On OpenAI's research-acceleration disclosure — the measured variables are all inputs

Argues that OpenAI's disclosure on models accelerating its own research reports only input-side quantities — inference compute, experiments per researcher, tokens, lines of code — and none of the outcome variables that would show whether those inputs became capability. Names the specific missing measurement: algorithmic efficiency as a function of capability, the feedback edge that makes recursive self-improvement a loop rather than a pipeline.