Having a reasoning model write out its "thinking" process in natural language, so researchers can read that text and watch for problematic intent or behavior — currently one of the few safety monitoring methods that doesn't require dissecting a model's internal structure, relying instead on simply reading what the model says about itself. But its effectiveness rests on a fragile premise: whether the reasoning written out actually reflects the model's real decision-making process.

Instead of pouring all computational resources into training to make a model bigger, this approach spends extra compute at the moment a model actually answers a question — letting it "think longer" by exploring multiple reasoning paths, self-checking, and repeatedly revising — trading that additional compute for better answer quality. It's a separate axis driving AI capability progress, distinct from <a href="/en/glossary/scaling-laws/compute-scaling/">Compute Scaling</a>.