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Frontier models concentrate economic value

2026-06-26 · 03:48 UTC ·@johnloeber ·2 min read

Brief

John Loeber (post 2026-06-26) argues that the new U.S. AI licensing regime will crystallize a contest over 'frontier' models because economic value concentrates at the frontier—customers pay for Anthropic and OpenAI while Mistral and XAI lag—creating winner-take-most (and sometimes winner-take-all) markets. He warns this will spur China to race to supply the best models, noting the R&D gap may be under one year and switching providers is often easy. Loeber says U.S. bans or sanctions on Chinese models would be ineffective given porous digital borders and resellers/wrappers, and that regulation without winning the technological race is a Red Queen's race. He also highlights that 'frontier' is hard to define—benchmarks depend on test‑time compute, FLOPs/parameter thresholds are imperfect, and claims like Karpathy’s (<1B parameters for AGI) imply licensing could ultimately extend to most models and hardware.

Why it matters

Frontier models concentrate economic value: customers currently pay for Anthropic and OpenAI while firms like Mistral and XAI are falling behind, producing winner-take-most dynamics and (in the most valuable markets) potentially winner-take-all outcomes.

Key details

  • The U.S. licensing regime will motivate China to develop frontier models because switching providers is often easy; the author estimates the U.S.–China R&D gap is under one year, so China could close it and capture customers if it offers the best models.
  • Attempts to ban or sanction Chinese models would likely fail in practice because digital borders are porous and a variety of resellers/wrappers can enable access; analogous EU attempts to regulate U.S. tech became ineffective and risk leaving regulators with outdated technology.
  • ‘Frontier’ is ambiguous: benchmark ranking depends on test-time compute (potentially requiring weeks or months), simple thresholds like FLOPs or parameter counts are imperfect, and with claims such as Andrej Karpathy expecting AGI in <1B-parameter models, the practical result could be licensing that eventually covers most models and model-capable hardware.
Source evidence

More Thoughts on the New AI Licensing Regime

  1. The economic interest is in the frontier. People are paying for Anthropic and OpenAI's frontier models. They want the best. Companies like Mistral, XAI, etc. are falling behind because they are not on the frontier, and we are seeing winner-take-most dynamics. Some markets -- potentially the most valuable ones -- are winner-take-all where the smartest model wins.

  2. Therefore, OpenAI and Anthropic could suffer the same fate as Mistral if they are no longer offering frontier models, while someone else is.

  3. Therefore, this shoots the true starting gun for China to develop frontier models: if they offer the best models, they will get the customers. Switching model providers, in most instances, is still easy; we've seen lots of evidence of this recently. Right now, the R&D gap is less than a year: perhaps they can close it.

  4. Some people think that the US and China might team up and (effectively) create a global AI licensing regime. But I view it as unlikely that China will cooperate with the US with a similar licensing regime, for two reasons:
    (a) My understanding of the Chinese government's approach is far less eschatological than the US': not framing AI as technology on par with nuclear weaponry, but rather as another normal technology that offers a chance at prosperity. It's important to note that the trend in China for the past fifty years has been one of steep, exponential progress in technological advancement offering enormous prosperity. It would take much more than what's going on currently to shake confidence in that trend.
    (b) If they do take the technological lead on supplying the world with frontier models, obviously that's a huge geopolitical and economic advantage!

  5. Some people are suggesting that this will result in the US banning Chinese models. While plausible, this would be an ineffective policy move: digital borders are porous and a myriad resellers and wrappers would enable access, if not providing US customers with plausible deniability.

  6. Perhaps the US could go further in trying to ban Chinese models, impose sanctions, etc. but this would end up being structurally equivalent to the EU's attempts to regulate US technology. We know how those end: ineffective while dooming themselves to irrelevance with the technology of yesteryear.

  7. What I've described is a Red Queen's race. If you wish to regulate, you must compete, and you must win.

  8. As a final thought, what does it mean to be "frontier"? This is a real question. As @polynoamial recently wrote about, benchmark performance is increasingly becoming a function of test-time compute, so it becomes very hard to tell which models are better than others: benchmarks might need to run for weeks or months to tell.

So maybe you say "frontier" is anything above a certain number of FLOPs. Or above some number of parameters.

But training techniques are becoming more efficient all the time. @karpathy said he expects to see AGI in a <1B parameter model.

=> There's no clear line. The result is that while today's "frontier" models may need to be licensed, eventually all models will be licensed. Maybe even all model-capable hardware.