China catches up
Has the US been focused on the wrong things?
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.
Gary, I thought you might be interested in how fusion has in some respects failed in the same way as AI. My father was a professor of plasma physics for 30 years, and his opinion was that, instead of focusing on the tokamak, we still needed much more science to find a better model. In other words, the fusion sector jumped on the first technology that had some kind of return (eg, the ITAR project in France).
The same thing is happening in AI. Instead of trying many different paths, they jumped on one that, while very flawed, yielded some results, and since there was so much money sloshing around and venture capitalists like to hear good stories, the entire industry is wasting trillions on the wrong approach.
I couldn’t agree with your closing thought more. I am excited about how Seed IQ’s active inference based AI agents acted as a control layer and created stability on IBM’s NISQ hardware. If Quantum leaps ahead we might not need all the data center compute.