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  • Alexandr Wang - Wikipedia
    Alexandr Wang (born 1997) is an American billionaire entrepreneur He is the co-founder and former CEO of Scale AI, an artificial intelligence company that provides data labeling and model evaluation services to develop AI applications [2] [3] At age 24 in 2021, he became the youngest self-made billionaire in the world at that time
  • Who Is Alexandr Wang, the Founder of Scale AI Joining Meta . . .
    Scale AI founder Alexandr Wang, 28, is a billionaire and an MIT dropout He became the world's youngest self-made billionaire in 2022 at age 24
  • From MIT Dropout to Billionaire: How Alexander Wang Built . . .
    When Alexander Wang founded Scale AI in 2016, the AI landscape was still in its early stages The data labelling startup, driven by Wang’s vision, quickly became a key player in the AI landscape, growing to over 1,500 employees and securing crucial partnerships across industries and governments
  • Alexandr Wang - Forbes
    #1763 Alexandr Wang on the 2025 Billionaires Alexandr Wang is the CEO of Scale AI, the startup he cofounded in 2016 to help companies put their raw data to work for AI and machine learning
  • Who is Alexandr Wang? The Co-Founder of Scale AI and Newest . . .
    Founding Scale AI: Alexandr Wang's Vision for Data-Driven AI In 2016, at the age of 19, Alexandr Wang co-founded Scale AI with Lucy Guo through the Y Combinator accelerator program The idea for Scale AI stemmed from Wang’s realization that the bottleneck in AI development was not algorithms or computing power but the availability of high
  • Alexandr Wang - Scale AI | LinkedIn
    View Alexandr Wang’s profile on LinkedIn, a professional community of 1 billion members Scale AI #39;s mission is to accelerate the development of AI applications
  • How a Young Visionary Built Scale AI and Changed the Future . . .
    The story of Scale AI starts with Alexandr Wang, a math prodigy from New Mexico, who entered MIT to study machine learning and computer science While at MIT, Wang interned at tech giants like Quora and Addepar, where he noticed a recurring issue: companies needed massive amounts of high-quality labeled data to train machine learning models





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