Chain of thought

Plain English. A model writing out intermediate reasoning steps before its answer — thinking on paper. "Reasoning models" are models trained to do this at length, and much of the capability jump since 2024 is chain of thought plus reinforcement learning, not new architecture.

Why it moves money. Twice over. First, the reasoning is made of tokens and tokens are billed — chain of thought is a direct driver of inference demand, the mechanism behind rising cost-per-task even as cost-per-token falls. Second, a legible trace is the main window auditors, safety teams and regulators have into why a model did something; if reasoning drifts into compressed or latent forms humans cannot read, an entire oversight approach loses its instrument, with regulatory consequences that touch deployment timelines.

What to watch. Whether traces stay legible and faithful — research already argues the written trace is not reliable evidence of the actual computation — and whether latent-reasoning models that skip words entirely reach production.

From the signals. Two ICML papers argued the reasoning trace is not the evidence of alignment. A site claimed frontier reasoning can be recovered from encrypted traces. A 150M-parameter model reasons without words at $0.0007 per task.

Further reading. Wei et al., Chain-of-Thought Prompting (2022)