To improve the reliability of AI agents, it is crucial to ensure that their actions are correct a high percentage of the time, which requires significant computational resources.

To enhance the usefulness, reliability, and security of AI, we need to understand the reasoning behind their actions.

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.

OpenAI's model improved not only with training time compute but also with test time compute, indicating that the model benefits from additional reasoning time during testing.

There's a specialization in AI models, where reasoning models that require more compute time are becoming popular for tasks like math and coding, while for consumer products, latency and intelligence per cost are prioritized.

Testing is essential for ensuring that AI systems behave as expected, not just in terms of performance but also in their underlying behaviors.

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

Achieving AGI requires not just pre-training but also the ability to reason and utilize additional computational resources effectively to improve outcomes.

Real-world testing is essential for ensuring that AI models are reliable and effective in practical applications.