Read Dan Selsam on AI Risk
Dan Selsam’s statement on AI risk is my recommended reading this week, alongside some thoughts on the debate about AI safety funding.
Hello, I'm Jacques. I work on AI alignment.
The transition to advanced AI, and how to make it go well.
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How I Came to Take AI Risk Seriously
My path from dismissing AI risk to working on AI safety, and the effort to think clearly amid fear, social pressure, and uncertainty.
Automated alignment research needs a better plan than 'Stop if we catch them scheming'
My PIBBSS Symposium talk from September 2025, with the deck and the full transcript, on why the dual-use worry is overblown.
Fooled by Vast Knowledge
Models look like they generalise because the training data is vast enough to hide the difference between interpolation and extrapolation. I think that confusion is leading safety research astray.
Difficulties in Building an AI Safety Startup
Guardrails, evals, RL environments, control infrastructure. I work through each one and keep landing in the same place, too far from what happens inside a frontier lab.
Gaining clarity on Automated Alignment Research
Four different things get conflated in the phrase automated alignment research, and the crux is whether applying techniques we already have is a short step from inventing a new paradigm.
Automating AI Safety: What we can do today
A concrete list of things to build now so a coding agent can actually run an interpretability experiment, and why the METR slowdown result does not say what people think.
But is it really in Rome? Limitations of the ROME model editing technique
The ROME edit does not generalise the way you would expect. It runs in one direction only, and cheese and fromage have to be edited separately.
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