The ultimate culmination of the “no moat => more competitors => price wars => profits are scarce” argument that I have been making here regularly since the summer of 2023, has arrived — and may wreck the U.S. AI industry:
It is hard to see how Anthropic and OpenAI are going to pull off trillion-dollar IPOs in light of this news, especially given the newfound industry-wide price sensitivity in token budgets. In the light, it is hard to see how all the massive data center investments will pay off, with price wars dropping token prices to near zero; the meagre profits are unlikely ever to justify the massive outlays.
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The fundamental flaw in the current paradigm is threefold. First, it is wildly inefficient, a brute force paradigm that requires a model to train on the entire internet in order to approximate intelligence — hence expensive to develop; it is also difficult to operate, because the approximation, being derivative of the entire internet, requires vast resources in order to run.
Second, because the systems are not reliable, charging premium prices was never really viable in the long term.
Third, the basic approach is easily replicated, leading to the price war dynamics and small or negative margins.
The combination of high operating costs, unreliability, and small margins is not a winning formula—and certainly not one that we should be structuring our entire economy around.
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Then again, maybe the whole AI race has been misconceived. As Robert Wright just noted in a Washington Post essay,
“America’s preoccupation with “winning” the AI race with China could well lead to unprecedented catastrophes, even catastrophes on a global scale. Not all games are zero-sum, and if this fact doesn’t start playing a bigger role in American policy discourse, the AI revolution could turn out very badly.”
Maybe, for example, instead of racing to sell the cheapest LLMs, a battle we are unlikely to win, we should be focusing on cultivating new forms of AI that are better suited for science and medicine.