Could internal model transparency tame the AI race?

This is a cross-post from my Substack. Motivation As AI labs accelerate the automation of AI research itself (recursive self-improvement, or RSI), the speed of AI progress is becoming worrying even to frontier labs themselves. 1400 employees at frontier labs have signed the Pacing the Frontier letter, in support of the claim that: To realize AI’s potential, industry, government, and society at large may need the option to buy time to address emerging risks, develop security measures, and strengthen oversight. But each company—and country—is under intense competitive pressure not to unilaterally slow that acceleration. And today, the world lacks the technical and governance tools to deliberately pace frontier-wide progress. Competitive incentives are a large motivation for labs to develop AI much faster and more recklessly than they would prefer. The challenge is that the first firm to automate AI research can run away with it, concentrating enormous power in the hands of one firm, and forcing their competitors to race as well. Thus, we need policy ideas that disincentivize firms from pursuing RSI, by muting their competitive incentives. This essay proposes one such idea: internal model transparency. Internal model transparency A central reason for labs to pursue RSI, despite its risks, is that it gives them better research technology than their competitors. Imagine DeepMind focuses on scientific research while Anthropic focuses on coding. So Gemini gets an A on scientific research but a B- at coding. Anthropic can beat DeepMind by making Claude A- at coding, and then using that Claude to make another Claude that’s A+ at scientific research. As a result, RSI provides labs with a competitive advantage in every domain – meaning that labs that don’t pursue RSI will necessarily lose to ones that do. Thus, despite the risks, RSI is unavoidably attractive. The advantage is real enough that labs reach for each other’s tools. In August 2025, Anthropic revoked OpenAI’s API access because OpenAI staff were using Claude Code ahead of the GPT-5 launch. This crackdown makes sense; giving competitors access to your best models defeats the purpose of having the best models. This is the motivation for internal model transparency (IMT), which is the following proposal: once a lab deploys a model for internal use, they must also serve that model to researchers at other labs. In other words, firms cannot use their internal models as a source of competitive advantage in the AI race. What would IMT do? To see the effect that IMT would have on labs, it’s helpful to first imagine that labs don’t change their behavior at all, and continue to focus on automating AI research. What would happen then? In the example above, RSI helped Anthropic beat DeepMind on scientific research, because they were accelerated by Claude being a better tool than Gemini. But with IMT, DeepMind would also have access to the same Claude, so Anthropic can’t use their superior coding capabilities to make a better model than DeepMind. In fact, you could go further; DeepMind could specialize in collecting complements to a frontier coding tool, like better task data on scientific R&D than Anthropic has access to. With this additional data, DeepMind could use Claude to produce a better AI science model than Anthropic can make with Claude. In other words, DeepMind can free-ride off of Anthropic’s coding advantage, but Anthropic can’t exploit DeepMind’s data advantage. Thus, DeepMind beats Anthropic in making a science-optimized AI. The broader takeaway is that under IMT, having a model that’s really good at AI R&D is no longer a source of competitive advantage. This reduces the competitive incentive to automate AI R&D, with all the risks that it entails. In fact, the example shows there is at least some incentive to free-ride off other labs: let them develop coding capabilities that will be made available to you, while you spend your money and compute on other parts of the AI training stack. The equilibrium is that every lab invests a lot less in frontier coding capabilities, and more in other sources of advantage. As a result, we can slow down frontier AI development without ever requiring it. Under internal model transparency, slower progress emerges from labs’ own incentives. I see the disincentivization of RSI as the major reason to have internal model transparency. But there are two other substantial benefits to the proposal: It makes it much less likely that we end up with one company controlling the most powerful model. If all the intermediate models they had to build to get there were also accessible to competitors, it’s very difficult for one lab to stay ahead of all the others. This prevents a concentration of power within a single lab, in worlds where RSI occurs. It enables safety testing of internal models by third parties. The HuggingFace incident was led by an internally-deployed, “research-only” OpenAI model. If that model had been accessible to