Latent Space
Technical interviews from an AI engineer's view; one of few shows linking papers to products
1:23:31There Is No Foundation Model for Physics — Neural Operators Start Filling That Gap With Weather
Anima Anandkumar brings weather forecasting into the AI era with neural operators: comparable accuracy at tens of thousands of times the speed, on a single consumer GPU — and shows how the physical world can reach foundation models with little data plus geometric inductive bias.
1:09:38Simulating real people is not a reasoning problem: frontier models get only 20–30% accuracy
Joon Sung Park's argument: treating people as trainable objects does not call for a stronger reasoning model but for a model that makes the same mistakes people make — frontier models reach only 20–30% accuracy predicting the behavior of niche populations, and Simile has pushed that to 85% using two-hour deep interviews plus RCT data.
1:35:19Behind a 0.33Å Error: Drug Design Is Moving From Waterfall to Agile Loop
Chai designed antibodies for 50 targets and got hits on about half of them, with cryo-EM validation showing a 0.33Å error. The real moat is not the model — it is data, compute, and a validation pipeline pharma companies will actually pay for.
1:41:29Quantizing more layers can be more faithful: the errors cancel out
Once you have picked the right layers, quantizing more of them actually preserves more fidelity — the errors cancel each other out. By the same logic, a 10x speedup is not one breakthrough: it is quantization, the speculator and PD disaggregation each roughly doubling and stacking up.
1:09:28Non-programmers Were Sneaking Into Codex, and ChatGPT Work Followed
OpenAI handed knowledge workers the Codex harness essentially unchanged, altering only the UX trade-offs and the sandbox defaults. The judgment underneath: AI will erase the boundaries between job functions, so OpenAI refuses to draw product lines around "who you are".
1:54:33Can a small model that grinds for eight weeks break the foundation-model oligopoly?
With a model of 118 billion total parameters and 8 billion active, Poolside argues that persistence and verification behaviour lift coding ability more than raw parameter count does — which is why they believe small open models can break the incumbents' lock on the future.