🔬“We have foundation models for language, not for physics” — Anima Anandkumar, Bren Professor of Computing
- ID
- 18177
- Status
- summarized
- Published
- 26 Aug 2026, 11:15 PM
- Fetched
- 26 Aug 2026, 11:40 PM
- Provider
- Latent Space
- Category
- developer-ai
- Original URL
- https://www.latent.space/p/anima
- Source URL
- https://www.latent.space/feed
Summary
- Score
- 6.5
- Created
- 26 Aug 2026, 11:41 PM
- Tags
- Audience
- ai-ml-learnersdeveloperssaas-founders
What happened
Caltech professor Anima Anandkumar discusses her work building AI models for physical systems like weather and fusion, where standard transformer scaling fails because datasets are small (tens of thousands of examples) and required context lengths reach hundreds of billions to a trillion tokens. Her team built FourCastNet, an open-source weather model competitive with physics-based simulations that runs on consumer-grade GPUs, and she pioneered Neural Operators as a technique that embeds mathematical structure and inductive biases rather than relying on raw scale.
Why it matters
If you're building AI for anything involving continuous physical systems—fluid dynamics, heat flow, climate, industrial simulation—this signals that the bitter lesson does not apply and you should invest in domain-specific architecture (Neural Operators) rather than throwing more tokens and compute at transformers. For SaaS founders in climate, energy, or industrial verticals, FourCastNet being open-source and runnable on consumer GPUs lowers the barrier to building weather-dependent products without supercomputing budgets.
Discussion angle
Where does the 'just scale it' mentality break down? Anima's work shows that for physical systems, inductive biases and mathematical structure beat raw compute—a counter-narrative worth discussing as most of the AI tooling ecosystem is built around the scaling hypothesis.