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

At Scale, we power the data infrastructure for the entire AI industry, with every major large language model built on top of our data engine, and we partner with enterprises and governments to implement this technology for their unique challenges.

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 story of Scale AI exemplifies a classic startup journey, where founders pivoted from an unrelated idea to successfully address the demand for data labeling in AI, particularly for self-driving cars.

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

At Scale AI, we foster a culture of meritocracy where anyone can propose ideas, and those with good ideas are empowered to realize them, regardless of their position.

At 19 years old, I dropped out of MIT to launch Scale, driven by the understanding that progress in AI relies on data availability, not just algorithms.

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