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.
