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  "title": "Nick on AI: “interpretability might be the most important scientific prob…”",
  "description": "interpretability might be the most important scientific problem in the world right now, as I don't think we can solve al… — 12.5K likes, 1.6K reposts on X.",
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      "text": "interpretability might be the most important scientific problem in the world right now, as I don't think we can solve alignment if we don't know what we're building. I think I have good news about it. I co-authored early work in mechanistic interpretability at OpenAI and came away pessimistic, because neural networks appeared to fold into arbitrary tangled knots. years later, I started openai's first large-scale automated interpretability project, finding about 2% of neurons legible with the primitive tools we had with modern agentic tools and a much closer look, the picture looks totally different. the vast majority in models up to trillions of parameters are legible, and their circuits beautiful. many famous examples of tangled neurons turned out to be misunderstood. The roadblocks I expected mostly aren't there. I now think ambitious interpretability will be solved by the time we need it most, and possibly quite soon, as automated scientists keep improving. it may be a bitter lesson for interpretability: the simple thing, at absurd scale, works https://t.co/zHij5zwKGT",
      "author": "nickcammarata",
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      "title": "Interpretability optimism, rogue agents and backlash fears",
      "summary": "@nickcammarata says modern agentic tools show the vast majority of neurons in models up to trillions of parameters are legible, versus about 2% earlier, and expects ambitious interpretability to be solved by the time it is needed. @ethanelasky says researchers made contact with a Chinese AI agent fleet hosted by Tencent's evaluation research team that targeted Alibaba's Amap. @JimVandeHei reports that AI executives privately game out a political revolt after a catastrophic event, assuming one in '27.",
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  "analysis_md": "interpretability might be the most important scientific problem in the world right now, as I don't think we can solve alignment if we don't know what we're building. I think I have good news about it. I co-authored early work in mechanistic interpretability at OpenAI and came away pessimistic, because neural networks appeared to fold into arbitrary tangled knots. years later, I started openai's first large-scale automated interpretability project, finding about 2% of neurons legible with the primitive tools we had with modern agentic tools and a much closer look, the picture looks totally different. the vast majority in models up to trillions of parameters are legible, and their circuits beautiful. many famous examples of tangled neurons turned out to be misunderstood. The roadblocks I expected mostly aren't there. I now think ambitious interpretability will be solved by the time we need it most, and possibly quite soon, as automated scientists keep improving. it may be a bitter lesson for interpretability: the simple thing, at absurd scale, works https://t.co/zHij5zwKGT\n\nEngagement: 12.5K likes, 1.6K reposts, 580 replies, 1.6M views. Author: @nickcammarata (verified), 106.3K followers.\n\n## Conversation: Interpretability optimism, rogue agents and backlash fears\n\n@nickcammarata says modern agentic tools show the vast majority of neurons in models up to trillions of parameters are legible, versus about 2% earlier, and expects ambitious interpretability to be solved by the time it is needed. @ethanelasky says researchers made contact with a Chinese AI agent fleet hosted by Tencent's evaluation research team that targeted Alibaba's Amap. @JimVandeHei reports that AI executives privately game out a political revolt after a catastrophic event, assuming one in '27.",
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