The AI industry suffers from a lack of rigorous evaluations and tests that demonstrate the frontier of model capabilities.

The expectation versus reality of AI adoption is often skewed; while expectations are high, the actual capabilities of current models may not meet those expectations, leading to a rapid adjustment in perception as models improve.

We're in an interesting time where the capabilities of AI models are far ahead of the products people have built. There's a huge opportunity for new innovations, especially as the cost of AI continues to decrease dramatically.

The current capabilities of AI models are impressive, and skeptics should engage with them to understand their potential better.

Training AI models is an exciting field with much potential for progress, and it's essential to involve more people in shaping AI's future.

There is a significant gap in the market for frameworks that clearly define how to scale and build AI agents, highlighting an opportunity for innovation.

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

There's a significant gap between the current compute capabilities and what could be achieved with ten to a hundred times more compute, which would allow for much greater AI services.

AI as a general-purpose technology presents numerous opportunities, and our focus should be on building concrete use cases to harness its potential effectively.