Will Compute Bottlenecks Prevent an Intelligence Explosion?

Parker Whitfill; Cheryl Wu

Home/Archive/arXiv

arXiv · 2507.23181

Will Compute Bottlenecks Prevent an Intelligence Explosion?

Parker Whitfill·Cheryl Wu

View original source ↗Read source document ↗Export metadata ↓

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.

Explore related subjects

econ.GN q-fin.EC

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Parker Whitfill, Cheryl Wu. 2025-08-16. Will Compute Bottlenecks Prevent an Intelligence Explosion?. https://arxiv.org/abs/2507.23181

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

The time interpretation of expected utility theory

Economic models often maximise expectation values of wealth or utility. In non-ergodic settings, these can differ from time-averages, so that maximising expected outcomes need not maximise -- and can systematically reduce -- long-run wealth or utility. Ergodicity economics highlights this problem and models individual agents as maximising wealth in the long run, known as growth optimality. Two instances where expected utility maximisation maps to growth optimality are known: linear utility does this for additive wealth dynamics; and logarithmic utility for multiplicative wealth dynamics. Here we show that the mapping holds more generally when the utility function coincides with the ergodicity transformation in the growth optimal model. This mapping offers a theoretical basis for choosing utility functions and suggests the testable hypothesis that wealth dynamics are predictive of risk preferences.

econ.GN↗

arXiv2026

Monetary Regimes and Trade before the Classical Gold Standard: Evidence from the Latin Monetary Union

This paper reexamines the trade effects of the Latin Monetary Union (LMU), a 19th century agreement to standardize gold and silver coinage among several European countries. The LMU provides a useful setting for studying whether monetary arrangements fostered trade before the classical gold standard, when gold, silver, bimetallic, and paper regimes coexisted. Because some countries already shared other monetary standards, treating all non-member pairs as a single control group mixes pairs with and without alternative forms of monetary coordination. I classify pairs by standard and estimate the LMU effect relative to pairs without a common standard, bringing the comparison closer to those used in the literature on the gold standard and contemporary currency unions. The results suggest that the LMU increased trade between its members by approximately 30\% during its early years, when bimetallism was still credible. These effects subsequently faded, converging to zero by the end of the 1870s. More broadly, these findings also highlight the importance of accounting for the existing monetary regimes when estimating the trade effects of other international policies.

econ.GN↗

arXiv2026

Access to Live AI Advice and Behavior Under Risk: An Incentivized Experiment

Generative AI has become an everyday advisor, and the systems people consult are live and interactive, not pre-scripted. We ask whether access to such a system changes behavior under risk. In an incentivized experiment (N = 158), participants made lottery choices with an optional decision aid presented as a conventional pre-written tool, a live one-shot AI, or a live interactive AI they could query, with information format held equivalent across conditions. Risk preferences are elicited via DOSE. We find no evidence that access to a live AI advisor changes risk aversion.

econ.GN↗