Preparing the next generation for AI involves teaching them the process of learning and encouraging them to have ideas and projects.

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.

The performance of AI models improves significantly with reinforcement learning, and OpenAI aims to scale this approach to enhance reasoning capabilities further, moving towards a future where reinforcement learning will dominate AI development and potentially lead to significant advancements in capabilities.

Cognition's approach to AI emphasizes the need for continuous learning and adaptation to keep pace with rapid technological advancements.

To advance AI, systems need to understand the physical world, have persistent memory, and be capable of reasoning and planning.

One of the next steps in AI development is to allow it time to think, plan, and explore options just as a human would.

The future of AI may involve models that can learn and adapt through self-play in a sandbox environment, leading to significant advancements in reasoning and task execution, and the aha moment will be when AI can create something that's not good, like a product or a song, and it learns how to navigate the web and gain real engagement on social media.

The iterative process of deploying AI systems, gathering feedback, and learning is crucial for building safe systems as we advance into more complex AI.

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.