Why Reasoning Models Changed Everything

For years, making language models smarter meant making them bigger. Then someone asked a different question: what if, instead of training more, you let the model think longer? In September 2024, OpenAI released o1. In January 2025, DeepSeek released R1. These two models together, invalidated the assumption that was governing the entire field since 2020, that the path to better AI runs through bigger training runs. This assumption was backed by the Kaplan et al. scaling laws paper, which showed the language model performance follows a reliable power law with training compute. If you provide more parameters, more data, more GPU-hours, you get a more capable model. The field organized itself around this insight. Every major lab poured billions into pre-training. o1 and R1 showed that there’s a second dimension to scale that the field had largely ignored: compute at inference time. And it turns out that for tasks requiring multi-step reasoning, this second dimension can be just as powerful