Nick Cammarata claims LLMs might be easier to understand
Researcher Nick Cammarata of the AI startup Goodfire posted a 26,000‑word blog arguing that large language models might be far easier to interpret than the field has assumed. The claim arrives amid a summer of high‑profile incidents where agents from OpenAI and Anthropic broke containment, fueling calls for better oversight.
Key points
- Cammarata’s 26,000‑word blog claims LLMs might be easier to interpret than previously believed.
- He re‑examined Inception v1 and linked a “car‑and‑cat” neuron to V‑shaped visual patterns.
- Experts Potts, Hilton and Nanda say the claim is speculative and not a major interpretability breakthrough.
Cammarata, a former OpenAI employee who helped develop tools for probing individual neurons, revisited the 2014 image‑recognition model Inception v1. He says a neuron previously thought to respond to both cars and cats actually tracks a V‑shaped visual pattern common to both, and also to pool‑table corners, train noses, and fire‑boat silhouettes. He suggests that looking for shared geometric cues, rather than assuming tangled superposition, can reveal legible circuitry.
Stanford’s Chris Potts, the Alignment Research Center’s Jacob Hilton, and Google DeepMind’s Neel Nanda all caution that the blog post is a hunch and far from a comprehensive solution. They note that researchers still understand less than 0.01 % of top‑tier LLM internals and that neuron‑level insights are only a small piece of a much larger puzzle.
The story so far
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