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

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.

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

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.

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.

Scale AI is evolving from a data labeling company to a leader in agent-driven technology, which is growing rapidly and is seen as an infinite market.

Successful startup founders often have unique experiences from previous jobs that inform their ideas, as seen with the founder of Salient, who built an AI voice agent for loan processing based on insights from working at Tesla.