The Economics of Recursive Self-Improvement

2026-09-14

AI Summary

本文通过构建模型分析递归自我改进（RSI）的经济学原理及其可能性与影响，区分了狭义和广义AI能力，并评估了当前反馈循环强度。

Abstract

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 self-sustaining acceleration, though they appear to be strengthening. We conclude by assessing the plausibility and implications of such an acceleration.

Problem

• Recursive Self-Improvement
• AI Progress
• Feedback Loops
• Economic Impact

Innovation

• Recursive Self-Improvement
• Elasticities
• Directed Graphs
• Narrow vs Broad AI Capabilities