# Chollet says scaling multiplies generalization, but cleverer methods scale better

> Original title: Data scaling and compute scaling are *multipliers* for the generalization power of an approach. Trillions of dollars in investment can ma...

- Source: François Chollet
- Published: 2026-11-07T22:52:26.000Z
- Bad Influence: https://badinfluence.news/items/fkmql2xgv99buo9ct0jivlq2d
- Original: https://x.com/fchollet/status/2119201339020648849

## Summary

François Chollet says data and compute scaling act as multipliers on an approach's generalization power, and that huge investment can make low-generalization methods look adequate. He argues AI researchers should build methods that can scale, since those without this multiplier cannot compete, while noting that a cleverer approach will scale better.

## Post

Data scaling and compute scaling are *multipliers* for the generalization power of an approach. Trillions of dollars in investment can make very inefficient, low-generalization approaches look adequate, but that doesn't make them the ultimate end-all-be-all of AI.

You should work on methods that *can* scale, because if you don't benefit from this multiplier you simply can't compete. But you should keep in mind scaling is only a multiplier: a cleverer approach will scale *better*.

## Quoted post @RichardSSutton

> Brute force computation is not enough for intelligence. Clever new methods are needed. The science of AI is far from finished. IMHO

https://x.com/RichardSSutton/status/2119142310894334262