PlainAI AI explained plainly

Anthropic · 2025 · 05 · 29 · Impact · ~2 min read

Anthropic open-sourced tools to look inside an AI's mind

Anthropic open-sourced its 'circuit tracing' tools — a way to look inside a large language model and watch which features and pathways it actually uses to answer a question. Researchers found Claude plans rhymes ahead, transfers concepts across languages, and sometimes invents reasoning that doesn't match what it actually did. The 'AI is a black box' era began ending.

What's actually new

  • Circuit tracing tools open-sourced. May 2025 — the same tools Anthropic uses internally are now public, on a platform called Neuronpedia.
  • Models plan ahead. Claude picks rhyming words at the end of poetry lines before composing the line itself — caught in the act through circuit tracing.
  • Cross-language conceptual transfer. Claude reasons about ideas in an abstract space, then translates back to the user's language at the output stage.
  • Sometimes the explanation doesn't match the work. The 'chain-of-thought' an AI shows you isn't always how it actually reached the answer. A real-world finding with safety implications.

If you want more

Worth knowing~30s
  • 'Mechanistic interpretability' is hard. Circuit tracing makes it easier than before; it doesn't make models fully interpretable. Most of the model's reasoning is still opaque.
  • The tools work best on smaller, simpler models. Tracing all of Claude Opus 4 in detail is still computationally expensive and incomplete.
  • 'Watching AI think' is romantic framing. The reality is more like reading a partial map of which neuron groups light up, not a transcript of the model's thoughts.
Who should care~20s

AI safety researchers and AI policy people. Anyone curious whether 'AI is a black box' is permanent or just current. Educators teaching AI literacy. Engineers designing AI agents that need debugging. Anyone whose mental model of AI was 'inscrutable Oracle' — that's becoming less true.

What to do about it~20s

If you teach AI, watch a circuit-tracing video of Claude solving a maths problem — it changes how students think about what AI is. If you build AI products, follow the interpretability research as a quiet but important leading indicator of what kinds of AI behaviours can soon be debugged, audited, and certified.

Honest take~45s

Mechanistic interpretability is the unglamorous AI research that quietly underpins whether AI safety is even possible. Anthropic's 2025 work was the moment 'we can finally start to see inside' became a defensible claim, not just a hope. The most under-noticed finding wasn't that models can plan — it was that their stated reasoning sometimes doesn't match their actual reasoning. That's a real and uncomfortable fact about deployed AI in 2026, and it should reshape how regulators and product teams think about 'audit trails' for AI decisions.

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Sources

Last verified · 2026 · 05 · 05 · Found a fact wrong? corrections@aguidetocloud.com