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.
Karp is an irresistibly noxious figure, a would-be philosopher king who can’t seem to sit still on a TV set or a conference stage as he holds forth about kill chains or tai chi or the woke left. Perhaps even more than Elon Musk, Karp seems to understand that in an attention-economy economy, the job of an executive is to always be pitching.
He is also perhaps the most visible spokesman of Silicon Valley’s increasingly significant defense tech sector; Anduril, Palmer Luckey’s drone manufacturer, is another of its tent poles. For the last few years, a new Silicon Valley has taken shape in a kind of cluster around defense tech, robotics and artificial intelligence, with the leaders of each sector speaking almost in unison to describe the urgency of an American industrial renaissance and present technological competition with China in terms of a new Cold War. In this view, which has been mostly embraced by the Trump administration, nothing should be allowed to get in the way of technological progress, and the march of that progress could be measured crudely in capital expenditure.
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. In recent weeks, out of a mix of concerns about political vulnerabilities, national sovereignty and privacy, 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.
But his rant highlighted several genuine and growing questions about the way those big A.I. labs have sold the rest of us on their paths to global domination. He is not alone in asking them. And he is not wrong to. Even if plenty of indicators suggest that the A.I. economy as a whole is rapidly growing, it looks less inevitable than it used to seem that the big, brand-name labs will be the titanic profit engines of that future. Perhaps they will function more like utilities, providing machine intelligence almost like electricity to other innovators, entrepreneurs and enterprises.
As I discussed with Natasha Sarin last month, the hype cycle of the last few years has flourished — and generated a lot of investment — on the supposition that artificial intelligence was the kind of arms race it was possible to outright win, and that enormous or even monopolistic profits would flow inevitably to the winner.
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.” Beyond that threshold, the leading L.L.M.s would be so powerful, and would be improving so rapidly, that even small initial advantages would compound quickly into something like a natural monopoly on intelligence, which could then be sold to users at almost any price. 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. When A.I. companies began raising prices on their premium products to more closely match the cost of producing them, many of their clients balked, realizing that frontier models were not generating enough profit to justify the expense. Partly as a result, corporate uptake of frontier models flatlined; much cheaper, open-source models exploded.
This helped illustrate a broader pattern, visible at least since the explosive release of a cheap, open-source model from China’s DeepSeek in 2025: that even if copycats never quite caught up to the best-in-class standards, they’d also never fall so far behind. The frontier labs have grown so worried about this that they are desperately appealing to Congress to take legislative action against what they say are lesser companies, many of them foreign, effectively stealing their I.P. through a process known as “distillation.” This looks almost like an existential threat because for most users — even most corporations with capital to burn — it might not make intuitive sense to pay a superpremium for an only slightly better product. For many, it would probably make more sense to pay a lot less for a slightly inferior product — especially given that, because of the rate of progress, no model is likely to retain its advantage for very long.
This is one reason 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.
A few years ago, it was fashionable to say, as Peter Thiel liked to, that cryptocurrency was a libertarian technology, enabling individuals to conduct even large-scale financial business outside the reach or oversight of any government, and that A.I. was a “communist” technology, by which he meant authoritarian, concentrating and centralizing godlike planning powers into a single machine intelligence whose judgment would presumably supersede even that of the market.
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.
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