The Sequence Knowledge #740: Is AI Interpretability Solvable ?
One of the biggest questions surrounding the new generation of AI models.
Today we will Discuss:
The core arguments in favor and against the viability of solving AI interpretability.
A review of a famous paper by OpenAI, DeepMind, Anthropic and others about using chain of thought monitoring for safety interpretability.
💡 AI Concept of the Day: Is Interpretability Solvable?
To conclude our series of AI interpretability, I wanted to debate a controversial idea. Is AI interpretability for frontier models even solvable? Whether AI interpretability for frontier models is “solvable” depends on what we mean by solving it. If the goal is perfect transparency—being able to map every internal computation to a human-legible concept—then no: general limits from computability, non-identifiability of internal representations, and sheer combinatorial complexity make full explanations unrealistic. If, however, “solved” means an engineering discipline that reliably produces actionable, falsifiable, and scalable explanations sufficient to audit risks, debug failures, and enforce governance constraints, then a qualified yes is possible. The right target is sufficiency, not omniscience: explanations good enough to catch dangerous capabilities, verify safety properties, and support regulation and incident response.