Recursive artificial intelligence improvement can involve models helping optimize serving stacks, kernels, research code, experiments, evaluation, data, or training. Verified gains can make later development or inference faster, cheaper, or more capable, creating another opportunity for AI assistance.
The loop is not automatically unbounded. Verification, compute, data, experiments, human judgment, physical infrastructure, safety controls, and diminishing returns constrain how quickly generated ideas become reliable improvements.
