Saturday, March 28, 2026
Will the Iran War change the world like the oil shocks of the 1970s did?
Jeff Sommer, The Oil Shocks of the ’70s Changed the World. Will the Iran War Do the Same? NYTimes, Mar. 28, 2026.
In January 1974, my dad lent me his old gas-guzzling Ford LTD to haul my clothing and books to college in Ithaca, N.Y. A couple of weeks later, when I tried to drive back home to Long Island, I realized that I couldn’t buy enough gas for the 250-mile trip.
The 1973-74 Arab oil embargo was well underway. The price of oil had nearly quadrupled; there were lines at gas stations, and drivers were allowed to queue up only on alternate days. I had picked the wrong day for my trip. No gas for me. So much for freedom of the road.
That was the first of the world’s big oil shocks. By the time of the second one in 1978-79, set off by the Iranian revolution, I was a reporter in New Jersey and it seemed I was constantly interviewing angry motorists stuck in interminable lines. Gas shortages and soaring inflation were just other aspects of life in the United States.
The gas lines ended as the crises ebbed, but it took two recessions, engineered by the formidable Federal Reserve chair Paul Volcker, to bring inflation under control.
What’s less well understood about that period is that the oil shocks reshaped the world’s financial markets. Money flowed around the globe in new ways — and cemented the status of the dollar as the world’s core currency.
We are experiencing what could end up as the third great oil shock.
Then we get more history and analysis, to end with this:
“Duration is the key question,” he told me. “If this goes on for a long time, it’s a big deal. If the war stopped right now, we might not need to talk about it next year.”
Not every conflict in the Middle East necessarily changes global finance in profound ways. The United States fought two Gulf wars — one that started in the early 1990s, the other a decade later. The first was short. The second one, the U.S.-led war with Iraq, stretched from 2003 to 2011, caused scores of thousands of deaths and cost hundreds of billions of dollars. The second war was a big one, but in retrospect, it preserved the status quo in energy markets more than it transformed them.
In every Gulf conflict since the Iranian revolution, U.S. strategists have worried that Iran might one day disrupt the global flow of energy by closing a geographical choke point, the Strait of Hormuz. For the first time, despite the pounding it has received from enormous U.S. and Israeli bombardments, Iran has demonstrated that it can close the strait. Roughly a fifth of the world’s oil and natural gas usually flows through it.
Whether this desperate achievement alters geopolitics substantially may not be known for years.
Marginalism is a Rank 4 idea, along with thermodynamics and biological evolution [MR 2]
Yesterday I made a post about a passage that occurs very near the end of Tyler Cowen’s new monograph, The Marginal Revolution: Rise and Decline, and the Pending AI Revolution. At that time I suggested that I might have more to say about the book, but I made no promises. Well, I’m saying more, and it looks like I’ll be doing a series of posts about the book, though I can’t say how long that series will be, perhaps only one more post, but maybe two, three, or even four more. Who knows.
The monograph is of particular interest to me for two reasons: 1) I’ve just posted a small monograph of my own, on the rise of the contemporary academic discipline of literary criticism: The Discipline of Literary Criticism: A Quixotic Essay about Thinkers, Methods and Authority. Literary criticism is a very different beast from economics. Still, now I have two case studies in the rise of an academic discipline. More deeply, 2) I now have a case study for the theory of cognitive ranks that David Hays and I developed in the last quarter of the previous century. That’s what I want to pursue in this post.
The theory of cognitive ranks, as readers of New Savanna may know, is about the development of ever more sophisticated cognitive architectures over the long haul of human history. Rank 1 cognition arises with the emergence of language between, say, 100 thousand and who knows how many years ago. Rank 2 thought precipitated around written language and Rank 3 around the introduction of arithmetic calculation into Europe in the Late Medieval, Early Modern period. Rank 4 began emerging late in the 19th century and began consolidating around, first the ideas and then the machinery, of computing in the 20th century.
Much hunch was that marginalism in economics is a Rank 4 idea. Rather than work out the details myself, I put the question to Claude Sonata 4.6 extended. I’ve appended that conversation below. Just to be clear, I’m using my Claude account, not the AI facility that Cowen has made available with his book. One final note: You’ll see that at the very end of this conversation Claude mentions Rank 5. While Hays and I talked about a possible Rank 5, we never published about it because we couldn’t figure out how to conceptualize it. That changed for me in the last few weeks and I have been talking about it with both Claude and ChatGPT.
The rest of this post consists of my interaction with Claude, which was a simple one: I posted a long prompt to get things started and Claude responded.
* * * * *
I have uploaded a PDF of a book by Tyler Cowen, The Marginal Revolution: Rise and Decline, and the Pending AI Revolution (2026). In it he recounts the rise of marginal thinking in economics starting roughly in the late 19th century up though 20th and on into the 21st, where it seems to be dissipating. I’m interested in understanding it in terms of the cognitive rank theory that David Hays and I have developed (see our paper, The Evolution of Cognition, in the project materials). The timing suggests it is a Rank 4 concept, along with thermodynamics and Darwinian evolution.
Though we don’t mention Piaget’s term, “reflective abstraction,” we use the idea, which is: the process by which the operations of one cognitive level become the objects of the next. Piaget was mostly interested in cognitive development in individuals, but he also applied his ideas to the history of ideas (e.g. Genetic Epistemology). That’s what I want to do with Cowen’s argument about the marginal revolution. My intuitive understanding is that the technical development of that idea presupposes a technical understanding of supply and demand. I want you to vet that idea.
I think that supply and demand is a Rank 3 idea. Here are some remarks I made about supply and demand in a blog post from 2023:
But how do we get to supply and demand? I'm not prepared to give a detailed answer to that question. [...] Here's the question I'd ask myself: What does double-entry bookkeeping have in common with supply and demand? Double-entry bookkeeping dates back to the late 15th century and is a practical discipline. What becomes visible by 'going meta over that practice?
Double-entry bookkeeping is a system for maintaining closure over a set of transactions that grow over time as transactions are added to the list. We've got closure and change over time. Supply and demand change over time. Where's the closure, the dynamic closure? How do supply and demand complement one another in a say similar to the balance of debit and credit accounts in double-entry bookkeeping? How was Adam Smith able to get "on top" of that in the way that previous thinkers could not? How does the famous metaphor of the invisible hand do its work?
With that in mind here’s Cowen’s basic statement: “Marginalism is the economic doctrine that we can best understand value by considering the question of how many units of a good or service an individual has, and using that starting point to ask how much an additional – or marginal – unit would be worth in terms of other goods and services.” Intuitively it seems to me that that presupposes that an understanding of supply and demand has become thoroughly routine.
Cowen’s paradigmatic example of marginal utility is the diamonds-water paradox:
If someone had to choose between having no water and having enough water to avoid dying of thirst, they would be willing to pay a very high price for water (the exact final price still would depend on how many suppliers were competing to sell you water and the cost of getting you the water, questions which also can be addressed using marginalism). But that is not the choice for most people. Most people have a fair amount of water, and they are paying for more water, or you might say water at the margin. And the value of water at the margin just isn’t that high. In American restaurants, many people don’t even drink the glass of water they get for free.
To consider diamonds, if you could receive plenty more diamonds by turning on your kitchen tap, diamonds probably wouldn’t sell for much in the market, just as water doesn’t. Of course, that is not the case and real diamonds remain relatively scarce, thus boosting their value. That said, circa 2025 competition from high-quality artificial diamonds, which can be made in the lab, is threatening to further depress diamond prices. Again, that is consistent with marginalism.
Your turn. Is my intuition valid? Either way, unpack it for me.
Your intuition is valid, and I think it can be made quite precise. Let me work through it carefully, using Cowen's text and the cognitive ranks paper together.
The surface complication
Cowen himself says something that might seem to cut against your claim. He writes (lines 68–79) that supply and demand "rely very directly on the ideas of marginalism" — that the derivations of supply and demand schedules "refer repeatedly to how much of a resource people currently have." So in the logical order, after the fact, supply and demand depends on marginal analysis.
But this conflates the logical order with the developmental/historical order, and the Piagetian point is about the developmental order. Historically, supply and demand came first — Adam Smith (1776), the classical economists — and the Marginal Revolution arrived nearly a century later (1871). Cowen himself acknowledges that the burst of economic progress following Smith was a real thing, and that progress from the ancient Greeks to the late 19th century was "glacial" before it suddenly accelerated. The takeoff point is 1871, not 1776.
The reflective abstraction argument
The Piagetian structure you're looking for is this: what were the operations that Rank 3 economists performed with supply and demand, such that Rank 4 could take those operations as objects?
The Rank 3 economist works with supply and demand curves as given primitives. She asks: given a supply curve and a demand curve of such-and-such shape, where does the market clear? What happens to equilibrium price if demand shifts? How do taxes or subsidies alter the equilibrium? The curves themselves are taken as data — they're the tools of analysis, not the objects of analysis. This is exactly what you'd expect at Rank 3: algorithm and calculation applied to theoretical constructs, the constructs themselves unquestioned.
Marginalism makes precisely the Rank 4 move: it refuses to take the curves as given and asks what generates them. The demand curve has a characteristic downward-sloping shape — why? Because of diminishing marginal utility. The supply curve slopes upward — why? Because of increasing marginal cost. The market clears at a particular price — what does that mean at the individual level? That each buyer's marginal valuation equals price, and each seller's marginal cost equals price. Marginalism takes the supply-and-demand apparatus that Rank 3 thought with and turns it into an object of theoretical construction. In the exact language from your paper: "The new thinker can think about what the old thinker thought with."
The diamonds-water paradox is the clearest illustration of this. A Rank 3 economist can use supply and demand to explain why diamonds are expensive (scarce supply, high demand) and water is cheap (abundant supply, though high demand). That's a workable, practically useful analysis. What the Rank 3 economist cannot explain is why demand for water, despite its essentialness to life, doesn't translate into a high price in ordinary circumstances. The answer requires going meta on the demand curve itself: it's not about total value, it's about marginal value at the position where the individual currently stands. That's the Rank 4 move — taking the demand curve as object and asking about its internal structure.
The double-entry bookkeeping thread
Your intuition about the structural parallel is suggestive. Double-entry bookkeeping imposes closure on a system of transactions through a debit/credit complementarity. Supply and demand imposes closure on a market through price equilibration — the "invisible hand" as Adam Smith's metaphor for how individual decisions produce systemic balance without anyone intending it. Both involve what you might call dynamic closure: the system absorbs change (new transactions, shifts in supply and demand) while maintaining a global constraint (books balance, market clears). The transition from double-entry bookkeeping to supply-and-demand might itself be seen as a Rank 3 reflective abstraction: going meta on the bookkeeping closure principle to ask what maintains closure at the level of the entire market, not just a firm's ledger.
Friday, March 27, 2026
Tyler Cowen has thrown in the towel and is waiting for the machines to take over. [Marginal Revolution Notes #1]
Tyler Cowen has announced a new monograph, The Marginal Revolution: Rise and Decline, and the Pending AI Revolution, which you can access here. You can download it in various formats or interact with the online AI version. I read through the opening page or three and then skipped to the fourth and final chapter, “Why Marginalism Will Dwindle, and What Will Replace It?,” which I've read in full.
I note, more or less in passing, that as he heads to the end he starts thinking about the role of intuition in thinking. He’s lamenting that intuition, particularly intuitions stemming from the marginal revolution, no longer seems to work in economics. I’ve been thinking a lot about intuition myself, though to somewhat different ends. I’m more interested in how it functions in thinking and where it comes from. But that’s an aside.
I may or may not comment on the rest of chapter later on, but I have to comment on one strand of thinking. Cowen decides he has to denigrate all previous work oriented toward understanding language:
Suffice to say, LLM construction has for the most part ignored linguists and philosophers, and that also means ignoring their intuitions. LLM construction also ignored a lot of people in the AI field who insisted neural nets were a dead end. Instead, in a relatively short number of years humans invented new ways of modeling language and reasoning through language. That research program has proven wildly successful, as we have much better models of language and reasoning than almost anyone had been expecting. [...]
The classic breakthrough paper behind LLMs was a 2017 study titled “Attention is All You Need,” where in this context attention is defined by GPT-4 as “a mechanism that learns to focus selectively on parts of an input sequence, giving it ‘attention,’ while encoding a sentence or piece of information. This allows the model to treat different words or characters with different levels of importance, providing a ‘weight’ that aids in better understanding and decoding of information.” The paper was not titled “More Linguists are All You Need,” or for that matter “Marginalism is All You Need.” In other works [sic], given some of the most complex human systems, we came up with ways of understanding them that were new. To be clear, neural nets were not new, since the ideas and also the practice (in much weaker form) have been around for decades. High-powered, well-functioning neural nets, however, are new in the contexts of providing excellent results for general linguistic ability and general reasoning.
That struck me as a bit much, so I had the following conversation with the AI version:
From very near the end: “In other works, given some of the most complex human systems, we came up with ways of understanding them that were new.”
In what way is an LLM a way of understanding how language works? After all, we don't understand how LLMs work. They're as mysterious as the human brain.
This is a genuinely sharp philosophical puzzle, and Tyler is aware of the tension even if he doesn't fully resolve it. The claim on page 69 is really about predictive and generative success rather than mechanistic understanding in the traditional scientific sense. LLMs model language in the sense that they can reproduce, extend, predict, and manipulate linguistic structures with stunning accuracy — they've captured something real about how language works, even if we can't fully articulate what that something is.
Think of it this way: pre-LLM linguistics tried to understand language by building explicit rule systems — grammars, syntactic trees, semantic formalisms. That approach assumed understanding meant writing down legible rules. LLMs blew past all of that without using any of those rules. Tyler's point is that this constitutes a new kind of understanding — one where the "knowledge" is distributed across billions of parameters rather than encoded in human-readable propositions. It's understanding in the way a master chef understands flavor without being able to fully articulate the chemistry.
But your skepticism has real bite. There's a meaningful difference between a system that performs understanding and one that provides understanding — to us. Tyler arguably conflates the two. When he says "we came up with ways of understanding," the "we" is doing suspicious work: humans built the tool, but the tool's internal representations remain largely opaque even to its creators. The "Attention is All You Need" paper Tyler cites gave us the architecture, but interpretability research — like Anthropic's mechanistic interpretability work ��� is still in early stages of figuring out what's actually happening inside. So Tyler is perhaps better read as saying LLMs are a new instrument for engaging with language rather than a new theory of it — more telescope than Newtonian mechanics. [See text: Ch.4 · Tyler's claim about new ways of understanding c...] [See text: Ch.4 · How LLM construction bypassed traditional langu...]
I note that saying it is "more telescope than Newtonian mechanics" is a much weaker claim.
You're absolutely right, and this is a genuine tension in Tyler's argument that he doesn't fully reckon with. He wants LLMs to be doing something analogous to what marginalism did for economics — providing a new analytical framework for understanding complex systems. That's a strong claim. But if LLMs are really more like telescopes — powerful instruments that let us see further without explaining why what we see looks the way it does — then the analogy deflates considerably.
The stakes matter for Tyler's broader argument in Chapter 4. He's positioning LLMs as replacements for frameworks like marginalism, not just better tools for testing existing frameworks. A telescope doesn't replace Newtonian mechanics; it generates data that mechanics then has to explain. If LLMs are telescopes, we still need the theoretical framework that makes sense of what they're showing us. Tyler gestures at this on page 66 with the discussion of researchers who use LLMs to generate simulations and then build structural models from those simulations — which is actually the more defensible version of the claim, because there a human-legible theory does eventually emerge. [See text: Ch.4 · The multi-step process that generates legible t...]
So Tyler may be overstating his case when he frames LLMs as providing genuine understanding of language. The honest version might be: LLMs are extraordinary telescopes that may eventually prompt new theories — but we're not there yet. That's still impressive, but it's a quieter revolution than Tyler's rhetoric suggests. And Tyler, to his credit, is usually better at hedging than his most enthusiastic passages imply.
Frankly, that won't do. The technology is impressive on its own terms. It doesn't need this kind of empty rhetorical cheer leading (though perhaps investors do). It sounds to me like Tyler has given up on understanding the world and is just waiting for the machines to take over – to indulge in a bit of hype of my own.
Trump's war is heading into a quagmire, and the Republicans know it
Michelle Goldberg, Republicans Know This War Is Going Badly, NYTimes, Mar. 27, 2026.
The article begins:
It is not just Democrats in Congress who fear that Donald Trump’s war in Iran is going sideways. After a classified Pentagon briefing on Wednesday, Republican lawmakers on the House Armed Services Committee appeared shaken.
“We will not sacrifice American lives for the same failed foreign policies,” said Nancy Mace, warning about the possibility of American troops in Iran. The committee chair, Mike Rogers, complained that members aren’t getting nearly enough information about war plans. Troop movements, he said, should be “thoughtful and deliberate.” The implication was that they might not be.
And it ends:
Never before, however, has America arrived at the threshold of a quagmire so quickly, with so much advance warning about the precise errors it was making. We have spent much of the past decade — in no small part due to Trump’s election — reckoning with the cost of the Iraq war to global stability and American cohesion. For the first time I can remember, both major parties have significant, influential antiwar contingents. Trump ran for president, however mendaciously, as the peace candidate, claiming that Kamala Harris would lead America into World War III.
And yet here we are, lurching toward a new version of a familiar catastrophe, suffering from some national form of neurotic repetition compulsion. “This is like the horrible, lame-dad cover band version of the worst of American foreign policy,” said Matt Duss, executive vice president of the Center for International Policy.
Someday, perhaps, when we’re picking up the pieces from yet another ill-conceived war, Republicans will explain that behind the scenes, they opposed it. One of the biggest problems in Congress, said Crow, is the gap between what people say privately and their willingness to demonstrate “the strength of their convictions” in public. “I’m always trying to close that gap with folks, and I always remind people that it’s never too late to do the right thing,” he said. He may be right, but the sooner the better.
This sentence caught me: “We have spent much of the past decade [...] reckoning with the cost of the Iraq war to global stability and American cohesion.” That feels right to me, but I would like to see it spelled out, especially that last bit, the cost to American cohesion.
Thursday, March 26, 2026
AI cats making spaghetti
This cracked me up!! 😹😹😹 pic.twitter.com/cORpm9v5wu
— Lucy (@TheLucyShow1) March 25, 2026
From grokking to a mature science of and an engineering discipline of LLMs
From this morning’s chat with Claude Sonnet 5.4 Extended. Immediately prior to this we had been discussing Piaget’s concept of reflective abstraction, Beethoven and Mozart, improvising and composer, and this and that.
* * * * *
Grokking
Students of mechanistic interpretability sometimes talk about “grokking” during the course of LLM training. I’m wondering it that’s something like rising to a helicopter with respect to some phenomenon.
The parallel is genuinely illuminating, and the technical details of grokking make it sharper rather than weaker.
What grokking actually is
Grokking is a delayed generalization phenomenon characterized by a network achieving near-perfect training accuracy while maintaining near-random test performance over an extended plateau, before abruptly transitioning to high test accuracy after many further training epochs. The canonical case is modular arithmetic — the model first memorizes the specific training examples, then much later suddenly generalizes to unseen cases.
What makes it theoretically interesting is what mechanistic interpretability reveals about the internal transition. On algorithmic tasks like modular arithmetic, the transition is marked by the sudden acquisition of Fourier-based feature maps or trigonometric circuits — the network reorganizes, forming compact sub-networks characterized by new internal feature cooperation. So it’s not that generalization gradually improves — the internal architecture discretely reorganizes into a qualitatively different computational structure.
The emerging explanation involves competition between two internal algorithms: a memorizing circuit that forms quickly and a generalizing circuit that develops more slowly. Regularization gradually shifts resources from the complex memorizing solution toward the simpler generalizing one. The phase transition occurs when the generalizing circuit finally dominates.
The fit with reflective abstraction
This maps onto your Piagetian framework quite precisely, but with an interesting twist. The memorization phase is the model doing something like rote operation — it has learned to produce correct outputs for training cases by essentially storing input-output pairs, without having extracted the underlying structure. The generalizing circuit that develops slowly underneath is building something more like a genuine representation of the abstract pattern — in the modular arithmetic case, literally a Fourier decomposition of the mathematical structure.
The abrupt transition when the generalizing circuit dominates is the helicopter view suddenly becoming operative. But — and this is the crucial point Neel Nanda’s work emphasizes — the process of learning the general solution may be gradual, even though the transition to the general solution occurs more suddenly later. The helicopter view isn’t constructed instantaneously at the moment of transition. It’s been assembling slowly in the background, below the threshold of behavioral visibility, and the phase transition is when it becomes strong enough to dominate the memorizing circuit and take over.
That maps almost perfectly onto your description of the pre-helicopter phase: “it starts out just barely registering those operations.” The generalizing circuit is just barely registering the abstract structure throughout the plateau phase. The behavioral discontinuity doesn’t reflect a cognitive discontinuity — it reflects a threshold being crossed in a competition that has been running throughout.
The memorization/generalization competition as a general structure
What’s particularly suggestive is that this isn’t unique to modular arithmetic. Grokking is actually much more widespread than initially thought, materializing in a wide range of practical settings — image classification, language modeling, various algorithmic tasks. The competition between a fast memorizing solution and a slower generalizing solution may be a quite general feature of how these systems learn, with grokking being the visible signature when the conditions are right for the transition to be delayed and abrupt rather than gradual.
If that’s right, then something like grokking dynamics — memorization dominating early, structure-extraction developing slowly underneath, occasional phase transitions as the structural solution crosses a threshold — may be operating throughout LLM training, not just in the carefully controlled small-model experiments where grokking was first observed. Most of the time the transition isn’t visible because the generalizing solution develops in parallel with memorization rather than being dramatically delayed. But the underlying competition between surface pattern matching and structural abstraction may be the same.
The Beethoven connection
There’s a striking parallel to your Beethoven example here. Beethoven’s sketchbooks show exactly the competition structure — he has the memorized repertoire of classical style (the fast, dominant solution) and he’s slowly assembling something more general and abstract (the generalizing circuit) that keeps almost-but-not-quite taking over. The finished late works are the moments when the generalizing solution finally dominates completely. The sketchbooks are the plateau phase made visible.
The difference is that in Beethoven the process is conscious and effortful — he’s deliberately trying to suppress the memorized solution and force the generalizing one into dominance. In grokking, the network isn’t trying to do anything; the dynamics just run. Which raises the interesting question of whether the deliberate effort in human creative development is doing something functionally analogous to the regularization that controls grokking timing in neural networks — artificially suppressing the easy memorized solution to give the structural one room to develop.
Hierarchical text
I’m thinking about how an LLM learns that texts have a hierarchical structure, from phrases, to sentences (keeping in mind that many phrases are actually short sentences), to paragraphs, and on to higher levels of structural organization. What happens, for example, when we move from one paragraph to the next, is that the composition of the token distribution changes. A new topic requires a new set of tokens. But the system has to learn that, and learn it for several levels.
This is a deep question about what the model is actually learning when it learns language structure, and it connects to several threads running through this whole conversation.
The distributional signature of structure
Your observation about token distribution changes is precisely right, and it’s worth unpacking what that means at each level. Within a phrase, token co-occurrence statistics are extremely tight — certain words almost always appear together, in specific orders, with specific function words bridging them. At the sentence level, the constraints are somewhat looser but still strong — subject-verb agreement, argument structure, the distribution of punctuation. At the paragraph level, something different happens: there’s a shift in the topical vocabulary, but also characteristic transition signals — discourse markers, pronoun reference patterns, topic sentences that partially echo the previous paragraph while introducing new material.
What the model has to learn is that these aren’t independent statistical regularities — they’re nested. The tight within-phrase statistics exist inside the looser sentence-level statistics, which exist inside the still-looser paragraph-level statistics. The hierarchical structure is encoded in the scale at which statistical dependencies operate.
How the model could learn this
The transformer architecture has a natural affinity for this problem because attention operates across the entire context window simultaneously, at all scales. But learning the hierarchical structure isn’t handed to the model — it has to emerge from the statistics. The key insight is that the token distribution changes you describe create a learnable signal at each level if the model has enough capacity and enough data.
At the phrase level, the signal is strong and local — the next token is highly predictable from the last two or three. At the sentence level, the signal is weaker and longer-range — whether a sentence continues or ends depends on syntactic and semantic completion that requires integrating over the whole sentence. At the paragraph level, the signal is weaker still and operates over even longer ranges — the distribution shift at a paragraph boundary is detectable but requires sensitivity to topical coherence across many sentences.
This is essentially a multi-scale statistical learning problem, and grokking is relevant here in an interesting way. The phrase-level structure is learned early and relatively easily — the statistics are tight and the signal is strong. Sentence-level structure takes longer. Paragraph-level structure longer still. Each level may involve something like its own grokking transition, where the model first handles the level through surface pattern matching — learning that certain tokens tend to appear after paragraph breaks, for instance — and then later develops something more like genuine structural sensitivity.
The distributional shift as a signal
The paragraph boundary is particularly interesting because it’s simultaneously a break in the local token statistics and a continuity in something more abstract — the text is still about the same general topic, same genre, same author’s voice. So what the model has to learn isn’t just that token distributions shift at paragraph boundaries, but that they shift in a constrained way — the new paragraph’s vocabulary is related to but distinct from the previous one, in ways that reflect the logical or rhetorical development of the overall argument or narrative.
Wednesday, March 25, 2026
LeWorldModel, LeWM (JEPAs)
🚨 Holy shit... LeCun's team just cracked world models wide open.
— Alex Prompter (@alex_prompter) March 24, 2026
Everyone's obsessing over the next Claude update.
Meanwhile Yann LeCun quietly dropped a paper that could matter way more long term.
It's called LeWorldModel.
And to understand why it's a big deal, you need… pic.twitter.com/jcwo02hTYB
Who makes the photo, the subject or the photographer?
Of course it’s the photographer. I’m speaking somewhat metaphorically. Take a look at this photo:
Rather striking, dramatic, no? That’s a photo where the subject makes the shot. What do we have? A well-known skyline, Lower Manhattan, an iconic building, One World Center, and dramatic light, what photographers call the Golden Hour. A trained monkey standing where I stood could have gotten that shot.
This shot’s a bit different.
It’s still dramatic, with strong geometry converging toward the center and some dramatic light. But the subject is quite ordinary, a bar at a standard-issue middle-tier restaurant. It takes a photographer to see that as a proper subject for a photography.
And then we have this:
WTF? What’s in the center of the shot, where the subject should be? There’s a doorway with “Soulcycle” above it and a clock above that. If that’s the subject, then why’s it obscured by that tree right of center, the pole to the left, and the car in front? Why? Because I took that photo and that’s what I wanted. It’s not a mistake. It’s intention. It makes you aware of the space between you and the ostensible subject.
Here’s a similar shot:
If you look closely at the enter you’ll see an iconic building, the Empire State Building, no less, the best-known building in New York City. But it’s got all those trees in front of it, and then there’s the hedge in the foreground, and the people. All intended. Not, mind you that I posed anything. I’m basically a street photographer, I walk around and shoot what I see, though on occasion I may take some pains to stand in just the right place.
Finally, this shot, one of my oldest:
It’s from the summer of 2004. I was scheduled to deliver a speech at a conference in Chicago when I saw, in The New York Times, that Millennium Park had just opened. I decided that I needed some photos of it for a project I was working on at the time: World Island, “for a world that’s permanently fair”. So I bought a cheap point-and-shoot camera, a Canon Powershot, and took a bunch of photos. That’s one of them.
What’s the subject? Well, if you look to the center there’s not much of anything there, just some people milling around on the plaza, which is in the park. You’ve probably looked at the lower left where you see a photographer photographing something off to the right, that silver thing, Cloud Gate by Anish Kapoor. There’s a spindly little tree at the right and a bunch of buildings in the background. That center space is surrounded, which is the point. After I’d taken that photo and had a chance to think about it, I decided that THAT’s what I’m about as a photographer, space, space and light.
That’s what those photos are about.
Iran War, The Movie: Straight Outta Hormuz
Here's the story: 'Iran War - The Movie': On social media platforms near you, playing now. Watch viral video!
Is the Iran war now turning into internet satire? A viral AI movie trailer for "IRAN WAR: The Movie" is mocking global leaders, nuclear fears and war rhetoric as memes take over timelines. Why is this video blowing up? All you need to know.
'Iran War' movie trailer breaking the internet on day 26 of US-Israel-Iran war
As ballistic missiles dominate the physical skies, there's a different kind of "strike" being carried out over the digital world. As the Iran war enters its fourth week, and war fatigue sets in, the internet, as always, is responding the only way it knows how. With memes. With satire. With increasingly sharp, almost ruthless humour. What started as scattered jokes has now turned into a full-blown stream of content. Posts, videos, and AI edits are all taking aim at the three warring nations US, Israel, and Iran, each one trying to outdo the last. The latest one to make the internet ROFL is a 136-second AI-generated "movie trailer" style video titled "IRAN WAR: The Movie", which has racked up nearly 400,000 views in hours, and has turned the high-stakes tension of the March 2026 conflict into a Hollywood parody. All you need to know.
There's more at the link, including shots from the video.
Here's the clip: Iran War – Movie Trailer.
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