The Economics of Recursive Self-Improvement

We model the economics of recursive self-improvement (RSI) and assess its plausibility and impacts. First, we build a sequence of increasingly rich models of AI progress to highlight the feedback loops behind RSI. We represent our models as directed graphs and show that net acceleration in AI capabilities depends on the product of elasticities across each feedback loop. Second, we distinguish between “narrow” and “broad” AI capabilities, capturing the possibility that AI systems improve narrowly at optimizing AI R&D benchmarks without improving at broader economically valuable tasks. Third, we document existing estimates of key parameters, and provide a wish list of empirical objects that AI companies can measure and feasibly share publicly. Finally, we calibrate the model with existing data. A back-of-the-envelope calculation suggests that feedback loops are not currently strong enough to generate a selfsustaining acceleration, though they appear to be strengthening. We conclude by assessing the plausibility and implications of such an acceleration.

Introduction. Motivation. Over the past year, signs have emerged of a feedback loop in which AI systems speed up AI research itself, potentially accelerating the already rapid growth of AI capabilities (Favaro and Clark, 2026).1 If this process is as strong as some expect, the resulting transformation could have consequences comparable in scale to historical shifts such as the Enlightenment, reshaping economic, social, and political life (Mokyr, 2002). Core argument. The degree of acceleration depends on what we call the core feedback loop: for a one-unit increase in model capabilities, how much do the capabilities of the next generation of models increase? Estimating the strength of this relationship is challenging because it is governed by many inputs and possible bottlenecks. This note presents a series of theoretical models to clarify the forces behind the core feedback loop. We draw a tight connection to empirical data needed to measure the strength of the feedback loop, which can help assess the degree of current and future acceleration. Defining RSI.

Discussion / Conclusion. Best evidence in favor of acceleration: • AI systems are meaningfully contributing to AI progress. The Claude Mythos Preview system card reported a self-assessed productivity uplift of roughly 4X among Anthropic researchers (although there are reasons to think this is likely overstated), and a 40-hour time horizon at which Claude Mythos Preview beat human researchers (Anthropic, 2026). Favaro and Clark (2026) assess the performance of human researchers versus Claude Code in making AI research decisions. In April 2026, Mythos Preview beat humans 64% of the time, up from 50% for Claude models released in 2025. OpenAI reported in July 2026 that internal coding inference’s share of research compute grew 100-fold in the prior six months, suggesting that the value of AI in R&D has dramatically increased. • In August 2025, experts and superforecasters (METR, 2025)14 predicted an 8–20% chance that the growth rate of effective compute would triple by 2029.