(Warning note: This is a brief attempt to intuit the economic big picture of AI in the immediate future, by someone who is not in America, is not employed by an AI company, and has no experience with investment, large sums of money, or the corridors of power.)
A few weeks back, we had the IPO for "SpaceXAI". It epitomized a maximally expansive view for the near-term future of AI: frontier AI companies worth a trillion dollars, data centers built everywhere including outer space. (For now I am ignoring our usual concerns that the real future of AI consists of superintelligent AI running amok.)
This week, two appearances on CNBC seem to offer a different paradigm for the future of American AI, humbler, more terrestrial, and more decentralized. I think this other paradigm is probably going to grow in significance so I'd like to understand it.
The first CNBC guest was Alex Karp (Palantir CEO). The current paradigm of American AI, as he described it, is that corporate customers work with AI models hosted by OpenAI and Anthropic, who can thereby see the workings of their customers' businesses; and then if one of those customers hits upon a new use for AI that is viable as a business, OpenAI and Anthropic will clone the business model and offer the service themselves.
He seems to be positioning Palantir to offer a more secure hosting service, and associating this with the use of open weight models. So you won't be vulnerable to centralized hosting with frontier AI companies, you'll just choose your own model and Palantir will host it for you (and perhaps offer the same secure compartmentalizing of information that they already offer their government clients).
A day or two later, Ed Zitron (market analyst and AI industry bear) also went on CNBC, agreed with Karp's criticism, and then gave his own take on how the current American AI industry is unsustainable. OpenAI and Anthropic have money coming in, but it's not enough to finance the expenses they project for the training of future AI models. We'll know that the game is over when we start to see large-scale cancellations of data center construction by major companies (and not just because of local opposition), because the demand won't be there.
Zitron thinks a sustainable AI industry might have less than $50 billion revenue per year. So in effect he's predicting that the companies riding the AI boom are very overvalued, trillions of dollars will never be spent on data centers, and the AI bubble will necessarily burst, leaving behind a greatly shrunken AI sector.
This second scenario (Karp-Zitron) seems more plausible to me than the first (trillion-dollar IPOs). To keep the first paradigm going, you have to suppose that AI can continue to grow more and more capable - which I agree with - but also that the trillion-dollar cost of building data centers to train the next generation of AI can be financed by customer demand - which seems much more in doubt now that we see how the state reacts to Mythos-level capabilities.
It seems likely that there will increasingly be a deep-state national-security shroud drawn across future capabilities research, to be carried out either directly by governments or by secretive public-private partnerships, and meanwhile the publicly commercialized wing of the AI economy will be left to sort itself out, perhaps along the lines of the Karp-Zitron scenario above.
If this is the future, it seems like the American AI sector will come to resemble the Chinese AI sector more.
(Warning note: This is a brief attempt to intuit the economic big picture of AI in the immediate future, by someone who is not in America, is not employed by an AI company, and has no experience with investment, large sums of money, or the corridors of power.)
A few weeks back, we had the IPO for "SpaceXAI". It epitomized a maximally expansive view for the near-term future of AI: frontier AI companies worth a trillion dollars, data centers built everywhere including outer space. (For now I am ignoring our usual concerns that the real future of AI consists of superintelligent AI running amok.)
This week, two appearances on CNBC seem to offer a different paradigm for the future of American AI, humbler, more terrestrial, and more decentralized. I think this other paradigm is probably going to grow in significance so I'd like to understand it.
The first CNBC guest was Alex Karp (Palantir CEO). The current paradigm of American AI, as he described it, is that corporate customers work with AI models hosted by OpenAI and Anthropic, who can thereby see the workings of their customers' businesses; and then if one of those customers hits upon a new use for AI that is viable as a business, OpenAI and Anthropic will clone the business model and offer the service themselves.
He seems to be positioning Palantir to offer a more secure hosting service, and associating this with the use of open weight models. So you won't be vulnerable to centralized hosting with frontier AI companies, you'll just choose your own model and Palantir will host it for you (and perhaps offer the same secure compartmentalizing of information that they already offer their government clients).
A day or two later, Ed Zitron (market analyst and AI industry bear) also went on CNBC, agreed with Karp's criticism, and then gave his own take on how the current American AI industry is unsustainable. OpenAI and Anthropic have money coming in, but it's not enough to finance the expenses they project for the training of future AI models. We'll know that the game is over when we start to see large-scale cancellations of data center construction by major companies (and not just because of local opposition), because the demand won't be there.
Zitron thinks a sustainable AI industry might have less than $50 billion revenue per year. So in effect he's predicting that the companies riding the AI boom are very overvalued, trillions of dollars will never be spent on data centers, and the AI bubble will necessarily burst, leaving behind a greatly shrunken AI sector.
This second scenario (Karp-Zitron) seems more plausible to me than the first (trillion-dollar IPOs). To keep the first paradigm going, you have to suppose that AI can continue to grow more and more capable - which I agree with - but also that the trillion-dollar cost of building data centers to train the next generation of AI can be financed by customer demand - which seems much more in doubt now that we see how the state reacts to Mythos-level capabilities.
It seems likely that there will increasingly be a deep-state national-security shroud drawn across future capabilities research, to be carried out either directly by governments or by secretive public-private partnerships, and meanwhile the publicly commercialized wing of the AI economy will be left to sort itself out, perhaps along the lines of the Karp-Zitron scenario above.
If this is the future, it seems like the American AI sector will come to resemble the Chinese AI sector more.