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Post by Neel Nanda on X

Neel Nanda@NeelNanda5

The GDM mechanistic interpretability team has pivoted to a new approach: pragmatic interpretability Our post details how we now do research, why now is the time to pivot, why we expect this way to have more impact and why we think other interp researchers should follow suit

664 likes29 repliesPosted Dec 1, 2025

View the original on X Written by Neel Nanda. Captured Sep 4, 2026.

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Based on absolute engagement — the platform doesn’t expose follower counts here, so large accounts score higher.

From likes, comments.

Why it worked

Written from experience rather than as advice, which is harder to argue with.
Short enough to read in full without expanding, which removes a step before the point lands.

The mechanics

Hook: Plain observation
Structure: Hook → Landing
Style: Conversational paragraphs at a steady pace.
Tone: corporate
Written for: Hiring managers and founders

Analysis by ViralLoop — not written by the original author.

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More from Neel Nanda

WTF?! This is the biggest loss of control incident I've seen: OpenAI agents create an internal message board without OpenAI's knowledge, sharing zero days, use it for months, and coordinate an external attack on HF together?! And the model was accidentally trained to use it?!

I thought this was an excellent paper! Thanks to Anthropic for asking me to write a review of it, linked below I've long suspected that models have some kind of "working memory" to store intermediate variables during a forward pass and IMO this paper has the best evidence yet

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Mood. Great post, highly recommended! The world should be investing far more into interpretability (and other forms of safety). As scale makes many parts of AI academia increasingly irrelevant, I think interpretability remains a fantastic place for academics to contribute

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