The Information Machine
Updated today·New·in the edition since 17 Oct 2026·2 sources

Nick Cammarata's interpretability reversal

The gist

Cammarata Reverses Pessimism: Most Neurons in Large Models Legible

Mechanistic interpretability is a core method for understanding what neural networks are actually doing, with direct bearing on AI alignment research. A shift from 2% legibility to a large majority changes the practical outlook for that research program.

The full picture

Nick Cammarata, who co-authored early mechanistic interpretability research at OpenAI, has reversed his earlier pessimism about the field. Where a large-scale automated interpretability project at OpenAI once found only about 2% of neurons legible using primitive tools, Cammarata now says the vast majority of neurons in models up to trillions of parameters are legible and their circuits comprehensible. He attributes the earlier pessimism partly to misunderstanding: many neurons previously cited as tangled or uninterpretable turned out not to be genuinely illegible. Using modern agentic tools and closer examination, the picture has changed substantially. Cammarata now believes ambitious interpretability will be solved before it is critically needed, and potentially soon as automated scientific tools continue to improve.

How it developed
First in the edition · 17 October 2026
16 October 2026

Nick Cammarata, who co-authored early mechanistic interpretability research at OpenAI, reversed his earlier pessimism about the field on October 15-16, saying that where primitive automated tools once found only around 2% of neurons legible, modern agentic tools show the vast majority of neurons in models up to trillions of parameters are legible and their circuits comprehensible.

He attributed the earlier pessimism partly to misunderstood cases that were not genuinely uninterpretable. Sam Altman responded positively to the announcement.

Sources
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