Thursday, April 30, 2026
Wednesday, April 29, 2026
AI spending is out of control [Ahab in pursuit of Moby Dick]
Karen Weise, A.I. Spending Sets a Record, With No End in Sight, NYTimes, April 29, 2026.
For the past two years, Amazon, Google, Microsoft and Meta have repeatedly set records for how much they are spending on artificial intelligence.
On Wednesday, the four giants did it again.
In the first three months of the year, the four companies reported in their financial results, they plowed a total of $130.65 billion into capital expenditures, largely spending on data centers that power A.I. That figure — which was another record — was more than three times what the Manhattan Project cost to develop nuclear bombs and 71 percent higher than what the tech giants spent in the same quarter a year earlier.
All of the companies said they would be spending even more, totaling roughly $700 billion this year. Meta, for one, raised its spending forecast for 2026 to between $125 billion and $145 billion, up from its previous prediction of $115 billion to $135 billion. Google also boosted its projection, to at least $180 billion, and said its spending would be “significantly” higher next year.
The big four – Google, Microsoft, Amazon, and Meta can afford it because they “continue to dominate in core businesses that spew cash, such as serving ads on YouTube or Instagram, delivering items in a few hours or tallying cells in Excel.” They've entered into circular relationships and Anthropic and OpenAI which have, in turn, “committed to spending hundreds of billions on computing power that the tech giants provide.” Yada yada so forth and so on et cetera et cetera:
Some of the tech companies have justified their building binge by saying they cannot meet all the demand. But analysts said there were risks if the companies became too dependent on two young customers: OpenAI and Anthropic.
More than 40 percent of Microsoft’s $625 billion in outstanding cloud contracts, for example, come from OpenAI, the company said in January. This week, Microsoft and OpenAI announced new terms that loosened their ties.
Betting so much on OpenAI and Anthropic is a gamble. But even if the start-ups flop, the tech giants are likely to weather the losses because of their size, scale and other businesses, said Matt Stucky, who manages tech investments for Northwestern Mutual.
“The core business,” he said, “is good.”
I think they're taking the economy for a Nantucket sleigh ride.
Excerpts from Séb Krier's Omni-Thread from February
https://x.com/sebkrier/status/2018351274127962300?s=20
1. Existing models will continue improving and getting better. And they will continue to be trained while accounting for all sorts of things like cost, efficiency, steerability, personality etc. as we already see today. I think it’s more obvious than ever that there is likely no convergence to the One Big Model. [...]
4. Here, there is still a lot to work out, and I expect high complementarity with human workers for at least the next decade. Roles will evolve: as you start doing less coding, your work looks more like technical product management. [...]
5. You just keep going up layers of abstraction, and humans continue steering complex multi-agent systems, until fixed costs bite. Part of the reason why humans always stay at the top of the chain is that many decisions made are normative: about what you want to happen, where you want things to go, how you want to react to changes. This requires inherently human inputs, since there's no point in having an AI decide this alone no matter how smart without eliciting more information about what the relevant humans prefer. Put differently: the telos of the whole system is the amalgamation of what users/consumers/businesses want, and tracking whether you're actually achieving that requires human input. This is already the case today with highly complex gigantic companies that make 1000 opaque decisions a minute.
6. Remember, this doesn’t violate the basic fact that market-coordinated economic activity is downstream of consumer and business demand. Capital isn’t some sort of independent force of the universe. What is being built depends on buyers/consumers that are ultimately human, even if occasionally intermediated by agents. The "AI decides everything" frame misses something fundamental about what economic and political systems are for. But as we go through these transitions, there are also costs or externalities (both pecuniary and non-pecuniary). Some people lose jobs. New industries cause unforeseen harms. Terence Tao has a great analogy: the abundance of food solved famine, but of course also led to harms like obesity. The solution is not to slow down abundance, but to develop the right norms, technologies, and laws to curb the excesses.
7. Accounts of full disempowerment assume democracy disappears, but I don't think all roads lead to autocracy. I don’t think ‘this time it’s different’. Growth and innovation historically benefited from free trade and liberal democracy, and this will be the case here too because of its impacts on investment, human capital, institutional quality, self-correction mechanisms, and ensuing fly-wheel effects. [...]
8. As the world goes through these transitions, we will probably continue to see many commentators gloss over the vast benefits and improvements humanity will see. Progress in longevity, cured diseases, consumer welfare, massive reduction in poverty and famine, better education and so on. The arguments for market coordination over some sort of early-Soviet or Maoist collectivism apply even more in this world, not less. The world will generally become materially richer. [...]
9. If we allow sufficient deployment of technology, robots, AI and so on, while ensuring the supply of energy, housing, and other important inputs isn’t constrained to a strangling degree, then the production of many goods and services will go down in price. [...] In general I am more concerned with customer service operators in Bangalore than I am with upper middle class white-collar professions in the West. I think FDI [foreign direct investment] and aid will be critical if we want humanity to thrive.
10. But this doesn't justify regressive populist policies or a 'pause'. It's not even optimal if we were being maximally selfish, and the equivalent of saying "poverty, misery and illness should be preserved for a longer period of time, for the benefit of a particular group of workers in time." Opposing AI or technological progress is a particularly nasty version of degrowth: it kills people, it entrenches poverty, and generally locks in all sorts of tragedies for the benefit of a comfortable elite who can easily thrive with the status quo. However, this does mean ensuring the right welfare systems, democratic protections, ‘societal resilience’, public infrastructure etc is important, as many have repeatedly noted over time. Just because things net out positively doesn’t mean ignoring those who lose out in the short run is the best we can do. There’s so much work to be done still if you want to build a better world, and I think we desperately need new, better economists, scientists, sociologists, artists, and politicians more than ever. I have more faith in the zoomers than some of my peers!
[Hmmmm.... I'm not so sure of 10. Don't know what it means.-BB]
12. In the future, I expect politics and governance to be an increasingly important component of people's lives: many will care deeply about how things are organised and managed at the local or national or international level. Personally, I think it’s fine if a large fraction don’t care much about those issues most of the time, since I don’t think there’s an obligation for everyone to have an opinion on everything, and that preference will likely be easy to satisfy. [...]
13. And I do think status games will continue, albeit in a much more diverse ecosystem of sub cultures and geographies. But again: always has been. Even today plenty of people more interested in art have zero envy for techbro founder lifestyles, and conversely many engineers couldn't care less about being perceived as cultured. As people get richer, much of this will evolve too. [...]
14. Ultimately, AGI will bring about huge positive transformations for the world, many of which are hard to describe: could anyone at the dawn of the Industrial Revolution have told you about video games, eye surgery, deep sea diving, street tacos, and mRNA vaccines? [...]
15. Lastly, so much of the field uses "this time it's different" as hand-wavey justifications for flouting norms, justifying unusual political measures, ignoring fragile progress built on centuries of trial and error, and various yet-to-be seen proposals for haphazard action (made confidently despite the uncertainty that one might guess would come with handling unprecedented phenomena). I think this is misguided: AGI will be huge, and of course will affect everything around us; but in many ways it’s also not different, and as always, there's a lot to learn from History. Much still needs to be built, except that this time you will also have millions of agents by your side to make progress. 🚀
Does Peter Thiel Think He's GOD? [Pope Leo XIV and AI]
You Tube:
Jon Favreau is joined by Christopher Hale to discuss Pope Leo XIV's warnings against A.I.
Is the internet slowly breaking our brains, and if so, what can we do about it? Offline with Jon Favreau is a place where you can take a break from doom-scrolling and tune in to smarter, lighter conversations about the impact of technology & the internet on our collective culture. Intimate interviews between Pod Save America host Jon Favreau and notable guests like Stephen Colbert, Hasan Piker, ContraPoints, Margaret Atwood, and Megan Rapinoe spark curiosity and introspection around the various ways our extremely online existence shapes everything from the ways we live, work, and interact with one another. Together we’ll figure out how to live happier, healthier lives, both on and offline.
At some point they mention Ross Douthat's NYTimes interview with Peter Thiel and how there's a moment where Thiel wasn't sure whether humanity was worth saving. Here's that moment"
Douthat: But the world of A.I. is clearly filled with people who, at the very least, seem to have a more utopian, transformative — whatever word you want to call it — view of the technology than you’re expressing here. And you mentioned earlier the idea that the modern world used to promise radical life extension and doesn’t anymore. It seems very clear to me that a number of people deeply involved in artificial intelligence see it as a mechanism for transhumanism — for transcendence of our mortal flesh — and either some kind of creation of a successor species or some kind of merger of mind and machine.
Do you think that’s all irrelevant fantasy? Or do you think it’s just hype? Do you think people are raising money by pretending that we’re going to build a machine god? Is it hype? Is it delusion? Is it something you worry about?
Thiel: Um, yeah.
Douthat: I think you would prefer the human race to endure, right?
Thiel: Uh ——
Douthat: You’re hesitating.
Thiel: Well, I don’t know. I would — I would ——
Douthat: This is a long hesitation!
Thiel: There’s so many questions implicit in this.
Douthat: Should the human race survive?
Thiel: Yes.
Douthat: OK.
Thiel: But I also would like us to radically solve these problems. And so it’s always, I don’t know, yeah — transhumanism. The ideal was this radical transformation where your human, natural body gets transformed into an immortal body. And there’s a critique of, let’s say, the trans people in a sexual context, or, I don’t know, a transvestite is someone who changes their clothes and cross-dresses, and a transsexual is someone where you change your, I don’t know, penis into a vagina. And we can then debate how well those surgeries work. But we want more transformation than that. The critique is not that it’s weird and unnatural, it’s: Man, it’s so pathetically little. And OK, we want more than cross-dressing or changing your sex organs. We want you to be able to change your heart and change your mind and change your whole body.
And then orthodox Christianity, by the way — the critique orthodox Christianity has of this, is these things don’t go far enough. That transhumanism is just changing your body, but you also need to transform your soul and you need to transform your whole self. And so ——
Tuesday, April 28, 2026
On Method: Computational Compressibility in Complex Natural and Cultural Phenomena
New working paper. Title above, abstract, contents, and introduction below:
Academia.edu: https://www.academia.edu/166054951/On_Method_Computational_Compressibility_in_Complex_Natural_and_Cultural_Phenomena
SSRN: https://papers.ssrn.com/abstract=6666638
ResearchGate: https://www.researchgate.net/publication/404263330_On_Method_Computational_Compressibility_in_Complex_Natural_and_Cultural_Phenomena
Abstract: Various machine learning techniques have been used to develop models of complex systems from empirical data. Through discussions with Claude, this paper examines several examples, including: weather, protein folding, chess, language, asset pricing, ticket sales for movies, the 19th century English-language novel. These models differ from one another in various ways, but all are fundamentally descriptive in character. Explanations must necessarily reside with their respective disciplines. In some cases we already have fundamental accounts of the phenomena, while in other cases we do not. With respect to economics in particular, it is clear that such models reveal phenomena for which no explanations are currently available, presenting a challenge to economic theory.
Contents
Part I: Computational Compressibility, Implications for Economics, Description and Explanation 5
Part II: Weather, Protein Folding, Chess, and Language 16
Part III: Interim Summary: Compressibility Without Reducibility 26
Part IV: Pricing Theory, Movies, 19th Century Novels, and Cultural Evolution 28
Part V: To Infinity and Beyond! – Hollywood Redux, Blockbusters, the Spreadsheet, Economics Going Forward 38
Introduction: Describing Computationally Compressible Systems
This a transcription of a dialog I had with Claude 4.6 and 4.7 on April 21 - 23, 2026. While I started it with a specific case from Chapter 4 of Tyler Cowen’s recent monograph on marginalism, now that the dialog has concluded with Chomsky’s distinction between descriptive and explanatory adequacy (Aspects of the Theory of Syntax), I realize that I’ve been thinking about the underlying issues for some time. While I read Aspects in about 1970, give or take a year, I didn’t think much about description as such until the 2000s, and then I was thinking about describing individual texts; but that’s not directly relevant to these cases in this paper. Then in the second decade of this century I began thinking about computational criticism, aka digital humanities, which typically involve some kind of statistical or machine learning investigation of a corpus of texts. In particular, I gave a great deal of attention to Macroanalysis (2013), where Matthew Jockers studied a corpus of roughly 3000 English-language novels published in the 19th century. That investigation culminated in a directed graph showing depicting relationships of close-similarity among the novels in the corpus. I decided that that graph, in effect, was fundamentally descriptive in character, depicting, in effect, the 19th century Anglophone Geist, or Spirit.
But Jockers’s graph wasn’t on my mind when I started my dialog with Claude. Rather, I was thinking about the distinction between computationally reducible and irreducible phenomena that Stephen Wolfram had introduced in his New Kind of Science (2001). As Claude notes in its summary of the dialog, “a reducible system admits shortcuts through its dynamics; an irreducible one must be simulated step by step.” My target was a paper about asset pricing that Cowen discussed in his monograph, which produced a model having 360,000 parameters but which defied intuitive understanding.
The weather is a canonical example of phenomenon that is computationally irreducible. Thus forecasting the weather generally involves running a simulation of the weather and stepping through it interval after interval. This requires enormous computing resources and takes time. But DeepMind has created a machine learning system, GraphCast, that abstracts over historical data in a way that allows more accurate forecasts with less compute. Thus the weather system is computationally irreducible, but it is also compressible.
I take that as my paradigm case of computational compressibility (pp. 16 ff.) and then move on to other examples: protein folding (another physical phenomenon, pp. 18 ff.), chess (human activity, pp. 22 ff.), and natural language (a different human activity, pp. 23 ff.), each of which is compressible using machine learning techniques. Each example sharpens and extends the idea of computational compressibility. At that point I asked Claude to summarize the discussion (pp. 26 ff)..
Then, and only then do I ask Claude to consider Cowen’s problematic example, AI Pricing Theory (pp. 28 ff.). In its analysis of the paper, Claude notice that it introduces something fundamentally new to the discussion, reflexivity. Asset pricing is done by a large group of actors over time who thus influence one another’s decisions. And that, in turn, brings up Arthur De Vany’s work on Hollywood Economics (pp. 30 ff.). De Vany discovered that box-office success cannot be predicted by such analytic variables as producer, screen writer, director, movie stars, or opening weekend box office. Rather the success of a film depends on a word-of-mouth cascade which cannot be predicted. That leads me, in turn, to suggest a thought experiment involve a hypothetical system capable to abstracting over entire films and developing a high-dimensional model which could be used to predict the success of individual films.
And that, in turn, led me to the work that Matthew Jockers had done on 19th century English-language novels (pp. 33 ff.), something that had not been on my mind when I began this dialog on April 21. Jockers used machine learning, albeit nothing so elaborate and computationally expensive as using a transformer to create an LLM – it only had roughly 600 parameters. What his model revealed, and what made it so fascinating to me, is that there is an inherent directionality to the production of novels over the course of a century. It’s not simply that later novels are systematically different from earlier ones, but that that difference has a direction in the 600-dimensional measurement space. What we’d really like to know, now, is a say to characterize that diction. The model shows us that there is a direction, but it doesn’t tell us what that direction is. Though the model is much simpler than that asset pricing model – it has three orders of magnitude fewer parameters – its significance is no more legible.
After that I have two discussions that are not based on existing models, but that do have implications for economists who want to study them. First, I consider the phenomenon of the blockbuster, arguing that it reveals audience preferences that had previously been unrecognized (pp. 41 ff.). Then I consider the spreadsheet (e.g. VisiCalc), which transformed the personal computer market from a small niche market into a large mainstream market (pp. 43 ff.). How do you create a model that allows you to predict markets that don’t even exist at the time you make your model? What kind of a problem is that? After that I took a brief look at Cowen’s argument in The Great Stagnation (pp. 45 ff.), where Claude remarked:
If the VisiCalc model is right, then what matters about ChatGPT and its successors is not primarily that they do existing things faster or cheaper—though they do—but whether they are constitutive technologies in the VisiCalc sense. Do they reorganize the space of possible wants, making new activities imaginable and practical that previously had no well-formed representation in anyone's preference space? With that I brought the exploration to a halt.
I then asked Claude to summarize the entire dialog, which I’ve placed immediately following these remarks (pp. XX ff), with a special emphasis on implications for economics (pp. 7 ff.). Then I introduce Chomsky’s distinction from the 1960s, description vs. explanation (pp. 9 ff.). Each of these cases involves a complex phenomenon that is irreducible, but can be compressed into a model that is descriptive in character. They have that in common. As for explanations, those must necessarily be specific to each phenomenon. Note that in some cases we have explanatory theories grounded in a fundamental understanding of the underlying system (weather, protein folding) while in others we do not (chess, asset pricing, cultural evolution).
Finally, I’ve added a coda from a different conversation with Claude (pp. 13 ff.), one I had with the AI that accompanied Cowen’s book. That conversation is about Hollywood Economics and Rational Ritual and argues that the factoring of intellectual space that we’ve inherited from the 19th century German university has outgrown its usefulness.
Monday, April 27, 2026
The Platonic Representation Hypothesis [Not surprising]
MIT proved every major AI model is secretly converging on the same "brain."
— How To AI (@HowToAI_) April 27, 2026
It’s called the “platonic representation hypothesis,” and it’s one of the most mind-blowing papers you’ll ever read.
You train a vision model purely on images. You train a language model purely on text.… pic.twitter.com/1fubIZUJuT
— How To AI (@HowToAI_) April 27, 2026
In our recent work, we show that representational similarity metrics used to measure such trends can “converge” just because models get wider/deeper. After calibration, we show that the global/spectral scaling trend largely vanishes and propose the alternative Aristotelian view…
— Fabian Gröger (@FabianGroger) April 27, 2026
Those new AI metrics: From AGI to bragawatts
Erin Griffith, How Do You Measure A.I. Firms’ Gargantuan Energy Plans? In ‘Bragawatts.’ NYTimes, April 26, 2026
The term started more than a decade ago in the energy industry, used to describe power from a solar or wind project that had no chance of being built. Last year, A.I. executives began boasting with increasing boldness about their plans. A.I. watchers, including Waldemar Szlezak, the head of infrastructure at the private equity firm KKR, repurposed the term in a Financial Times column imploring investors to look past the A.I. hype and focus on the reality of today’s power grid. Since then, the term has popped up in media headlines, analyst reports and on social media, typically with a healthy dose of skepticism about how quickly such projects can realistically be built.
The numbers being announced are staggering. Nvidia estimated that as much as $4 trillion would be spent on A.I. infrastructure this decade. OpenAI said it had committed to spend $1.4 trillion to build data centers around the world. (It later lowered that target to a mere $600 billion.)
Brad Gastwirth, global head of research and market intelligence at Circular Technology, a supply chain services firm, said that projects highlighting a gigawatt or more of energy are the most likely to be bragawatts.
“That’s where you can have some scratching of the heads,” he said.
Likewise for any infrastructure projects announced by companies that haven’t already secured the land to build the project, he noted. “That’s definitely the braganomics.”
There's more at the link.
Sunday, April 26, 2026
Perhaps Ukraine has replaced America as leader of the free world
David French, Meet the New Leader of the Free World, NYTimes, Apr. 26, 2026.
A remarkable thing has happened on the world’s battlefields. Ukraine — a nation that was supposed to dissolve within days of a Russian invasion — has fought Russia to a stalemate, revolutionizing land warfare in the process. It has become an indispensable security partner in the western alliance, including in the war against Iran.
Now, Volodymyr Zelensky, Ukraine’s president, is taking the next step, one that would have been unthinkable even as recently as 2024. By word and deed, he’s showing Europe and the world how the post-American free world can preserve its liberty and independence. This is what happens when, as Phillips Payson O’Brien wrote in a piece for The Atlantic, “Kyiv appears to have given up on the United States.”
If that is true — and it looks as though it is — it may be worse news for the United States than it is for Ukraine.
Events on the ground and in world capitals are moving so quickly that it’s hard to keep up. First, the strategic situation in the Ukraine war seems to have changed. Last week, Mick Ryan, a retired Australian major general and one of the most astute analysts of the war, wrote that Ukraine has largely stabilized the frontline in eastern Ukraine, deepened its coalition, isolated Russia diplomatically and developed an indigenous arms industry that makes it less dependent on external support.
It’s no longer accurate to think of Ukraine as a desperate underdog; it’s becoming an independent power. Even as it fights for its life against Russia, it’s reportedly reaching defense deals with the Gulf states and with the United States — and this time it’s Ukraine that’s providing military assistance.
In February 2025, Donald Trump mocked Zelensky in the Oval Office. “You’re not in a good position. You don’t have the cards right now,” Trump said. In April 2026, Ukraine has enough cards left that it’s sharing them.
This might be difficult for many readers to grasp — given our nation’s longstanding military supremacy — but the largest and most battle-hardened land force in the western world may well be the Ukrainian Army. While the precise numbers are classified, the Atlantic Council estimated in 2025 that Ukraine had roughly a million men and women under arms, the vast majority of whom serve in the ground forces.
America’s total force is larger than Ukraine’s, but to put the size of Ukrainian land forces in perspective, the combined size of the U.S. Army and Marine Corps is around 620,000. It’s also worth noting that the U.S. forces have much less combat experience than Ukraine forces — especially when it comes to combat with a great power.
There's more at the link.
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