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.