The efficiency of AI agents in conducting research will depend on their ability to learn from experiments and adapt their strategies, which may lead to breakthroughs in various fields.

Both algorithmic improvements and computational advancements, such as better chips and larger data centers, are essential for progress in AI.

The ability to prompt AI effectively is crucial, as it determines the quality of the output, which is a reflection of agency and taste in AI development.

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.

While AI isn't conducting new scientific research, it enables scientists to complete their work more quickly and effectively.

The best AI startups are actively refining their prompt engineering practices, gathering insights from numerous companies to improve the quality and reliability of AI outputs.

Having a dedicated experimentation team allows for faster innovation and the ability to fail fast, which is essential in a rapidly evolving AI landscape.

High-quality training data is crucial for the success of Deep Research, as the quality of the input data significantly influences the model's output quality.

The bottleneck for AI development isn't the algorithms or technical limitations; it's actually the availability of data.