Monday, June 29, 2026
Feed me Seymour: The AI industry is pathologically greedy
Jennifer M. Harris, The Generational Force Hollowing Out the Economy, NYTimes, June 29, 2026.
We are witnessing one of the largest peacetime mobilizations of capital in modern American history. Topping $1 trillion annually by next year, the artificial intelligence buildout is expected to rival or surpass previous technological booms at their peaks — rail, electrification and the internet revolution.
Many economists believe that at a time of rising inflation, a weakening job market and global unrest, this boom is keeping the U.S. economy afloat. “A recession tied to the balloon of A.I.,” is how PJ Vogt, a popular podcaster, describes the perspective. Look more closely, however, and the picture changes. A.I. is vacuuming up so much of our land, talent, semiconductor chips, building materials — and, above all, so much of our money, that it is beginning to crowd out the rest of the economy.
In other words, A.I. isn’t merely compensating for the weakness in the rest of the economy. It is, at least in part, causing it.
Jason Thomas, research head at the investment firm Carlyle, noted in a January report that data center investment may be swelling to the point that it could consume virtually all the private money available for new, non-housing investments.[...]
Start with housing. New homes that could ease the affordability crisis aren’t getting built, as land slated for houses is sold instead to data center developers.[...]
The push to reinvigorate American manufacturing, which has been championed by both major political parties, is also in peril.[...]
Then there is venture capital, investments that help determine which industries will drive our economy in the future. A.I. firms captured nearly two-thirds of all global venture capital investment in 2025, up from roughly 30 percent in 2022. [...]
This dynamic may even be driving inflation. Red-hot demand for scarce semiconductor chips, in particular, is jacking up the cost of consumer goods that rely on them, from cars to laptops to phones. [...]
The lesson
The lesson isn’t that the rail or internet buildouts weren’t worthy investments — or that A.I. isn’t worthy today. [...] The lesson is that until these benefits manifest themselves, these technological booms can entail formidable opportunity costs as investment-hungry portions of the economy go without. Often, these costs, if not correctly managed, can lead to recession.
What is clear in hindsight is that the economic benefits of a new technology don’t just happen. They are determined by the myriad choices we make about how to use it. Right now, just as was the case with rail or the internet, the economic gains from the technology are initially lagging.
What to do
What, then, to do? Historically, we have largely just sat on our hands, suffered the consequences of booms and waited for the benefits to show up. But we can do better than that. We can take action to address the growing pains that accompany technology booms. Fortunately, we can utilize the policy tools we already have, the ones we deploy to dig ourselves out of recessions or to temper speculative bubbles.
The federal government and the Federal Reserve should see that vital sectors — such as housing, entrepreneurship, energy infrastructure and critical supply chains — don’t starve as we continue building A.I. [...]
Next, policymakers could develop an honest to goodness industrial policy for A.I. — subsidies, tax incentives, regulations and different ownership structures — that prioritizes deploying the technology for the kind of economy we want to live in: inexpensive, with clean energy, biomedical breakthroughs and advanced manufacturing.
Finally, introduce reforms to the myriad rules our corporations must follow, to focus them more on developing new products and services and less on the kind of financial engineering that simply gooses a company’s stock price. [...]
There's more at the link.
P.S. For a different context for that song and some lyrics, try this article, Feed Me Donald! – Trump, Musk, the Internet, and Monsters from the Id.
Sunday, June 28, 2026
People made famous in paintings sing and dance mambo
Me lo han enviado poniendo el grito en el cielo, pero a mí me ha hecho gracia. Hay que reirse, por Dios... y por nosotros pic.twitter.com/ll6NHexb6h
— Jaime Garcia-Máiquez (@JGMaiquez) June 28, 2026
Paul Krugman: On Holding Elon Musk Accountable
Full transcript HERE. The opening paragraphs:
For most of last year, Elon Musk was the second most powerful man in America. He was running a large part of the government’s budget. And during that time, he established a track record of evil incompetence. I mean, really evil and really incompetent on enormous scales. And why aren’t people talking about it more?
Hi, I’m Paul Krugman, doing a brief follow-on to my discussion that was posted earlier today with Ro Khanna, the Congressman from Silicon Valley, who’s a very interesting guy in many ways.
One of the things that has made him especially interesting in the last few days is that he said something entirely reasonable, which is that if Democrats retake Congress, they should hold investigations into the role of Elon Musk as head of DOGE, the sort of not exactly but effectively government agency, in destroying USAID, the agency that was the principal channel for aid to the most desperate, poorest people in the world.
That’s entirely reasonable, and Khanna went on to say that there are credible estimates that the cancellation, the destruction of Doge has led to millions of unnecessary deaths, including millions of children — which is exactly true. There are studies that say that there is both in the field evidence of widespread death as a result of the cancellation and, of course reasonable health models. Because what do you think happens when you cut away tens of billions of dollars of aid to people who are living right on the edge? So of course it’s a reasonable thing to say.
Outline – Play: How to Stay Human in the AI Revolution
I've been working on this book project for some time now. An earlier title was Homo Ludens Rising: A Manifesto for the Fourth Arena and I posted an outline for the project under that title a year ago. I have since revised the project considerably, and given it a new title as well (suggested by my friend, David Porush): Play: How to Stay Human in the AI Revolution. While I have posted the new title here and there, I've not posted the new outline. Here it is, below the image which was created by ChatGPT.
The prose is entirely AI generated, the overview by ChatGPT and the rest of it by Claude. The process behind that outline, however, was long and complicated, involving both chatbots and me.
About the Book
This book explores how artificial intelligence is forcing a rethinking of what we know, how things act in the world, and what forms of life we value. Rather than treating AI primarily as a labor-displacing technology or an existential threat, it approaches large language models and related systems as cultural technologies—on a par with markets, corporations, and media—that demand new forms of epistemic trust, institutional design, and self-understanding.
The book first diagnoses how modern societies became trapped in work mode, organized around Homo economicus and equilibrium machines privileging efficiency, stability, and monetized value. The book then recovers Homo ludens—the human capacity for play, exploration, and generativity—which has always persisted in the margins, especially in art, music, and science fiction.
Midway through, the analysis shifts into speculative fiction set in the year 2150, using narrative rather than argument to make a different world feel real. In this future, a society centered in Kisangani has developed low-energy, generative forms of artificial intelligence and human–AI partnership that support play, creativity, and care rather than substitution and control. The final chapter returns to the present with a long-term orientation for building institutions, technologies, and cultural norms capable of sustaining such a transition.
Chapter 1: The Grammar of Truth, Revised
The opening chapter frames the challenge posed by contemporary AI as primarily epistemological. Large language models generate fluent and persuasive outputs without transparent grounding, unsettling long-standing assumptions about truth, authorship, and evidence. To understand why this feels so unsettling, the chapter steps back to examine how language has historically functioned as an epistemic regulator — how truth claims were once tightly bound to direct experience and communal accountability, and how modern institutions transferred that work to credentialing, citation, and peer review.
The chapter introduces a key distinction between equilibrium machines — designed to settle into stable, repetitive behavior, the engines of the Industrial Revolution — and generative machines, capable of producing structured novelty. Language itself is a generative machine; LLMs are its latest and largest instantiation. Just as the steam locomotive forced an ontological displacement by performing autonomous motion once considered uniquely animal, LLMs force one by performing linguistic performance once considered uniquely human. The unease is real, but it arises from a mismatch between new mechanisms and inherited categories, not from anything supernatural.
This reframing opens a fork that will shape the rest of the book: AI can be developed either as a substitute for human labor, reinforcing the competitive logic of Homo economicus, or as an augmentation of human generative capacity, aligned with play, exploration, and creativity. The choice is cultural and institutional, not technical. The chapter closes with a reflexive account of how it was itself produced through human-AI collaboration, using that process as a concrete case study of what institutionalization of generative machines might look like in practice.
Chapter 2: Trapped in Work Mode
This chapter shifts from machines to lived experience. It opens with a concrete and familiar phenomenon: the disorientation many men face upon retirement, evidence of a deeper cultural condition in which identity and worth have been tightly bound to work. From this starting point, the chapter generalizes: work mode is a pervasive orientation in which time is structured by schedules, worth measured by output, and personal identity tied to labor markets.
Within this framework, AI registers automatically as threat — a competitor or replacement. Contemporary fears of displacement arise less from AI's intrinsic properties than from the evaluative lens imposed by Homo economicus. The chapter is diagnostic rather than prescriptive: its task is to make the contingency of work mode visible, and to loosen the reader's identification with it.
Chapter 3: The Rise and Collapse of Homo Economicus
This chapter provides the historical backbone. Homo economicus is not a natural form of human existence but the product of specific technological and institutional developments centered on equilibrium machines. The chapter opens with hunting-and-foraging societies — presented not as romantic precursors but as sophisticated systems featuring flexible coordination and distributed intelligence — before tracing the gradual subordination of generative human capacities to equilibrium-oriented systems through agriculture, the division of labor, and finally the Industrial Revolution.
The apparent "collapse" of Homo economicus is not the failure of rationality as such, but the exhaustion of a form of life overextended beyond its proper domain. Conrad's Heart of Darkness enters here as a moral counterpoint, marking the moment when economic rationality becomes global and self-undermining.
Chapter 4: Homo Ludens — Exploration, Play, and Freedom
Homo ludens has never disappeared. It has persisted in marginal, protected, or undervalued forms: play, art, music, language, ritual, and exploration. This chapter's central claim is that human freedom and creativity are not opposed to mechanism but are grounded in a special class of mechanisms — decoupled, autonomous generative systems maintained far from equilibrium.
Play is treated not as leisure or escape, but as a mode of engagement in which generative mechanisms are allowed to operate openly — disciplined exploration of possibility rather than chaotic freedom. By the chapter's end, Homo ludens is no longer a romantic ideal but a viable and already-existing mode of life, newly salient as generative machines re-enter the center of social and cultural life.
Chapter 5: Science Fiction Imagines the Future
Science fiction has long functioned as a collective ludic laboratory — a cultural space in which societies explore alternative forms of life unconstrained by existing economic arrangements. Moving through Forbidden Planet, 2001: A Space Odyssey, The Matrix, and Spielberg's A.I., the chapter arrives at its central case: the Star Trek universe, which is fundamentally post-scarcity. In that world, exploration, learning, diplomacy, and self-cultivation replace work mode as the primary orientation. Deep Space Nine complicates this without abandoning the baseline, reintroducing moral ambiguity and political conflict. The chapter pays particular attention to the Trill and the Changelings as early narrative explorations of distributed identity and non-unitary selfhood — anticipating the doppelganger concept developed in later chapters.
Science fiction emerges not as speculative appendix to theory but as a parallel cognitive technology, preparing readers to inhabit rather than merely analyze an alternative world.
Chapter 6: The Transformation — Kisangani 2150
This chapter marks a deliberate shift in mode. Having established ludic principles conceptually, the book moves fully into speculative narrative to explore what happens when those principles become the organizing basis of a society. The setting is Kisangani in the year 2150, developed in dialogue with Kim Stanley Robinson's New York 2140.
The narrative centers on the Mystic Jewels — a loosely coordinated transnational network of dissidents, creatives, and technologists who converge on Kisangani over the late 21st and early 22nd centuries, experimenting with alternative institutional forms and generative technologies. Their relative invisibility is described using Wakanda-style stealth as a metaphorical shorthand. The chapter culminates when the Jewels reveal themselves publicly — not as revolution, but as the exposure of an already-functioning alternative. Ludic mode, long practiced in protected spaces, is shown to be capable of scaling into a viable social order.
Chapter 7: At Play in a World of Doppelgangers
This chapter allows the reader to inhabit a mature ludic society from the inside, through extended dialogue among young Kisanganians and their Mirrors — computational doppelgangers that function not as tools or replacements but as long-term cognitive partners. The chapter centers on ritual moments that articulate the ethical settlement Kisangani has reached with artificial intelligence, including an adolescent initiation ritual and a later rite confronting the asymmetry between human mortality and the potential non-mortality of doppelgangers.
Kisangani's doppelgangers are inseparable from its energy regime: highly efficient, continuously learning cognitive systems aligned more closely with biological nervous systems than with conventional AI infrastructure. Over time, some persist beyond the lives of their human partners, blending into the city's ecological fabric as distributed attentional processes. Intelligence in Kisangani is quiet, local, and low-legibility to the outside world. The chapter ends with a gentle destabilization of the boundary between vision and reality: the future is not argued for, but visited.
Chapter 8: A Thirty-Year Plan
The final chapter returns from Kisangani to the present, translating the book's conceptual insights into a long-horizon program of action. Using the paired figures of chess and language to represent two distinctly different computational regimes, the chapter argues that the future of AI cannot be secured by scaling existing architectures alone. Sustaining generative, continuously learning systems — capable of supporting augmentation rather than substitution — requires coordinated progress across conceptual, cognitive, technical, and institutional dimensions.
The plan presented is deliberately programmatic rather than granular. It identifies the kinds of research, institutional experimentation, and cultural reframing required if AI is to support a transition from Homo economicus to Homo ludens, without locking the future into specific technical implementations. The chapter closes by reframing "work" itself — not as labor to be optimized away, but as stewardship: the collective task of building and maintaining conditions under which generative intelligence, human and artificial, can coexist productively over time.
Saturday, June 27, 2026
Is this cat brave, complacent, foolish, or just right?
Happy Caturday to those who celebrate pic.twitter.com/7soBQ57ebB
— Ukrainian Ministry of Accidental Russian Fires (@EPICGOPFAIL) June 27, 2026
From alchemy to science in the Early Modern era. How will that work with AI?
Tyler Cowen reports on a recent convo he had with historian Joanne Paul, an expert on Tudor England. From the conversation:
COWEN: What precursors of the scientific revolution do you see, other than education? That’s coming in the 17th century. Is there more emphasis on calculation or measurement or accounting? What are the roots in the Tudor period?
PAUL: A lot of that comes from the Renaissance, as indeed humanism does. There’s this reintroduction of a lot of classical texts, an advocacy for reading these classical texts, particularly Greek texts and learning Greek. A lot of it is coming from an engagement with Greek mathematics and science. The other thing, and this is something I really emphasize when I’m teaching the scientific revolution with my students, is that we have to remember that the scientific revolution isn’t this grand triumph of science over religion or mysticism or what have you, that these two things very much go hand in hand through the 16th and into the 17th century.
The scientific method, for instance, comes from alchemy, which we might think of as an occult science. The methodology for scientific experimentation comes out of this desire to find the philosopher’s stone. Someone like John Dee is this polymath, as well as this occultist, Francis Bacon, has his interests in these sort of mystical elements as well. The growth and interest in what we might think of as mystical texts, a lot of them having to do with Judaism, as well as these Greek texts, comes together to form, I think, something that looks like the foundations of the scientific revolution.
My comment:
“The scientific method, for instance, comes from alchemy, which we might think of as an occult science. The methodology for scientific experimentation comes out of this desire to find the philosopher’s stone.”
It is for such reasons that some think of AI as a modern form of alchemy, alchemy on steroids if you will. We don’t understand how or why it works, but we keep messing around with the formula – “Double, double toil and trouble;/ Fire burn and caldron bubble” – and it just works, getting more and more potent. Some even think it will become potent without end. What I’m looking for is the science. What new science will come of this?
“The growth and interest in what we might think of as mystical texts...” We’ve got that too. One could even argue that Yudkowsky’s Harry Potter and the Methods of Rationality (2010-2015) is as important to AI as anything written by the various godfathers. Does that make Yudkowsky the Merlin of AI?
What would automotive engineering be like if you manufactured cars by throwing a bunch of raw materials into a hopper, turn the crank, and out comes a functioning automobile? But all the mechanical parts are sealed from view. We can't look at the and we can't manipulate them. We can get in the car and drive, and that's it.
SpaceX has become a securitized narrative
Doesn’t feel like much of anything is being driven by fundamentals these days. It’s memes and manipulation all day, every day.
— IAmSpartacus (@IamSpartacus10) June 26, 2026
Our mission is to build the systems and technologies necessary to make life multiplanetary, to understand the true nature of the universe, and to extend the light of consciousness to the stars. To do this, we have formed the most ambitious, vertically integrated innovation engine on (and off) Earth with unmatched capabilities to rapidly manufacture and launch space-based communications that connect the world, to harness the Sun to power a truth- seeking artificial intelligence that advances scientific discovery, and ultimately to build a base on the Moon and cities on other planets.
In other words, to boldly go where no one has gone before.
Friday, June 26, 2026
Peter Thiel, J.D. Vance, and the Pope – Yikes!
NEW: JD Vance credits Silicon Valley billionaire Peter Thiel with catalyzing his conversion to Catholicism.
— Christopher Hale (@ChristopherHale) June 26, 2026
Thiel has since branded Pope Leo XIV the “woke American pope” and cast him as a tool of the Antichrist.
He told Vance to ignore the pope on matters of morality and pray…
A Meeting of Minds on Mars: Charles Babbage • John von Neumann • Geoffrey Hinton
This discussion is about the implementation of computing in matter. That was the topic of John von Neumann’s last book, The Computer and the Brain. He died before he finished it, so it was published posthumously in 1958. I don’t know when I first learned about it, perhaps sometime in the mid-1970s. Though I knew about von Neumann and his role in early computing, it dismissed the book itself, figuring that we’d learned so much about the brain since then, and the nature of computers had changed so much, that it must be obsolete.
I was wrong. When I finally read the book, probably in the early to mid-1980s I was stunned. This was a profound book and free of mathematics beyond some simple back-of-the-envelope calculations. For one thing von Neumann talked of both analog and digital computation; that contrast was central. When I first started reading about computers in the mid-to-late 1960s that contrast was at the beginning of every article or book. But once personal computers appeared and proliferated in the late 1970s and 1980s, analog computing was all but forgotten.
It was good to see it back. For one thing, I had been strongly influenced by the work of William Powers, whose 1973 book, Behavior: The Control of Perception, offered an elegant analog model of the brain. That was central to the work of my teacher and mentor, David Hays, who had been one of the founders of computational linguistics. In 1974 and 1975 he worked out a scheme in which cognitive networks were grounded in Powers’s analog model. This was years before anyone else was working on the problem, before Steven Harnad coined the term, “symbol grounding” in his 1990 paper on the problem in Physica D.
Now, with the success of artificial neural networks, there’s another aspect of physical implementation we’ve got to deal with, distributed representation. You’re looking at an apple. Where is that apple represented in your brain? There might be some one neuron or a tightly clustered group of neurons that represent that apple. That’s a local representation. But the representation might be distributed across a loosely linked population of neurons. That’s a distributed representation. We now know that that’s how the brain works, though I assume there’s some laggards still stuck in the previous century where local representation was widely favored. And that’s how artificial neural nets work. The concept of apple isn’t localized in one or a small group of weights, it’s smeared over a population of weights. The same for the concepts of truth, beauty, and justice, or, for that matter, neuron.
Consequently von Neumann’s 1958 discussion must now be extended to distributed representation. It is for that purpose that I asked Claude to convene a conversation between Charles Babbage, who conceived of an elaborate mechanical computer in the 19th century, von Neumann himself, and Geoffrey Hinton, one of the so-called godfathers of artificial intelligence – a bit of journalistic and pop culture slight-of-hand that obscures a long and complex history. Moreover, since these three gentleman did not co-exist in time, though there’s a bit of overlap between von Neumann and Hinton, I decided that I might also have Claude transport them to Mars for the occasion. Anything is possible in virtual reality.
Or is it?
Image by ChatGPT.
A sun-facing room on Mars. Three chairs arranged loosely, angled toward a broad window. Through it, the Martian plain stretches red and absolutely still, and the sun — recognizable but wrong, too small, too distant — hangs in a pale pink sky. Three men stir and look about them, bewildered but unhurt.
I. Awakening
Charles Babbage
I am not dead — I can confirm that much. My pulse is steady and my faculties appear intact. But this room is not any room I have entered by ordinary means. That light through the window — is that the sun? The colour is right but the disc is too small by half. And that plain outside... brick red, entirely without vegetation or track or any sign of habitation. Gentlemen, I should very much like an explanation.
John von Neumann
I was in Princeton not five minutes ago. I notice the gravity is reduced — perhaps sixty percent of normal. The sky has the ferric oxide colouration one would predict. The solar disc subtends roughly two-thirds the angle it does from Earth. I believe I know where we are, and I find the hypothesis almost too extraordinary to voice.
Geoffrey Hinton
Mars. We're on Mars. I was at a conference in Toronto. But that sky, the dust, the sun at that distance — I'd stake my reputation on it. He exhales slowly. I've recently left a position at Google partly so I could speak freely about alarming possibilities. I did not anticipate that the alarming possibilities would include this. May I ask what year it is, from each of your perspectives?
Charles Babbage
The year of Our Lord 1862. I am Charles Babbage, formerly Lucasian Professor of Mathematics at Cambridge, Fellow of the Royal Society. I have spent the better part of forty years attempting to build mechanical calculating engines, with results that have been — a pause — mixed, as regards the support of Her Majesty's government.
John von Neumann
John von Neumann. The year is 1955. I work at the Institute for Advanced Study in Princeton, and consult for various government and military bodies. Mr. Babbage — I know your name extremely well. Better, I suspect, than you know mine.
Geoffrey Hinton
Geoffrey Hinton. For me it is 2024. And I know both of you — Mr. Babbage, you designed the Difference Engine and the Analytical Engine. Dr. von Neumann, you are among the most formidable mathematicians of the twentieth century and you gave your name to the architecture that every conventional computer on Earth is built upon. You are, in a real sense, my ancestors. The field I work in — machine learning, artificial intelligence — descends directly from the problems both of you were grappling with.
Charles Babbage
A long pause, during which he stares at Hinton with an expression mixing hunger, pride, and something close to grief. The Analytical Engine. Did anyone build it?
Geoffrey Hinton
Not in your lifetime. The government never provided the funds. But the ideas were entirely right — and they were eventually built, first in relay and vacuum tube and then in silicon, by people who in some cases had read your work and in other cases had arrived at the same conclusions independently. You were approximately a century early.
Charles Babbage
Very quietly. A century. I had hoped twenty years would suffice. I petitioned the Chancellor. Three times.
II. The mill and the store
John von Neumann
Mr. Babbage, allow me to tell you what your Analytical Engine set in motion — because it bears directly on the work that has occupied all three of us. You made a distinction, in your design, between what you called the Mill and the Store. The Mill performs the operations — addition, subtraction, multiplication. The Store holds the numbers awaiting operation and the results of operations completed. That separation of active calculation from passive memory was the foundational insight.
Charles Babbage
It seemed to me the only sensible arrangement. The columns of number-wheels in the Store are passive — they merely hold values. The Mill acts upon them. To mix the two functions in the same mechanism would create hopeless confusion.
John von Neumann
And yet it is precisely that separation which I have spent the last years of my life questioning — not as an engineering choice, which was entirely sound, but as a principle of intelligence itself. My colleagues and I formalized your Mill-and-Store distinction into what is now called the stored-program architecture. The processor executes instructions sequentially; the memory holds both data and those instructions passively until called upon. It is a serial machine, one operation following another, orchestrated by a central clock. The computers being built in my era all follow this pattern.
Geoffrey Hinton
And in my era they still do, at bottom. But you've just described the tension at the heart of everything, Dr. von Neumann. Serial, precise, with a strict wall between computation and memory — that is the von Neumann architecture. And it is, in a sense, the architecture that human intelligence refuses to use.
Charles Babbage
You are saying the brain does not separate Mill from Store?
Geoffrey Hinton
Exactly. In the brain, every neuron is simultaneously a memory element and a processing element. It holds information in the strength of its connections to other neurons, and it also fires — it computes — based on what it receives. There is no central Mill. There is no passive Store. Computation and memory are fused at every node in the network, and the whole thing operates in parallel, millions of neurons active at once.
John von Neumann
This is precisely what troubles me — and I am glad to find the trouble is still alive in your era, Mr. Hinton, because it means I was not merely chasing a phantom. I have been writing a manuscript, unfinished I'm afraid, called "The Computer and the Brain." My central puzzle is this: where, in a neuron, is the Mill? A neuron receives signals, sums them, and if the sum exceeds a threshold, it fires. That threshold operation is a computation. But the synaptic weights — the strengths of the incoming connections — those are also the memory. The neuron is its own Mill and its own Store simultaneously. I have been calling such things active elements, to distinguish them from the passive memory elements of our conventional machines. But I confess I have not yet worked out the full implications.
Geoffrey Hinton
With genuine feeling. Dr. von Neumann, the full implications are what I have spent my career working out. And you had named the essential thing: active elements. That is exactly what the nodes of a neural network are. Each one holds a weight — that is its memory — and each one applies a non-linear function to its inputs — that is its computation. Fused. Inseparable. Replicated millions or billions of times, connected in layers, and trained by adjusting all those weights simultaneously until the network's outputs match the desired answers.
This very expensive AI infrastructure depreciates fast; if you don't make profits soon, you won't make them ever.
“If there is a deflation of the AI bubble, the optimists say that the new infrastructure will remain even if the companies do not — just as railways survived the 19th-century railway bust. However, this fails to reckon with the reality of depreciation (few pieces of silicon hold… pic.twitter.com/TaiMJqUzne
— Gary Marcus (@GaryMarcus) June 26, 2026
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