The need for having large amounts of data to train AI systems has become mainstream over the last couple of years, but Scale CEO Alexandr Wang had read the writing on the wall all the way back in 2016.
Wang, who recently joined Meta as Chief AI Officer following a $14.3 billion investment deal that valued Scale AI at $29 billion, had an unusual prescience about the critical importance of data in the AI ecosystem. Speaking about his decision to drop out of MIT and start Scale AI, Wang demonstrated the kind of forward-thinking that would eventually make him one of the most influential figures in artificial intelligence infrastructure. At just 28 years old, he now leads Meta’s newly formed Superintelligence Labs, overseeing the company’s ambitious quest to build AI systems that could surpass human intelligence.
“NVIDIA was already a very clear leader in building compute for these AI systems, but there’s nobody focused on the data,” Wang explained when discussing his entrepreneurial journey. This observation would prove to be remarkably prescient as the AI industry has since recognized data quality and availability as perhaps the most critical bottleneck in developing powerful AI systems.
Wang’s insight went beyond just identifying a market gap. “It was really clear that over the long arc of this technology, data was only gonna become more and more important,” he said. This long-term perspective drove his decision to make a dramatic career pivot. “And so in 2016, I dropped out of MIT, did Y Combinator, and really started Scale to solve the data pillar of the AI ecosystem and be the organization that was gonna solve all the hard problems associated with how do you actually produce and create enough data to fuel this ecosystem.”
The implications of Wang’s early bet on data infrastructure have proven transformative. Scale AI became a San Francisco-based data annotation company that provides data labeling, model evaluation, and software to develop AI applications. The platform employs a distributed workforce of over 100,000 skilled annotators who label data according to specific project requirements, supported by machine learning tools that increase efficiency and consistency.
Wang’s vision has materialized at a time when the entire AI industry has awakened to the data challenge. Major AI companies now spend billions on data acquisition and processing, with OpenAI, Google, and others facing lawsuits over training data usage. The quality and scale of training data has become the defining factor in AI model performance, making Scale’s services essential infrastructure for companies building everything from autonomous vehicles to large language models. The recent Meta acquisition not only validates Wang’s 2016 thesis but positions him to influence the next phase of AI development at one of the world’s most ambitious technology companies, where he’s been tasked with leading Meta’s quest for superintelligence despite coming from a company that was not in the business of actually building large language models but rather providing the foundational data that makes them possible.