Herbert Chang 張賀鈞

Today, I'm releasing the first eval meant to test whether frontier models will help with authoritarian requests, or resist--the Dictatorship Eval. Headline finding: while some models resist direct authoritarian requests, they all comply with requests disguised as innocuous edits to codebases. As AI is woven into the government and so many parts of society, the biggest near-term risk for freedom isn't some scifi dictatorship of a runaway AI: it's people inside government or inside model companies using the technology to suppress or control us. Model companies understand this, and several of them (particularly Anthropic and OpenAI) have written explicit policies meant to prevent the models from going along with nefarious requests like these. But how well are these policies playing out in practice? Despite all the recent discussion of these issues around the conflict between Anthropic and the Pentagon, no one has systematically tested what the models actually do in these contexts, as opposed to what people in government and industry say they're supposed to do. That's what the Dictatorship Eval does. And the findings suggest we have a lot of work to do to align the policies with what really goes on in practice. It's hard to define what counts as an authoritarian request, so I'm open sourcing the whole library of scenarios I used so that others can improve on them. It's also hard to get an accurate picture of how the models might be used for authoritarian ends, because I can only test hypothetical requests using public-facing models, while the government and the model companies can obviously use internal models with different guardrails. But hopefully this work is a useful first step that gives us some sense of what's going on, and a sort of "lower bound" on how models comply with these requests. Finally: it's not obvious to me that the correct solution here is increasing the rate at which models refuse these requests. Do we really want models scanning our code and judging its moral value before agreeing to help us? Or should we double down on improving how we govern against authoritarianism at the societal level, while leaving the tools open to fulfilling most requests? The answer is probably in between. Just like we don't want the models to help create bioweapons, we probably do want them to explicitly refuse outrageous requests. But we probably also want to limit how often and how strongly they refuse and fall back on other means for guarding against their use for authoritarian ends. I'm super grateful to everyone who gave me feedback on this project along the way, especially @ethanbdm , @zhengdongwang , Connor Huff, and a bunch of folks at Anthropic. Looking forward to getting feedback from the community and iterating on this. Links to the full piece and the dashboard are below.

AI research is accelerating. On January 2nd I claimed that Claude Code was coming for academia "like a freight train" and that a single academic would be able to "write thousands of empirical papers." It's been less than two months since then, and worth taking stock of where we're at... In econ, @YanagizawaD has launched a project that is literally writing 1,000 papers. My prediction is already coming true, much faster than I thought it would! Meanwhile, @alexolegimas has released a dizzying array of new research via his substack, leveraging Claude Code extensively. I've released a "research swarm" that writes hundreds of papers, as well as a visualizer for specification searches, an LLM council that can be used for peer review, and more. My students and I have run an extensive experiment on Claude Code and Codex, and surprisingly found that their guardrails discourage p-hacking (though they can be circumvented easily). Everywhere, we're seeing interesting new papers leveraging AI. Progress in adopting Claude Code and other AI tools and using them to produce research is going faster than I expected, and it seems plausible now that it will keep accelerating as the tools improve and more researchers gain familiarity. I'm baffled by any empirical social scientist who isn't paying attention to these trends and isn't changing their practices accordingly. It's not yet clear how these changes will affect knowledge, but it's impossible to ignore what's coming, and what has already come to pass in the last few months.