Recursive self-improvement (RSI)

Plain English. The idea that an AI system improves the systems that build AI — writing the training code, designing the experiments, tuning the hardware — so each generation arrives faster than the last. In the strong version the loop closes entirely and progress compounds without humans. Nothing public today is the strong version: what labs actually report is AI helping engineers with narrow parts of the pipeline.

Why it moves money. RSI is the assumption hiding inside the largest valuations. If the loop compounds, today's leader converts capital into capability faster than anyone can catch up, and a US$2tn valuation is arithmetic; if it doesn't, model-making stays an ordinary R&D business with rising input costs. Claims of "the model helped build itself" are therefore worth more scrutiny than any benchmark score.

What to watch. Whether the claim survives being restated boringly. "Automated performance engineering" — real, useful, bounded — fits most published evidence as well as "self-improvement" does. Ask what fraction of the gain is attributed to the model, measured how, against what baseline.

From the signals. Tencent said Hy4 helped train itself and put 31.8% on the throughput — its framing, with the boring restatement fitting the same facts. Ornith-1.5 put the harness inside the training loop, the most direct public test of the mechanism so far.