The shift from merely scaling LLMs to incorporating reasoning techniques has opened new avenues for AI development, allowing models to perform better by thinking longer and more deeply.

The emergence of reasoning models is expected to enable AI agents to perform actions on behalf of users, enhancing their reliability and effectiveness in real-world applications.

The addition of a reasoning paradigm to AI models can significantly enhance their performance, as demonstrated by the difference in capabilities between versions of AlphaGo with and without this feature.

Recent developments in AI show models are increasingly capable of externalized reasoning, which can be monitored for control purposes.

By allowing models to think longer, large language models can leverage more compute on the fly, enhancing their intelligence for complex problems.

The surprising interpretability of thinking models allows us to understand how they reach their conclusions, which was not expected in earlier AI developments. The fact that AI models can articulate their reasoning provides a level of comfort regarding safety, even though it's not infinite comfort.

The o1 model's ability to reason and learn from feedback on its processes is a step towards teaching AI to think more like humans, rather than just providing correct answers.

Understanding how AI models think can lead to safer and more reliable systems, similar to how neuroscience improves health outcomes.

The reasoning model of DeepSeek allows for deeper answers by thinking through the steps of a problem, which was not released with previous models like OpenAI's.