# MIT, Stanford, Allen Institute Reveal Chain-of-Thought Reasoning Scaling Laws

> A collaboration between MIT, Stanford, and the Allen Institute has published a detailed look at how chain-of-thought reasoning actually scales, and the findings complicate the tidy story the field has told itself. For years, capability has tracked neatly with model size and data. But the researchers report that reasoning ability does not scale predictably with parameter count the way general language modeling does. The most striking detail is a cost paradox. Reasoning models can consume on the order of 100 times more tokens internally, working through their hidden chains of thought, than they ever produce as visible output. That means the expensive part of a thinking model is largely invisible, and it does not necessarily buy proportional gains as you scale up. If it holds, the implication is that simply making models bigger may not be the road to better reasoning, and that the economics of test-time compute deserve harder scrutiny. It is a reminder that the scaling laws which have guided billions in spending were derived for one task, and reasoning may not obey them.

_Section: [Daily AI Updates](https://www.wortins.com/daily-ai) · Source: Skycrumbs · Published Saturday, August 15, 2026_

## Wortins' read

A collaboration between MIT, Stanford, and the Allen Institute has published a detailed look at how chain-of-thought reasoning actually scales, and the findings complicate the tidy story the field has told itself. For years, capability has tracked neatly with model size and data. But the researchers report that reasoning ability does not scale predictably with parameter count the way general language modeling does. The most striking detail is a cost paradox. Reasoning models can consume on the order of 100 times more tokens internally, working through their hidden chains of thought, than they ever produce as visible output. That means the expensive part of a thinking model is largely invisible, and it does not necessarily buy proportional gains as you scale up. If it holds, the implication is that simply making models bigger may not be the road to better reasoning, and that the economics of test-time compute deserve harder scrutiny. It is a reminder that the scaling laws which have guided billions in spending were derived for one task, and reasoning may not obey them.

## Source

[Read the full story at Skycrumbs](https://skycrumbs.com/blog/ai-research-august-2026)

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