The question is not whether AI is a bubble. Nor is it whether NVIDIA is expensive, whether Michael Burry is early again, or whether productivity gains will eventually lower inflation. The better question is an accounting one: who needs to earn what return for the AI capex cycle to make sense?
At the bottom of the stack, GPU, HBM, networking, power, and cooling suppliers are already earning. The capex is real. The checks have cleared. That was the first phase of the trade.
The harder question sits one layer above. Hyperscalers and neoclouds are converting capital into compute. Compute becomes tokens. Tokens must become revenue. Revenue must become gross profit after depreciation, power, financing, and model costs. And finally, the buyers of those tokens must earn a return high enough to keep spending.
Only then does AI become a true macro productivity shock rather than a capital-spending boom with better branding.
Then comes the long analysis, followed by these concluding remarks:
The AI cycle will not be resolved by asking whether GPUs are expensive or whether chatbots are useful. It will be resolved by a chain of returns.
Can infrastructure suppliers earn margins without oversupply?
Can hyperscalers and neoclouds sell enough tokens to cover depreciation, power, financing, and obsolescence?
Can enterprises earn more from those tokens than they spend?
Can the economy convert those firm-level returns into productivity growth?
And if it can — does the bond market understand that higher productivity may mean a higher neutral rate, not just lower inflation?
That is the real AI macro debate. Each layer has a hurdle, and each hurdle has a date with evidence. The next time an AI headline crosses your screen, skip the bubble question and ask the ledger question: which layer is this, and what return does it need?
The framework carries its own falsifier, as it should: monetizable AI revenue clearing the hurdle band for years while aggregate productivity and corporate operating leverage outside the supplier base stay flat. That would mean buyers funding sellers indefinitely without a return — the one thing this piece says cannot last.
The first phase was about capex. The next phase is about returns.
Chris Murphy, the Democratic senator from Connecticut, offered the graduates of Wesleyan University wise counsel in his commencement speech a few weeks back. “You are about to step out into a world that prizes efficiency and the annihilation of drift and friction above all else,” he said. “Our entire economy is built on rewarding companies that are efficient at making a profit, not based upon how they treat their workers, the social value of their product or the impact they have on the community.”
“You didn’t design this world,” he continued. “You didn’t choose it. But you will live with the consequences of this cult of efficiency. And you will have to choose which side you are on.”
Efficiency inflicted on pigs:
In 2016 and 2018, voters in Massachusetts and California passed ballot initiatives banning, among other things, the sale of pork from pigs confined in gestation crates. These crates confine breeding sows — large animals, often 400 to 500 pounds — in two-by-seven-foot cages in which they cannot so much as turn around, much less root or socialize. Because sows are often reimpregnated about a month after their piglets are born, they can spend years of their lives in these crates.
I watched, in the interest of fairness, a video from an arm of the National Pork Board on why gestation crates are good for pigs. It features row upon row of sows penned between bars so narrow they cannot turn around. It is no way for any animal to live, particularly not one as smart and as social as a pig.
There are studies I can cite on the psychic and physical violence these crates inflict on pigs — the elevated cortisol levels, the sores, the obsessive biting of metal bars — but I think the way Kristof puts it is simpler and more honest: “Think of your dog enduring what pigs face, and you realize that the moral cost is incalculable.” The difference between dogs and pigs is neither their intelligence nor their sentience. It is our willingness to admit their intelligence and their sentience. It is our decision to extend them our compassion and concern.
Homo economicus:
In traditional economics, prices are the informational lifeblood of an economy: They reveal the cost of materials and labor, the balance of supply and demand. But much can be hidden in prices. Perhaps it is artificially low because waste is being dumped into the rivers or workers are being robbed of their wages or the burden is borne by animals that will spend years of their lives without the comfort of their herd or the ability to feel grass beneath their hooves or turning around when curious about a sound. When that happens, we have sacrificed compassion for cost.
Most of us know by now that the lives animals lead in factory farms are often hideous. A 2019 survey by the Johns Hopkins Center for a Livable Future found that a majority of Americans wanted stronger oversight of confined animal feeding operations and a plurality wanted a ban on new ones. Those numbers were even higher when the same pollsters asked Iowans and North Carolinians, where majorities favored a ban on new concentrated animal feeding operations, in part because these operations often impose terrible costs on the human beings who live near them.
Burton Malkiel, best known for his 1973 book, A Random Walk Down Wall Street, has some interesting remarks in the NYTimes for those who are wary of being stuck with a stake on SpaceX (and perhaps OpenAI and Anthropic as well) as a consequence of having index funds in their 401(k).
Given that so many millions of Americans are suddenly having SpaceX shares foisted upon them, I understand why some financial experts are criticizing the practice of index investing itself. Right now, just a handful of A.I.-related stocks represent almost half the value of the total stock market index. If A.I. stocks collapse, so will the worth of your index fund.
This is the paragraph I find particularly interesting:
Unlike prior initial public offerings, SpaceX shares are already so expensive there isn’t a lot of upside potential left. When Facebook, now Meta, went public in 2012 with a valuation of $100 billion, shareholders were able to benefit financially from its growth to a $1.5 trillion giant. Amazon went public with a generous (for 1997) valuation of $440 million, and shareholders profited as it grew into a $2.5 trillion behemoth. Not so SpaceX. Because it was owned by private investors for so long, much of the gain will immediately be handed off to its venture and private equity backers rather than preserved for new investors.
He goes on to point this as well:
Moreover, unlike other public companies, SpaceX is employing a dual class share structure that gives Elon Musk essentially complete control with no independent oversight. Public shareholders will, comparatively, have no voice in corporate decisions. Mr. Musk controls multiple related corporate enterprises, raising the possibility of conflicted transactions within the Musk ecosystem. Many investors will be uncomfortable giving him so much power and holding an index fund in which he has so large a share.
He then says:
These are all legitimate reasons to worry. But in my view, it would be a mistake to abandon an indexing strategy. Timing the market is impossible. Yes, the stock market is unusually concentrated today, and it is likely to get even more so over the next period with Anthropic and OpenAI looking to go public soon.
You can read the rest of the article, if you wish. But it's that paragraph about upside potential that caught my attention.
I had a very interesting conversation with Google's AI – by which I mean the AI on the standard search page. I asked Claude to summarize it. Pay particular attention to the penultimate paragraph about alignment.
An exercise for the reader: What are the implications of this conversation for the idea of super-intelligence? In the words of Aretha Franklin, “Who’s zoomin’ who?”
Overview
This is a transcript of a wide-ranging conversation between you and Google's AI, structured around the concept of AI's "jagged" capabilities — the phenomenon where AI excels at complex tasks but stumbles on apparently simple ones, with no predictable boundary between the two.
The Arc of the Conversation
The document moves through ten topics:
Jagged Skills & Moravec's Paradox — You open by asking about the origins of the "jagged frontier" concept (traced to Harvard Business School researchers in 2023, popularized by Ethan Mollick). You immediately point out that this is essentially a replay of Moravec's Paradox from the 1980s — the AI agrees, but notes some differences: the modern jaggedness is intra-domain (within knowledge work) rather than the macro divide between symbolic reasoning and physical/perceptual tasks, and human intuition about where the failures will occur has now completely broken down.
Cyborg & Centaur Workflows — You steer toward practical implications. The AI explains two human-AI collaboration strategies: Centaurs (clean division of labor, human handles reality, AI handles execution) and Cyborgs (deeply interleaved real-time co-authorship). You frame the underlying issue as being about the relationship between a computing system and the nature of the world it computes over — a framing the AI endorses.
Hallucinations — The AI argues (and you presumably agree) that "confabulation" is a better term than "hallucination" for LLM errors: like neurologically impaired patients, the LLM's narrative engine runs flawlessly while its error-checking against reality is absent.
Scaling — Discussion of whether scaling (more data, more compute) will smooth the jagged frontier. The AI describes the "scaling wall" now being hit: data drought, model collapse from training on AI-generated content, and diminishing returns — pointing toward structural, not just quantitative, limits.
Miriam Yevick & Holographic Logic — Here your own intellectual history enters the conversation. You surface Yevick's 1975 Pattern Recognition paper on Holographic vs. fourier logic, which you discovered in 1978 via a comment she made on a Haugeland article in Behavioral and Brain Sciences. The AI treats this as a profound, forgotten piece of computer science that precisely explains the mechanism behind the jagged frontier.
Principles and Development of Natural Intelligence (1988) — You describe how you and David Hays incorporated Yevick's insight into your 1988 Journal of Social and Biological Structures paper. The AI discusses how that paper, if injected into current debates, would reframe LLMs as having "hacked" the top-level indexing principle of intelligence (via language) while lacking the foundational lower layers — modal, feedback, Gestalt — that ground intelligence in reality. Hallucinations become not a bug but an architectural inevitability.
Ramesh & Wikipedia — You mention your ongoing collaboration with Ramesh Viswanathan (machine vision, Goethe University Frankfurt) and the Wikipedia entry on Yevick that now exists, connecting her Fourier logic to David Bohm's holonomic brain theory and your 1988 paper.
ChatGPT Tells Stories — You describe your working paper "ChatGPT Tells Stories, and a Note about Reverse Engineering," in which you apply Lévi-Strauss's structuralist transformation method to probe ChatGPT's internal constraints. Ramesh has students working with the data. The AI frames this as demanding a "top-down behavioral interpretability" that the industry's current bottom-up neuron-level approach entirely misses.
Suggestions for Students — The AI offers three research frameworks for your upcoming Zoom with Ramesh's students: a "Narrative Stress-Test" benchmark, 3D vector-space visualization of holographic ripple effects through story transformations, and a co-authored manifesto paper for venues like NeurIPS.
AI Researchers Need Their Alignment Adjusted — You wrap up with a characteristically dry observation: it's "fitting and ironic" that LLM-based chatbots can understand Yevick's work better than the engineers who built them. You suggest the researchers need their alignment adjusted — and sign off to get breakfast.
The Document's Character
It reads as both an intellectual exploration and a kind of vindication narrative — the AI functions as a surprisingly capable interlocutor for ideas that the mainstream AI research community has largely overlooked. The irony you close with is the document's real punchline: the systems built on these mathematical structures understand them better than their builders do.
Hockney was one of the world’s most successful artists, and could have done anything, anywhere. But his late-life art was laser-focussed on the transient seasonal beauty of the English, particularly Yorkshire countryside, his best works drawn digitally on an IPad. pic.twitter.com/EPOZUfdBzZ
In this town, almost every communal space is private property. A company controlled by the world’s richest man owns nearly all of it. He shapes its future.
This is Starbase, Texas, the city that Elon Musk built on America’s ragged hem at the southern border as the home for SpaceX, his aerospace and artificial intelligence company. Locals describe a highly secretive environment overseen by a company-affiliated city commission that rubber-stamps Mr. Musk’s vision, a place where even kindergartners are guided by his philosophies. Starbase is the newest manifestation of Mr. Musk’s political power. It is a beta test for a rising oligarchy that seems intent on transforming America from the inside out. [...]
On May 12, Mr. Musk announced on social media that “SpaceX is considering several locations domestically and internationally to build the world’s most advanced spaceports!” His announcement came on the heels of reports that a large parcel of land in coastal Louisiana may have been acquired by an anonymous aerospace company, widely rumored to be SpaceX.
These spaceports will allow Mr. Musk to create his own reality for other people to live in. He doesn’t need Mars. Mr. Musk has already built a colony of his own.
Mr. Musk often cites “Star Trek” as inspiration for founding SpaceX. “We want to make ‘Star Trek’ real, OK?” he said in January. But Starbase bears less similarity to the enlightened wonderland depicted in that 1960s television show than it does to the autocratic company towns of the 19th and early 20th centuries. Like Mr. Musk, the industrial titans of that era built their own private fiefs, not only to cement control over workers, but to realize their vision of an ideal society.
Perhaps the most grandiose company town of them all was Fordlandia, the sprawling city that Henry Ford built in the Brazilian rainforest to grow rubber trees. Fordlandia was Ford’s personal Utopia, an expression of his social views, his personal predilections and even his vegetarianism. Workers were forced to subsist on a diet heavy on brown rice, oatmeal and canned peaches, as detailed in Greg Grandin’s “Fordlandia: The Rise and Fall of Henry Ford’s Forgotten Jungle City.” For amusement, there was square dancing — Ford loved square dancing — and poetry readings.
Fordlandia’s ghost haunts Mr. Musk’s colony. Corporate control is so all-encompassing at Starbase that a warning on the menu at its Astropub restaurant alerts diners to the “confidentiality and proprietary nature” of the fare. Students at its private Ad Astra school are guided on “hands-on experiential missions.” The interplanetary mission is even written into the job description for a facilities supervisor overseeing waste management and janitorial needs.
There's much more at the link. It's not pretty reading.
Judah Goldfeder, Philippe Wyder, Yann LeCun, Ravid Shwartz-Ziv, AI Must Embrace Specialization via Superhuman Adaptable Intelligence, arXiv:2602.23643v1 [cs.AI], 2026.
Abstract: Everyone from AI executives and researchers to doomsayers, politicians, and activists is talking about Artificial General Intelligence (AGI). Yet, they often don't seem to agree on its exact definition. One common definition of AGI is an AI that can do everything a human can do, but are humans truly general? In this paper, we address what's wrong with our conception of AGI, and why, even in its most coherent formulation, it is a flawed concept to describe the future of AI. We explore whether the most widely accepted definitions are plausible, useful, and truly general. We argue that AI must embrace specialization, rather than strive for generality, and in its specialization strive for superhuman performance, and introduce Superhuman Adaptable Intelligence (SAI). SAI is defined as intelligence that can learn to exceed humans at anything important that we can do, and that can fill in the skill gaps where humans are incapable. We then lay out how SAI can help hone a discussion around AI that was blurred by an overloaded definition of AGI, and extrapolate the implications of using it as a guide for the future.
In view of the articles in this special double-issue of Dædalus, AI & Science: What Is the Future of Discovery?, I must agree. Yes, the ascent of Mount AGI will continue, but at the same time we will be developing more specialized AIs for specific tasks, AlphaFold is one example, but it is only one of many. Back in 1990 David Hays and I published an article in which we asserted, "Sooner or later we will create a technology capable of doing what, heretofore, only we could." We didn't put any dates on that, nor did we envision today's technology, but we could see the long-term trend. And that trend certainly includes specialized AIs. Think of them as intelligent instruments.
[Hmmm... Why don't we think of trains, planes, and cars as superhuman vehicular transportation (SVT)?]
Cortical pyramidal neurons possess elaborate dendritic trees with diverse nonlinear membrane conductances and thousands of plastic synapses, suggesting substantial computational capabilities at the single-cell level. Yet, what can a neuron compute remains an open question, largely due to the lack of a systematic framework to quantify its computational capabilities. We introduce TwinProp, a digital-twin-based backpropagation algorithm that enables gradient-based optimization of synaptic strengths and dendritic locations in detailed neuron models via a millisecond-accurate deep neural network (DNN). Using TwinProp, we demonstrate that a detailed model of rat layer 5 pyramidal cell (L5PC) can perform naturalistic image and audio classification tasks at a remarkably high accuracy, significantly surpassing perceptron and leaky integrate-and-fire baselines. The same neuron solves high-dimensional nonlinear problems, including exclusive-or (XOR), 10-bit parity, and random Boolean tasks, demonstrating capabilities typically attributed to multilayer networks. Mechanistically, increasing task complexity recruits distributed dendritic nonlinearities, including NMDA- and voltage-dependent mechanisms; removing these or collapsing dendritic structure markedly impairs performance. These findings identify dendrites as a substrate for high-order feature binding and position single cortical pyramidal neurons as powerful, noise-robust, general-purpose analog computational units. Our results offer testable in vivo predictions and provide a systematic framework linking cellular morpho-electrical properties to computation in both brains and artificial systems.
