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.

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.

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 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.

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 scaling laws in AI are still valid, but as we scale, challenges such as data management and system complexity become more pronounced, necessitating innovative solutions.

We realized that there needed to be an infrastructure platform, like AWS or Stripe, specifically focused on data to power the AI industry.