Outside inside out

 

Homo Economicus and the subordination of women

I’ve been working away on my book: Play: How to Stay Human in the AI Revolution. Chapter 3, The Rise And Collapse Of Homo Economicus, is about the corrosive effects of organizing society around work where the economic that work is measured in cash. This is from a recent conversation with ChatGPT.

* * * * *

This is just a note about Ch. 3, Homo economicus. I grew up in the 1950s. My mother was a traditional stay-at-home mother. There’s nothing wrong with that; as Martha Stewert would say, that’s a good thing. What’s wrong is that it also meant the subordination of women to men, and that’s surely because Homo economicus was in charge. Since women did not earn money, there was no way to afford them an overall position in society equivalent to that that men had. The real economic value of women’s work, raising and caring for the next generation, wasn’t measured by the metric society used, cash income. With AI threatening the job system, it will be much harder to justify the social and political superiority of men.

That’s an important clarification, and it lands squarely in Chapter 3’s conceptual center of gravity.

A few quick reactions, staying at the level you want:

• You are exactly right that Homo economicus does not merely organize work; it organizes legitimacy. Cash income becomes the proxy for social worth, and anything not mediated by wages is structurally devalued, no matter how indispensable it is. That makes the subordination of women not an accident or a moral lapse, but a systemic consequence of the metric.

Framing your mother’s role as “nothing wrong with that—but…” is the right tonal move. It avoids anachronistic moralizing while still making clear that the injustice lies in the accounting system, not in the work itself.

This fits very cleanly with the chapter’s argument about embedded vs abstracted economies. Care work, child-rearing, and household labor are paradigmatic examples of value that is real, causal, and future-oriented, yet invisible under Homo economicus.

The final point is strong and worth keeping conceptually central, whether or not it appears explicitly in the proposal: if AI destabilizes the wage system, it also destabilizes gender hierarchy. Once income is no longer the primary gatekeeper of status, the rationale for male social and political superiority collapses with it. That’s not a side effect; it’s a structural implication.

In short: this note doesn’t redirect Chapter 3; it sharpens it. It shows that Homo economicus didn’t just reorder markets—it reordered families, genders, and moral standing. That’s exactly the level at which the chapter should operate.

ChatGPT and Claude are beginning to change how I think

I don't code, but I do a LOT of thinking through prose. The bots are beginning to change the way I think, and I like it.

— Bill Benzon, BAM! Bootstrapping Artificial Minds (@bbenzon) January 22, 2026

Sausage, waffle, and sugar

Heart of Darkness as a critique of Homo economicus

From a conversation with ChatGPT 5.2:

Benzon: What’s Heart of Darkness about? Its mode is more suggestive than literal. And, sure, it’s about the West vs. Africa. But isn’t it also about Homo economics vs human beings? Why was Kurtz in Africa? To collect ivory. Why was Marlow sent after him? Sure, to recover Kurtz. But really, it was about the ivory. The fact that Kurtz died is of little consequence to the company that sent him there. But it’s of great consequence to the fiancé he left behind. And why did Kurtz go to Africa in the first place? Because he was poor and her family was well-to-do. He went to Africa to make the money that would make him marriage worthy. What we have is an allegory about how Homo economicus wreaks marriage and reduces people to property.

ChatGPT: You’re right to insist that Heart of Darkness is not primarily about “the West vs. Africa” in any simple sense. Its power comes from the way it stages economic rationality as a corrosive moral force without ever theorizing it explicitly. Conrad doesn’t give us an argument; he gives us an allegory whose logic only becomes visible when you ask the question you just asked: why is everyone really there?

Once you ask that, the answers line up cleanly:

• Kurtz is in Africa to extract value (ivory).
• Marlow is sent to retrieve Kurtz, but the real object is the flow of value.
• Kurtz’s death is incidental to the company.
• Kurtz’s fiancée is incidental to the company.
• Marriage itself becomes contingent on capital accumulation.

That is Homo economicus avant la lettre—not as a theory, but as a way of organizing human life such that relationships, vocation, even moral aspiration are subordinated to extraction.

* * * * *

Now back to me, no longer in conversation with ChatGPT. Consider the treatment of economic issues in Pride and Prejudice, Tess of the d'Urbervilles, of Madam Bovary. The nineteenth century novel was about marriage (at least in Europe). Heart of Darkness occurs at the very end of the century and, in effect, turns that novel inside-out and up-side down.

Elysian Park on a sunny winter day

ChatGPT on Literary Texts as Cognitive Instruments, with Notes on Tyler Cowen and Harold Bloom

As some of you may know, I’ve been working on a book about the social and cultural implications of AI, Play: How to Stay Human in the AI Revolution. I’m planning it to have eight chapters, with the sixth and seventh being in science fiction mode, rather than the non-fiction mode I use in the other chapters and that is standard for such books. In discussing this with ChatGPT 5.2, it (he, she, they – I’ve never asked it what pronouns it prefers) remarked:

When prediction fails, fiction becomes a legitimate cognitive instrument. That is why Chapters 6 and 7 are not “illustrations.” They are epistemological machinery.

That struck me, hard. I decided to follow up on that and entered into a brief discussion about academic literary criticism after World War II and its avenging nemesis, Harold Bloom. The rest of this post consists of excerpts from that conversation along into which I have inserted a bit from Tyler Cowen’s essay on novels as models.

The academic discipline of literary criticism

Here’s the prompt I gave to ChatGPT:

Now I understand what academic literary criticism (in America) has been about since WWII. It’s obvious that literature is about pleasure and beauty, but also escape. That yields a certain critical practice. And there’s critical practice around the notion of literature as moral instruction. But literary texts as cognitive tools, as epistemic instruments? That didn’t get institutionalized until after WWII with the rise of interpretive criticism. And THAT’s why there was all that controversy in the 1960s and 1970s about validity in criticism. Critics were now treating texts as authoritative instruments of cultural cognition. So it became imperative to determine just how the critic could know what the texts were saying. Otherwise it collapses down on the critic as mere opinion.

ChatGPT’s response:

You’re right: American literary criticism after WWII did quietly institutionalize the idea that literary texts function as epistemic instruments, even if it never quite said so in those terms.

Once critics moved beyond:

• pleasure,
• beauty,
• moral uplift,

and began treating texts as:

• sites of cultural knowledge,
• vehicles of historical consciousness,
• instruments for thinking about identity, power, subjectivity,

then validity became unavoidable.

That’s exactly why the 1960s–70s debates mattered so much:

• If texts are just aesthetic objects, disagreement is taste.
• If texts are moral instruction, disagreement is ethics.
• But if texts are tools for knowing, then disagreement becomes epistemological.

And epistemology demands standards.

The famous anxiety about “valid interpretations” was not pedantry; it was the growing pain of a discipline that had, without quite realizing it, claimed cognitive authority.

Cowen: Novels as Models

Just what does it mean to think of a literary text as a cognitive instrument? Consider an unpublished essay by an economist, Tyler Cowen, “Is a Novel a Model?” Here’s the abstract:

I defend the relevance of fiction for social science investigation. Novels can be useful for making some economic approaches – such as behavioral economics or signaling theory – more plausible. Novels are more like models than is commonly believed. Some novels present verbal models of reality. I interpret other novels as a kind of simulation, akin to how simulations are used in economics. Economics can, and has, profited from the insights contained in novels. Nonetheless, while novels and models lie along a common spectrum, they differ in many particulars. I attempt a partial account of why we sometimes look to models for understanding, and other times look to novels.

I can’t imagine a more direct way of asserting that literary texts are cognitive instruments.

To be sure that is not the way literary critics talk, and Cowen knows this. He notes that “there is a massive literature on what novels are all about, there is virtually nothing comparing novels to models,” which is linked to a long footnote referencing discussions of what novels are about from thinkers in a variety of disciplines, including literary critics. Literary critics will have been trained in Continental modes of thinking – with its philosophical, psychoanalytic, and Marxist strains – which are quite different from the empirical and mathematical economics Cowen learned at Harvard. Thus the cognitive style of literary criticism is different, perhaps even unrecognizably so, but it IS cognitive. Cowen is aware of this difference, which he acknowledges early in his article:

But in the larger picture social scientists should consider, for research reasons, spending time reading novels, and humanities professors should consider spending time with formal models. The two methods of investigation, despite notable and persisting differences, have more in common than is commonly supposed. Since this article is directed at social scientists, it should be thought of as pro-novel, relative to economic science as a whole, although individuals from the humanities may experience discomfort at seeing novels compared to models at all.

With that behind us, let’s turn to a literary critic, Harold Bloom, whom Cowen admires a great deal.

Harold Bloom

Bloom interests me because he has rejected post war literary criticism and thus has, implicitly, rejected the idea that literature can serve epistemic ends – though I suspect, if the issue were put to him in those terms, that he would claim, quite the contrary, that literature speaks to out deepest possible cognitive aspirations.

Here is the prompt I put to ChatGPT:

This also explains why I find Harold Bloom so puzzling and uninteresting as a critic. Brilliant he was, but he rejected the idea of literary texts as cognitive instruments. That’s what’s behind his rejection of those “critics of resentment.” Did they sometimes/often go overboard into polemic, sure. But they were treating texts as cognitive instruments. And thus they were not being reductive as Bloom claimed. I find it telling that when he rejected those critics (e.g. in his book on the canon) in favor of an aesthetic reading, he never developed an explicit account of just what an aesthetic reading was. He just asked the reader to accept of faith that’s that what Bloom was up to and that it was good. He was so brilliant in his range of reference and discourse that many were happy to accept him on face value.

The Wine of God

14:19 And the angel thrust in his sickle into the earth, and gathered the vine of the earth, and cast it into the great winepress of the wrath of God. https://t.co/qpcHebgQG7 pic.twitter.com/YwWIxKaZ8h

— Apocalypse Animated (@ApocalypseAnim) January 19, 2026

Nahre Sol reworks "Fly Me to the Moon" in a Ravel Style

Call and response, clarinet and vocal

The expressive way the clarinet player does a musical call and response to the singer is just mesmerizing. I couldn't stop watching her. pic.twitter.com/sUOSFuxnnY

— Love Music (@khnh80044) January 19, 2026

On the other side of Weehawken Cove

Trump is using Latin America as target practice. This is no way to conduct foreign policy.

Greg Grandin, Trump Picked the Right Stage to Act Out His Imperial Ambitions, NYTimes, Jan 19, 2026.

The article opens:

President Trump has become increasingly entangled in Latin American politics. Less than a year into his second term, he has seized the president of Venezuela, imposed sanctions on Cuba and Nicaragua and threatened Mexico with airstrikes and Colombia with a coup. He has imposed crushing tariffs on Brazilian exports, sent deportees to El Salvador’s infamous supermax prison, pressured Panama to limit China’s influence and meddled in the internal politics of Honduras and Guatemala.

Mr. Trump’s turn toward Latin America isn’t surprising, for often during times of global turbulence, like the moment we now find ourselves in, presidents seek safe harbor there. Latin America was where U.S. leaders have projected power beyond their borders not only with brute force, including all those coups Washington orchestrated, but with moral suasion as well.

For presidents of both parties, Latin America has served as a wellspring of perpetual reinvention and the source of much of their ideological creativity. This is especially true for what the political scientist Stephen Skowronek calls “reconstructive” presidents, leaders who work to build new political orders and restore political legitimacy after periods of acute crisis.

Grandin then goes on to discuss Franklin D. Roosevelt and Ronald Reagan as reconstructive presidents. Then...

Here we are again: a nation turning away from the rest of the world toward Latin America.

Mr. Trump, too, has a story to tell about Latin America, and it isn’t pretty. He likes to play the role of the business cutthroat — a corporate pirate devoid of any ideology, save greed. “We are going to be taking out a tremendous amount of wealth out of the ground,” he said shortly after U.S. troops captured Venezuela’s Nicolás Maduro and his wife, Cilia Flores.

Such unadorned venality and disdain for diplomatic politesse cramp Trumpism, preventing it from forming the basis of a majoritarian coalition. Roosevelt and Reagan differed sharply in politics, yet both grasped how depicting Latin America as aligned with the United States could convert partisan alliances into durable governing majorities. Turning foreign policy into a mirror of national identity, they renovated Americanism as humane and universal.

Mr. Trump, in contrast, seems unconcerned with transforming dominance into hegemony or with broadening his base. He seeks only raw power in which the United States dominates the hemisphere because it can dominate the hemisphere — where it kills speedboat captains because it can kill speedboat captains. He is using the region to send a message to the nations of the world: Don’t “cross us,” as JD Vance recently said, referring to the Venezuela strike. [...]

Whereas Roosevelt held up the Western Hemisphere as a model of international cooperation for the world to emulate, the Trump administration uses Latin America as target practice. Whereas Reagan’s sunny capitalist Pan-Americanism silenced anti-migrant extremists in his coalition, Mr. Trump’s tribalism stokes them, with a growing number of young self-identified conservatives openly embracing white supremacy, antisemitism and Nazism.

Mr. Trump’s hemispheric turn leads not out of the crisis but deeper into it.

Citibike in Hoboken

The failure of zero-sum economics, with a note about cognitive ranks

Noah Smith, Zero-sum economics keeps failing, Noahpinion, Jan. 18. 2026.

The biblical story called the Judgement of Solomon isn’t just meant to illustrate what a wise king Solomon was. It’s also supposed to demonstrate a central principle of economics, and of society in general — that the world isn’t a fixed lump of resources waiting to be divided up. In the story, two women are arguing over which one is the real mother of a baby; Solomon proposes to cut the baby in half and give half to each woman, causing the baby’s actual mother to be instantly horrified. The lesson is that a baby is much more than the sum of two halves of a baby.

I feel like modern American leaders and intellectuals often forget this important lesson. There are plenty of thinkers and leaders on both the right and the left who think of society’s main task as slicing up and handing out a lump of “resources”. And yet when they make economic policy based on this idea, it keeps failing.

A prime example is Trump’s immigration crackdown. During the 2024 presidential campaign, Trump and his people swore up and down that kicking millions of illegal immigrants out of the country would result in a bonanza of jobs for the native-born. They probably still believe this. But people are now flowing out of the United States on net, and native-born employment rates haven’t risen:

Smith then presents 2 graphs and 2 tables that make that argument.

If the Trump administration had bothered to ask economists, they would have replied that the overwhelming majority of the empirical evidence indicates that immigration — even low-skilled immigration — doesn’t take jobs from Americans. Immigrants also produce goods and services, growing the pie and creating labor demand that helps provide work for native-born workers. But the only economist they seem to have bothered to ask was George Borjas, a man who has spent his life unsuccessfully trying to prove that immigration is bad for America. The new jobs numbers illustrate the failures of Borjas’ zero-sum economics.

Smith then presents a chart making the same point about tariffs.

Zero-sum thinking failed on immigration because the U.S. economy isn’t a lump of labor. It failed on tariffs because the global economy is not a lump of manufacturing.

Now it’s also probably going to fail Trump on geopolitics as well. Trump recently overthrew the leader of Venezuela, and he has made it clear in speeches and statements that one of the reasons he did this was to seize control of the country’s oil.

And now Greenland:

Or consider Trump’s desire to conquer Greenland. Simply adding a large chunk of land to America’s map would not mean riches for the U.S. economy. The U.S. already has access to Greenland’s natural resources and shipping routes; conquering the island would simply earn the enmity of both the Europeans and of Greenland’s people themselves. The U.S.’s previous relationship with Greenland was positive-sum and cooperative; switching to zero-sum piracy would not be an improvement.

Smith then goes on to criticize progressives and leftists for zero sum thinking as well.

I note that the notions of zero sum and non-zero sum interactions are from game theory, which is post WWII. I’m pretty sure it’s rank 4 thinking, to invoke the theory of cultural or cognitive ranks that David Hays and I have developed. I wonder how far such thinking is distributed in the population. The popular notion of win-win thinking derives from game theory; it’s zero sum games without the technical analysis. One can “get” the idea of win-win situations without understanding the game theory analytical account. But I suspect that such understanding is purely local to the situation and doesn’t lend itself to more general reflection about human affairs.

Anyhow, I recommend reading Smith’s whole column

Poetry in humans and machines: Who’s great?

Hollis Robbins, LLM poetry and the "greatness" question, Anecdotal Value, Jan, 7, 2026.

I posted the following comment to Hollis’s article:

An oblique observation. To the extent that an LLM can be said to "simulate" a human brain, it would be simulating the neocortex. With only 16 B neurons the neocortex contains only 19% of 86 B neurons in the brain. Thus LLMs, no matter how many parameters, are operating with only a fraction of the capacity of a poet's brain. To be sure, the poems they're trained on were created by full-brained poets, so those poems bear the traces of full brains. But I figure a full-brained poet would be able to find new kinds of paths through the brain's full state space. An LLM's ability to do that would seem to be rather truncated.

It's not clear to me how this bears on greatness in poetry. But I would like to think that at least some poems are great because they opened up new whole brain possibilities.

The purpose of this post is to develop those ideas more carefully. First I’ll talk about the idea of the brain’s state space. Then we move to the idea of a text as tracing a path through that space. Finally we’ll take up greatness in poetry.

The phase space of a brain

The notion of phase space, as I’m using the term, derives from statistical mechanics. It’s a way of thinking about how a physical system changes over time. Change is a movement from one state of the system to another.

The phase space of a system depends on three things: 1) how many elements the system has, 2) how many states each element can take, and 3) how the individual elements are coupled. The phase space has a dimension for each element of the system. The system’s state at any given moment will be a point in the phase space.

If the system has only three elements, then its phase space has three dimensions. Its state at any moment will be a point in that space. Notice that time isn’t one of those dimensions. But as the system changes over time its position in the phase space will change as well. It is possible, however, keep track of those positions.

A phase space having only three dimensions is easy to visualize. What about a phase space with four, five, six, thirty-eight, of 999,000 dimensions? Impossible to visualize.

The human brain has roughly 86 billion neurons. A phase space with that many dimensions will have 86 billion dimensions; impossible to visualize. But the individual neuron might not be the appropriate element to be the primitive unit in the brain’s phase space. The individual synapse – a connection between one neuron and another – may be the appropriate phase space primitive. Each neuron is connected to an average of 7000 other neurons, making the number of dimensions in the brain’s phase space much higher. The exact number doesn’t matter; the point is simply that the phase space is huge.

Note that neurons do not operate independently of one another. They are coupled together. The brain is divided into a number of functional areas (NFA, my term) where the connections between neurons within an area are more numerous that the connections each neuron has to neurons in other NFAs.

At this point let me introduce a query I put to ChatGPT 5.2:

The human brain has roughly 86 billion neurons. How many different functional regions does it have? I believe that the neocortex has between 100 and 200 different functional regions, but only has 16 billion neurons or so. How many different functional regions do we have in subcortical area?

The resulting discussion was a bit messy and complicated, as these things can be, but the upshot is that that brain seems to have roughly 700 NFAs. Only half of them are in the cortex. That’s very important (recall my original comment to Hollis). Later in the conversation ChatGPT observed:

• If LLM behavior resembles anything in the brain, it most closely resembles cortical linguistic competence: high-dimensional, distributed patterning; associative completion; flexible recombination of learned material.
• But cortex is only one part of the full system. Even taking the cortex generously as “the cognitive engine,” it is not the whole engine.

Thus, “At best it’s like a subsystem of the brain, and specifically one that is downstream of bodily regulation, affect, action selection, and reinforcement gating.”

Text as trace

I tend to think of texts, any texts, as the trace of a trajectory through the brain’s state space. But, at best, an LLM models only part of that space. Thus, returning to ChatGPT:

Even when an LLM produces a convincing poem, its method is necessarily missing the full set of whole-brain constraints that shaped the poetic corpus it learned from. Therefore its exploration of possibility space is structurally truncated relative to the exploration performed by a whole-brain poet.

ChatGPT offered me this paragraph:

If we treat a text as a path through a brain’s phase space, then poetry is not simply the output of a language module. It is the trace of a whole-brain trajectory: language regions interacting with memory, interoception, affect, reward, attention, and social cognition. A large language model is trained on the textual residue of those trajectories — the fossil record, not the living process. The poems in its corpus were written by full-brained poets, so the text contains evidence of that fuller generative machinery. But the model’s own generative dynamics are constrained to what can be recovered from token sequences and their statistical regularities. However impressive the simulation, it is necessarily missing the deeper gating systems that, in humans, determine what becomes sayable, what becomes bearable, and what becomes necessary. My hunch is that some poems are great not merely because they are well-formed linguistic artifacts, but because they open new whole-brain possibilities — new stable pathways of feeling-and-thought — that alter what the mind can do afterward.

What of humans and AI’s

I then prompted ChatGPT:

The issue that’s hanging over this kind of discussion is ultimately: Are there good roles for humans in a world of intelligent machines or are we replaceable (and so will be replaced)? That’s a messy question that begs many issues as formulated and so is rather meaningless. [...]

At the moment I’m working on a book about the social and cultural implication of AI. I started working on the book last summer. I broke off work from September through about a week ago. I’m now back at it. Last year I had a title, scads of notes and drafts and two or three versions of an outline. I’ve now arrived at an outline that I don’t think I could have produced without the help of Claude and ChatGPT. Many of the chapters I was working on last year are in this new version, 6 of them in fact. But I’ve got two chapters I didn’t have last year and an overall architecture that I didn’t have. Moreover I don’t think I could have produced that architecture on my own. The AIs gave me the means of holding the whole thing in my head and working at different scales so that I’ve been able to do something new.

I’m a good thinker. I’m better working with AIs. I should think the same would be true for poets.

We had some further discussion and ChatGPT offered me this paragraph:

The question “Will AI replace us?” is too blunt to be useful. My own experience suggests something more precise: large language models can function as cognitive instruments that let a person think at scales they could not reliably manage alone. I can now hold a book-length architecture in view, revise it coherently, and move between local detail and global structure without losing the plot. I am not a poet, but I am a thinker, and I know the difference between producing text and producing understanding. The models are not merely faster typists with larger memories. They are interfaces that make new kinds of conceptual control possible. If that is true for expository thinking, it is likely true for poetry as well: not that the machine becomes the poet, but that the poet gains a new way to explore and stabilize trajectories through a much larger space of possibilities.

I’m willing to let things rest there for the moment.

(Oh, I also asked ChatGPT to produce that illustration at the head of this post.)

A breakfast story

Hon. Martha A. Mills (ret): Lawyer, Activist, Judge

Martha Mills is a friend of mine. She took a course on the history of technology taught by my teacher, David Hays, and became fascinated by his ideas. That's how I know here. I published a review of her 2015 book, Lawyer, Activist, Judge: Fighting for Civil and Voting Rights in Mississippi and Illinois, in 3 Quarks Daily in 2016.

Here's a short professional biography on the website of the American College of Trial Lawyers:

Mills was appointed in 1995 by the Illinois Supreme Court to serve as a Judge of the Circuit Court of Cook County. She served for two years, primarily in the Juvenile Justice, Child Protection Division. She was recalled in 2007, and was Supervising Judge of the Parentage and Child Support Court until December of 2012. Under her guidance, the court started a highly successful pilot restorative justice program with the help of Elizabeth J. Vastine and Peter Newman. Since her retirement she has continued to to teach and offer services in community building and restorative practices.

After law school, Mills was the first woman lawyer with White & Case in NYC. She then joined the staff of the Lawyers Committee for Civil Rights Under Law in Mississippi where she tried many cases, civil and criminal, getting the first jury verdict in Mississippi since Reconstruction for over a million dollars on behalf of the estate of an African American man murdered by the Klan. She was Chief Counsel of the Lawyers Committee Office in Cairo, IL prior to moving back to Chicago. She has practiced law in Illinois since 1969, both as a trial and appellate attorney. The American Bar Association in 2013 published her book: Lawyer, Activist, Judge: Fighting for Civil and Voting Rights in Mississippi and Illinois.

Judge Mills was a trial lawyer for more than forty years. She is licensed to practice in the states of Illinois, New York and Mississippi, and in numerous federal courts. She has argued cases in the Mississippi, Illinois and U.S. Supreme Courts. She has not only arbitrated, mediated and facilitated many cases and conflicts, but also taught others to do so. As founder and Executive Director of Transforming Communities, a not-for-profit corporation, she engaged consulting, practice, education and training in restorative justice from 1996-2007, as well as work in violence prevention and intervention.

Friday Fotos: Vibrant Matter

Now’s the time, free at last

Something I found on the web:

When it comes to changing the world:

It's always too late,
always too soon,
the time's never right,
Now's the time.

I found it deep in the Wayback Machine, here. You’ll have to scan down the lefthand panel until you come to the phrase, "Now is the time"; it'll be in grey type. Click on that phrase; it's a hypelink. You'll see new text appear in the righthand panel. The text i quoted above is there. That’s from a tribute to Martin Luther King that Bill Berry put on the web in the mid-1990s in the early days of the web. If you’re at all curious about how that got there, this post tells the story: I was on the web before the New York Times was, me and Cuda Brown at Meanderings.

This post is about that little four-line ditty. It’s not attributed to anyone. I created that page so I assume that I wrote those lines. But I don’t remember having done so. It’s not a big thing. I have no trouble imagining that I’ve forgotten all about it; it was 30 years ago. That I’ve just forgotten is more plausible than that I’d gotten the lines from someone, somewhere, and just didn’t attribute them. That’s not my style.

About the clave rhythm, central to Latin music

The clave rhythm developed in Cuba from the African bell pattern brought to the Americas via the trans-Atlantic slave trade. The rhythm is used to lock in the many rhythms of Afro-Cuban music, as well as other Latin music like the bossa nova of Brazil. This video focuses on three claves -- rumba clave, son clave, and the Brazilian clave.

Selfie-stick in Hoboken confronts Hudson Yards

Among universities, the Chinese are moving up, USA is moving down

Just think, a century ago most Americans thought of Charlie Chan, a movie detective, when they they thought on the Chinese. And Chinatown, of course. In the 1960s a character named "Hop Sing" appeared as comic relieve in the hit TV program Bonanza. He was the house boy for the wealthy Cartwright family – note the word "boy." And then came the war in Vietnam along with Chairman Mao and his Red Book. When I was in graduate school in the 1970s I took a course on radical approaches to literature, taught be Art Efron. We read a collection of essays on aesthetics written by Mao.

Mark Arsenault, Chinese Universities Surge in Global Rankings as U.S. Schools Slip, NYTimes, Jan 15, 2026. The article opens:

Until recently, Harvard was the most productive research university in the world, according to a global ranking that looks at academic publication.

That position may be teetering, the most recent evidence of a troubling trend for American academia.

Harvard recently dropped to No. 3 on the ranking. The schools racing up the list are not Harvard’s American peers, but Chinese universities that have been steadily climbing in rankings that emphasize the volume and quality of research they produce.

The reordering comes as the Trump administration has been slashing research funding to American schools that depend heavily on the federal government to pay for scientific endeavors. President Trump’s policies did not start the American universities’ relative decline, which began years ago, but they could accelerate it.

“There is a big shift coming, a bit of a new world order in global dominance of higher education and research,” said Phil Baty, chief global affairs officer for Times Higher Education, a British organization unconnected to The New York Times that produces one of the better-known world rankings of universities.

In the middle of this long article:  

The number of international students arriving in the U.S. in August 2025 was 19 percent lower than the year before, a trend that could further hurt the prestige and rankings of American schools if the world’s best minds choose to study and work elsewhere.

China has been pouring billions of dollars into its universities and aggressively working to make them attractive to foreign researchers. In the fall, China began offering a visa specifically for graduates of top universities in science and technology to travel to China to study or do business.

“China has a boatload of money in higher education that it didn’t have 20 years ago,” said Alex Usher, president of Higher Education Strategy Associates, a Toronto education consulting company.

Mr. Xi has made the reasons for the country’s investments explicit, arguing that a nation’s global power depends on its scientific dominance.

There's much more in the article.

Hays, Forth, and creativity in man-machine-interaction

I've just been talking with ChatGPT about David Hays back in the 1970s. From my prompt:

In 1977 or 1978 he got a personal computer, one of those based on the Z-80 chip, and started learning to program it in Forth. He also sent out a weekly snail-mail newsletter entitled “The Diary of a Man and His Machine.” In one issue he asked for replies from people whose work feel between the cracks. He got about 100 replies. We read them all. We concluded that most of them were just oddballs; they didn’t even seem to know the standard literature in the field(s) relevant to their work. But a handful seemed legit. I don’t think we ever did anything with them.

...working between the cracks is not the same as working without foundations

From ChatGPT's response:

First, David Hays’s early adoption of a Z-80–based personal computer and Forth in 1977–78 places him well ahead of the curve, not just technically but cognitively. Forth, in particular, is telling: it is intimate with the machine, minimalist, stack-based, and reflective. It invites thinking about process, control, and representation in ways that high-level languages often conceal. That choice alone aligns with his lifelong interest in cognitive architecture rather than surface performance.

working between the cracks is not the same as working without foundations

Later:

Third—and this is perhaps the most interesting part—the call for people whose work “fell between the cracks,” and the response it generated, reads like an early, analog precursor to today’s online intellectual ecosystems. The fact that you and Hays received about a hundred replies, and that most of them struck you as oddballs, is not a disappointment; it’s a sociological constant. When disciplinary boundaries loosen, noise floods in first. What matters is that a handful were clearly legitimate, even if nothing came of it at the time. [...] Finally, your retrospective judgment—that many respondents lacked grounding in the relevant literature—underscores a distinction you’ve been circling throughout this chat: working between the cracks is not the same as working without foundations. Hays was interested in the former, not the latter.

Another prompt:

Your comments about Forth are apt. Hays valued the intimacy with the machine. He eventually programmed a whole environment using Forth and used it for keeping notes and generating text. Some of the text for our articles came out of that machine. That detail completes the picture—and it makes the point even stronger.

Chat replies:

What David Hays was doing with Forth was not “early word processing” or “personal productivity.” He was building a cognitive prosthesis—a writable environment in which thinking, note-keeping, and text generation were continuous with computation rather than merely supported by it.

A few things are worth making explicit.

First, the intimacy you mention is decisive. Forth collapses distance between intention and execution: no elaborate compilation pipeline, no abstraction layers to hide what the machine is doing. That intimacy encourages a style of thought in which process is visible. For someone like Hays—already committed to understanding cognition as structured activity—that mattered more than convenience or speed.

Second, the fact that he built an entire environment—not just scripts, but a living workspace for notes and text—means that the machine was not ancillary. It was part of the thinking loop. That some of the prose in your joint articles emerged from that environment is not incidental; it means the machine participated, materially, in the production of theory. This is not metaphorical augmentation. It is literal.

23% chance that the USA will buy part of Greenland for $500 to $700 billion

BREAKING: Secretary of State, Marco Rubio, overseeing Greenland acquisition — $500-$700 billion offer being prepared.

23% chance it happens.https://t.co/r6FFiR9iPE

— Polymarket (@Polymarket) January 14, 2026

Can AI generate new mathematical ideas?

Cade Metz, Can A.I. Generate New Ideas? NYTimes, Jan 14, 2026 

Here's how Claude 4.5 summarized the article:

AI Tackles Mathematical Problems - But Questions Remain

An AI startup called Harmonic, using OpenAI's GPT-5 technology, recently solved a mathematical "Erdos problem" - a collection of challenging puzzles designed to test mathematical ingenuity. This sparked debate about whether AI has reached the point of conducting legitimate academic research.

Key Points:

The achievement generated mixed reactions. While some experts see it as evidence that AI can do genuine research, others, including renowned mathematician Terence Tao, argue the solution resembles "a clever student who memorized everything" rather than demonstrating true understanding. The solution was largely based on well-known methods. Initially, OpenAI claimed GPT-5 had solved 10 Erdos problems, but researchers discovered it had merely identified existing solutions buried in decades-old papers. Despite this, the technology proved valuable - it could find obscure sources humans might never locate.

Current Capabilities:

Modern AI systems use "reinforcement learning" to reason through problems, sometimes working for hours. While they can't yet generate truly novel ideas, they've become powerful research tools that can:

• Analyze and store far more information than humans
• Suggest hypotheses researchers hadn't considered
• Help scientists narrow down experiments from 50 to 5

The Verdict:

Experts agree AI is a rapidly improving research assistant, but it still requires experienced human collaborators to guide it, interpret results, and separate useful insights from noise. Whether AI can independently generate breakthrough ideas remains an open question.

A black and white photo of a dessicated plant

Artificial Intelligence and the Future of Work

Konrad Kording and Ioana Marinescu, (Artificial) Intelligence Saturation and the Future of Work, Brookings Institute, 2025, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6051694

Abstract: Macroeconomic models typically treat AI as just another form of capital, and predict a slowly evolving world, while computer science scaling laws applied to the whole economy predict explosive growth and the potential for a singularity-like event. Both views gloss over the asymmetric reality that intelligence capital or AI scales at computer-science speeds, whereas physical capital and labor do not. What’s missing is a unified, parameter-driven framework that can nest assumptions from both economics and computer science to generate meaningful predictions of AI’s wage and output im- pacts. Here we use a constant elasticity of substitution (CES) production function framework that separates physical and intelligence sectors. Whereas physical capabilities let us affect the world, intelligence capabilities let us do this well: the two are complementary. Given complementarity between the two sectors, the marginal returns to intelligence saturate, no matter how fast AI scales. Because the price of AI capital is falling much faster than that of physical capital, intelligence tasks are automated first, pushing human labor toward the physical sector. The impact of automation on wages is theoretically ambiguous and can be non-monotonic in the degree of automation. A necessary condition for automation to decrease wages is that the share of employment in the intelligence sector decreases; this condition is not sufficient because automation can raise output enough to offset negative reallocation effects. In our baseline simulation, wages increase and then decrease with automation. Our interactive tool shows how parameter changes shift that trajectory. Wage decreases are steeper at high levels of automation when the outputs of the physical and intelligence sectors are more sub- stitutable. After full automation, more AI and more physical capital increase wages, a classic prediction from standard production functions in capital and labor. Yet, when intelligence and physical are complementary, the marginal wage impact of AI capital saturates as AI grows large. More broadly, the model offers a structured way to map contrasting intuitions from economics and computer science into a shared parameter space, enabling clearer policy discussions, and guiding empirical work to identify which growth and wage trajectories are plausible.

H/t Tyler Cowen.

Trinidad GG

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 so 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).

Gringos Tacos

Rodney Brooks on the state of AI and Robotics

As you may know, Rodney Brooks has been keeping an annual scorecard for various categories of high-tech activity. He puts it online on the first of the year. I’ve listed some excerpts from the 2026 scorecard below. The scorecard has much much more that I haven’t excerpted.

The Falcon 9

Eight years ago, Falcon 9 had been launched 46 times, all successful, over the previous eight years, and it had recently had a long run of successful landings of the booster whenever attempted. At that time five launches had been on a previously used booster, but there had been no attempts to launch Falcon Heavy with its three boosters strapped together.

Now we are eight years on from those first eight years of Falcon 9 launches. The scale and success rate of the launches has made each individual launch an unremarkable event, with humans being launched a handful of times per year. Now the Falcon 9 score card stands at 582 launches with only one failed booster, and there have been 11 launches of the three booster Falcon Heavy, all successful. That is a sustained growth rate of 38% year over year for eight years. And that it is a very high sustained deployment growth rate for any complex technology.

There is no other modern rocket with such a volume of launches that comes even close to the Falcon 9 record. And I certainly did not foresee this volume of launches. About half the launches have had SpaceX itself as the customer, starting in February 2018, launching an enormous satellite constellation (about two thirds of all satellites ever orbited) to support Starlink bringing internet to everywhere on the surface of Earth.

[Not AI or robotics, I know. But it interests me.] 

Humanoid Robots

My blog post from September, details why the current learning based approaches to getting dexterous manipulation will not get there anytime soon. I argue that the players are (a) collecting the wrong data and (b) trying to learn the wrong thing. I also give an argument (c) for why learning might not be the right approach. My argument for (c) may not hold up, but I am confident that I am right on both (a) and (b), at least for the next ten years.

I also outline in that blog post why the current (and indeed pretty much the only, for the last forty years) method of building bipeds and controlling them will remain unsafe for humans to be nearby. I pointed out that the danger is roughly cubicly proportional to the weight of the robot. Many humanoid robot manufacturers are introducing lightweight robots, so I think they have come to the same conclusion. But the side effect is that the robots can not carry much payload, and certainly can’t provide physical support to elderly humans, which is a thing that human carers do constantly — these small robots are just not strong enough. And elder care and in home care is one of the main arguments for having human shaped robots, adapted to the messy living environments of actual humans.

Given that careful analysis from September I do not share the hype that surrounds humanoid robotics today. Some of it is downright delusional across many different levels.

At the end:

Meanwhile here is what I said at the end of my September blog post about humanoid robots and teaching them dexterity. I am not at all negative about a great future for robots, and in the nearish term. It is just that I completely disagree with the hype arguing that building robots with humanoid form magically will make robots useful and deployable. These particular paragraphs followed where I had described there, as I do again in this blog post, how the meaning of self driving cars has drifted over time.

Following that pattern, what it means to be a humanoid robot will change over time.

Before too long (and we already start to see this) humanoid robots will get wheels for feet, at first two, and later maybe more, with nothing that any longer really resembles human legs in gross form. But they will still be called humanoid robots.

Then there will be versions which variously have one, two, and three arms. Some of those arms will have five fingered hands, but a lot will have two fingered parallel jaw grippers. Some may have suction cups. But they will still be called humanoid robots.

Then there will be versions which have a lot of sensors that are not passive cameras, and so they will have eyes that see with active light, or in non-human frequency ranges, and they may have eyes in their hands, and even eyes looking down from near their crotch to see the ground so that they can locomote better over uneven surfaces. But they will still be called humanoid robots.

There will be many, many robots with different forms for different specialized jobs that humans can do. But they will all still be called humanoid robots.

As with self driving cars, most of the early players in humanoid robots, will quietly shut up shop and disappear. Those that remain will pivot and redefine what they are doing, without renaming it, to something more achievable and with, finally, plausible business cases. The world will slowly shift, but never fast enough to need a change of name from humanoid robots. But make no mistake, the successful humanoid robots of tomorrow will be very different from those being hyped today.

Neural networks

Despite their successes with language, LLMs come with some serious problems of a purely implementation nature.

First, the amount of examples that need to be shown to a network to learn to be facile in language takes up enormous amounts of computation, so the that costs of training new versions of such networks is now measured in the billions of dollars, consuming an amount of electrical power that requires major new investments in electrical generation, and the building of massive data centers full of millions of the most expensive CPU/GPU chips available.

Second, the number of adjustable weights shown in the figure are counted in the hundreds of billions meaning they occupy over a terabyte of storage. RAM that is that big is incredibly expensive, so the models can not be used on phones or even lower cost embedded chips in edge devices, such as point of sale terminals or robots.

These two drawbacks mean there is an incredible financial incentive to invent replacements for each of (1) our humble single neuron models that are close to seventy years old, (2) the way they are organized into networks, and (3) the learning methods that are used.

That is why I predict that there will be lots of explorations of new methods to replace our current neural computing mechanisms. They have already started and next year I will summarize some of them. The economic argument for them is compelling. How long they will take to move from initial laboratory explorations to viable scalable solutions is much longer than everyone assumes. My prediction is there will be lots of interesting demonstrations but that ten years is too small a time period for a clear winner to emerge. And it will take much much longer for the current approaches to be displaced. But plenty of researchers will be hungry to do so.

LLMs

So we all know we need guard rails around LLMs to make them useful, and that is where there will be lot of action over the next ten years. They can not be simply released into the wild as they come straight from training.

This is where the real action is now. More training doesn’t make things better necessarily. Boxing things in does.

Already we see companies trying to add explainability to what LLMs say. Google’s Gemini now gives real citations with links, so that human users can oversee what they are being fed. Likewise, many companies are trying to box in what their LLMs can say and do. Those that can control their LLMs will be able to deliver useable product.

A great example of this is the rapid evolution of coding assistants over the last year or so. These are specialized LLMs that do not give the same sort of grief to coders that I experienced when I first tried to use generic ChatGPT to help me. Peter Norvig, former chief scientist of Google, has recently produced a great report on his explorations of the new offerings. Real progress has been made in this high impact, but narrow use field.

New companies will become specialists in providing this sort of boxing in and control of LLMs.

A note on embodiment

But since 1991 I have made a distinction between two concepts where a machine, or creature can be either, neither, or both situated and embodied. Here are the exact definitions that I wrote for these back then:

[Situatedness] The robots are situated in the world—they do not deal with abstract descriptions, but with the here and now of the world directly in-fluencing the behavior of the system.

[Embodiment] The robots have bodies and experience the world directly—their actions are part of a dynamic with the world and have immediate feed-back on their own sensations.

At first glance they might seem very similar. And they are, but they are also importantly different. And, spoiler alert, I think much of the work at companies, large and small, right now, is trying abstract out the embodiment of a robot, turning it into a machine that is merely situated.

Later:

Being both situated and embodied is still a challenge to robots in the world. [[Now here is the most important sentence of this whole blog post.]] I think the training regimes that [are] being used for both locomotion and dexterity are either ignoring or trying to zero out the embodiment of physical robots, their inertias and forces, reducing them to merely being situated, just apps with legs and arms, characters in video games, not the reality of real physical beings that the tasks we want them to do requires.