Henry Farrell on "The political economy of billionaire derangement"

Henry Farrell, The political economy of billionaire derangement, Programmable Mutter, July 15, 2026.

He opens with two somewhat different quotes about billionaires, Tyler Cowen more or less in favor of them and Tim O’Reilly deeply skeptical of them. He suggests we breed them together:

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

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

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

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

Farrell then goes on to quote Thiel’s book at some length, analyzing while going along. And so:

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

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

And then a bow to Keynes’s “animal spirits” in the market place:

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

There’s more at the link.

Creating Imaginary Bank Notes with ChatGPT: AI as cultural technology and collective creativity

At the beginning of this week I published an article at 3 Quarks Daily, Tyler Cowen is the Tycho Brahe of Economics. I then decided to turn it into a PDF that people could download from my Academia page. So I uploaded the article to ChatGPT and asked it to create an illustration suitable for the cover. Here’s what it produced (Note: this image, as well as the others in this post, has a lot of detail; you can click on the image to see a larger version):

I told it to make an image suitable for the front page of that article. It then had to “read” the article and come up with an image. I liked the first image it produced, but that had the aspect ratio of a portrait, which isn’t what I wanted. I told it to make a square image. I liked that so much that I accepted it without any editing. What was my role in creating that image? I didn’t give ChatGPT any specific instructions.

A day or so later I asked to use that image as the basis for the design of an imaginary bank note. I told it that the note was to be issued in the name of the Free Principality of Mercatus and should be in the denomination of Five TC Busks. That’s it. ChatGPT did the rest. Here’s what it produced:

Another day later I created two bank notes for the Free Republic of 51 Pacific. 51 Pacific is the address of a building in Hoboken, NJ, where a friend had loft space in which he curated graffiti and held late night raves. The note was to be denominated in Knowledge Dollars. I uploaded this picture to use as source material:

Here’s the resulting bank note:

I then asked for a 20 dollar note with a pit bull on the bill (my friend has two pit bulls):

Again, what was my role?

I then repeated the process for these four Kisangani bank notes:

ChatGPT obviously did a lot. But what did I do?

I decided that it was time for a discussion with ChatGPT. This discussion bears on the notion of AI as a cultural technology, which has been advocated by Alison Gopnik and Henry Farrell among others, and on creativity as a collective phenomenon. Pay particular attention to an analogy I use midway in the dialog; it’s about strings of colored dots instead of strings of alpha-numeric characters. Here’s the dialog.

* * * * *

I assume you’ve been trained on lots currency and ornate certificates so you have a good idea of how those things are designed. I assume as well that each chat starts with a “blank slate” as it were, no memories of prior chats, no inherited context. But, since I asked you to make those currency designs from within the Kisangani Materials project, you do have access to project-level materials (documents I’ve uploaded). Do you have access to the other chats within the project?

Yes, broadly right, with a few qualifications.

I was almost certainly trained on many examples of paper currency, certificates, stock certificates, passports, banknote engravings, seals, guilloché patterns, holographic security devices, ornate borders, typography, and so forth. I don’t “retrieve” a specific banknote template, but the model has learned a high-dimensional statistical/compositional sense of what makes an object look like currency: denomination placement, corner numerals, intaglio-like linework, central emblems, security strips, microprint, official-looking typography, controlled symmetry, and dense ornamental fields.

On context: a new chat is not exactly a blank slate, but it is not a complete archive either.

In this Project, I have access to several kinds of context:

1. The current chat.
I see the banknote sequence we have just made: 20 Riffs with jivometric percussion, 5 Riffs with dancers and Homo Ludens, 50 Riffs with cell/solar-system science, and 100 Riffs with heart/neurons/circuits for human–doppelganger comity.

2. Project-level materials.
I can access uploaded Project documents when they are made available to me in this workspace. For example, the Kisangani working paper frames the whole project around Homo Economicus versus Homo Ludens, the Fourth Arena, play, and human-AI interaction, which clearly influenced the designs.

3. Some project conversation context may be summarized for me.
I may see summaries or selected snippets of other conversations in the project if the system has included them in the context for this chat. That is not the same as having free, searchable access to every prior Project chat.

4. Persistent memory / profile context may also be present.
I may have durable memories about your interests, projects, preferences, and recurring concepts. That is why I can often pick up the thread of “Kisangani,” “doppelgangers,” “Homo Ludens,” “jivometric,” “Fourth Arena,” and so on without needing you to re-explain everything.

But the important limitation is this: I do not have a general browser-like view of all your other chats inside the Project. I cannot just open a sidebar and inspect every previous conversation. I only know what is in the current chat, what has been injected as project context or memory, and what I can retrieve from uploaded files through the available file tools.

So for your friends: the banknotes came from an interaction among three sources of constraint:

• my general learned visual knowledge of currency and ornate official documents;
• your immediate prompts specifying denomination, motifs, slogans, and Kisangani themes;
• Project context about Kisangani as a post-economic, play-centered, human/AI civilizational experiment.

The “design intelligence” is not a stored memory of prior banknote conversations so much as a context-sensitive recombination engine operating over learned visual conventions plus the specific Kisangani conceptual field you have built.

Would it be reasonable to say that you can pick up a “vibe” from the Kisangani conceptual field?

Yes. “Vibe” is a reasonable informal term, provided we cash it out carefully.

What I can pick up is not an occult atmosphere or a hidden essence. It is a pattern of constraints, motifs, values, narrative cues, and stylistic expectations distributed across the Kisangani materials and our conversation. In practice, that “vibe” includes things like:

Kisangani is futuristic but not cyberpunk dystopian; technological but not technocratic; African/Afrofuturist but not reducible to ethnic ornament; musical, improvisatory, playful, civic, ritualized, and humane. It is organized around Homo Ludens, the Fourth Arena, doppelgangers, non-instrumental play, post-work social meaning, mutuality between humans and AI-like beings, and a suspicion of Homo Economicus. So when you ask for a banknote, I don’t merely think “make a fictional currency.” I infer that the note should look like a civic artifact from a society whose deepest value is not sovereign power, extraction, or corporate futurism, but a cultivated ecology of play, knowledge, ritual, and companionship.

That is what “vibe” amounts to operationally: a soft but real attractor in the design space. It biases choices of symbols, palettes, slogans, compositional balance, and exclusions. For example, a Kisangani note should probably avoid militarized insignia, grim cybernetic domination, heroic CEOs, corporate logos, or cold transhumanist triumphalism. It should prefer music, dance, river/city life, science-as-wonder, neural/circuit reciprocity, and ceremonial public beauty.

So yes: I can pick up a Kisangani vibe. But it is better understood as an emergent conceptual gestalt than as a memory of a single doctrine. The “feel” comes from many local signals aligning: Homo Ludens, jivometrics, Mystic Jewels, doppelgangers, civic ritual, Afrofuturist urbanity, play after work, humane AI, and the refusal of both Silicon Valley domination and anti-technology nostalgia.

So imagine that I asked you to produce with this prompt: “I want a bank note for Kisangani. It should have a futuristic look and be denominated in Riffs. Let's start with at Twenty Riff note. “Jivometric” should be somewhere on the note, as should a djembe and a cowbell.” But I didn’t deliver that prompt within this context but, rather, it was in an independent chat, there wouldn’t be any “emergent conceptual gestalt.” So the result wouldn't have the same vibe.

Yes, that’s basically right.

In an independent chat, with only that prompt, the result would likely be good, but it would not be guided by the same rich Kisangani field. It would have to infer almost everything from the surface cues in the prompt alone:

• “Kisangani” suggests an African setting or at least a place-name with cultural specificity.
• “Riffs” suggests music, improvisation, rhythm.
• “Jivometric” suggests some hybrid of rhythm, geometry, and perhaps biomorphic/futurist patterning.
• djembe and cowbell strongly steer it toward percussion and musical symbolism.
• “futuristic” pushes it toward speculative or advanced design language.

So even in an isolated chat, those cues would still generate some vibe. It would not be random. It might well come out as a futuristic, musically inflected, Afrofuturist-style banknote.

But it would be missing the deeper, project-specific layers, such as:

• Homo Ludens as a civilizational principle
• the contrast with Homo Economicus
• the Fourth Arena
• the role of doppelgangers
• the sense of Kisangani as a humane, playful, post-work, post-instrumental society
• the accumulated stylistic continuity from the other notes in the series

So I’d put it this way:

• Independent chat: a plausible and possibly striking design based on prompt-level inference.
• Within the Kisangani Materials project: a design shaped by a thicker conceptual ecology, hence more likely to have the “right” Kisangani vibe.

In short: the isolated version would have a vibe, but not the same vibe. It would be thinner, more generic, and less organically connected to the broader Kisangani world you’ve been building.

New Savanna has 911 tags as of 13 May 2026 [my 16-year production function is 57 tags per year]

Here’s my previous post on this topic: New Savanna has 665 tags as of 13 Nov 2023 [my 13-year production function is 48 tags per year]. You can find all my posts on this topic at this tag: tag_count.

This is how I explained my production function in that earlier post:

If you are a fan of Tyler Cowen, as I am, then you know that in his Conversations with Tyler he often asks his guests about their production function, how they do whatever it is they do. Well, I now have a way of estimating the output of my production function over the course of this blog. Let’s divide the total number of tags, 655, by the age of the blog in years, 13 years and 7 months is 13.6 years, giving me an output of 48 tags per year.

Given that a large fraction of my intellectual output turns up on New Savanna in one way or another, that’s not an unreasonable number to consider. It would be easy enough to calculate how many posts I produce a year if I wished, but I’m not going to do that, because that particular number is not what I have in mind. What I’m thinking is that that count would thereby give me two measures.

As a first approximation, I’m going to say they measure different things. The post count is a simply a word count, or rather, a words-plus-photos count. The tag count is a new-idea count. To be sure, it’s a very crude approximation to new ideas, but, on first glance, it does seem to me that that’s what it is. I only add tags when I want a convenient way to refer to a new topic. One could, of course, write about the same thing year after year, in which case the tag list would remain constant from year to year. That’s how you succeed in the academy, publish the same three ideas year after year after year until you go emeritus. That’s just not how I roll.

For this time out the number of tags is 911 and the age of the blog is 16.1 years. Divide the former by the latter and you get 57 tags per year.

Here’s the full list of tags as of May 13, 2026:

(the) singularity #Canon/Archive #ccourses #ccresearch #DH #gpt3 #GVM002 #GVM003 #GVM004 #GVM008 #GVM019 #GVM021 #GVM022 #GVM023 #GVX #HEX01 #HumanProgress #LARB #longwoodgardens #nugget #whyiteach 2_war 2set 3300 node graph 3blue1brown 3QD 51 Alley 51 Pacific 5FF 60s abstraction abundance academia academia_stats activism Adam Savage Adam_neeley Adam_Roberts ADHD adolescence adulthood adventure aesthetics affective_tech Africa African-American AI AI Alchemy AI Limit AI values AI_Agents AI_Backlash AI_Chess_Lang AI_companion AI_Emo_Support AI_hallucination AI_idolatry AI_Paradox AI-creativity AI-epistemology AI-future AI-integrate AI-whaling AI30 Alan Liu alien alien phenomenology alignment altered states of consciousness altieri Altman America American myth Amodei analog-computing analogical_reasoning Andreessen Andrew_Piper animals animation anime Anthropic anthropology anti-nuke anti-Semitism ants anxiety AOC apocalypse Apocalypse Now Araki archaeology Arena Arena_4 arithmetic art art_club artificial minds arts ASC attachment Attractor-net Attridge&Staten autobiography awesome B_Russell bacon Baltimore_Years BAM BAnvil BAPC bardolatry BB & Computing BB-reflects BBjazzEdu BCSmith bees behavior behavioral mode bell benchmarks Bennett Bergen Arches BFJO Bill_Gates billionaires-in-space biology bird black black church blogistan blogs blues body Bordwell Born2Groove Bostrom Bostrom-simulation Bourdain brain-as-computer brain-to-brain brains breakfast breaking bad Brian Phillips British Bruce_Jackson Bryant Buddhism Buffalo bundling business C_Keil Carroll cartoon CE Direction CE workshop center-point Chain_of_Thought change change-mind Changizi charting-AI-space Chat_paints chatbots ChatGPT ChatGPT conversation ChatGPT interpretation ChatGPT MTL ChatGPT stories ChatGPT summary ChatGPT_poems ChatGPT-memory cherry_blossoms chess chess_lang_core Chess_Lang_Series childhood children chimpanzees China Chinese-room chocolate factory Chomsky chrysler bulding chuck jones citizen science classify Claude Claude stories claude_colab Claude_describes Claude_partner claude_summary Cld_melancholy_notes clouds coaching Coates Cobra_Kai cog poetics cog-poverty cogart cognition cognitive science Coleridge collapse collective creativity Collier color comedy comfort common sense knowledge communication community complexity compositionism computation computational envelope computers conduit metaphor connect-course consciousness contrail control copyright corruption cosmos coupling COVID-19 creativity culler Cultural Beings cultural evolution cultural identity cultural ranks culture cyberization cybernetics cyberspace daffodil DamiLee danah boyd dance dandelion davidsimon death deep learning DeepSeek default mode deGrasse_Tyson demonstration Dennett depression DeptofPeace description design development DGH Diamond_Age Dick_Macksey digital humanities digital wilderness digital_doppelganger disarmament Disney Dizzy Gillespie DOGE dolls Douthat Douthat-decadence downsize drugs duckdodgers Duke_Ellington Dumbo Dwarkesh eating Ebert eclipse ecology econ_growth economics economy ecstasy education Egypt emergence emergent-ventures emotion empire state building EndofScience energy engineering entangle environment epistemic_struct_cosmos Eric_Jang Erie Cut erotic essay Ethan_Iverson ethical criticism Everett evol-force evolution EVs exploration expressive systems eye Ezra_klein F Me Pump Facebook family Fantasia Farrell fast lane Father Faust FBorFreedom FDR federal government Felski Feyerabend fictional characters Fifty_Shades film flix flow flowers fluid mind food foraging foreign_policy form forsythia fractal free range kids Freeman French toast Friday Fotos Fridman FTH FTH_Class fun funny fusion future G_Hinton g-zone gags game theory garden Gärdenfors Gary_Marcus Gavin Gavriil gender genius genji genre gift Girard GITS glenn_loury glia global warming globalization GOATLiC god Godfrey-Smith Goethe gojira golf Golumbia Gopnik GPT GPT_classroom GPT-3 GPT-4 gpt-4.5 GPT-5 graf-then-now graffiti graffiti aesthetics graffiti chaos graffiti detail graffiti essay graffiti site graphic novel grass Great Chain greatest siam green grief Groucho groups Guillory Haggard Halloween hallucinate hallucinated-city handbook happiness Harman Harold-Bloom Hartman Hassabis HCRichardson HCW health heart darkness Heather_Cox_Richardson Henry Lau high culture Hillis Miller Hiromi history Hoboken Hoboken_2225 Hollis_Robbins home homeless Homo_economicus Homo_ludens hope Horgan Hossenfelder House_MD Howard_Jam Hudson River human nature human_origins human-swarm humanities humans vs. machines humor hunter-gatherer Hurricane Sandy hydraulic_press hyperobjects identity ideology-intellect Iliza_Shlesinger illusion immigration implementation improvise India Industrial_Revolution inequality information Inside_Out Instagram institutions_broken intel_case intellectual history intelligence intention interdisciplinary international affairs internet interpretability Interpretation intuition invent_future IP Iran irises Israel-Palestine Issues-in-CultEvol its complicated Jakobson Jamie jamming Japan jasmine Jaws jazz JC Rising JCPhotoEssay JCwall Jersey City JGGChronicles jim morrison jivometric Jockers Joe Rogan John_Oliver JumpandKong justice K-Pop Kambhampati kami Keil_12/8 Kelly Sax Keynes Kim Stanley Robinson Kimono Mom King Kong Kisangani2150 KK encounter Korea Kubla_Khan Kwak DaKyung LAARB Lafayette_Garden language language-computation-litform Lantz LARB-DH Latouor-modes Latour Latour Assemble laughter law leaks learning leaves LeCun Lee_Morgan Leonard_Bernstein Letterman Lévi-Strauss life light linguistics links literary criticism literary Darwinism literature LLM LLM_collab LLM-Meta Loser love low culture lump MacArthurFP MacPaint macroanalysis maestro magic maker malick man_machine man-in-space man-machine-interaction mana mandala manga manhattan Maplewood marilyn_monroe Mark_Twain mark-turner Mars Mary_Douglas masculinity math maynard McCarty McCulloch McDonalds meals meanderings meaning meaning_semanticity meaningful_life media notes medicine meeting_of_minds melancholy_mind memes memory mental health Meta_struct_world metalingual definition method mhacks Michael_Jordan microchips Miles Davis Millennium_Park mimi and eunice mind mind-inside mind-or-machine mindfulness Miyazaki Mnozil mom moon moral injury morality Moretti Morton Morton-Spacecraft MR-Notes music Musk my intellectual life my_mood myth-logic narrative NASA nation-state nationalism natural geometry naturalist criticism nature NCIS networks neural-holography neural-weather neuromorphic neuroscience neurosymbolic new government New Hope City new humanities new savanna New York City new-descriptivists next-word Nina Paley NLH rejection No_Kings Northrup Frye notes novel NY2140 NYTimes Obama object-oriented ontology objectification Occupy ontological cognition OOCRnB open letter OpenAI operadoc orange orgasm Oshii P_Thiel pansies 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LLMs, hallucinations, and language as cultural technology @3QD

I’ve got a new article at 3 Quarks Daily:

Of Grammar and Truth: Language Models and Norms, Truth and the World

I start with an obscure topic in linguistics, evidentials, and then move on to so-called hallucinations and into the Gopnik, Farrell, Underwood account of AI as cultural technology. I conclude the article by explaining how I got Claude to create the text and discussed the issues that raises for attribution.

The penultimate section is entitled: What Language Turns Out to Be: Mechanistic. But I never really explain that. I’m going to do that here.

Or rather I’m going to let Claude explain:

The success of modern chess programs and large language models shows that language and reasoning are mechanistic, but not in the familiar steam-engine sense of mechanism. These systems are better understood as machines with trillions of interacting parts, whose behavior emerges from distributed internal dynamics rather than from transparent, human-scale causal chains. Such mechanisms operate autonomously: once set in motion, they carry out sustained symbolic activity without continuous human or animal control. This autonomy is not accidental; it is the defining consequence of scale. Just as early steam locomotives violated pre-industrial ontologies by exhibiting self-propelled motion without life, contemporary computational systems violate inherited ontologies by exhibiting structured linguistic and cognitive behavior without minds. What we are confronting is not the end of mechanism, but the emergence of a new kind of mechanism—one that forces us to revise the categories by which we distinguish agency, control, and understanding.

We decided that steam-engine mechanisms are best called equilibrium machines while machines of a trillion parts are generative machines:

By equilibrium machines I mean mechanisms designed to settle into stable, repetitive behavior, minimizing deviation and surprise. These are the machines of the Industrial Revolution, and they underpin the worldview of Homo economicus. By generative machines I mean mechanisms maintained far from equilibrium, whose internal dynamics produce structured novelty and exploration. Language is the paradigmatic generative machine, and Homo ludens is the form of life that emerges when such machines become central rather than marginal.

The world of Homo economicus is organized around equilibrium mechanisms: machines designed to settle, repeat, and minimize deviation. These are the mechanisms of the Industrial Revolution, whose success shaped not only our technologies but our intuitions about causality, control, and value. Homo ludens inhabits a different world. Its characteristic institutions and practices arise from generative mechanisms—systems maintained far from equilibrium, whose internal dynamics support exploration, play, and the continual production of novelty. Human freedom does not stand opposed to such mechanisms; it depends on them.

This allows me to observe (in Claude’s words:

Human freedom and creativity are not opposed to mechanism. They are grounded in a special class of mechanisms—decoupled, autonomous mechanisms whose internal standards of coherence allow sustained activity independent of immediate worldly constraint. Language is paradigmatic of this class.

That is an idea I’ll be developing in my book, Play: How to Stay Human in the AI Revolution.

AI for the Next 30 Years: Four kinds of activity that should be pursued

Here’s the prompt I used to elicit a text from Claude:

I want to do a blog post setting forth those four programs. I want it to be between, say, 1000 and 2000 words, no more. It should have an introduction, sections for each of the four programs, and some final remarks. Give it a title like: AI for the next 30 years, an open-ended plan. Assume a college-educated readership that is generally sophisticated. I’m going to put it in my New Savanna blog along with another post in which I present excerpts from Rodney Brooks’ current remarks on technology.

I’ve spent a lot of time over the last three days conceptualizing those four programs, interacting with both ChatGPT 5.2 and Claude 4.5. Those chats, in turn, rest on work that I’ve done with both chatbots over the last three years. Moreover, I have uploaded a fair number of documents to those chatbots, both articles from the formal literature and informal working papers, going back five decades.

Note that AGI is not mentioned anywhere nor did I ask ChatGPT to make dated prediction. Predicting where the earth will be in the solar system in 30 years, that’s easy. We’ve known how to do that since Newton. Predicting the weather 30 years out, very difficult to impossible. But maybe we can come up with rough estimates of average temperature for the year, and precipitation. Predicting the 30-year evolution of a complex socio-cultural-technical system? Not on your life.

I’ve edited Claude’s text in some minor ways and added some links at the end of each section.

AI for the Next 30 Years

Large Language Models represent something fundamentally new in computing: systems that have learned vast amounts about the world but encode that knowledge implicitly, in billions of inscrutable parameters. We can use these systems—often impressively—but we don't truly understand what they know or how they organize that knowledge. It's as if we've discovered a vast wilderness: we can explore parts of it, but we lack comprehensive maps.

Over the past few years, I've been thinking about what it would take to map this territory systematically and transform it from mysterious wilderness into reliable infrastructure. This thinking has crystallized into four parallel research programs, each essential, each reinforcing the others. Unlike the prevailing Silicon Valley vision of one lab developing a superintelligent system that does everything, this is a distributed, collaborative, multi-decade effort requiring both technical innovation and institutional creativity.

Activity 1: Ontology Extraction

The challenge: LLMs generates texts that distinguish between dogs and cats, animate and inanimate, concrete and abstract—but this knowledge exists only implicitly in weight matrices. We need to extract this latent ontological structure and make it explicit and inspectable.

Recent work by Christopher Manning and colleagues at Stanford has shown that neural networks encode rich linguistic structure—syntax trees, for instance—that can be extracted through systematic probing. I'm proposing we extend these methods from linguistic structure to ontological structure: the categories, hierarchies, and affordances that organize conceptual knowledge.

The key insight is that ontology is implicit in syntax. Verbs select for certain kinds of subjects and objects based on categorical presuppositions. "Eat" requires an animate agent and edible patient. These selectional restrictions reveal the categorical structure underneath. By systematically probing syntactic behavior, clustering words by shared patterns, and validating through transformation tests, we can extract the ontologies LLMs have learned.

This work must be distributed across many research groups, each focusing on specific domains—medical ontologies, legal ontologies, physical systems ontologies, and so forth. No single lab has the expertise or resources to map the entire territory. We need shared infrastructure (probing tools, ontology repositories, validation benchmarks) and coordinated standards, but the actual extraction work happens in specialized communities with deep domain knowledge.

The payoff: explicit ontological structure that can be verified, debugged, systematically improved, and integrated with symbolic reasoning systems. We transform opaque neural networks into hybrid systems that combine learning with legible structure.

Some background:

Christopher Manning et al. Emergent linguistic structure in artificial neural networks trained by self-supervision, PNAS 2020, https://www.pnas.org/doi/full/10.1073/pnas.1907367117

William Benzon, ChatGPT: Exploring the Digital Wilderness, Findings and Prospects, https://www.academia.edu/127386640/ChatGPT_Exploring_the_Digital_Wilderness_Findings_and_Prospects (see especially pp. 28-38, 42-44]

Activity 2: Cognitive Models and Multimodal Grounding

The challenge: Extracting ontologies from language gives us how language talks about the world, not how minds represent the world for perception and action. A robot needs more than linguistic categories—it needs grounded representations that integrate vision, touch, motor control, and yes, language, into a unified cognitive model. This distinction is standard in the cognitive sciences, including “classical” symbolic AI. I picked it up in the work I did with David Hays in the 1970s on cognitive networks for natural language semantics. We conceived of language mechanisms as operating on a separate cognitive model—language is an interface to the model, not the container of it. For embodied AI and robotics, this becomes crucial.

Consider a cup. The linguistic ontology tells us: cup is-a container, is-a artifact, can-hold liquids. The cognitive model adds: cylindrical shape with hollow interior, graspable via handle, stable on flat surfaces, rigid, will break if dropped, liquid spills if tilted beyond 45 degrees. This is sensorimotor knowledge grounded in perception and action, not purely linguistic.

Current multimodal systems (like GPT-4V or Gemini) take vision and "linguistify" it—everything gets processed through language. What we need are systems where multiple modalities read and write to a common cognitive model. Vision contributes spatial structure, language contributes categorical relationships, action contributes causal understanding, and they all integrate.

This research connects directly to robotics. A robot exploring a new kitchen needs to build spatial maps, identify affordances, understand causal relationships (that knob controls that burner), and eventually respond to linguistic commands—all drawing on the same underlying world model. The cognitive model is where the "adhesion" component of meaning lives: the grounding in physical reality that pure language systems lack.

Some background: Gary Marcus, Generative AI’s crippling and widespread failure to induce robust models of the world, Marcus on AI, June 28, 2025, https://garymarcus.substack.com/p/generative-ais-crippling-and-widespread

Activity 3: Associative Drift and Discovery

The challenge: Current AI systems are reactive, not curious. They solve problems you give them but don't discover problems worth solving. They lack what I'm calling associative drift—the capacity for open-ended, low-bandwidth exploration that enables serendipitous discovery.

Think about how intellectual discovery actually works. When I searched "Xanadu" on the web years ago, I had no hypothesis—just idle curiosity. When I got 2 million hits, I had a hunch that seemed interesting (though I couldn't articulate why). The opportunity cost of investigating was low, so I poked around. Eventually I discovered distinct cultural lineages (sybaritic via Citizen Kane, cybernetic via Ted Nelson's hypertext project) that revealed something about how cultural memes evolve.

This is fundamentally different from task-directed reasoning. I wasn't trying to solve a predefined problem. I was in a low-bandwidth exploratory mode, sensitive to interesting patterns, following hunches without clear goals. Current LLMs operate only in high-bandwidth mode: given a prompt, they generate detailed responses. They can't "skim" or "wonder" or "notice something odd" without generating full text.

We need architectures that support dual-mode processing: high-bandwidth for focused problem-solving, low-bandwidth for pattern detection during exploration. This requires technical innovations (sparse attention patterns, adaptive computation, salience detection) and new ways of thinking about AI objectives. How do we train systems to explore productively without specific goals?

For robotics, this is essential. A robot with associative drift doesn't just execute commands—it develops intuitions about its environment through undirected exploration, notices regularities, forms hunches about what matters. It becomes genuinely curious rather than merely reactive.

The interesting twist: associative drift needs the other programs. Ontologies provide the structured space that makes certain patterns "interesting" (ontologically distant concepts appearing together). Cognitive models enable embodied drift (noticing patterns through physical interaction). And drift enables discovery in the other programs (finding ontological incoherences, noticing when modalities misalign).

Alison Gopnik, a conversation with Tyler Cowen (children as scientist, Freud & Piaget, AI)

Tyler Cowen, Alison Gopnik on Childhood Learning, AI as a Cultural Technology, and Rethinking Nature vs. Nurture (Ep. 265), Conversations with Tyler, Dec. 17, 2025.

The introduction:

Alison Gopnik is both a psychologist and philosopher at Berkeley, studying how children construct theories of the world from limited data. Her central insight is that babies learn like scientists, running experiments and updating beliefs based on evidence. But Tyler wonders: are scientists actually good learners? It’s a question that leads them into a wide-ranging conversation about what we’ve been systematically underestimating in young minds, what’s wrong with simple nature-versus-nurture frameworks, and whether AI represents genuine intelligence or just a very sophisticated library.

Tyler and Alison cover how children systematically experiment on the world and what study she’d run with $100 million, why babies are more conscious than adults and what consciousness even means, episodic memory and aphantasia, whether Freud got anything right about childhood and what’s held up best from Piaget, how we should teach young children versus school-age kids, how AI should change K-12 education and Gopnik’s case that it’s a cultural technology rather than intelligence, whether the enterprise of twin studies makes sense and why she sees nature versus nurture as the wrong framework entirely, autism and ADHD as diagnostic categories, whether the success of her siblings belies her skepticism about genetic inheritance, her new project on the economics and philosophy of caregiving, and more.

Kids as Scientists:

We have some good computational models of how scientific theory change works. It turns out that those apply to children as well. The specific thing that I’ve looked at is, what is it that scientists do? Here’s this big, hard problem. All we seem to get from the world are a bunch of photons at the back of our retina and disturbances of air in our ears, and yet, children know about people and things, and scientists know about quarks and quantum phenomena. How do we ever get from the data to the theory?

One subcategory of that is, how do we ever get causal structure which is so important in science? How do we ever figure out what causes what just from a bunch of data that we have?

What’s happened is that philosophers of science and computer scientists have found some systematic ways that you could talk about that. Scientists — I think, mostly, not necessarily consciously, but just as part of what they do — and little kids are looking at data and systematically figuring out what kind of structure out there in the world could have caused this pattern of data. That’s not the only thing, of course, that’s going on in science. There’re lots of other things, too, but it’s at least one central thing going on in science that we’ve started to really understand. [...]

If you asked a three-year-old, “Do you think that this pattern of conditional dependencies is giving you a confounding causal structure?” They would probably not give you a very sensible answer. Even when you ask scientists that, they don’t give you a very sensible answer. But when you look at their actual practice, what you see is that, in fact, kids, for example, are Bayesian, and so are scientists.

Now, the thing is that, in fact, in many respects, kids are better Bayesians than scientists, but a lot of it depends on your prior. If you have, as they say, a very peaked prior, you have a lot of experience, you have a lot of reason to believe that this prior assumption is right, then it’s rational not to change it when you just have a little bit of evidence. You should require a lot of evidence to overturn something that you have a lot of confirmation for.

It’s interesting that the kids, actually, are better at solving problems that involve unusual outcomes than the scientists are. I think what happens in science — we’ve just been doing some work about this — is that there’s also a social factor, where having a big distribution of people who are more likely to go with the prior versus people who are more likely to go with the evidence, which seems to be true in science, that collectively can get you to the right answer. There’s no arbitrary principle you can have about when should you abandon the theory and when should you hold onto it. [...]

One thing you can do, which is like what you’re describing about the money supply, is just make little changes to what you already know. That’s what you mean about moving in the predictable direction. You’re just changing things a little bit. Then seeing, “Okay, if I change it a little bit, is it doing a better job of accounting for the data?” That’s what people think of as a low-temperature search. The other kind of search you can do, the high-temperature search, is just bounce around the space. Try wild, crazy things. Exactly as you were saying, have just a more random walk.

The strategy that you see in computer science, this annealing, is start out with this wild, crazy, out-of-the-box, high-temperature search through the space, and then cool off and just fill in the details. If you think about your four-year-old, who do they sound like? Do they sound like the creature that’s just moving a little bit, or do they sound like they’re noisy and bouncy and random and doing all sorts of weird things? The four-year-old seemed to be a really good idea of this kind of random search. [...]

I think you see both things happening. When you get big paradigm shifts, as Kuhn said, when you get big changes in science, a lot of times it’s because someone found an idea that looked like it was improbable. The nice thing about kids is, because they don’t have to worry about grant proposals, they can be off in the wild space all the time.

[...]

With scientists, we underestimate how much that — we sometimes dismissively call it a fishing expedition — how much that very general experimentation is playing a role in scientific progress. In the grant, you’re supposed to say, “Here’s my three hypotheses, and here are the four experiments I’m going to do to test them.” But I think in practice, a lot of times, scientists are being like the little boy with the avocado and the spoon. They’re saying, “I don’t know, what will happen if I try this? What will happen if I try that?” Then they write the grant to get money to do the things that they’ve already done by doing all these experiments.

FWIW, I've known about simulated annealing for years, a couple of decades at least. For awhile I was one of my go-to metaphors/analogies, though I've not used it recently. In terms that I've been developing in other posts and in some working papers, high-temperature search is ludic (Homo Ludens) while low-temperature filling-in-the details is economic (Home Economicus).

LLMs tend to be used in economic ways. All those benchmarks are based on specific problems in well-specified domains. That's why they aren't particularly creative. My series of blog posts on humans in the loop contains case studies of three of my own ludic explorations.

LLMs as cultural technologies: Four Views

Henry Farrell, Large language models are cultural technologies. What might that mean? Programmable Mutter, Aug. 18, 2025.

It’s been five months since Alison Gopnik, Cosma Shalizi, James Evans and myself wrote to argue that we should not think of Large Language Models (LLMs) as “intelligent, autonomous agents” paving the way to Artificial General Intelligence (AGI), but as cultural and social technologies. In the interim, these models have certainly improved on various metrics. However, even Sam Altman has started soft-pedaling the AGI talk. I repeat. Even Sam Altman.

So what does it mean to argue that LLMs are cultural (and social) technologies? This perspective pushes Singularity thinking to one side, so that changes to human culture and society are at the center. But that, obviously, is still too broad to be particularly useful. We need more specific ways of thinking - and usefully disagreeing - about the kinds of consequences that LLMs may have.

This post is an initial attempt to describe different ways in which people might usefully think about LLMs as cultural technologies. Some obvious provisos. It identifies four different perspectives; I’m sure there are more that I don’t know of, and there will certainly be more in the future. I’m much more closely associated with one of these perspectives than the others, so discount accordingly for bias. Furthermore, I may make mistakes about what other people think, and I surely exaggerate some of the differences between perspectives. Consider this post as less a definitive description of the state of debate than a one man presentation exchange that is supposed to reveal misinterpretations and clear the air so that proper debate can perhaps get going. Finally, I am very deliberately not enquiring into which of these approaches is right. Instead, by laying out their motivating ideas as clearly as I can, I hope to spur a different debate about when each of them is useful when and for which kinds of questions.

Gopnikism

I’m starting with this because for obvious reasons, it’s the one I know best. The original account is this one, by Eunice Yiu, Eliza Kosoy and Alison, which looks to bring together cognitive psychology with evolutionary theory. They suggest that LLMs face sharp limits in their ability to innovate usefully, because they lack direct contact with the real world. Hence, we should treat them not as agentic intelligences, but as “powerful new cultural technologies, analogous to earlier technologies like writing, print, libraries, internet search and even language itself.”

Behind “Gopnikism” lies the mundane observation that LLMs are powerful technologies for manipulating tokenized strings of letters. They swim in the ocean of human-produced text, rather than the world that text draws upon. Much the same is true, pari passu, for LLMs’ cousin-technologies which manipulate images, sound and video. That is why all of them are poorly suited to deal with the “inverse problem” of how to reconstruct “the structure of a novel, changing, external world from the data that we receive from that world.”

Interactionism

Interactionist accounts of LLMs start from a similar (but not identical) take on culture as a store of collective knowledge, but a different understanding of change. Gopnikism builds on ideas about how culture evolves through lossy but relatively faithful processes of transmission. Interactionism instead emphasizes how humans are likely to interpret and interact with the outputs of LLMs, given how they understand the world. Importantly for present purposes, cultural objects are more likely to persist when they somehow click with the various specialized cognitive modules through which human intelligence perceives and interprets its environment, and indeed are likely to be reshaped to bring them more into line with what those modules lead us to expect.

From this perspective, then, the cultural consequences of LLMs will depend on how human beings interpret their outputs, which in turn will be shaped by the ways in which biological brains work. The term “interactionism” stems from this approach’s broader emphasis on human group dynamics but by a neat coincidence, their most immediate contribution to the cultural technology debate, as best as I can see it, rests on micro-level interactions between human beings and LLMs.

Structuralism

I’ve recently written at length about Leif Weatherby’s recent book, Language Machines, which argues that classical structuralist theories of language provide a powerful theory of LLMs. This articulates a third approach to LLMs as cultural technologies. In contrast to Gopnikism, it doesn’t assume that culture’s value stems from its connection to the material world, and pushes back against the notion that we ought build a “ladder of reference” from reality on up. It also rejects interactionists’ emphasis on human cognitive mechanisms:     

A theory of meaning for a language that somehow excludes cognition—or at least, what we have often taken for cognition—is required.

Further:

Cognitive approaches miss that the interesting thing about LLMs is their formal-semiotic properties independent of any “intelligence.”

Instead of the mapping between the world and learning, or between the architecture of LLMs and the architecture of human brains, it emphasizes the mappings between large scale systems. The most important is the mapping between the system of language and the statistical systems that can capture it, but it is interested in other systems too, such as bureaucracy.

Language models capture language as a cultural system, not as intelligence. … The new AI is constituted as and conditioned by language, but not as a grammar or a set of rules. Taking in vast swaths of real language in use, these algorithms rely on language in extenso: culture, as a machine.

The idea, then, is that language is a system, the most important properties of which do not depend on its relationship either to the world that it describes or to the intentions of the humans who employ it.

Role play

Weatherby is frustrated by the dominance of cognitive science in AI discussions. The last perspective on cultural technology that I am going to talk about argues that cognitive science has much more in common with Wittgenstein and Derrida than you might think. Murray Shanahan, Kyle McDonell and Laria Reynolds’ Nature article on the relationship between LLMs and “role play” starts from the profound differences between our assumptions about human intelligence and how LLMs work. Shanahan, in subsequent work, brings this in some quite unexpected directions.

I found this article a thrilling read. Admittedly, it played to my priors. I first came across LLMs in early/mid 2020 thanks to “AI Dungeon,” an early implementation of GPT-2, which used the engine to generate an infinitely iterated role-playing game, starting in a standard fantasy or science fiction setting. AI Dungeon didn’t work very well as a game, because it kept losing track of the underlying story. I couldn’t use it to teach my students about AI as I had hoped, because of its persistent tendency to swivel into porn. But it clearly demonstrated the possibility of something important, strange and new.

There's much more at the link.

Needless to say, I am very sympathetic to this line of thinking. 

Cultural Technology, Old School (in Jersey City)

Role play with large language models

Shanahan, M., McDonell, K. & Reynolds, L. Role play with large language models. Nature 623, 493–498 (2023). https://doi.org/10.1038/s41586-023-06647-8

Abstract

As dialogue agents become increasingly human-like in their performance, we must develop effective ways to describe their behaviour in high-level terms without falling into the trap of anthropomorphism. Here we foreground the concept of role play. Casting dialogue-agent behaviour in terms of role play allows us to draw on familiar folk psychological terms, without ascribing human characteristics to language models that they in fact lack. Two important cases of dialogue-agent behaviour are addressed this way, namely, (apparent) deception and (apparent) self-awareness.

Main

Large language models (LLMs) have numerous use cases, and can be prompted to exhibit a wide variety of behaviours, including dialogue. This can produce a compelling sense of being in the presence of a human-like interlocutor. However, LLM-based dialogue agents are, in multiple respects, very different from human beings. A human’s language skills are an extension of the cognitive capacities they develop through embodied interaction with the world, and are acquired by growing up in a community of other language users who also inhabit that world. An LLM, by contrast, is a disembodied neural network that has been trained on a large corpus of human-generated text with the objective of predicting the next word (token) given a sequence of words (tokens) as context1.

Despite these fundamental dissimilarities, a suitably prompted and sampled LLM can be embedded in a turn-taking dialogue system and mimic human language use convincingly. This presents us with a difficult dilemma. On the one hand, it is natural to use the same folk psychological language to describe dialogue agents that we use to describe human behaviour, to freely deploy words such as ‘knows’, ‘understands’ and ‘thinks’. Attempting to avoid such phrases by using more scientifically precise substitutes often results in prose that is clumsy and hard to follow. On the other hand, taken too literally, such language promotes anthropomorphism, exaggerating the similarities between these artificial intelligence (AI) systems and humans while obscuring their deep differences1.

If the conceptual framework we use to understand other humans is ill-suited to LLM-based dialogue agents, then perhaps we need an alternative conceptual framework, a new set of metaphors that can productively be applied to these exotic mind-like artefacts, to help us think about them and talk about them in ways that open up their potential for creative application while foregrounding their essential otherness.

Here we advocate two basic metaphors for LLM-based dialogue agents. First, taking a simple and intuitive view, we can see a dialogue agent as role-playing a single character. Second, taking a more nuanced view, we can see a dialogue agent as a superposition of simulacra within a multiverse of possible characters. Both viewpoints have their advantages, as we shall see, which suggests that the most effective strategy for thinking about such agents is not to cling to a single metaphor, but to shift freely between multiple metaphors.

Adopting this conceptual framework allows us to tackle important topics such as deception and self-awareness in the context of dialogue agents without falling into the conceptual trap of applying those concepts to LLMs in the literal sense in which we apply them to humans.

There's more at the link (the article is ungated). 

H/t Henry Farrell. 

When tech CEOs are like grumpy ducklings

That's the title of Henry Farrell's interview with Le Grand Continent in Programmable Mutter.

Louis de Catheu - Something that is quite striking with the second Trump administration is the part taken by the Tech Bros in his entourage and his coalition. To you, what are the causes of this new alignment between the MAGA movement and Silicon Valley active bosses?

Henry Farrell - There are a number of areas where they share common interests. One thing, which is always very important to remember, is that they have common enemies. Many people on the Tech right are very much opposed to some of the measures that the Biden administration was associated with.

The Tech right also harks back to a particular vision of the politics of technology. [...] It more or less suggests: wouldn't it be incredible if we lived in a world in which we did not have to worry about government anymore?

That vision is very clearly part of the animating vision of Peter Thiel. [...] This will be a new world order in which technology founders will effectively be treated almost as the God Kings of these micro communities. People would be able to move back and forth between these communities according to Albert Hirschman’s logic of exit. [...]

The second thing that is important is a much more immediate set of political tensions, some of which are purely and simply due to people in the Silicon Valley right feeling that they did not get sufficient recognition from the Biden administration. [...]

The third thing is that there is an extreme hostility towards unions in Silicon Valley, which goes across the far right through to moderate left Democrat leaning people among the founders. The research of Neil Malhotra, David Broockman, and Gregory Ferenstein, suggests that in many ways Silicon Valley founders and funders are pretty left wing when it comes to a variety of social issues. They are more left wing than you might expect with respect to certain kinds of distributional issues, such as, being more favorable to basic income or to the distribution of welfare. But they are virulently hostile towards anybody who tries to tell them how to do their business. [...]

Opposition to woke is a significant part of the new way of thinking about things, but this is not because people have opposition to individual lifestyles, to people getting gender alteration surgery or any of these things. On principle, many people in Silicon Valley often tend to be radically libertarian when it comes to people's lifestyles. What they really objected to was the possibility that other people could tell them what they could or could not do to shape the workplace of their firms. These three forms of opposition helped to explain why you saw the Big Tech and Trump coming together.

Great Men:

You just spoke about God Kings and the need for respect. That make me think about your writings on the Silicon Valley canon. Some founders in Silicon Valley seem to have very specific views on history, the great men and their own place in history.

When I said that there was a lack of respect, I think that this involves changes in the broader conversation. Over the last number of years, Silicon Valley CEOs and venture capitalists have been treated with a significant degree of worship by the United States press — until around 2015-2016.

If Silicon Valley people wanted to pronounce on something, their grand statements were treated with enormous respect by the press. They had entourages, as if they were presidents. Mark Zuckerberg and other people were deemed by many to be world historical figures.

That, unsurprisingly changed the sense among Silicon Valley people about what their world historic role was. Here, one of the key essays for me is Marc Andreessen's piece, from 2011, called “Why Software is Eating the World,” which suggests that he and his colleagues are going to bring through a fundamental transformation in the way of things. [...]

When you talk to Silicon Valley people, you get the sense that they either read a lot or —which is in some ways nearly the same thing— they pretend to read a lot, because being well read is viewed as being part of the culture.

That is very different from 15 years ago. When my friend Aaron Swartz was alive and was still part of the debate, he used to be extremely grumpy about the lack of intellectual curiosity among Silicon Valley people. Now Silicon Valley people at least feel that they are obliged to pretend to read — and some of them actually do read. [...]

I think that the reason why Silicon Valley leaders read is because they are looking for models of how to act in the world as great men. [...]

We're in a world now where Silicon Valley people really began to identify with the idea that they were great men. This is not to say that great men are perfect, but they are people with very large virtues — and very large flaws as well. Silicon Valley founders were going to be world historical colossuses, bestriding the stage, reshaping the world in their image.

DOGE and its aftereffects:

First of all, there's DOGE's official goal, which is to achieve efficiency in the federal government. Indeed, there were probably a couple of people attached to DOGE for whom that was actually their primary goal.

A second goal was to effectively eliminate large chunks of the federal government. Of course, if you look at where DOGE acted, very often it was acting in those parts of the federal government, which were left coded rather than conservative coded. [...]

Finally, you could argue that a significant part of DOGE and efforts associated with it involved Elon Musk looking to position his own people in places of power. [...]

The first part, the efficiency part, I think never really took off in any very serious way. Perhaps under different Republicans or under Democrats, you might begin to see that take off again seriously. I don't think that was ever really a serious part of the project, as opposed to an ideological justification for it.

The second part, trying to rip away large chunks of the US government, I think is going to continue because it does not just involve DOGE, but is also about the people associated with Project 2025.

The final part - Musk installing his own people into the government - is obviously now unlikely to succeed. A lot of people are probably not going to continue on in the same way, or they will be asked to sign different loyalty oaths than the one that they felt that they were signing when they first came in. [...]

My personal theory is that if you really want to understand the Trump administration, read the autobiography of Cardinal de Retz or similar people who worked in these courts. The modern equivalents don’t have titles of nobility - perhaps they might like to - but they are engaged in the same squalid intrigues based around personal advancement, sex, rivalry. All of these things are going to be part of the Trump administration in a way that they were not part of the Biden administration — which of course had its own rivalries, but they tend to be much more technocratic ones.

AI and AGI:

We're now in a different world. It is very hard to say exactly how the Trump administration thinks about AI and AGI. It has not come out with any explicit documentation. But from what we can tell, this is much less of a coherent vision that the Biden administration had — whether you liked it or not — and much more a question of convergence between the interests of particular powerful companies and the national interest of the United States, which is defined in a somewhat flexible way.

The deals that were done in the Gulf a few weeks ago, or the apparent deal that has been done between the United States and China, which we have not seen the details of, suggest a much more flexible approach to AI. The United States still wants to be the country which really dominates the AI debate and discourse, but it also wants to take full advantage of the opportunities to do deals around AI, which will cement not simply US power, but also the power of particular people within the administration and of particular business interests which have strong connections with the administration. [...]

Throughout all of this we will see a perpetual opportunism in place of planning and strategy. I think that this is going to be a world of deal-making, rather than a world of the United States trying to fulfill a grand vision as it has in the past.

There's much more at the link.

Henry Farrell on science fiction, AI hype, and the need for usable futures

Henry Farrell, We need to escape the Gernsback Continuum, Programmable Mutter, July 2, 2025. The three opening paragraphs:

As I admitted at the time, my review essay on Adam Becker’s More Everything Forever: AI Overlords, Space Empires and Silicon Valley’s Crusade to Control the Fate of Humanity was a bit of a bait-and-switch. Becker’s book explains how the dreams of science fiction have shaped Silicon Valley’s dreams of technology in general. I deliberately made a comparison that was both narrower and more fantastical: emphasizing how debates over AGI resembled the dreams of Renaissance alchemy. In partial redress, here’s a more specific argument about the relationship between science fiction and Silicon Valley.

Again, it’s more a riff on Becker than a bald presentation of his argument, but the connections are much clearer, even if it isn’t quite the same argument that Becker makes. What Becker sees as rooted in 1950s and 1960s science fiction arguably goes back a few decades further: to the fusion of “scientifiction” and technocracy that happened in the early 1930s, right at the beginning of the so-called “Golden Age” of science fiction.

Silicon Valley is trapped in a new version of the Gernsback Continuum - a situation in which it is collectively haunted by the visions of an imaginary future of endless expansion that didn’t happen and never will. Our escape route, as Becker suggests, is to acknowledge the physical and social limits that we can’t escape, and to try to construct better futures within those limits.

Then we have a lot of this and that, about Becker's argument, about the Golden Age of science fiction, William Gibson, Hugo Gernsback, visions of the future, limitless energy, and Andreessen's techno-optimism. Then:

Equally, not all people who embrace this style of thinking are fascists, or even slightly fascist adjacent. If you read the liberal-leaning technocratic utopianism of Demis Hassabis or Dario Amodei, you’ll find the suggestion there too that technology - and in particular AI - is a limitless cornucopia of possibilities. See Hassabis’ WIRED interview a few weeks ago:

If everything goes well, then we should be in an era of radical abundance, a kind of golden era. AGI can solve what I call root-node problems in the world—curing terrible diseases, much healthier and longer lifespans, finding new energy sources. If that all happens, then it should be an era of maximum human flourishing, where we travel to the stars and colonize the galaxy. I think that will begin to happen in 2030.

Again, this leans on a particular reading of science fiction. The future that they aspire to is explicitly the future of Iain M. Banks’ Culture novels, in which vast intelligent AI Minds underpin a civilization in which people can do more or less whatever they want.

Amodei:

I think the Culture’s values are a winning strategy because they’re the sum of a million small decisions that have clear moral force and that tend to pull everyone together onto the same side. Basic human intuitions of fairness, cooperation, curiosity, and autonomy are hard to argue with, and are cumulative in a way that our more destructive impulses often aren’t. It is easy to argue that children shouldn’t die of disease if we can prevent it, and easy from there to argue that everyone’s children deserve that right equally.

Hassabis:

I would recommend “Consider Phlebas” by Iain Banks, which is part of the Culture series of novels. Very formative for me, and I read that while I was writing Theme Park. And I still think it’s the best depiction of a post-A.G.I. future, an optimistic post-A.G.I. future, where we’re traveling the stars and humanity reached its full flourishing.

On the one hand, this is obviously much more attractive, and far less sinister, than the Andreessen version. It’s an optimistic liberal bet on the boundless cornucopia of technology. Banks was a Scots socialist who detested authoritarianism with a passion.

On the other, Banks was making a much more complex and ambiguous argument than the version that Hassabis and Amodei present. The Culture is, to steal Ursula K. Le Guin’s term, an ambiguous utopia, in a universe that is emphatically not a mere backdrop for the playing out of the manifest destiny of mankind.

After some discussion of Iain Banks we get:

As Kim Stanley Robinson (another poet of hard physical limits) emphasizes in his recent work, we are almost certainly not going to be able to spread out among the stars. What we have on this planet is pretty well what we have. That doesn’t mean that we can’t do much better than we are doing. We can achieve greater forms of material abundance. But pretending that the hard problems simply don’t exist, or that they will be solved by some magical technology that is right around the corner, is a recipe for the embrace of fascism at the worst, and starry-eyed ingenuous optimism at the best. As I’ve written before, we need usable futures. But to get there, as Becker argues, we need to discard the imagined futures of a past world whose aspirations and understanding of the world are a very poor fit with the present that we find ourselves in.

Amen. Usable futures. That's what I'm up to:

• Kisangani 2150: Homo Ludens Rising, A Working Paper
• Chapter Outline: Homo Ludens Rising: A Manifesto for the Fourth Arena