The Economics of Recursive Self-Improvement: feedback loops aren't self-sustaining yet, but they're strengthening

The Economics of Recursive Self-Improvement [pdf]

This 36-page paper models recursive self-improvement as a set of measurable feedback loops. The core takeaway: current loops aren't strong enough for self-sustaining acceleration, but they appear to be strengthening. The authors use directed graphs to map how AI capabilities, algorithmic efficiency, human labor, and compute interact—net acceleration depends on the product of elasticities along each loop. They distinguish 'narrow' capabilities (good at AI R&D benchmarks) from 'broad' ones (economically valuable), warning against conflating the two. The paper includes a wish list of metrics companies could share publicly, but the post doesn't disclose any actual firm data. The calibration is back-of-the-envelope, not an empirical measurement.

Why it matters: METR and Stanford authors model RSI as measurable elasticity loops, concluding feedback isn't yet self-sustaining but is strengthening. The narrow-vs-broad capability distinction is a crucial contribution. Score held at 78 because it's a preprint with no product tie-in and lea...