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Are AI Capabilities Increasing Exponentially? A Competing Hypothesis

arXiv 2602.04836 ↗First posted 4 Feb 2026cs.AIcited by 0

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Abstract (arXiv)

Rapidly increasing AI capabilities have substantial real-world consequences, ranging from AI safety concerns to labor market consequences. The Model Evaluation & Threat Research (METR) report argues that AI capabilities have exhibited exponential growth since 2019. In this note, we argue that the data does not support exponential growth, even in shorter-term horizons. Whereas the METR study claims that fitting sigmoid/logistic curves results in inflection points far in the future, we fit a sigmoid curve to their current data and find that the inflection point has already passed. In addition, we propose a more complex model that decomposes AI capabilities into base and reasoning capabilities, exhibiting individual rates of improvement. We prove that this model supports our hypothesis that AI capabilities will exhibit an inflection point in the near future. Our goal is not to establish a rigorous forecast of our own, but to highlight the fragility of existing forecasts of exponential growth.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 16 references that are in the map. First posted on arXiv 4 Feb 2026.

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References · 9

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 8,851
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  4. 2025Measuring AI Ability to Complete Long Software Taskscited by 142
  5. 2025GDPval: Evaluating AI Model Performance on Real-World Economically Valuable Taskscited by 142
  6. 2025The Illusion of Diminishing Returns: Measuring Long Horizon Execution in LLMscited by 62
  7. 2025Artificial Intelligence Index Report 2025cited by 58
  8. 2025A Rosetta Stone for AI Benchmarkscited by 29
  9. 2025AI Governance to Avoid Extinction: The Strategic Landscape and Actionable Research Questionscited by 6

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