Tag: recursive-self-improvement
Ryan Greenblatt argues that AI research is verifiable enough to automate itself, that automating it buys four or five years of progress in one, and that what comes out the other end can transform the world without ever learning to play politics. His median for full automation is 2030–31 and his odds on takeover by 2040 are 35–40%.
Inference engineers now write training pipelines, RL rollouts bottleneck on inference speed, and Baseten reports running GPU kernels inside their inference engine that GLM-5.2 wrote for itself from its own profiling traces. Plus the argument that continual learning belongs in the KV cache rather than in the weights.
What it would concretely take to train a model to do AI research — the environments, the three scales they run at, and the one part of the job Greenblatt thinks stays unverifiable. Plus the argument that algorithms rather than bought human judgment have been driving progress, which is what makes the whole thing work.