Recursive self-improvement needs a better measure
Blog post from Factory
Recursive self-improvement in AI should be evaluated through measurable, bounded changes rather than assumed to imply rapid or unlimited progress. Matan Grinberg distinguishes a system’s ability to help improve itself from the speed, quality, and generality of those improvements, emphasizing that successful experiments or repairs do not necessarily demonstrate broader capability gains. For engineering teams, the recommended approach is to measure accepted outcomes, total AI spending, failed attempts, preparation time, and human review required for recurring workflows, while testing changes against representative tasks not used to create them. The discussion also advises separating model choice, provider flexibility, API compatibility, privacy controls, and safety claims, since available alternatives may behave differently or involve different data flows. Teams can improve workflows through clearer task specifications, reproducible test commands, reusable procedures, and feedback loops, but should retain stable acceptance criteria, preserve failures, and expand automation or permissions only when evidence supports doing so.
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