tl;dr: Many critiques of *Situational Awareness* have been qualitative; one year later we can check the numbers. I did my best to verify his claims using public data through June 2025, and found that his estimates mostly check out.
This is inherently noisy work and should be taken with a grain of salt.

Thanks to Kai Williams & Egg Syntax for their critical feedback.

Abstract

Leopold Aschenbrenner’s 2024 essay Situational Awareness forecasts AI progress from 2024 to 2027 in two groups: "drivers" (raw compute, algorithmic efficiency, and post-training capability enhancements known as "un-hobbling") and "indicators" (largest training cluster size, global AI investment, chip production, AI revenue, and electricity consumption).^[1] Drivers and the largest cluster size are expected to grow about half an order of magnitude (≈3.2×) annually, infrastructure indicators roughly doubling annually (2× per year), with AI revenue doubling every six months (≈4× per year).^[2]

Using publicly available data as of June 2025, this audit finds that global AI investment, electricity consumption, and chip production follow Aschenbrenner’s forecasts. Compute, algorithmic efficiency, and unhobbling gains seem to follow Aschenbrenner’s forecasts as well, although with more uncertainty. xAI’s Grok 3 exceeds expectations by about one-third of an order of magnitude.^[3] However, recent OpenAI and Anthropic models trail raw-compute trends by about one-third to one-half an order of magnitude,^[4] and AI-related revenue growth is several months behind. Overall, Aschenbrenner’s predicted pace of roughly half an order-of-magnitude annual progress is ~roughly supported by available evidence.

Graph. At-a-glance scoreboard.

Introduction

“It is strikingly plausible that by 2027, models will be able to do the work of an AI researcher/engineer. That doesn’t require believing in sci-fi; it just requires believing in straight lines on a graph.” — Leopold Aschenbrenner

AI forecasts, such as the AI Futures Project’s modal scenario that reaches artificial super-intelligence by 2027,^[5] have generated a lot of debate recently. These forecasts often implicitly rely on extrapolation of recent trends, an approach that critics claim overlooks constraints that could slow progress. Leopold Aschenbrenner’s 2024 essay, Situational Awareness, frames its timeline using quantitative forecast...

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