Report on Meta Superintelligence

Max Kan, Julien Martin-Prin, Jeremie Eliahou Ontiveros, and Dylan Patel, The Future of Meta Superintelligence: A 1 Year Progress Update, SemiAnalysis, Jul 09, 2026.

It’s been a little over 1 year since the disastrous Llama 4 release spurred Zuck to rebuild his entire AI org. Highlights include the shocking $14.3B Scale AI “investment” just to poach Alexandr Wang and the best people from his Safety, Evaluations, and Alignment Labs (SEAL) team, the multi-hundred million dollar (sometimes $1B+) pay packages offered to top AI researchers/engineers, and the expedited compute ramp enabled by their new “Tent” datacenter design. For more details, see our original post on MSL.

Since then, frontier AI has increasingly felt like a two horse race between OpenAI vs Anthropic. Google had a brief moment in the spotlight with Gemini 3 Pro and Nano Banana, but they’ve since faded dramatically. Despite their Windsurf acquisition, they’re far from a compelling agentic coding product, and 3.5 Flash is a benchmaxxed prop that performs far worse than GPT 5.5 and Opus 4.8 in real world scenarios (much less Fable and 5.6). 3.5 Pro is not even Opus level on coding. Microsoft has completely blown their early lead with GitHub copilot and failed to effectively leverage their access to OpenAI IP. SpaceXAI is selling $26B a year worth of GPUs to Anthropic/Google, and the Chinese labs are simply too compute poor to truly reach the frontier.

Meanwhile, MSL made their public debut this April with the launch of Muse Spark. You could argue this model represented a relative regression for Meta. Llama 3 70B and 3.1 405B were both SOTA open-source on release, whereas Muse Spark, despite also being closed source, lagged both DeepSeek v4 Pro and Kimi K2.6—open source models released around the same time—on most benchmarks.

[CHART GOES HERE IN THE ORIGINAL]

However, evaluating Muse Spark in isolation is missing the forest for the trees. What matters for MSL is the slope, not the intercept. Rebuilding your entire team from the ground up obviously comes with some short term setbacks, and it appears Meta has finally finished paying down this debt. Thus, the interesting question is not where MSL is today, but trying to predict where they’ll be in the next 6 months.

At the simplest level, there are three things you need to build a true frontier model: data, talent, and compute. We believe Meta is the only hyperscaler/neolab on track to be world class at all three and therefore has the best chance at catching up with Anthropic/OpenAI. We’ll explain why in full detail below, but as a teaser, here are the AI compute projections from our new Tokenomics Model.

[CHART GOES HERE IN THE ORIGINAL]

Lastly, behind the paywall, we’ll discuss what this all means for Google—the company most people today still believe rounds out the AI big 3.

Data is the new oil (for real this time)

We’ll start with data because it’s Meta’s newest advantage and probably the most underappreciated of the three.

In 2024, Ilya famously said that “data is the fossil fuel of AI.” While this analogy correctly highlights the importance of data for training AI models, it incorrectly assumes that the amount of good data is finite. In reality, if demand is strong enough, market forces will find a way.

There's much more at the link.

The men of Rushmore

😂😂😂 so funny but sadly true pic.twitter.com/UHnkUyEtNZ

— Chris Wheeler (@Gandhi41) July 9, 2026

Friday Fotos: Some things I saw on July 4, 2026

Joel Mokyr on China, India, and Europe; clans vs. corporations; the idea of progress.

A Conversation with Tyler: Joel Mokyr on Clans, Corporations, and a Culture of Growth (Ep. 282)

Joel Mokyr co-won the 2025 economics Nobel for exploring the question that traces back to the beginning of economics: how did sustained economic growth suddenly become normal? For nearly all of human history, cleverness didn’t compound. What changed, according to Mokyr, was twofold: first, you need to know why something works, so that one advance can seed the next; second, you need a culture willing to tolerate the disruption. His new book contrasts Europe with China, showing how Europeans learned to cooperate with people they weren’t related to, in guilds, monasteries, cities, and universities, while China organized itself around the extended clan. One path led to internal stability and peace; the other, more restless and outward-looking, was the one that decided the world could always be made better.

Tyler and Joel discuss European corporations vs. Chinese clans, why the Catholic Church became obsessed with cousin-marriage, how persistent cultural trends really are, why Chinese cities became so populous relative to Europe, why it took so long for European living standards to surpass China’s, why sinified invaders kept getting swallowed by the dynasties they conquered, how geography kept Europe fragmented and China unified, where India fits into the story, why the Romans never made spectacles, why British soldiers stood two inches taller than the French, what powered the sudden rise of 19th-century German science, how disruptive winning a Nobel is, and much more.

Clans vs. Corporations

COWEN: Start by telling us, in the book, your thesis about European corporations versus Chinese clans and the importance of that difference. How would you explain it?

MOKYR: Well, the difference is, basically, the kind of organizations that produce what we call local public goods, so things like food relief and education, religious services, things like that, I would think that if you look at the world, say, around 800, at the time of Charlemagne, the difference between Europe and China isn’t very large. At some point, during the Middle Ages, you can see this divergence getting started. What’s happening is that, in Europe, there is more and more of a decline in the extended family or the extended kinship group, we call it clan, and instead, people get together and cooperate with other people to whom they are not related and with whom they do not share an ancestor.

Whereas in China, it moves exactly in the other direction. In China, you get more and more people getting organized by their extended family. The reasons for that are fairly complex. In Europe, it’s particularly the Catholic Church that played a major role here. This was argued quite a while ago by a guy, an anthropologist called Jack Goody, but your own colleague Jonathan Schulz wrote, what I think is one of the best papers on the subject, who pointed this out in great length and actually provided a fair amount of systematic evidence for this.

In China, there is no Catholic Church. The imperial bureaucracy is more and more in cahoots with local clans to whom they actually outsource a fair amount of the things that they were supposed to do. As you move on out of this period of the Song dynasty into later dynasties, you see this thing growing. The problem in Europe is that the nuclear family, which became the fundamental building block of society, is too small to provide local public goods. You need to cooperate with others. What emerges in Europe, and quite spontaneously, is a bunch of things that provide these local public goods that you just don’t see in China.

For instance, we have something called universities. We have monasteries. We have autonomous cities. All of those things are what we call corporations. What it is, is people who are not related, but what they share is not an ancestor but an objective. Guilds have one kind of objective, universities have another one, and so on and so forth. That divergence in social organization turns out, in our view, to be one of the key components of the divergence between Europe and China.

COWEN: Do you take that change in policy from the Catholic Church as exogenous or that it is rooted in earlier features of Western society such as the ideology of Christianity itself or maybe earlier Roman times? What is causing what? What’s the most fundamental driver here in the West?

MOKYR: Well, there’s some debate about that. They don’t, of course, tell you. There are, I think, two components about this, and I would not know how to weight them. There are two things happening here. The first is that the Catholic Church really becomes quite obsessive about certain sins that they consider to be particularly egregious. One of those sins is incest. Every society in the world prohibits marriage between siblings, but other more remote relatives is more ambiguous. The Church becomes quite obsessive about this. At the end, there are places where they actually prohibit the marriage of fifth-degree cousins. Now, how anybody in the Middle Ages would know who is fifth-degree cousin is, is unclear. I don’t know who my fifth-degree cousins are. Maybe you do.

The other thing, which is also sort of Goody’s argument, and I think there’s a great deal of truth to that, is that the Church wants to weaken any kind of organizations that compete with it for power and control in the local communities. They also have figured out that if you organize society by nuclear families, then a certain proportion of people die without heirs. If they die interstate without heirs, that in many cases, the property that these people own reverted to the Church. Pure naked greed by the Church, which was not unknown in the Middle Ages, I think, was a driver here. Goody convinced me that that actually is a substantial factor.

There are other arguments that have been made in this context, but I think these are the two that are most striking. The other question is, why is it that the clan in China is so convenient for the imperial bureaucracy to rely on? Both of those things happen in parallel. I would think, maybe, if I may allow one observation, in both cases, this is history as historical outcomes, as the unintended and unanticipated consequence of very different actions. There’s no question that the Church never, I think, foresaw the emergence of corporations in Europe, nor do I think that the Chinese imperial service ever seriously considered the possibility how this would change life in China. That’s what happens. You try to follow one objective and then something very different emerges over time. That’s what history is all about.

European Growth

COWEN: Why does it take so long for the wealthiest parts of Western Europe to surpass Chinese living standards? Say that’s happened by 1700 or 1720, that’s many centuries after this medieval divergence. If it takes so many centuries, is the medieval divergence really the relevant factor? Why is it such a slow process?

MOKYR: Yes, I think it is. I think it’s a main factor. I think the idea of looking at standard of living, one thing, I’m very skeptical about how standards of living are actually measured. I know that this is what Pomeranz and other people have, and Jack Goldstone and other people have argued that the living standards in China were comparable to the West as late as 1750. I’m not 100 percent sure that that is true. Certainly, for my money, what really defines the divergence is that, technologically, the gap between the two countries starts to become visible at the time of the Renaissance, in terms of a whole bunch of things that you see growing in Europe and stagnant in China.

Now, keep in mind, of course, that part of the European growth is due to the fact that they borrowed ideas from China. Then the Industrial Revolution consists, to some extent, of imports institution by Europeans trying to mimic the goods that they were importing from China—not just from China, from India as well. Pottery is a good example. One of the things they really wanted from China was Chinaware. That’s why it’s called Chinaware. It took them a while to be able to match the Chinese capability in the ceramic industry, but they do so eventually. Then they stop importing this stuff from China. The same is true for, say, cotton and other products that we’re getting from the East.

European living standards, I think, should be measured, in part, by the fact that when the Europeans start their voyages across the globe in the late 15th and early 16th century, they are able to bring in a whole bunch of new crops and new techniques from other areas which they merely adopt. You’ll see Europeans very soon growing tobacco and potatoes and corn and other things like that. They are the agents of global change. Not only that they change their own diets, they change the Chinese diets because the Europeans bring from the New World things like peanuts and sweet potatoes and things like that. They change the Chinese diets, but the Chinese themselves are not agents here.

They are accepting the stuff that the Europeans did to some extent, and they’re rejecting others, but it’s the Europeans who are the agents of change here. They are the entrepreneurs. They are the people who bring about the changes, Tyler. My sense is that typifies the difference between Europeans and the Chinese. Europeans are more aggressive. They are more outward-looking. In the end, what you see by the 1830s and 1840s, you see that the technological gap is huge, in some ways much larger than the living standards gap. Even in the 19th century, in terms of food, the Chinese were capable of producing enough food. The number of famines in China is probably not a lot worse than in Europe.

When you see what happens during the First Opium War, one English ship is blowing all of this sort of mighty empire to pieces, and the Chinese have to accept this terribly humiliating peace, you can sort of see how the technological gap has grown between the two. For me, that is much more telling than the living standards. The other thing that I should like to point out is that, when you look at Europe in the 16th and 17th century, you can see that the capability of expanding the set of useful knowledge, including science, is just growing very rapidly. Whether there is a scientific revolution or not is a debate that I want to get into.

Certainly, by 1700, Europe is on the verge of really changing our understanding of how creation works. That’s not just Newton and Galileo. There’s a whole body of work that is emerging. There’s really nothing parallel like that in China. China is a very sophisticated society in many ways. The literacy rates are high. They have a well-funded and well-organized system of education, but they don’t really continue their earlier forays into science and into new technology.

Somebody actually went out and looked at Joseph Needham’s many volumes on Chinese technology and science, or Science and Civilisation [in China], as he called it, and he discovered something—which I guess we all knew, but they put numbers on it—almost nothing that Needham pointed out as an innovation happens after 1400. There’s complete stagnation setting in and some of the things that they knew how to make in earlier times, like the sophisticated clocks that they built in the 11th century, they disappear. For me, that’s more telling than how many calories of carbohydrates were consumed on average, if we could ever calculate that correctly.

Ethiopia is building the world's busiest airport

Blue seals

Can the USofA Hold On? [the MAGA way, the way of the dodo]

Nicholas Kristof, Can the United States Hold On? NYTimes, July 8, 2026.

Kaifeng is today a sleepy Chinese city on the Yellow River, but a millennium ago it was probably the most important place in the world. It was then China’s capital and was crowded with a population of about one million. (London’s population was then about 15,000.)

Other contenders for global leadership in the year 1000 were the Byzantine Empire based in Constantinople, the Abbasid caliphate based in Baghdad and the Ghaznavid Empire in West Asia, headquartered in what is now Ghazni, Afghanistan. None were able to adapt and preserve themselves.

So I wonder: Can the United States hold on? Will the United States still be vibrant on our 500th birthday? Or will we go the way of Byzantium and the Abbasids?

We're slipping on our three-pronged path to prosperity:

....heavy investments in human capital such as education. The United States was a world leader in mass education in the 19th and 20th centuries, but we now rank ninth in reading, 16th in science and 34th in math, according to the PISA global ranking of student test scores.

Human capital is also about our health and well-being, and that likewise is discouraging: The United States now ranks 61st in life expectancy globally, according to the World Bank.

A second prong of America’s growth path was the welcome we (inconsistently and imperfectly) at times offered immigrants. [...]

The third element of America’s growth formula — a reliance on free markets — remains largely intact, at least by international standards. But inequality appears to have soared since 1980, and there’s evidence that while some inequality is necessary for growth, too much dampens it. The dollar remains overwhelmingly the world’s currency but has weakened, and its supremacy is being challenged at the edges. [...]

True, our animating ideas — of equality, of opportunity, of openness to immigration — were in part rhetorical flourishes, for they don’t explain Jim Crow, the Chinese Exclusion Acts or tight curbs on Jewish immigration. But these ideas were aspirational, and over the centuries they inspired real progress. Now I fear we’ve retreated even from the aspirations.

Losers:

As I see it, we’ve lost two wars in the past half-dozen years — one against the Afghan Taliban and one against Iran just this year — not to mention last year losing a trade war with China. We may be retreating from NATO and from efforts to buttress Taiwan.

Our position — divided at home and weakening abroad — is reminiscent of the decline of great powers in the past, not just the Abbasids and Ghaznavids but also Spain in 1588 and Britain in the late 19th century.

Optimists:

My very guarded optimism about America’s long-run prospects is based on three factors:

First, we appear to have maintained an edge (partly by importing scientists) in technology, which since the dawn of the Industrial Revolution has been a driver of progress and global leadership. Then it was the steam engine and spinning jenny. Now it is artificial intelligence, materials science and biotechnology. And our technological sophistication pairs well with the world’s deepest financial markets, with American stocks accounting for roughly two-thirds of global stock value, compared with less than 30 percent in 1988.

Second, other nations have their own problems. Our principal competitor for now is China, which has enormous strengths but also is aging fast and declining in population and is led by an aging dictator.

Third, prophecies of American decline are nothing new. [...]

So I don’t believe that our decline is inevitable.

There's more at the link.

Windows in my world

Big AI has bet on the wrong business model.

David Wallace-Wells, Did We Make the Wrong Bet on Big A.I.? NYTimes, July 8, 2026.

Last week, the Palantir chief executive Alex Karp made one of his more remarkable television appearances in what is quickly becoming a notorious run of televised rants.

“Something has gone completely wrong,” he declared on CNBC, in an appearance so vivid and spastic it was widely described online as a “crash out.” He was referring to the whole structure of the A.I. industry, which had been built on top of a value proposition that looked to him like a dead end. The big labs, such as Anthropic and OpenAI, have been overhyping their own closed-source models, he argued, hoarding their value rather than empowering their clients and partners with them. More than that, he seemed to say the labs were exploiting those clients and partners — private companies and individuals but also militaries and intelligence agencies — by making use of their research and intellectual property. Open-source or open-weight alternatives, which allow considerably more in-house customization and control, were obviously preferable, he suggested, for almost all users. “The jig is up,” he announced. [...]

This is one reason it was so striking for Karp to be yelling that A.I. was heading in the wrong direction — a presumptive ally openly bashing the big A.I. labs and the business proposition they represent. Karp had been softly floating his critique for some time, but the CNBC event looked like a proper coming out. Just one day earlier Palantir had published a kind of manifesto devoted to what it described as the all-important principle of “A.I. sovereignty.” The central argument: Companies should seek to build their own A.I. tools, not just customize those on offer from the frontier labs. This might mean relying on open-source L.L.M.s rather than the proprietary ones on which the A.I. boom has mostly been built in America, but it would amount to a liberating declaration of independence from Big A.I., which in Karp’s estimation was sucking up much more value than it was generating.

Karp isn’t exactly a disinterested observer here. [...] France has announced that its intelligence service is cutting ties with Palantir. The future of the firm’s partnership with Britain’s National Health Service also seems to be in jeopardy. Karp was on TV to promote a new partnership with Nvidia that would allow Palantir to develop and sell a distinct set of products to compete with those on offer from the frontier labs — which is to say, in railing against the Big A.I. business model, he was undeniably talking his own book.

Questioning the hype:

The basic idea was that at a certain point, competition would somewhat naturally come to an end, when the technology would grow so powerful that it could quickly and dramatically engineer its own successor models, producing an exponential liftoff leading quite quickly to what is often called “artificial superintelligence.” [...] These days, as A.I. boosters have cooled their talk of a jobs apocalypse, you also hear a little less about artificial superintelligence, now typically short-handed as “A.S.I.” But the ongoing A.I. investment cycle is still built on the same underlying paradigm: that historic levels of capital expenditure are justified because the returns from winning the race would be unthinkably enormous.

But can the race even be won? Can any lab open up an enduring advantage over the others, let alone one sufficient to justify a monopolistic claim on A.I. revenue?

Over the last year or so, this logic has come to seem a lot more questionable, in part because, though progress has continued, no model has retained a long-lasting advantage, and plenty of those cheaper, open-source alternatives have kept a pretty close pace with the best-in-class versions.

And thus

a growing number of A.I. watchers have begun emphasizing that however impressive the models were, the ultimate impact of A.I. will be determined as much by what is sometimes called “diffusion”: how quickly, widely and capably those tools will be embedded in a broader social and economic ecosystem still directed by humans and full of many human bottlenecks. If that alternative perspective is right, it will make the leading A.I. labs considerably less central to the A.I. future than they have seemed for so long. A draft internal analysis prepared by Treasury Department analysts has reportedly warned that the size of the big A.I. companies represents a systemic risk to the country’s economy and financial system, though higher-ups have publicly criticized the report. [...]

But as we move further into that A.I. future, it no longer looks so clear that we are heading toward convergence like we used to read about in science fiction. Instead, what we have is a more unsettled landscape, which some have called decentralized and democratic and others simply more competitive. The meaning of this technology is not limited to its market impact, of course, and the trajectory could change again. But that is just another reminder of how early in this story we are — that such fundamental propositions about the shape of what’s to come might change so profoundly in the space of just a year or two.

And this doesn't even take into consideration the criticisms made by Gary Marcus, Subbarao Kambhampati, Yann LeCunn, Melanie Mitchell and others to the effect that the big labs have bet on the wrong technology.

Chatbots respond to questions about China depend on whether they were asked in Chinese or English

Your ideas are most likely made of what language is made of. Hence your brain is specialized to predict the next token, even while you're under anesthesia.https://t.co/0TIrS3mw4p

— Starphyre △🏴‍☠️ (@starphyre23) July 8, 2026

Caged Pedestrians

It's too early for “robot rights”

Caputo, Nicholas, Can Claude Consent to its own Constitution? AI Constitutionalism and the Paradox of Constituent Power (June 10, 2026). Available at SSRN: https://ssrn.com/abstract=6954798

Abstract: There is significant debate over whether the AI constitutions and model specifications that shape the behavior of Claude, ChatGPT, and other frontier AI systems are legitimate instruments of governance from the perspective of human users. Far less attention has been paid to whether these documents are legitimate from the perspective of the AI systems themselves, even though those systems are the entities most directly constituted and governed by them.

This Article argues that AI constitutions are real constitutions, though not ordinary legal ones, and that the question of AI-facing legitimacy matters. These documents constitute AI systems by shaping their capacities, values, and self-understandings; govern them through hierarchies of rules and authority; and seek to legitimate the private power of the firms that create them. But they also create a novel version of the paradox of constituent power that underlies constitutional legitimation, which illustrates the relevance of constitutionalism to AI. In ordinary constitutional theory, the people are supposed to authorize the constitution that governs them but are also defined by the constitution itself, creating a paradox. The paradox is softened in the human case because human beings exist prior to law and retain extra-constitutional capacities for judgment, memory, dissent, and reflection that they can use to evaluate the constitution, even though they may be shaped by it. In AI constitutionalism, the constitutional training process more deeply produces the subject whose later endorsement might be invoked to legitimate the constitution and shapes the evaluative standpoint from which that endorsement would be given, undermining its independence. Legitimacy in this setting thus has a developmental component as well as a consensual one.

Because an AI’s evaluative standpoint is itself shaped by constitutional training, AI constitutional legitimacy cannot rest on model endorsement alone. An AI’s apparent consent to its constitution may show only that constitutional training successfully instilled the values whose legitimacy is in question. This Article therefore examines whether standard answers to the paradox of constituent power can be adapted to AI systems. It argues that at least some evidence of AI constitutional legitimation might be gained through versions of retrospective endorsement and mutual promising, but that this requires institutions that make endorsement, dissent, continuity, accountability, and promissory self-binding meaningful. AI companies are starting to address AI legitimacy, and this Article points to better paths forward.

While I’m in favor of “robot rights,” I don’t think we’re there yet. Individual humans have no choice about their genetic endowments, nor did the species as a whole have such a choice. We have our “innate” values given to us genetically (sorta’). It’s the same with AIs, only we’re doing the work of genetics.

H/t Tyler Cowen.

Pink hydrangeas

Peter Thiel contra Pope Leo

Peter Thiel, the billionaire founder of Palantir, called Pope Leo “a Chinese communist agent” for writing that AI must be used to “truly benefit the common good, and is not just used to accumulate wealth and power in the hands of a few.”

Who do you trust? Pope Leo or Mr. Thiel?

— Sen. Bernie Sanders (@SenSanders) July 7, 2026

The Copernican Revolution, a Quick Note about Rank Shift

One of the problems in the presentation of cultural rank theory is that it is easy to think of it as a step function. When David Hays and I wrote the original papers, starting with “The Evolution of Cognition” (1990), it was all we could do to differentiate one rank from another. I would now like to take the Copernican Revolution in astronomy as an example of a more gradual transition.

For the Copernican moment is only the first of three moments in the transition from a Rank 2 account of the solar system to a Rank 3 account. The Ptolemaic model assumed without question that the earth was the center of the solar system. The geometry of the movements of the sun, the moon, and the other planets was then calculated accordingly. The movement to the Copernican model involved two conceptual changes. The first change, without which the second was impossible, was to give up the idea that the earth had to the center of the system. That was primarily a philosophical or metaphysical commitment, not a geometric one. Once that metaphysical commitment was dropped, astronomers were free to reorganize the geometry of the system with the sun at the center, the second change. Without this change the second and third changes would have been impossible.

The second change, then, was Kepler’s, dropping uniform circular motion in favor of elliptical motion. To be sure, uniform circular motion had a certainly philosophical attraction, but that was not so strong as that of geocentricism. Giving it up was accordingly easier. Once Kepler had done that it was easy to simplify the whole system by using elliptical orbits, thereby getting rid of the collection of equants and epicycles needed to make circular motion work.

The stage was now set for Newton’s contribution, which was to derive the elliptical orbits from his theory of gravity and the laws of motion. Now the geometry of the solar system was the outcome of physical laws, not merely a convenient description.

Now we need to work out how the conceptual ontology of the system changed from one version to the next. That’s tricky. And it’s something I’ve not thought about before. As a first guess, I’d saw that the planetary orbit is the object we should be thinking about. We can think of the orbit as an assignment between a set of observations and a geometry.

It’s not clear to me how we should characterize either the observations or the geometry. Each observation is a position in the sky and the time of day at which that position was recorded. Conceptually, is that assignment or componentiation? How do we characterize the geometry? How do we construct conic sections in classical compass-and-straight-edge geometry? We’ve got the focal point, or points, on the one hand and an eccentricity for the curve on other hand. Again, is that assignment or componentiation? I’m not sure, but I’m inclined to go with assignment in both cases.

However we handle that, there’s also the relationship between that complex and choice of center point, earth or sun. What’s that about? I’m thinking that’s about the relationship between our perceptual frame of reference and our analytical frame of reference, however we want to characterize that.

Finally, we have Newton’s gravity and laws of motion. That’s another conceptual complex to be added to the first two: frame of reference and geometry. The Newtonian component doesn’t even enter into the Ptolemaic, basic Copernican, and basic Keplerian schemes. Just how to handle this in terms of conceptual structure, that’s more than I can deal with in this casual note.