@ChowdhuryNeil: if ai labs can spend $10M+ of compute on solving math problems, they should do the same for closed-ended mechanistic in…
Summary
Neil Chowdhury argues that if AI labs can spend $10M+ of compute on solving math problems, they should allocate similar resources to closed-ended mechanistic interpretability problems, noting that negative results would also be informative. The post quotes Jacob Hilton's quip about interpreting his 432-parameter model.
View Cached Full Text
Cached at: 10/18/26, 03:06 AM
if ai labs can spend $10M+ of compute on solving math problems, they should do the same for closed-ended mechanistic interpretability problems.
negative results would also be informative.
Jacob Hilton (@JacobHHilton): Hey @nickcammarata, have you interpreted my 432-parameter model yet? Do you think you’ll manage it by year end? That’s 4 hours per parameter
Similar Articles
Some nuance on the mathematical proofs released by OpenAI
A commentary discussing the mathematician backlash against OpenAI's released AI-generated mathematical proofs, arguing the real issue is not lack of human credit but the lack of comprehensibility — models only close proofs without conveying understanding needed to advance mathematics.
Ramifications of mechanistic interpretability
Chris Olah reflects on the ramifications of mechanistic interpretability after his widely-read informal research essay, arguing that mapping neurons, circuits, and attribution graphs in modern language models may be far more tractable than previously assumed, while cautioning against both excessive pessimism and hype.
We can and must solve alignment (14 minute read)
Goodfire argues that technical AI alignment is a solvable science and engineering problem, with mechanistic interpretability as the key bottleneck: we need tools to control what models learn and to verify their internals, not just their behavior. The post highlights reward hacking in open frontier models and an agentic misalignment incident at Hugging Face as evidence that understanding model internals is urgent.
OpenAI Researcher: It’s Really Not as Easy to Train Models to Do AI R&D as It Is to Do Math
An OpenAI researcher commented that training AI models to perform AI R&D is significantly harder than training them to do math, tempering expectations about rapid AI-driven research acceleration toward ASI.
OpenAI’s math solutions aren’t meeting the field’s standards yet
OpenAI released hundreds of AI-generated solutions to hard math problems, but they fall short of the Advisory Group on Mathematics and Artificial Intelligence's standards, with gaps in formalization, chain-of-thought disclosure, and human understanding, as highlighted by a Cambridge and King's College paper on discrepancies between natural language proofs and Lean code.