Alexandr Wang is the CEO and co-founder of Scale AI, a data-labelling startup that helps other companies train and deploy frontier AI models. Over the years, Wang has built his startup into the backbone of the AI boom, quietly enabling everything from autonomous vehicles to large language models (LLMs). Now, Wang finds himself at the centre of a potential $15 billion shake-up as Meta taps him to lead its newly formed research lab that will focus on building AI systems capable of ‘artificial superintelligence’. The $15 billion investment deal is further expected to bring other Scale AI employees to Meta, which is also reportedly offering seven to nine-figure compensation packages to AI researchers from the likes of OpenAI and Google who would like to be a part of its new 50-member lab. The new lab comes at a crucial time for Meta, which is perceived to be struggling to pull ahead of its competitors Google, Microsoft, and OpenAI in the high-stakes AI race. CEO Mark Zuckerberg has pushed for AI to be incorporated across the company’s products such as its Ray Ban smart glasses as well as social media platforms Facebook, Instagram, and WhatsApp. Meta has also sought to define its competitive edge by releasing its foundational Llama series of AI models under open-source licences, allowing developers to freely download and integrate the models into their own tools. But internal issues such as employee turnover and underwhelming product launches have reportedly hampered Meta’s AI efforts lately. So far, the company’s research efforts have been overseen by its chief AI scientist, Turing Award winner Yann LeCun, who is widely recognised for his groundbreaking research contributions on convolutional neural networks (CNNs). However, LeCun’s views on AI are not aligned with others in Silicon Valley as he has argued that LLMs are not the path to artificial general intelligence (AGI). Meta is now betting on Wang to not only help regain its lead in the AI race but also push toward another frontier known as artificial superintelligence (ASI) — a hypothetical AI system with intelligence exceeding that of the human brain. Who is Alexandr Wang? What are his views on AI? Alexandr Wang was born in New Mexico, US, to Chinese immigrant parents who worked at Los Alamos National Laboratory as nuclear physicists. Before heading to college, Wang reportedly worked at knowledge-sharing website Quora. He dropped out of Massachusetts Institute of Technology (MIT) after just one year and joined Y Combinator, the popular startup accelerator that used to be led by none other than OpenAI CEO Sam Altman. At Y Combinator, he teamed up with Quora alum Lucy Guo to start a new company called Scale AI in 2016. Two years later, both Wang and Guo were named in Forbes’ 30 Under 30 list in enterprise technology. Shortly after, Guo exited Scale AI “due to differences in product vision and road map,” according to a report by Forbes. Other reports say that she was ousted from the company. Meanwhile, Wang continued running the startup, which was minted as a unicorn in 2019 after raising $100 million from Peter Thiel’s Founders Fund followed by another $580 million fundraising round that put the company at a $7 billion valuation. At 24, Wang became the youngest self-made billionaire in the world. His co-founder, Lucy Guo, recently became the youngest self-made woman billionaire due to her stake in Scale AI. Wang was also reportedly Sam Altman’s roommate during the COVID-19 pandemic. The two AI industry leaders were also photographed sitting next to each other at US President Donald Trump’s swearing-in ceremony in January this year. What is Scale AI? How fast is it growing? Scale AI was founded in 2016 as a startup that labelled mass quantities of data and organised them into datasets required to train AI systems, particularly autonomous vehicles (AV). As a result, most of its data services were primarily offered to self-driving automakers at first. By essentially cornering the market for training data that helped self-driving cars distinguish between objects on the road, Scale AI positioned itself early for the AI boom that was to follow. LLMs are trained on massive amounts of data to generate text and other content. Scale AI hires thousands of contract workers to sift through vast amounts of data, label the information, and clean the datasets that are then supplied to tech companies to train their AI models. Scale AI’s client list includes major automakers such as Toyota and Honda as well as Waymo, Google parent Alphabet’s AV subsidiary. It has also partnered with Accenture to help the consulting giant build custom AI apps and models. OpenAI, Microsoft, and Toronto-based AI startup Cohere count among Scale AI’s customers as well, according to a report by Forbes. The US government has also reportedly sought Scale AI’s data services in order to help analyse satellite imagery in Ukraine. Last valued at nearly $14 billion, the company gathered about $870 million in revenue in 2024. It expects to more than double revenue in 2025 to $2 billion, which would put Scale AI’s valuation at $25 billion, according to a report by Bloomberg. However, the AI boom has also given rise to a wave of relatively new competitors such as Surge AI, which offers data labeling tools to AI companies, as well as data labeling startups Labelbox and Snorkel AI, which primarily cater to non-tech enterprises. Companies like Scale AI have drawn criticism for outsourcing rudimentary tasks like manually labelling and annotating data to workers in countries with low-cost labour such as Kenya, Venezuela, Philippines, and even India. Scale AI has reportedly set up an in-house outsourcing agency called Remotasks which is responsible for training these workers in data-labelling work at several facilities across southeast Asia and Africa. Previous reports have highlighted the poor working conditions of data labelers, who are often paid less than $1 an hour even as AI companies continue to attract massive investments.
