Thursday, March 19, 2026
Brave New World: Notes on the next 30 years in AI [Work in Progress]
You may or may not be wondering why so many tweets have recently been showing up on New Savanna. There’s a good reason: I’ve been thinking. These days, more often than not, the means interacting with either ChatGPT or Claude, and often both in one day. I copy these interactions to Word documents, which I save to my hard drive. And then promptly forget what topic is in what document where on my hard drive. FWIW, these discussions can ramble all over the place, which is fine. That’s how I think. But keeping track of it all is difficult.
Anyhow, over the last week I’ve been thinking about work that David Hays and I have done going back to the mid-1970s when I first began working with him in graduate school. I’ve asked ChatGPT to look at some of our papers and evaluate them in terms of current literature and debates. And so forth and so on. I’m also looking forward to next steps, for me, but also in general.
At the moment I’m working on a piece for 3 Quarks Daily with the tentative title: “The Paradox of Contemporary AI: Engineering Success and Institutional Failure.” Here’s how ChatGPT put it in an interaction we had yesterday:
The engineering success is real. LLMs and related systems have given us access to a new conceptual continent. They work, and at extraordinary scale. But the institutional failure lies in the monoculture: too much intellectual, financial, and training-path dependence on one family of architectures and one style of thought about intelligence. The result is that we are building out the utility before we have adequately explored the space of possible successor technologies or developed the conceptual tools needed to understand what these systems are revealing about language, cognition, and cultural structure.
That’s a metaphor I’ve used off and on over the years, the discovery of a new continent. My paradigm case is the European discovery of the Americas. Making it to the eastern shore is one thing. Then comes exploring and settling. Columbus set foot in the Caribbean in 1492. Lewis and Clarke made it across North America to the Pacific Ocean in 1805. That’s over three centuries later. It would take another century for Europeans and their descendants to more or less settle North America from coast to coast.
Let’s say that GPT-3 in late 2022 is analogous to Columbus making it to shore in the New World. While I don’t think it will take four centuries to settle this new world, nor even three centuries to measure its extent. Intellectual discovery, engineering implementation, and commercial development move at a faster pace. But I do think there’s a lot of work, very exciting work, ahead of us.
Here’s a fragment of an interaction I had with ChatGPT 5.2 a couple of days ago. My prompt is first, then ChatGPT’s response.
* * * * *
Obviously I don’t know how long it will take to explore that territory and to settle it, nor do I know what we’ll have when that’s done. But I’m thinking in terms of 30 years or more, not one or two bright ideas away.
Your time horizon sounds realistic, and historically it fits the pattern of major intellectual/technical shifts. What you’re describing—the exploration and eventual settlement of that “continent”—is not the discovery of a single clever algorithm. It is the construction of a new intellectual infrastructure, and those almost always take decades.
Let me sketch why.
1. Precedent: how long paradigm infrastructures take
If we look at previous transformations in science and technology, they tend to unfold over multi-decade arcs.
Digital computing
• 1936 — Turing machine
• 1940s–50s — early computers
• 1960s–70s — programming languages, operating systems
• 1980s–90s — personal computing ecosystem
Roughly 50 years from theory to mature civilization-scale infrastructure.
Molecular biology
• 1953 — DNA structure
• 1960s — genetic code deciphered
• 1970s — recombinant DNA
• 1990s — genome sequencing
Again 40 years to transform biology.
Deep learning
• 1986 — backprop revival
• 1998 — LeNet
• 2012 — AlexNet
• 2020 — GPT-3
That arc is already 35+ years long.
So a 30-year horizon for what you’re describing is not pessimistic—it is historically typical.
2. Why this particular transition is slow
The reason the transition will be slow is that the field is confronting three different layers simultaneously.
1. Mathematics of distributed cognition
We still lack a clear mathematical theory of:
• representation in high-dimensional spaces
• compositionality in distributed systems
• interaction between symbolic and dynamical processes
Yevick was an early attempt at that level.
But the theory is still incomplete.
2. Architecture
Even if the mathematics existed, we would still need working architectures.
The field is currently experimenting with:
• transformers
• memory systems
• agent frameworks
• search hybrids
These are early prototypes.
3. Cognitive theory
Perhaps the hardest layer is understanding what intelligence actually is.
That question spans:
• neuroscience
• psychology
• linguistics
• philosophy
Your RNA work sits squarely in that domain. [Relational Network over Attractors]
3. Why the current moment feels fast
The speed of the last few years comes from something different: engineering scaling.
Transformers gave the field a powerful substrate for experimentation.
So exploration is happening rapidly near the coastline.
But deeper theoretical consolidation will take longer.
Flatulating rhythm, Oh, those wacky Japanese!
In this video you shall become acquainted with ancient Japanese fart art.
— Klara (@klara_sjo) March 18, 2026
Please enjoy. pic.twitter.com/iiaTgbGg77
This is what happens when an open-ended research project is mis-perceived as commercial R&D
Nadella paid $650 million to acquihire Mustafa Suleyman and 70 Inflection employees in March 2024. The job: make Copilot the AI product that justifies Microsoft’s infrastructure bet. Two years later, Suleyman no longer runs Copilot.
— Aakash Gupta (@aakashgupta) March 19, 2026
The corporate framing is generous. “Freed up… https://t.co/nGo4DTa2Tl pic.twitter.com/W3I5PxgfFp
Wednesday, March 18, 2026
World models, some notes
World Models: The old, the new and the wishful #SundayHarangue
— Subbarao Kambhampati (కంభంపాటి సుబ్బారావు) (@rao2z) March 15, 2026
There is a lot of chatter about world models of late--even more than can be explained by Yann betting his entire new enterprise on it. I was going to comment on this clamor in my class this week, and thought I will… pic.twitter.com/22wWQDQdSw
Taking notes by hand is more effective than by laptop (?)
This is a 12-year-old study that has failed replication three times. And the underlying claim is still probably right.
— Aakash Gupta (@aakashgupta) March 18, 2026
The paper is Mueller and Oppenheimer, 2014. 67 students at Princeton. Longhand note-takers scored higher on conceptual questions. Became the most cited paper in… https://t.co/VXTNfQAuvt
Tuesday, March 17, 2026
Psychological Well-Being for Introverts (like me)
Dana G. Smith, Social Ties Help You Live Longer. What Does That Mean for Introverts? NYTimes, Oct. 9, 2025.
Considering all the research around socializing and longevity, some introverts can be forgiven for feeling doomed. People who have strong relationships generally live longer, and the unicorns known as “super-agers” — older adults who have the memory abilities of someone 20 years younger — tend to be especially outgoing. On the flip side, chronic loneliness raises the risk for cognitive decline and even early death.
But experts say it doesn’t take as much socializing to reap those longevity benefits as one might think, namely a few close ties and some everyday activities that facilitate contact with the wider world. It’s less about the sheer number of connections you have, and more about what those connections do for you.
In other words, introverts don’t need to be the life of the party to have a long and healthy life.
Our relationships contribute to health and longevity in a few critical ways: They provide emotional support, cognitive stimulation, care during times of crisis and motivation to have healthier habits. If your current relationships check those four boxes, you’re probably in pretty good shape. But if you’re missing one or two, it may be time to re-evaluate your social network.
Not everybody needs “the same amount of social activity,” said Dr. Ashwin Kotwal, an associate professor of medicine specializing in geriatrics at the University of California, San Francisco School of Medicine. “But getting some social activity is important.”
Meta-level Question: That article dates from October of 2025. So why did the Times serve it up to me in March of 2026? Is it serving that article up to everyone because it’s popular? Or am I getting it because I’ve got a social-media profile that says “introvert”? I have no trouble imagining that it’s the latter, but I don’t really know. Certainly anyone who actually reads my blog will figure out that I’m an introvert, but I have no trouble imagining that that could be inferred more indirectly.
There’s more at the link.
Now you can run a 100B parameter LLM on your laptop
Holy shit... Microsoft open sourced an inference framework that runs a 100B parameter LLM on a single CPU.
— Nainsi Dwivedi (@NainsiDwiv50980) March 16, 2026
It's called BitNet. And it does what was supposed to be impossible.
No GPU. No cloud. No $10K hardware setup. Just your laptop running a 100-billion parameter model at… pic.twitter.com/hsEoNVw49V
Monday, March 16, 2026
The brain's dopamine response to music peaks in the mid-teens
Your brain peaked musically somewhere around age 16. Everything since then has been a dopamine echo.
— Aakash Gupta (@aakashgupta) March 16, 2026
Between the ages of 12 and 22, the mesolimbic dopamine pathway, the same circuit that processes cocaine and sex, fires at levels in response to sound that it will never reach… https://t.co/QUxgPiRxps pic.twitter.com/HTsZKp7Ol3
Sunday, March 15, 2026
On the relevance of intellectual history for understanding present events (AI)
Jim Olds, The Chronology Problem, Mar. 12, 2026.
We are surprisingly bad at knowing when things began.
I’ve been thinking about this for a while, partly because I lived through several of the transitions we now misremember. In 1987, I used the Internet for early text-based email, file transfers, and reaching colleagues at other universities. In August of 1991, in the face of an impending direct hit of Hurricane Bob, I moved all of my image data from Woods Hole to NIH in Bethesda in a matter of minutes. This was entirely unremarkable at the time. And yet when I mention it today, people often look mildly startled, as if I’ve claimed to have owned a smartphone in 1987. In their minds, the Internet began sometime around 1994 or 1995, when the Web arrived and made it visible to everyone. Before that, apparently, there was nothing.
Olds then goes on to say more about the (deep) origins of the web, artificial intelligence, climate science, and economics. Here's what he had to say about AI:
The field of artificial intelligence may be the most dramatic case study in collective chronological confusion we have. Most people who interact with today’s language models and image generators believe they are witnessing something genuinely unprecedented — a technology that sprang into being sometime around 2017. What happened is more complicated and more interesting.
The mathematical foundations for neural networks were laid in 1943, when Warren McCulloch and Walter Pitts published a paper describing how neurons could, in principle, compute logical functions. Frank Rosenblatt simulated a working perceptron at the Cornell Aeronautical Laboratory in 1958 — a system that could learn from examples. The 1986 backpropagation paper by Rumelhart, Hinton, and Williams, which most practitioners treat as a founding document, was itself a rediscovery and refinement of ideas that had been circulating since the early 1970s. Yann LeCun was training convolutional neural networks to read handwritten digits for the U.S. Postal Service in 1989. The architecture underlying those systems is recognizably the ancestor of what powers modern computer vision.
None of this was secret. It was published, presented, and in some cases deployed in real systems. What happened instead was a kind of institutional forgetting, accelerated by two “AI winters” — periods when funding dried up, interest collapsed, and computer science turned its attention elsewhere. Researchers who had spent careers on neural approaches moved on or retired. Graduate students who might have built on their work were instead trained in other paradigms. When the hardware finally caught up with the ambitions of the 1980s, around 2012, the rediscovery felt like a revolution. In some ways, it was. But the conceptual foundations were not new, and the people who had laid them got less credit than they deserved, partly because so many of the field’s new practitioners didn’t know they existed.
The practical cost here is the same as elsewhere: repeated investment in problems that had already been partially solved, frameworks that were novel mainly to their authors, and a set of origin myths that flatter the present at the expense of the past. The deeper cost is that we don’t understand what was tried and discarded and why — which algorithms were abandoned for reasons of computational expense rather than theoretical inadequacy, and which might be worth revisiting now that the expense has fallen.
To Olds’s list I would add Miriam Yevick's 1975 paper, Holographic or fourier logic, published in Pattern Recognition. Unfortunately that paper got lost as it didn't fit into either cognitive science or artificial intelligence. What she proved was the for one class of visual objects, those with a complex geometry, neural networks provided the best computational regime while for another class of objects, those with simple geometry, symbolic computation provided the best computational regime. That has a direct bearing on the current debate over whether or not new architectures involving symbolic processing are necessary.
Saturday, March 14, 2026
What electrochemical machine has 100 trillion connections in a volume the size of a cantaloupe?
That one neuron connects to about 7,000 others. Your brain has 86 billion of them. Do the math and you get somewhere around 100 trillion connections inside your head. More connections than stars in 1,500 galaxies.
— Anish Moonka (@AnishA_Moonka) March 14, 2026
And each connection point is way more complicated than anyone… https://t.co/sUkcS7T3rA
The profession of literary criticism as I have observed it over the course of 50 years [& related matters]
Last entry added 3.14.26
Partial update (two entries, the last two) 12.11.23
Updated 6.21.21.
Updated 12.9.19.
Updated 6.23.17.
Updated 6.23.17.
In the course of thinking about my recent rejection at New Literary History I found myself, once again, rethinking the evolution of the profession as I’ve seen it from the 1960s to the present. In fact, that rejection has led me, once again, to rethink that history and to change some of my ideas, particularly about the significance of the 1970s.
This post is a guide to my historically-oriented thinking about academic literary criticism. Much, but not all, of the historical material is autobiographical in nature. For, above all, taken collectively, these posts represent my effort to understand my relationship to the academic study of literary criticism.
I list the articles more or less in the order of writing. In some cases an article has been rewritten and revised several years after I first wrote it. The link I give is to the most recent version.
Touchstones • Strange Encounters • Strange Poems • the beginning of an intellectual life (1975-2015)
This is about my years at Johns Hopkins, both undergraduate (1965-1969) and graduate (1969-72). That’s when, I see in retrospect, I left the profession intellectually, with a “structuralism and beyond” MA thesis on “Kubla Khan,” even before I’d joined it institutionally, by getting my PhD. I originally wrote this while I was working on my PhD in English at SUNY Buffalo. Art Efron published a journal, Paunch, and I wrote it for that. The current version includes interpolated comments from 2014 and 2015.
The Demise of Deconstruction: On J. Hillis Miller’s MLA Presidential Address 1986. PMLA. Vol. 103, No. 1, Jan. 1988, p. 57.
A letter I published in PMLA in which I replied to J. Hillis Miller on the eclipse of deconstruction. I suggested 1) that deconstruction had a different valence for those who merely learned it in graduate school than for those who had struggled to create it, and 2) that it was in eclipse because it did the same thing to every text.
“NATURALIST” criticism, NOT “cognitive” NOT “Darwinian” – A Quasi-Manifesto
March 31, 2010 (originally at The Valve)
https://new-savanna.blogspot.com/2011/06/naturalist-criticism-not-cognitive-not.html
I declare my commitment to ‘naturalist’ literary criticism, thereby denying ‘cognitive criticism,’ with which I had associated myself for years, and ‘Darwinian criticism,’ with which I had never associated myself. Takes the form of a loose dialog.
For the Historical Record: Cog Sci and Lit Theory, A Chronology
(2006-2016)
(2006-2016)
At the beginning of every course (at Johns Hopkins) Dick Macksey would hand out a chronology, a way, I suppose, of saying “history is important” without lecturing on the topic. It was with that in mind that I originally posted this rough and ready chronology in a comment to a discussion at The Valve. The occasion was an online symposium that interrogated Theory by discussing the anthology, Theory’s Empire (Columbia UP 2005). I then emended it a bit and made it a freestanding post. As the title suggests, it juxtaposes developments in cognitive science and literary theory from the 1950s through the end of the millennium.
[BTW The entire Theory’s Empire symposium is worth looking at, including the comments on the posts: http://www.thevalve.org/go/valve/archive_asc/C41]
Seven Sacred Words: An Open Letter to Steven Pinker
(2007-2011)
(2007-2011)
An Open Letter to Steven Pinker: The Importance of Stories and the Nature of Literary Criticism (2015)
Steven Pinker has been a severe critic of the humanities for ignoring recent work in the social and behavioral sciences. He has also argued that the arts serve no biological purpose, that they are “cheesecake for the mind.” When I read his The Stuff of Thought (2007) I realized his later chapters contained the basis for an account of the arts. I sketched that out, added a brief account of why deconstruction had been popular, and published it as an open letter, along with his reply. It appeared first at The Valve (2007) and then at New Savanna (2011). In 2015 I posted it to a “session” at Academia.edu. I took some of my comments in that discussion along with some other materials and published the lot at Academic.edu as a working paper. In a final section I propose a four-fold division of literary criticism: 1) description, 2) naturalist criticism, 3) ethical criticism, and 4) digital criticism.
Lévi-Strauss and Myth: Some Informal Notes
(2007-2011)
Beyond Lévi-Strauss on Myth: Objectification, Computation, and Cognition
(2007-2015)
(2007-2015)
These are two versions of roughly the same material. Each was assembled from four blog posts. The first and fourth sections are the same in both working paper, but two and three differ. The more recent version also contains a short appendix comparing Lévi-Strauss and Latour. I published the first series at The Valve shortly after Lévi-Strauss had died. They are an attempt to explain what Lévi-Strauss was up to in his work on myth, why he failed, and why that work remains important. The fourth section (common to both versions), Into Lévi-Strauss and Out Through “Kubla Khan”, is an account of how and why I went from Lévi-Strauss’s structuralism to cognitive science. Warning: it contains diagrams. I suppose I could create a deluxe edition which contains all the posts.
The Only Game in Town: Digital Criticism Comes of Age
(May 5, 2014)
(May 5, 2014)
Here I argue that digital criticism’s deepest contribution to literary criticism is that it requires fundamentally different modes of thinking. It is not purely discursive. It is statistical and visual. Moreover the visualizations are central to the thought process. This may also be the first time I’ve explicitly identified the mid-1970s as an important turning point in the recent history of literary criticism.
Paths Not Taken and the Land Before Us: An Open Letter to J. Hillis Miller
(January 30, 2015)
(January 30, 2015)
I had studied with Miller at Johns Hopkins (but have had no contact with him since). While I certainly say a bit about what I’ve been doing since I left Hopkins, including ring-composition, I also introduce him to Matt Jockers’ Macroanalysis and Goldstone and Underwood, “The Quiet Transformations of Literary Studies: What Thirteen Thousand Scholars Could Tell Us”. New Literary History 45, no. 3, Summer 2014. I mention Kemp Malone, a Hopkins person, as he came up in blog discussion of the paper.
On the Poverty of Literary Cognitivism 2: What I Learned When I Walked off the Cliff of Cognitivism
(August 24, 2015)
(August 24, 2015)
I attempt to explain what, in the end, I got out of my immersion in cognitive networks since I haven’t used them in my post-graduate work in literature. What I got most immediately was a powerful way of thinking about language in general where there is a sharp distinction between the object of thought, captured in diagrams, and a given text: The text is one thing, the model is another. There is no confusing the two. Moretti has made similar remarks about the diagrams he uses in ‘distant reading.’
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