In the coming years, we will see more experiments on how to effectively deploy and manage multiple AI agents for complex tasks.

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.

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.

Ambient agents allow for scalability, enabling thousands of them to run in the background, which significantly increases our impact.

Once AI agents reach a serial speed of 90x compared to humans, further improvements in speed will not significantly enhance their performance due to bottlenecks in other areas.

There's a lot of excitement around AI agents, but many people are unsure of their practical applications, often trying to apply them to problems that simpler solutions could address.

The future landscape of AI may involve a combination of large models and smaller, specialized models that work in parallel, leveraging different areas of expertise.

One overhyped aspect of AI agents is their perceived capabilities, while the underhyped aspects include the importance of understanding when and how to use them effectively in production, as well as how they can save people time by automating tasks that take only a minute, significantly changing dynamics and allowing you to perform those tasks many more times than before.

Building a system that effectively utilizes AI agents requires careful orchestration and cannot be achieved merely by increasing model size.