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

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.

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.

With AI, we can generate high-quality educational content quickly, allowing us to expand into new subjects like chess, math, and music, enhancing our learning offerings. We started working on the chess course eight months ago, initiated by two people who had no coding or chess experience, and they managed to prototype it successfully before we added engineers to productionize it.

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.

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.

The rise of AI coding tools has led to a shift in hiring practices, with founders focusing on software engineers who can create processes using AI rather than hiring more personnel immediately.

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 next priority for us is AI, as the world is being completely overhauled by it, and we need to create our own AI innovations that align with our strengths in front-end and design.