Many people have written about advanced AI being too restricted, but my fear is we haven’t planned enough for a world where advanced AI will continue to proliferate.
This is despite the availability of models like GLM-5.2, despite the "Deepseek moment", and despite consistent evidence that open models lag closed models by a matter of months.
What happens in a world where, even with restrictions on US frontier models and compute, advanced AI continues to be broadly available?
Non-proliferation is likely to fail; at best, it provides a few months of buffer
A common response is to dispute the premise by claiming that the narrow open vs. closed gap is only a result of distillation by Chinese model developers, and if the US ecosystem shut itself off, Chinese competitors won't be able to keep up.
Maybe. But the evidence we have so far points the other way:
1) Existing defenses against distillation don't work. OpenAI, Anthropic, and Google already censor their models' reasoning chains to prevent distillation, and Anthropic has prevented Chinese customers from accessing their models. This hasn't led to an increasing gap between closed and open models.
2) Chinese companies have made genuine improvements to their model training stack that are discounted too heavily by US observers. Notably, preventing distillation might systematically push Chinese AI labs to innovate, rather than follow the lead of US labs.
3) AI has also seen the equivalent of the "four-minute mile": once a developer shows what's possible, others can prioritize and quickly improve their models on those capabilities, even when they previously weren't able to. We've seen this with reasoning models (Deepseek R1), coding agents (GLM-5.2), and will likely continue to see it with future advances.
4) There are many improvements that don't require large training runs. Updating scaffolds, finetuning models, using more inference compute can all narrow the gap between the capabilities elicited from US and Chinese models, even if the gap between the base models increases.
In other words, even if we ban open models, improving the agent scaffolds could lead to continued improvements in capabilities, such as by the creation of specialized scaffolds for cyberattacks (this is known as the "scaffold overhang"). So we are likely to continue seeing new misuse risks emerge for years.
5) Most importantly, there is no secret sauce for training advanced AI models. The main training techniques for developing and serving frontier models are well understood. Labs' roadmaps can be gleaned based on the roles they (and their third-party providers) are hiring for. The data being collected through these efforts doesn't require new innovations either.
If non-proliferation can at most buy us a few months, we should use that time to improve resilience
AI policy can't take for granted that US restrictions on models and compute will lead to a worldwide slowdown in the availability of advanced AI. What changes if we plan for a world with widespread access to advanced AI?
For one, we need to prioritize resilience. If licensing, export controls, and other restrictions can at most give the government a few months of lead time, that time is best spent giving defenders broad and deep access to AI systems to improve our ability to withstand attacks.
This becomes especially important since many of the misuse risks that the US govt. is concerned about, such as cyberattacks, are a result of AI's *absolute* capabilities, rather than *relative* the gap between open and closed models.
For example, even if US model developers can maintain a lead compared to foreign developers, this doesn't help unless the lead is used to find and fix cybersecurity vulnerabilities in the meantime, as @joshua_saxe and others have argued.
To their credit, OpenAI and Anthropic have both emphasized the importance of improving resilience. There is also widespread consensus within AI policy that resilience is important.
But resilience is often still described as a way to improve society's ability to withstand the availability of *US* frontier models, controlled in its pace of diffusion by US companies and the government.
This is also reflected in Anthropic's recent letter to US Senators, where they argue that if only we address distillation attacks and enforce export controls better, advanced AI capabilities would not be available broadly.
Because of all the reasons mentioned above, I think this is unlikely. This makes investing in resilience that much more important.
