AI is both a general-purpose and highly specialized technology, requiring local expertise to fine-tune models for specific needs.

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.

Experimenting with different AI models is crucial, as their performance can vary significantly across tasks like debugging, planning, and implementing features.

Energy requirements for AI training are significant, and a mix of energy sources, including advanced nuclear, gas, and solar, will be necessary to meet these needs.

As we approach mid-2027, the key factors in AI research will be the taste of AI agents and the amount of compute available for experiments, as these will determine the effectiveness of automated AI research.

The future of AI may involve new infrastructure and development tools that leverage the unique characteristics of AI models, differentiating them from traditional functions.

To optimize costs with AI agents, you can either choose a cheaper model or reduce the amount of data fed to it, but it's best to start with a good model and ample data first.

Building good products on top of AI models is incredibly difficult.

AI is a general-purpose technology that fundamentally changes how we build software and use machines, similar to the internet.