Will Compute Bottlenecks Prevent an Intelligence Explosion?

TL;DR

This paper investigates whether compute bottlenecks could hinder an AI intelligence explosion by analyzing the substitutability of compute and labor in AI research through economic modeling and empirical data.

Contribution

It introduces a novel dataset and applies CES production models to assess the relationship between compute and labor at leading AI labs, revealing divergent results.

Findings

Compute and labor are substitutes in a baseline model.

Compute and labor are complements in a scale-aware model.

Implications for AI progress forecasting are discussed.

Abstract

The possibility of a rapid, "software-only" intelligence explosion brought on by AI's recursive self-improvement (RSI) is a subject of intense debate within the AI community. This paper presents an economic model and an empirical estimation of the elasticity of substitution between research compute and cognitive labor at frontier AI firms to shed light on the possibility. We construct a novel panel dataset for four leading AI labs (OpenAI, DeepMind, Anthropic, and DeepSeek) from 2014 to 2024 and fit the data to two alternative Constant Elasticity of Substitution (CES) production function models. Our two specifications yield divergent results: a baseline model estimates that compute and labor are substitutes, whereas a 'frontier experiments' model, which accounts for the scale of state-of-the-art models, estimates that they are complements. We conclude by discussing the limitations of our analysis and the implications for forecasting AI progress.