make nice with the powers that be. Trump is absolutely committed to the bit. He is the type of politician who won’t deviate, because he simply can’t deviate.

Hence, Trump is reliably trustworthy to his constituents, in a very particular sense that is impossible for ordinarily strategic politicians to emulate. He will never suck up to the traditional power elite. As long as that is what you mostly care about, Trump has the advantage.

Equally, when he actually comes to power, this strength is liable to turn into weakness. You can be absolutely sure that Donald Trump will never become a narc for the traditional power elite - both native disposition and the larger system of social resentments that shape his understanding of America rule that out.

Still, you wouldn’t want ever to trust him to deal with you fairly if his interests pointed in the other direction, any more than you would any other heavily tattooed criminal. When his decisions shape your day-to-day life, that becomes a problem for you, and perhaps, increasingly for him, as it becomes more salient to voters.

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* To a Substacker’s first approximation. I’m not going to spend hours in Google searches trawling for disconfirming evidence, only to be trumped by some obsessive who finds the one shining example buried deep in an archived Truth Social posting.

I’ve spent the last couple of days in Italy at the Grand Continent Summit, a geopolitics meeting which takes the word summit seriously. It culminated in a trip to the Matterhorn glacier, 4,000 meters or so above sea level (I should be clear that this isn’t the kind of event that I usually get invited to). There was a lot of discussion, much of it skeptical, about the U.S.-Europe relationship, but no-one I talked to on the closing day had any idea of how wild the Trump Administration’s National Security Strategy would get.

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Back when I taught “Intro to International Relations," I always did a week on “grand strategy." Half the lecture talked about international relations theorists who dreamed of becoming the new George Kennan, drafting some sweeping and comprehensive approach to world order that would remake US foreign policy making for decades. The other half described the more mundane and important activity of crafting America’s National Security Strategy, a document that does its best to spell out a broad strategic vision, but inevitably gets pulled into the awkward realities of complicated issues, different factions in US politics, ally politics etc.

The Trump administration’s new strategy for the world is a kind of Groyper Grand Strategy Cosplay, which simultaneously purports to be a guide to specific policy.* It is set to fail, even by its own ludicrous and wildly offensive standards. As I used to tell my students, a National Security Strategy speaks to three audiences: the U.S. government itself; allies and friends, and adversaries. The new strategy can’t be coherently implemented by the first, will alienate the second still further, and will open up opportunities to the third.

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As mentioned, a National Security Strategy (NSS from here on in), sets the government’s national security priorities. New administrations have new goals and approaches to the world - each at some point comes up with its own NSS.

The NSS doesn’t have any binding force, but it is meant to serve an important practical use. The United States policy making apparatus is enormously complex, with many different institutions, agencies and departments which have some greater or lesser role in national security, but regularly get in each other’s way. That poses enormous challenges of coordination.

The NSS helps to mitigate these challenges by laying out the administration’s broad objectives, tradeoffs and preferred approaches, so that everyone has a rough sense of how policies are supposed to fit together. However, broad objectives and approaches don’t magically coordinate the government on their own. That is why the US has a National Security Council (NSC), which is primarily responsible for connecting the priorities of the president to the different parts of the government, building understanding across institutions and agencies, and banging heads together when heads need to be banged. Then, those institutions and agencies are supposed to do all the things that need to be done given these priorities, coming back and providing further information as needed. This is a gross simplification of an enormously complex cybernetic mess, but it gives the broad picture as I understand it.

The brand new NSS purports to be vastly clearer and more effective than its predecessors (which it describes as mere “laundry lists” of “vague platitudes”). Finally, after many false starts, America’s true national security interests have been discovered, heralding a new age of decisive national security policy making!

Or, perhaps, not. Laying out grand plans is not much help if the government is incapable of delivering on them. Who is going to make sure that these priorities get implemented? The NSC has been gutted in a series of ideological purges, egged on by those who interpret expertise and experience as codewords for Deep State sympathies. It is less than half its previous size, and even less able than that might suggest to connect priorities to process. Matters are even worse in the State Department, and the strategic decision making bits of major national security institutions aren’t doing great either. People are still fleeing and being fired. So too for the bits of government that are actually supposed to implement the detail of policy. All this is compounded by the president, who most likely hasn’t read the document, and will continue to do whatever the hell he feels like doing in the moment.

That all has big consequences, if you think through the implications. People blame the Trump administration’s foreign policy failures on bad ideas, self-dealing and inexperience. These indeed are important problems. But, as someone recently remarked to me, there is another, even more fundamental challenge. Even if the stupidity and cupidity magically evaporated, the Trump administration lacks the institutional bandwidth to execute the sweeping changes that it proposes. It has hollowed out the coordinating apparatus that the US government uses to set priorities and coordinate across the whole bureaucracy. Stuff still happens, but haphazardly. When underlings turn priorities into policy, they are likely to do so in different ways that may be contradictory or even mutually undermining. Sometimes this will be the product of sincere mistakes, and sometimes of deliberate misinterpretation, as different factions vie for advantage. There isn’t any effective NSC to manage clashes or ride herd.

The problems aren’t just within the US government. The NSS also gives allies and other friendly countries some sense of what to expect from America. That isn’t its direct purpose, but it is absolutely something that its drafters need to think about when they’re writing it. They know that the document will be read by other countries that want to figure out what US national security priorities are, and what their consequences might be.

What signals does Trump’s new NSS send to allies and potential allies? There is lots that could be said e.g. about the revival of the “Monroe Doctrine” (under which the US considers the western hemisphere its exclusive sphere of influence), and the “Trump corollary” of “enlisting” and “expanding,” whatever that is supposed to mean in practice (please don’t call these ‘vague platitudes’ - it would be rude). However, others are better suited to explain these questions than I am.

What I can talk about is Europe, having just sat through a couple of days of conversations among Europeans, and having listened to many similar conversations over the last several months. It has been clear for some while that the Trump administration has a … novel … understanding of America’s relationship with Europe. But it has not always been as clear as it ought be to European officials. These officials have often vacillated in response to previously unthinkable demands, sometimes making concessions, sometimes looking to preserve a little autonomy. Brief shocks (such as J.D. Vance’s speech at Munich) have not been sufficient to galvanize long term coherence.

I can’t say for sure that the NSS will do the galvanizing, but I think that it will push Europe a fair distance further along the road to resistance. The document soft-pedals America’s rivalry with China (the central theme of the first Trump administration’s NSS) while spitting malice and venom at America’s most established supposed allies. Its clear message is that Europe - as it is currently constituted - is a threat to U.S. wellbeing.

The National Security Strategy declares that Europe is not just in economic decline, but faced with the prospect of “civilizational erasure.” The “European Union and other transnational bodies” are undermining “political liberty and sovereignty.” Europe is riddled with “censorship of free speech and suppression of political opposition.” It is led by “unstable minority governments, many of which trample on basic principles of democracy to suppress opposition.” Most fundamentally, Europe is being turned into a zone of “strife” by migration policies, so that it will be “unrecognizable” in two decades. Certain NATO members will become “majority non-European”and no longer reliable allies. It doesn’t take much sophistication to decipher what terms like “majority non-European” are intended to mean.

However, the NSS says, America “cannot afford to write Europe off.” Instead, it will work to foster what it calls “genuine democracy, freedom of expression and unapologetic celebrations of European nations’ individual nations’ character and history.” America “encourages its political allies in Europe to promote this revival of spirit, and the growing influence of patriotic European parties indeed gives cause for great optimism.” To help all this along, the NSS says that America will undertake actions which include “[c]ultivating resistance to Europe’s current trajectory within European nations” and “[b]uilding up the healthy nations of Central, Eastern, and Southern Europe through commercial ties, weapons sales, political collaboration, and cultural and educational exchanges.”

This is, quite straightforwardly, a program for regime change in Europe, aimed at turning it into an illiberal polity. Accomplishing this transformation would involve undermining existing liberal governments in cahoots with Europe’s own far right, and turning Eastern Europe into an ideological wedge against its Western neighbors.

That all sounds horrifying: might it work? Theodore Roosevelt famously recommended that the U.S. should speak softly while carrying a big stick. Big talk and soft stick are likely less effective.

Again: the Trump administration’s capacity to turn rhetoric into concerted action is undermined by its self-created lack of bandwidth and capacity to coordinate policy. Nor does the Trump administration have the solid domestic support in the United States that it needs to make its threats and promises stick over time. It is electorally weak and looks, at least on current trends, to be growing weaker.

It probably isn’t a good idea to telegraph threats like a James Bond villain. Vacillating European liberal leaders are now less likely to be hesitant than in the past. The Trump administration has declared, in its defining national security document, that the EU and they themselves are a security threat to the United States. I suspect that this is more likely to build solidarity and resistance than to break it down.

We’ll likely see very soon whether this guess is right. The NSS depicts the EU as a threat to liberty, casting its restrictions on social media as censorship and oppression. The same morning that the NSS was published, the EU issued a preliminary finding that Twitter/X had breached EU law proposing to fine it 120 million euro. More findings and more fines are likely in the future.

Even before the finding was announced, JD Vance had condemned it. Elon Musk has asked that the US not only punish the EU, but the individual officials who were responsible for the decision. Will the US retaliate against the EU, and if so, how? If it does retaliate, will the EU back down, or will it stick to its guns? The NSS has likely lowered the odds of capitulation and increased the odds of resistance. So too has Trump’s decision to mostly back down from similar threats that he made against Brazil.

None of this implies that Europe is not in serious political and economic trouble. But the NSS likely undermines rather than bolsters the US ability to reshape Europe, which it would anyway find hard. Perhaps the US can help European far right parties a little. Alternatively, its efforts to help them might turn out to be counterproductive. Europe’s more serious political challenges are internal; if the US accomplishes anything it will likely be opportunistic, on the margin, and happen half by accident.

Finally, America’s rivals and adversaries will take lessons from the NSS too. The decision to target close US allies, rather than China or Russia, says a whole lot about America’s priorities right now. They are inward focused - the guff about civilizational collapse in Europe reflects the administration’s anxieties about the continued strength of liberalism within its own political system.

All this suggests that America isn’t going to pay much serious attention to the rest of the world for the next few years, except when it pays off for Trump and his cronies in very direct ways. The US government’s lack of available bandwidth may worsen as it gets mired down in some of the mistakes that it makes. When America does pay attention to the world, it will likely make bad choices, which may create sometimes create greater uncertainty and risk for America’s adversaries, but may also open up greater opportunities. Finally, America is now saying that key allies are in fact its greatest enemy. That gives those allies strong incentives to reduce their dependence on American power and technological and economic platforms, building closer connections among themselves and perhaps with others. All this is likely to the benefit of those who’d like to see America taken down a peg or three.

The Trump administration’s vision of American greatness is making the country poorer, weaker, and meaner. The new strategy document will do its own little bit to accelerate that process.

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* In fairness, George Kennan was not nearly as far from the groypers on race and democracy as his aristocratic mien might suggest.

[“A Fully Cut Fairy Tale”. Paper cutout by Hans Christian Andersen. From the collection at the Met, and remarkably apt for the post below]

When I wrote some months back about various ways of thinking about AI as a cultural technology, I left one approach out: humanism. This is a loose family of understandings that oppose Large Language Models on the grounds that they replace human culture with something that is machinic and alien. I couldn’t see a good way to reconcile this approach with the others that I discussed, which think about culture in different ways. Leif Weatherby, for example, is sharply critical of what he describes as ‘remainder humanism,’ which he sees as ignoring the large systems, most notably including human language, that actually produce culture.

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I’m intellectually sympathetic to Weatherby’s arguments; but I’m also sympathetic to certain flavors of humanism, and have been thinking about how to put the two in conversation with each other. Writing a piece that tries to do this has taken a while. These are not the kinds of argument I specialize in as a scholar. The weird intellectual path I’ve chosen leads through the science fiction of Gene Wolfe and the folklore studies of Albert B. Lord. I also take liberties with the ideas of friends and others, who bear no responsibility for any intellectual abominations that I commit. Caveat lector.

Both Wolfe and Lord died before LLMs became a thing. Nonetheless, both thought about the relationship between the immensities of language and human culture and the more particular desires of human beings to tell their own stories. I read their ideas as as implying that LLMs have much in common with the long existing traditions that storytellers draw upon. It is not simply that the compressions that LLMs derive from enormous corpora of text resemble, and arguably incorporate, the characters, stock phrases, tropes, situations and narrative structures that storytellers weave together. It is that LLMs and storytelling traditions are similar structures - large scale bodies of generic cultural knowledge that are drawn upon in somewhat predictable ways to create specific cultural instances.

These are not original claims on my part. Their proximate beginnings lie in an emailed side-remark by Cosma Shalizi about LLMs and The Singer of Tales, and his extended discussion of LLMs here. Kush R. Varshney independently struck upon the same comparison in this paper, which investigates the relationship between LLMs and Lord’s ideas from an engineering perspective. [Update: also Bill Benzon, David Smith, and, I suspect, others too]. I’m also influenced in ways it would be hard to set out by the work of my grand-aunt, Maire MacNeill. She was a folklorist who died too early for me to know well, but her writing and translations shaped my childhood; The Festival of Lughnasa, fairy tales collected from the oral tradition in West Donegal; the book of the storyteller, Sean O’Conaill, who lived on the same peninsula that John McCarthy’s people came from. More even than most children, I grew up amidst the cultural structures of folklore, without ever really thinking about what they involved.

LLMs resemble folklore in structure, but differ in their relationship to intentionality and performance. In one sense, stories are assemblages of tropes about third sons, enchanted rings, animal helpers and the like. In another, which is equally important, stories don’t exist until they are spoken and are products of the circumstances of their speaking. Their meaning doesn’t just reflect a shared tradition, but the particular circumstances under which they are told: an individual human speaking to other humans, snipping or stretching out the cloth of the tale as it is woven, to suit the desires, expectations and responses of his or her audience. In this, stories differ from LLMs, which instantiate the tradition itself, in its indifferent and inhuman nakedness. LLMs are not the singer, despite their apparent responsiveness, but the structural relations of the tales that are sung. Still, we can now listen to and even interrogate those structures without immediate human intermediation. That is new.

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Gene Wolfe is one of the great science fiction writers, though he is little known outside the genre. Ted Chiang cites him as an important influence. Ursula Le Guin compares him to Melville, who, like Wolfe, freighted adventure stories with theological and metaphysical speculation. Kim Stanley Robinson likens him to Proust, whose prose style and sense of time as a lucid dream was put by Wolfe to his own purposes. Wolfe has had little influence on debates over AI, unlike some of his lesser peers. Perhaps that should change.

Wolfe was an engineer, but his great love was for stories. As Severian, the imagined narrator of his great work, the five “New Sun” books, remarks after a story-telling competition:

it often seems to me that of all the good things in the world, the only ones humanity can claim for itself are stories and music; the rest, mercy, beauty, sleep, clean water and hot food (as the Ascian would have said) are all the work of the Increate. Thus, stories are small things indeed in the scheme of the universe, but it is hard not to love best what is our own—hard for me, at least.

From this story, though it was the shortest and the most simple too of all those I have recorded in this book, I feel that I learned several things of some importance. First of all, how much of our speech, which we think freshly minted in our own mouths, consists of set locutions. The Ascian seemed to speak only in sentences he had learned by rote, though until he used each for the first time we had never heard them. Foila seemed to speak as women commonly do, and if I had been asked whether she employed such tags, I would have said that she did not—but how often one might have predicted the ends of her sentences from their beginnings.

I’ll return to “this story” (told by an Ascian prisoner) below. For the moment, just note the tension that Wolfe identifies. Severian (presumably speaking for Wolfe) describes stories as something that is uniquely “ours,” uniquely human. Yet he also notes that they are composed of language that is predictable, a commingling of stock parts or “set locutions,” as is our ordinary language itself. Those locutions are joined together into sentences that are highly redundant.

As for language, so too for the tales we tell with it. They too are regularly built from stock parts, as Propp and others have argued. In an earlier book in the New Sun series, a child asks Severian to “find a story with a boy in it who has a big friend and a twin. There should be wolfs in it.” Boys, animal friends and twins appear regularly in folk tales, together with many other stock elements. One consequence of this is that folk tales can be recombined more readily than other narrative forms such as novels or written poems; they are mishmash from the beginning. The story that Severian tells the boy commingles The Jungle Book with the rescue of infant Moses, the rivalry of Romulus and Remus (who, like Mowgli, were suckled by wolves), and the founding of colonial America. In Severian’s far future earth, our stories and our history have commingled to become part of the common stock of collective human wisdom.

Misprision becomes a source of new variations. Another story recounted by Severian confounds the hero Theseus with a graduate student’s thesis, while the Minotaur and the steamship Monitor are merged into a fearsome giant with a “naviscaput,” roaming a maze of silted channels purpose-built to exhaust his prey. Wolfe resembles Joyce as well as Proust and Melville in his delight in wordplay and sometimes terrible puns.*

Yet this play is not completely open-ended. It builds on and from a system. Wolfe’s remark that “how often one might have predicted the ends of [Foila’s] sentences from their beginnings” is strongly reminiscent of the ideas of Claude Shannon. Such predictability is the core insight of Shannon’s predictive account of language, which in turn was the intellectual starting point for the development of Large Language Models. A “statistical model of language,” which makes the open-endedness of language (its particular version of the ‘curse of dimensionality’) tractable, combined with the particular affordances of the transformer architecture, powers the next-token prediction of LLMs.

Wolfe had great love for stories, but he was also an engineer. I suspect that the resemblance between Severian’s critique and Shannon’s ideas is the product of deliberate artifice. There’s no explicit evidence I’m aware of that Wolfe read Shannon (perhaps such can be found somewhere in his technical essays for Plant Engineering, which have never been collected), but elsewhere in the books Severian presents a theory of the knowledge of magicians that is based on signaling theory and lossy transmission.

Yet Severian does not love stories because they are a statistical extrapolation of underlying structures in language and culture. He loves them because they are human. So how do human stories emerge from vast systems, without being reducible to them?

*******

That question is explored in another extraordinary book, Albert B. Lord’s The Singer of Tales (available for free online). Lord built on the work of Milman Parry, who he had helped to record Yugoslavian singers of tales in the mid-1930s. From this experience and archive, Lord constructed a broader account of the oral tradition, which he believed encompassed Homer and other singers of epic histories. By extension, his argument also covers other forms of the oral tradition, such as the Irish fairy tales that my grand-aunt and others translated, compiled and sought to understand.

Parry and Lord listened to singers, whose tales described great heroes, villains and battles of the past (unsurprisingly, given the asperities of Yugoslav history, Christian and Muslim singers disagreed about who were the heroes and who the villains, though Christian singers could calibrate their stories for Muslim audiences and vice versa). The anthropologists recorded these songs on crude discs. These songs were the last whispers of a tradition passed down by illiterate singers and story tellers (although when Parry and Lord heard them, their singers were already beginning to be influenced by written culture). Some of the songs were very long, but accomplished singers could sing very many of them. How, given the faults and frailties of human memory, did they manage this?

The answer, according to Lord, lay not in rote memorization, but the nature of the oral tradition that they drew upon. While the singers themselves insisted that they could reproduce the songs perfectly and without error, their sense of perfection differed from ours. Neither the epic songs, nor the broader tradition they drew upon were unchanging texts. Instead, the specific epics they sang, and the epic form itself was better understood as a kind of “dynamic structure” that could generate new cultural instances.

This structure was a kind of ‘grammar,’ a set of rules that determined not simply the meter, but how the story flowed, which themes were emphasized and which suppressed, and so on.

In studying the patterns and systems of oral narrative verse we are in reality observing the “grammar” of the poetry, a grammar superimposed, as it were, on the grammar of the language concerned. Or, to alter the image, we find a special grammar within the grammar of the language, necessitated by the versification.

Singers constructed their tales around set “themes” or situations, such as the “council meeting” in which the possibility of battle is discussed. To describe these themes, they drew on a “common stock” of formulas they then deployed to reconcile characters, the style of the story, the expectations of the audience, and the meter of the song. Different singers had slightly different repertories of formulas, which might be greater or smaller, depending.

The singer never stops in the process of accumulating, recombining, and remodeling formulas and themes, thus perfecting his singing and enriching his art.

Singers might come up with their own variations, which could in turn be copied by others. All this perpetuated a tradition that was continually recreated in its own performance. In the oral tradition, epics are not fixed texts, but generative systems of songs, endlessly articulated, re-articulated and adapted in varying forms, according to the particularities of the singer and the circumstances they sing in. As Lord puts it: “we cannot correctly speak of a “variant,” since there is no original to be varied.”

As Cosma has pointed out, there is a remarkable resemblance between LLMs and Lord’s depiction of the oral tradition. That is because LLMs, like Lord’s singers, have mastered the tropes from which tales are made, but on the level of written culture itself rather than any narrative form or genre therein.

A huge amount of cultural and especially intellectual tradition consists of formulas, templates, conventions, and indeed tropes and stereotypes. To some extent this is to reduce the cognitive burden on creators: this has been extensively studied for oral culture, such as oral epics. …

The formulas make things easier to create and to comprehend once you have learned the formulas. The ordinary way of doing so is to immerse yourself in artifacts of the tradition until the formulas begin to seep in, and to try your hand at making such artifacts yourself, ideally under the supervision of someone who already has grasped the tradition. (The point of those efforts was not really to have the artifacts, but to internalize the forms.) Many of the formulas are not articulated consciously, even by those who are deeply immersed in the tradition.

Large models have learned nearly all of the formulas, templates, tropes and stereotypes. (They’re probability models of text sequences, after all.) To use Barzun’s distinction, they will not put creative intelligence on tap, but rather stored and accumulated intellect. If they succeed in making people smarter, it will be by giving them access to the external forms of a myriad traditions.

There is a lot of speculation that LLMs are returning us to something like oral culture. There is rather less that engages in any very intelligent way with the particulars of how oral culture works. Oral culture, like LLMs, involves lossy abstractions that also serve as generative systems. It too produces myriads of variations on common themes, adapting them to particular prompts and circumstances. It too is indifferently geared for verbatim transmission of the work on which it has been trained. When Varshney describes Lord’s formulas as “heuristic solutions to constrained optimization problems that must be solved in real-time,” he is using language that Lord might perhaps have found peculiar (though also perhaps not; Jakobson was on his dissertation committee), but that Wolfe would readily have recognized.

Again, it is a mistake to treat either oral epics or LLMs as fixed texts. From an analytic perspective, they are systems for producing particular cultural forms, which adhere to particular rules, stereotypes and expectations. They can be expected to be lossy in loosely similar ways. Lord notes that singers employ the “principle of thrift.” Once they have discovered a good technique for solving a particular class of textual problem (e.g. reconciling a particular kind of description with the expected meter), they will deploy it again and again. LLMs have their own principle of thrift, so that their compressions emphasize commonly encountered cultural patterns to the detriment of those encountered rarely in their training data.

As it turns out, LLMs are not very good at the rhyme and meter of language (perhaps since they are trained on written text rather than its heard performance).** But many aspects of human work and culture involve broadly similar combination of templates and stereotypes to those employed by the singers of tales. I suspect that this helps explain the facility of LLMs in carrying out many programming tasks, since programming too involves figuring out how to apply a common formula to a particular problem. The poiesis of the programmer is closer to the heroic poiesis of the bard than we think. As one of my old XKCD t-shirts puts it: stand back - I know regular expressions! So too for performance in math olympiads.

And perhaps also for much of the practice of social science? Dani Rodrik has written that a great deal of the art of the economist consists in accumulating a large mental library of mathematical models, and building an intuitive grasp of which model one ought to use when. Equally, there is reason to suspect that there are sharp limits to the capacities of LLMs to apply their compressions and transformations to unexpected challenges, and to aspects of the physical or social environment that don’t translate easily into spreadsheet form.

The fundamental point, then, is that a lot of human culture and endeavor does not just depend on lossy compressions, but involves dynamic systems that combine and apply these compressions in useful ways. These combinations may be surprising (involving unexpected juxtapositions), although they will likely have limited originality. Equally, originality may be unnecessary for many tasks, and even over-rated: Lord hints that our desire for artistic originality may be related to the anxieties of print and the other technologies of accurate production that have overtaken oral culture:

Expression is [the business of the singer], not originality, which, indeed, is a concept quite foreign to him and one that he would avoid, if he understood it. … There are periods and styles in which originality is not at a premium

Perhaps we might expect that in the near future we will return to some form of those norms of expression, to the extent that LLMs become culturally dominant. Perhaps instead, we ought anticipate a cultural reaction against them, doubling down on originality.

Whatever happens, Lord suggests that human culture does not have to be original to be human in the ways that writers such as Wolfe care about. Structural systems such as epics can produce possibilities for human meaning. As he says, “the style is not really so mechanical as its systematization seems to imply.” The tales would not work if they were mere jumbles of expected cliches.

Most importantly, stories are performed in settings that emphasize the relationship between the singer and the audience. When the singer sings, “the song produced in performance is his own. The audience knows it as his because he is before them.” Lord describes how the singer (apparently always male in 1930s Yugoslavia, though of course women are storytellers too) adapts his performance of the story to the audience and what they seem to want. If the audience appears to be growing restive, the tale may be changed, or truncated. While the tale draws on structure, it is performed in the moment, and in the context of human intentions and human relationships shared by singer and listener.

That, then, is an important - and from some perspectives crucial - difference between traditional generative cultural systems such as the oral epic tradition in Yugoslavia and its Homeric ancestor and cousin, and algorithmically generative cultural systems such as LLMs. The former are inseparable from their performance in human contexts: the system can be abstracted from their performances, but does not have any substantial being independent of them. The tradition’s existence is manifested through the stories that humans tell each other. LLMs, in contrast, are a condensation and actualization of the tradition itself and all the other traditions that have been folded and compressed into its statistical weights, stripped of the specifics of human relationships and reapplied to them as an algorithmic process. LLMs speak only when prompted, but their continuations of those prompts are not expressed through two-way human relationships (though the words used by the prompter, training via RLHF and fine tuning etc obviously affect the outputs). The person who prompts the LLM is interrogating a lossy representation of the tradition itself. Of course, LLMs may then shape human understandings, and fool people into treating them as human, but they are not. Their tangible abstraction is something that is novel and different in its application than the previously intangible cultural knowledge that they summarize.

LLMs, then, can reasonably be understood as a summarized composite of the tales that are sung. In their particular applications, they are even a version of the singing of the tales. But they are not, and cannot be, the singers. Our relationship to them, and our interpretation of what they tell us is necessarily different from our relationship with and interpretation of what we tell each other.

*******

To understand this, it’s helpful to return to Wolfe’s Severian. As I’ve already noted, Severian makes his remarks about stories in the context of a story telling competition. He has fallen ill, and is stuck in a lazaret with soldiers recovering from their wounds, where there are few other ways to distract from current circumstances than to talk. Two of the other inmates are vying for the hand of a third, Foila, who has said that she will marry whoever can tell the best story. This framing story itself partakes of the logic of the folk tale, but it is also a space for the play of agency. Foila changes the rules of the competition twice to suit herself; once to allow a third inmate, an Ascian prisoner, to join the contest; then to enter into the competition on her own behalf by telling her own story. For after all:

Even a man who courts a maid thinking he has no rivals has one, and that one is herself. She may give herself to him, but she may also choose to keep herself for herself. He has to convince her that she will be happier with him than by herself, and though men convince maids of that often, it isn’t often true.

Severian wonders whether the Ascian prisoner too has picked his story to convey a hidden message, and why Foila had allowed him to participate at all. None of this is obvious - the competition is a maze of ambiguous intentions.

I had not learned those things I had most wished to learn as I listened to the Ascian and to Foila. What had been her motive in agreeing to allow the Ascian to compete? Mere mischief? From her laughing eyes I could easily believe it. Was she perhaps in truth attracted to him? I found that more difficult to credit, but it was surely not impossible. Who has not seen women attracted to men lacking every attractive quality? … And what of him? Melito and Hallvard had accused each other of telling tales with an ulterior purpose. Had he done so as well? If he had, it had surely been to tell Foila—and the rest of us too—that he would never give up.

Stories can convey messages, but those messages are likely (except in the most debased and simple tales) to be partly obscured, and sometimes more obscure even than that.

The Ascian’s story is constructed as a most difficult case for individual meaning. He comes from a future North America that seeks deliberately to stamp out individuality in favor of imposed culture. In Ascia, people apart from children can only speak by reciting rote phrases, taken from a small set of approved texts that they have memorized. Hence, the story that the prisoner tells is a sequence of a couple of dozen of these phrases, from which the careful and informed listener can construct a story.

On its face, this seems a fantastical version of Orwell’s Newspeak, a victory for the imposition of the unchanging printed phrase, and the antithesis of the dynamic oral traditions from which new usages and conjunctions may appear. Yet even in this deliberately cramped and pinched linguistic system, ambiguity of intention can seep from the cracks, interstices and implied spaces of imposed tradition. The tale cannot fully be confined within the artificial prison house of language.

I learned once again what a many-sided thing is the telling of any tale. None, surely, could be plainer than the Ascian’s, yet what did it mean? Was it intended to praise the Group of Seventeen? The mere terror of their name had routed the evildoers. Was it intended to condemn them? They had heard the complaints of the just man, and yet they had done nothing for him beyond giving him their verbal support. There had been no indication they would ever do more.

Severian does not grasp the intention of the tale, nor the Ascian’s intention in its telling. Yet his belief that there is some intentionality to both the tale and teller, however murky or unclear - perhaps unclear even to the Ascian himself - is what makes the story human. Telling a story is a human speech act, through which one human looks deliberately to communicate with others. The selection of certain rote phrases rather than others combines with the circumstances of their utterance to convey meaning and ambiguity, both so entangled as to be impossible to separate. As Wolfe says elsewhere, the speaking of any word is futile unless there are other words, words that are not spoken. The meaning behind the choice of some words or stock phrases, certain tropes rather than others is hard to grasp, yet we struggle to understand this meaning, because there has been some choice that matters to us. We are more than the bearers of structure and ideology.

It is this that distinguishes human stories from the productions of LLMs. On some dimensions, LLMs are much more open-ended than the little red books of the Group of Seventeen. Indeed, as Varshney points out, LLMs are not in fact well captured by the common stock phrase “stochastic parrots,” since they can come up with novel combinations. Even back in 2023, they could do weird and unexpected things, such as inventing cod-Latin phrases, as I discovered when I prompted one to rewrite the plot of Hamlet in the style of a chemistry textbook.

However, they’re definitionally incapable of the kind of ambiguity that the Ascian prisoner can achieve through the mere combination of rote catchphrases. There is no valid room for wondering about LLMs’ motives for saying things, since they don’t have any motives to wonder about. As Weatherby argues, LLMs have “heat-maps” of summarized correlations between themes, tropes and words instead of intentions.

As I noted at the beginning, Weatherby dismisses “remainder humanism,” ideological efforts to distinguish human culture from the systems that perpetuate it. However, he still acknowledges the distinction between poetry - a system of conventions and usages, and the individual sonnet that “unites poetry with an intention.” Even if LLMs are made out of poetry, they are incapable of producing poems. Or in Wolfe’s language, both the epic form and LLMs are story, but are incapable of telling stories. That requires the marriage of structure and intention that human mediation provides. LLMs are a kind of composite of the singing of tales, but are not singers, even if we sometimes misconstrue them as such.

*******

Thinking about stories - and perhaps art more generally - in this way does not lead to any very emphatic conclusions about the relationship between humanistic values and vast impersonal structures. At most, I think, it provides a common space where people concerned with the one can more easily talk to the other, and where the spaces of agreement and disagreement become more visible. And perhaps not even that: I’m venturing well outside my own areas of expertise, and may have blundered. Even if so, I still believe that humanism is not incompatible with the acknowledgement of large structure, nor structuralism with acknowledging the importance of human intentions.

This leaves open the question of whether LLMs and their cousins (e.g. diffusion models) can produce art. Does art necessarily involve human intention? Perhaps not necessarily. I suspect that as artists begin to use these tools, many of the effects they produce will rely on what Mark Fisher calls the “eerie” - the sense that agency is missing where it ought to be, and that something strange has crept in to fill the void. Others may dispute whether such productions are, in fact art, or something different. Others still may argue that even if AI is at a remove from human intentions, it is still human made. And there will be all sorts of hybrid productions.

I’m not the right person to mediate such disagreements, let alone resolve them, but I would like to see them better and more sharply articulated. Technologies such as LLMs are neither going to transcend humanity as the holdouts on one side still hope, nor disappear, as other holdouts might like. We’re going to have to figure out ways to talk about them better and more clearly.

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* One joke that I have not seen picked up anywhere involves the “Cumaean,” who, in Wolfe’s version is a serpentine alien, covered in scales that resemble human faces. Naturally, Wolfe never uses the word “pythoness” in connection to the Cumaean or otherwise, leaving the discovery of the pun as an exercise for the reader.

** Casual experimentation suggests that the most recent version of Claude Opus has gotten quite good at reproducing iambic pentameter (a meter that I’ve chosen for the semi-arbitrary reason that a few of Wolfe’s aliens speak exclusively in it). Ironically, Claude Sonnet cannot. Nor can the most recent version of GPT.