Growing up, I was fascinated by solving math problems and understanding how machines work, which laid the groundwork for my interest in AI and its practical applications.

At Tesla, I became very interested in AI applications, particularly in autopilot technology, which led me to explore AI's potential in different domains.

The shift in AI development has seen a growing number of influential figures in the field coming from physics and math backgrounds, unlike 15 years ago when it was more about traditional engineering.

We initially explored automating mechanical engineering but pivoted to focus on programming, recognizing the potential for AI in that space.

Since joining Caltech in 2017, I have focused on using AI as a framework to address some of the hardest scientific problems that were previously considered impractical to solve.

I was fascinated by how people work and communicate, which led me to study psychology and computer science.

I have always been fascinated by intelligence and the potential of AI to help us understand the universe and answer fundamental questions about reality.

Our decision to pivot from CAD to coding was influenced by our personal interests and the realization that the existing science for 3D modeling wasn't ready for effective AI applications.

Initially, we attempted to create a co-pilot for mechanical engineering, but we pivoted to focus on coding because we were more passionate about it and recognized the potential for AI to transform programming.