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.

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.

My interest in computer science was influenced by the realization that AI could solve complex problems, leading me to shift from studying physics to focusing on AI and technology.

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.

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

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.

We were inspired by the success of GitHub Copilot, which demonstrated that useful AI products could be created, motivating us to pursue our own ambitious ideas in AI.

We prioritize tackling big problems in the industry, which leads to discovering larger opportunities, as we did when shifting focus from autonomous vehicles to AI's geopolitical implications.

As AI takes on more coding tasks, maintaining a balance between automation and preserving core engineering skills is essential for the development of effective programmers.