We need more decorrelated evidence

AI agents do a growing share of work on each new model. Then, through some process, the results are combined into an overall safety assessment, which labs use to estimate the probability that the next model is not catastrophically misaligned. I feel that, several months later, we appreciate further the foresight of Automated Alignment is Harder Than You Think (Bowkis et al. 2026): much is handed off to the machines because of the urgency of fast timelines, and we miss many things due to the problem of undetected systematic errors . MS Paint drawing by Fable 5.2. In this post, I’ll develop a simple argument (drawing heavily or even verbatim from Bowkis et al. at times) that our recent failures to catch misalignment can be framed as systematic errors shared across our evidence, i.e. a lack of decorrelated evidence. Currently, labs run some set of experiments to decide if a model is safe to use. Here are some examples: OpenAI (Astra system card, Sep 2026) : Astra models don’t go into internal use without passing blocking alignment evals. After the Hugging Face incident, this paused workloads that didn't comply with new safeguards. OpenAI (Sep 2026) : According to news reports, GPT-6.1