The first generation of AI applications in drug design often focused on local models, but our approach is to build a more comprehensive system that can generalize across various tasks, as demonstrated by AlphaFold 3, which allows us to create models that generalize across chemistry and target space, enabling application to any protein or small molecule without needing fine-tuning.

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

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

The advancements in AI, such as AlphaFold, are revolutionizing fields like medicine, enabling us to design proteins that can potentially cure diseases like cancer.

We released the protein structures to the world knowing that the scientific community would find countless applications for them, far beyond what we could imagine.

General-purpose technologies like supervised learning and generative AI are valuable for a wide range of applications, and there is still much work to be done in identifying and executing concrete use cases.

AI for science is a major focus, as it can significantly enhance productivity and lead to new scientific discoveries.

We've applied AI to various tasks, including chip design and scheduling, making processes more efficient, but it remains constrained to domains that are provably correct, like mathematics and coding.

To maximize improvement in AI, we need to scale existing techniques to their limits while also investing in future innovations that could lead to significant advancements.