[2511.23455] The Price of Progress: Price Performance and the Future of AI

Computer Science > Machine Learning arXiv:2511.23455 (cs) [Submitted on 28 Nov 2025 (v1), last revised 23 Mar 2026 (this version, v2)] Title:The Price of Progress: Price Performance and the Future of AI Authors:Hans Gundlach, Jayson Lynch, Matthias Mertens, Neil Thompson View a PDF of the paper titled The Price of Progress: Price Performance and the Future of AI, by Hans Gundlach and 3 other authors View PDF HTML (experimental) Abstract:Language models have seen enormous progress on advanced benchmarks in recent years, but much of this progress has only been possible by using more costly models. Benchmarks may therefore present a warped picture of progress in practical capabilities *per dollar*. To remedy this, we use data from Artificial Analysis and Epoch AI to form the largest dataset of current and historical prices to run benchmarks to date. We find that the price for a given level of benchmark performance has decreased remarkably fast, around $5\times$ to $10\times$ per year, for frontier models on knowledge, reasoning, math, and software engineering benchmarks. These reductions in the cost of AI inference are due to economic forces, hardware efficiency improvements, and algorithmic efficiency improvements. Isolating out open models to control for competition effects and dividing by hardware price declines, we estimate that algorithmic efficiency progress is around $3\times$ per year. However, at the same time, the price of running frontier models is rising between $...