The challenge of artificial intelligence is to explain the reasoning behind decisions, which is essential for trust in its applications.

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.

Reasoning and test-time compute are crucial for unlocking the potential of AI agents to perform actions on behalf of users reliably.

We need to manage transparency and control while using AI, ensuring users can trust the AI's actions on their behalf.

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.

AI is often described as a black box, where inputs lead to outputs, but the reasoning behind its responses remains unclear.

As AI systems become more complex, understanding their behavior requires a holistic view that considers all interactions and outputs.

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

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