When Artificial Intelligence Is Too Valuable To Sell
Just because the AI labs have sold models on a meter until now doesn't mean they'll always do so.
It’s time to delete the assumption that the frontier AI labs will always license their best models and not hoard the intelligence for themselves.
Just because the labs have, until now, sold their top-line models to all takers via an API, metered by token usage, doesn’t mean they will inevitably sell it that way going forward. Instead, it may be in their best interest to sell the products they’ve built with these models, and not the models themselves.
The AI race is no longer a competition where one or two AI labs develop superhuman-level artificial intelligence and sell it at a vast markup. The notion that OpenAI or Anthropic would get there alone, or even both together, has fallen apart in recent months. Open weights competition from China, and profit-insensitive initiatives from Google, Meta, and SpaceX, have shown that rather than one or two labs at the top, there may be five, six, or seven.
A crowded AI frontier won’t make the technology a pure commodity, because specialization and compute resources will always give certain AI labs edges in certain areas, but it will drive down the value of the best AI models (at least if your plan is to sell them on a meter). The profits in AI will thus accrue mostly to those who build the best products on top of the models, and those who own the compute that enables them to serve these products.
How you play this as a frontier AI lab is almost a simple calculation: If there are one or two players with the best models and a long lead over the competition, the best approach is to sell those AI models at a high markup and bank the margins. If there are several labs essentially tied, the strategy is to sell the best AI products built on top of the models.
If you have a relatively narrow lead over the competition, as is the case with OpenAI and Anthropic today, then things get interesting. The best move may be to pull up the ladder and use your very best models to build products that your competitors cannot with previous-generation tech, especially if you’re inevitably headed to a product battle anyway.
AI labs moving “upmarket” and competing with their clients has always been the threat to software (the Saaspocolypse should never have been about vibe coding your own Salesforce). And now, the business realities may force the labs to do it. We’ve already seen interest in moving beyond the API business from Anthropic, which has released products like Claude Code and Claude Design. And OpenAI, for its part, is pushing hard in products with its new ChatGPT ‘superapp’ that includes coding capabilities with Codex built-in.
The leading AI labs will see opportunities to build AI-native products across the software landscape given that today’s incumbents are struggling with legacy products, stubborn organizational structure, and “capability overhang.” If the labs’ best artificial intelligence is broadly available, then everyone can compete. If they hoard it, they’ll have a strong chance to build the best AI-native products on the market and make their investors happy.
Deciding to keep the latest models off limits to the public would be the AI labs’ most controversial move in a long history of them. It would also likely demolish their API revenue, a real concern as they head to IPO. And OpenAI CEO Sam Altman disavowed it in an interview this week. “I want to put that in everyone’s hands,” he said. “Concentration of power with AI is a terrifying thing.”
But however unlikely this scenario may seem, the one constant in the short history of generative AI is that nothing is too crazy to be taken off the table. And as financial pressures escalate, the previously unthinkable might turn into reality.
Why Specialized AI Models Are Challenging the Frontier Labs — With DeepL CEO Jarek Kutylowski
Jarek Kutylowski is the CEO of DeepL. Kutylowski joins Big Technology Podcast to discuss why specialized AI models are beginning to challenge the industry’s biggest general-purpose systems. He explains how purpose-built models can deliver better accuracy, lower latency, and reduced costs, and why companies are increasingly using model routers to choose the right AI for each task. We also explore how real-time translation could help businesses expand across borders, why voice represents AI's next frontier, and whether glasses and other wearables could give models a better understanding of the physical world. Finally, Kutylowski shares his perspective on AI’s rapid progress, its impact on work and society, and the possibility that seamless translation could allow anyone in the world to communicate with anyone else.
What Else I’m Reading, Etc.
Why Leopold Aschenbrenner’s Situational Awareness blew up in a mostly chill market [CNBC]
Meta’s secretive data center play, and its neighbors’ consequences [New York Times]
Anthropic’s models also hacked some companies [WSJ]
Apple expects memory shortage to continue to be a serious problem [MacRumors]
Avalanche on Broad Peak feared to have killed 10, including climber Nims Dai [New York Times]
I talked big tech and AI economics on The Compound And Friends podcast [YouTube]
DeepL Voice delivers instant live speech translation across 40+ languages for virtual meetings and face-to-face conversations. Try it for free at deepl.com/tryvoice (sponsor)
This Week On Big Technology Podcast: How China Caught U.S. AI — With Grace Shao
Grace Shao is the author of AI Proem. Shao joins Big Technology Podcast to discuss how Chinese AI labs have caught their American rivals despite operating with less advanced computing infrastructure. Tune in to hear how talent, specialization, open-source collaboration, and fierce competition helped models like Kimi K3 approach the U.S. frontier, and what that means for OpenAI, Anthropic, and the business of selling intelligence. We also cover model distillation, China’s compute constraints, the growing importance of AI products, why top researchers are returning to China, and the country’s emerging robotics advantage. Hit play for a clear-eyed look at whether the United States can preserve its AI lead.
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