How this channel uses it <p>In the channel's material, inference-time scaling appears in the context of OpenAI's o1: a genuinely new way to make AI models better where, instead of training on more data or compute, the model is trained to spend more time thinking before answering. The video frames this as a new axis of scaling — quality gains that come from extra effort at the moment a question is asked, not from a bigger model trained offline.</p><p>The same exploration connects the term to o1's native chain-of-thought reasoning: as more inference-time effort is spent thinking through the problem, the model's answers improve, and o1's benchmark results reflect that. It also changes how you prompt the model — prompting o1 differs from prompting GPT-4o because the model already reasons internally rather than being coached into it step by step.</p><p>Beyond the mechanics, the video treats inference-time scaling as a signal about a changing economy of intelligence: progress can now come from spending more compute while answering, rather than only from larger training runs.</p> - o1 trains the model to spend more time thinking before answering rather than scaling training data or compute.- More thinking time at inference produces better answers, which is why o1 smashes benchmarks.- The approach changes prompting practice: o1 reasons natively, unlike GPT-4o.