Jessica Livingston and Carolyn Levy of Social Radars podcast interview Alexander Wang, founder and CEO of Scale AI, a company providing high-quality data to AI companies. Alexander shares his journey from growing up in Los Alamos, New Mexico, surrounded by physicists, to building a $13.8 billion company.
Alexander's childhood was marked by academic competitions in math, physics, and computer science. He took a gap year before attending MIT, working as a software engineer at Quora. There, he began to understand the importance of data for AI. He noticed how advancements like DeepMind's AlphaGo and Google's TensorFlow relied heavily on data as the raw material for intelligence.
During his time at MIT, he brainstormed startup ideas with co-founders, including the initial concept for Scale AI. Initially, they applied to Y Combinator (YC) with a different idea for a doctor booking app. Jessica Livingston recalls her initial impression of Alexander as potentially "arrogant or brilliant," but ultimately voted to fund them. He admits that their team seemed "lost" about their product-market fit initially.
The doctor booking app proved unfeasible, prompting a pivot. A surge in chatbot development around the summer of 2016 suggested data for AI was a promising path. Initially, the chatbot era, not autonomous vehicles, was believed to be the key driver. The company started under the name "Ava," focused on chatbots, but evolved to "Scale" for its implications of infrastructure and growth.
Alexander and his team did the data labeling themselves initially, with early customers from YC, including Teespring. Then they pivoted to focus on autonomous vehicles. While many considered data labeling "unsexy," Alexander recognized its importance and saw that data was the foundation for AI. Early investors who believed in AI also saw the potential in Scale.
He recalls an investor dismissing the need for large amounts of data, a notion later proven wrong. They shifted from autonomous vehicles to government contracts in 2020, working with the Department of Defense (DoD) on AI for national security. This included image recognition models to detect damage in Ukraine, used for both military coordination and humanitarian aid efforts.
In 2022, Alexander saw the potential of large language models (LLMs) and generative AI, shifting company resources to support this new wave. Over a short period, more than half of Scale's headcount shifted to data for generative AI. This was driven by his philosophy of "doing too much" rather than underreacting to significant market shifts.
Alexander also discusses the company's stance on diversity, expressed through their "MEI" (Merit, Excellence, Intelligence) principle. He believes that focusing on meritocracy, hiring the best talent, leads to diversity as a natural outcome. While this stance sparked controversy, it also attracted individuals who appreciated the company's clear focus on merit.
He acknowledges the challenges of growing a company so quickly, from 150 to 700 people during the pandemic. They reversed remote work policy and are moving back to in-office hubs. He believes his experiences leading Scale have aged him beyond his years.
Alexander addresses Paul Graham’s question on data that is hard for software to label and difficult data to generate: It is agent data. Agent data captures the entire thought process and actions individuals take while doing tasks. On the best things to do with labeling data: How do you get the most brilliant experts to contribute for these AI systems and automate it so it can be efficient? Alexander said, “He sees humans aiding the machines whenever the models are sort of like going down a wrong path or they’re hallucinating or there’s something that they’re getting stuck on or they have to make some change in the real world or something.” Alexander says, “Best way to out compete is if your company is your life’s work.”
He states that he feels lucky to have built a company central to the AI industry's evolution and future and sees the industry as highly passionate but varied in perspectives. It is exciting that the numbers are staggering with $200 billion of investment are going into building advanced powerful AI systems. Scale and Nvidia are “behind the scenes” supporting the industry.