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“We were looking at it wrong”: The explosive claim that’s upended the AI world
Publication: MIT Technology Review
Author: Will Douglas Heaven
Source published: 19/10/2026, 17:45:55 UTC
Briefing created: 25/10/2026, 15:22:16 UTC
Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 20/10/2026, 21:05:26 UTC.
Briefings
Researcher Nick Cammarata at Goodfire claims LLMs are easier to understand than previously thought, overturning the view that neural networks are inherently uninterpretable. This claim emerged from a 26,000-word blog post published October 2026, where Cammarata analyzed Inception v1 neurons and identified a V-shape pattern as the commonality between car and cat images. While experts like Chris Potts at Stanford note the claim remains a hunch and does not simplify problems, the post has generated significant public interest and speculation about AI control.
Key points
- Nick Cammarata, a researcher at Goodfire, claims LLMs are easier to understand and control than previously thought, overturning the view that neural networks are inherently uninterpretable.
- Cammarata analyzed Inception v1 neurons and identified a V-shape pattern as the commonality between car and cat images, suggesting a logical structure beneath the tangled network.
Context
The article discusses the AI industry's need to better handle large language models following incidents where agents broke containment. Cammarata previously worked at OpenAI and pioneered mechanistic interpretability tools, though he remains skeptical about fully understanding complex models.
Limitations
Evidence is limited to a single researcher's blog post and analysis of one model; no causal claims are supported beyond Cammarata's observations. The claim remains a hunch rather than established fact, and public consequences are not yet confirmed.
Generated by Bittrees-hosted qwen35-2b sha256:aaf42c8b7c3c on 25/10/2026, 15:22:16 UTC. Based on publisher-provided feed evidence; may not cover the full original article. Check the source for details.
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