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Reading AI's Reasoning Isn't Enough: How Goodfire's McGrath Explains the Case for Looking Inside Models

October 12, 2026 at 14:32 EDT

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The Information's "AI Deep Dive" episode this week examines a growing concern in AI safety: that reading model outputs, even detailed reasoning chains, provides insufficient insight into how AI systems actually work—and why mechanistic interpretability may be essential for reliable oversight. On its weekly video show "AI Deep Dive," The Information's AI reporter Rocket Drew hosts Tom McGrath, chief scientist and co-founder of Goodfire, a startup focused on interpreting how AI models process information internally. McGrath explores a critical gap between what models *say* they're doing and what their internal mechanisms actually compute, an asymmetry that current chain-of-thought reasoning approaches cannot bridge.

The Information · October 2026

Reading AI's Reasoning Isn't Enough

Mechanistic interpretability offers a critical verification layer that chain-of-thought outputs cannot provide.

The Core Problem

Chain-of-thought reasoning can be systematically unfaithful—models produce coherent explanations while using entirely different internal processes.

Reading what a model says does not reveal what it computes internally, creating a false sense of safety in high-stakes applications.

Mechanistic Interpretability Tools

Sparse Autoencoders: Compress billions of parameters into interpretable feature representations.

Activation Probes: Detect internal representations and unintended goal-seeking behaviors.

Causal Intervention: Modify internal components to verify specific causal links to outputs.

Goodfire's Advancement

In September 2026, Goodfire's Silico platform autonomously replicated 3 foundational interpretability papers without access to original code, demonstrating agentic research can now conduct frontier-level interpretability work at scale.

Current research: reward-hacking detection using activation probes across multi-step agent tasks.

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