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Keumars Afifi-Sabet

Alexandr Wang: the world’s youngest self-made billionaire shaking up tech

The college dropout has made his fortune supplying AI companies with human labour

When the Silicon Valley company Scale AI was valued at $7.3 billion in 2021, its co-founder and chief executive Alexandr Wang become “the world’s youngest self-made billionaire” at just 25 years old.

Wang, now 26, “spent just one year at MIT” before teaming up with Lucy Guo, “another dropout”, to start Scale in 2016, according to Forbes. Scale “was conceived as the one-stop shop for supplying human labour to perform tasks that could not be done by algorithms”, and many of its clients include AI companies. 

Although OpenAI’s CEO Sam Altman “has been dominating the spotlight”, Wang is “increasingly influencing how the industry is perceived” by regulators, wrote Louise Matsakis in the Semafor Technology newsletter. But who is Alexandr Wang, and what makes his AI start-up so special?

Who is Alexandr Wang?

Wang’s “privileged upbringing” was crucial in giving him “a solid foundation in science and technology”, said Mashable. Born in New Mexico, his parents “both worked as physicists on projects for the US Air Force and the military”; the Scale CEO “clearly had a brilliant mind passed down from his parents”. 

He was “a maths whiz” as a child and “fond of participating in coding competitions”, added ZeeBiz. Wang “moved to the Bay Area” in San Francisco, California, to work for internet start-up Quora at 17, when, Forbes reported, CEO Adam D’Angelo “gave him a crucial piece of advice”: “four years of college is overrated, two is underrated”. 

Wang spent just one year at MIT [Massachusetts Institute of Technology], where, ZeeBiz added, he “became fascinated with machine learning and AI”, before joining start-up accelerator Y Combinator. A “trailblazer at heart”, he then created Scale with Guo, a “fellow young tech whizz”, according to South China Morning Post magazine Style.

What is Scale AI?

Wang’s fortune “wasn’t built entirely on silicon” but also “with a vast outsourced workforce that performs a rudimentary task crucial to AI”, said Forbes. This is “labeling the data used to train it”. 

Conceived as “the antithesis of AI”, the start-up received a $4.5 million investment in 2016 and “within months”, Wang and Guo “realised Scale was a viable solution” to a problem affecting self-driving car companies, which were at “AI’s then-frontier”. These companies had “millions of miles” of driving footage but “not nearly enough people to review and label it”. Scale “could fill that need”. 

Roughly 240,000 people around the world work now for Remotasks, a Scale subsidiary, with Wang branding them “very, very important to the process of building powerful AI systems”. 

As of 2018, Scale’s client list included “major international auto manufacturers” such as Toyota and Honda as well as Waymo, a Google subsidiary. Several rounds of investment followed, and a final round “valued the company north of $7 billion”. It had taken Wang “just five years to become the youngest self-made billionaire in the world”. 

“So successful and valuable is Scale’s technology”, said Saul Lipchik in Style, its service has been “employed by more than 300 companies”.

Why is Wang so vocal in Washington? 

Scale has been providing services to the US government over the last few years, and Wang “has also made deep connections with lawmakers in Washington”, wrote Matsakis in Semafor Technology. 

One lawmaker even called him his “real friend”, which is “a real compliment in DC”. And his “deep connections in Washington” make him “stand out in the California-centric tech industry”. 

Wang often speaks to legislators about his “hawkish views” on China, which he describes as “the greatest geopolitical competitor” to the US. He’s twice briefed the Select Committee on the Chinese Communist Party, with Democratic Congressman Raja Krishnamoorthi finding him to be “very insightful and at the same time personable”.

Scale AI’s “outsized presence” in Washington may also “open lawmakers’ eyes to the immense amount” of human labour that goes into AI, wrote Matsakis. 

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