To create effective AI models for engineering, we need to generate synthetic training data that is physics-based and informed by supply chain considerations for various physical product domains.

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

The challenge in AI is that while the underlying technology is evolving rapidly, it complicates product development because you have to predict where the tech will be in a year.

P-1 AI aims to build engineering AGI for the physical world, focusing on areas like data center cooling and aircraft design, which have yet to be radically transformed by AI.

AI applications that ingest real-world sensor data and robotics are crucial building blocks for achieving physical AI, which is essential for developing engineering AGI.

Design is about problem-solving and requires a deep understanding of user needs, which current AI models struggle to replicate.

To effectively use AI, one must understand the physics behind the problems being solved, allowing us to design AI that can provide answers directly applicable to real-world scenarios.

Current AI systems lack the ability to acquire common sense and learn from natural sensors like video, which presents a significant challenge.

Many potential AI projects exist, but outside of consumer software, few have the scale to apply a single piece of software to a billion users, making it challenging to generate similar financial value.