Since starting Scale, we've been at the forefront of every major development in AI, working with companies like General Motors and Toyota on autonomous vehicle programs and collaborating with the US government on pioneering AI initiatives.

The original insight behind founding Scale AI was the belief that as AI grows, the demand for data will increase exponentially, necessitating infrastructure to support that growth.

The initial business of Scale AI focused on generating data for self-driving car companies, which required us to stay ahead of AI trends.

The success of our operational data business created the momentum to explore building applications, which was a significant evolution for Scale AI.

In the early days of Scale AI, the focus was on refining data for autonomous vehicles, transforming raw data into labeled, high-quality data for machine learning applications.

The AI industry faces significant challenges, and companies often outsource large-scale problems to specialized firms like Scale AI to build infrastructure.

Scale AI acts as a refinery for data, converting large amounts of raw data into high-quality data that powers algorithms for various applications.

We realized that scaling laws in AI showed predictable improvements with increased data and compute, which encouraged us to focus on building a product for knowledge work as AI matured.

The scaling laws became a foundational principle for AI development after being popularized by researchers, including those at OpenAI and Google DeepMind, who emphasized that it's not just about making models larger, but also about training them on sufficient data.