On 12 June, Alexandr Wang stepped down as chief executive of Scale AI to spearhead Meta’s new “superintelligence” division—his most audacious venture yet. As part of the deal, Meta is investing £11.3 billion ($14.3bn) in Scale for a minority stake, but the true acquisition is Wang himself. Known for his intense drive and vision, Wang is expected to inject a new sense of urgency into Meta’s artificial intelligence efforts, which have recently been overshadowed by Chinese rivals such as DeepSeek. Despite his lack of formal academic accolades—having left MIT at 19—Wang brings strategic insight into the AI data landscape and a deep understanding of what powers next-generation models.
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Wang made his name by building Scale into a major player in the AI data labelling sector, becoming the world’s youngest self-made billionaire in the process. He is admired by AI figures like OpenAI’s Sam Altman, who once shared a flat with Wang during the pandemic. However, his relentless pursuit of growth has faced criticism, particularly in relation to the treatment of Scale’s vast contract workforce. Some contractors have reported issues such as delayed or reduced payments, a point of contention noted by Scale’s co-founder Lucy Guo. While Scale maintains that these cases are rare, they highlight the tensions between rapid expansion and ethical labour practices in the AI industry.
Now at the helm of Meta’s efforts in superintelligence, Wang co-authored a paper with former Google CEO Eric Schmidt warning of the existential risks such systems could pose—comparing them to the advent of nuclear weapons. In conversation prior to his transition, Wang spoke candidly about leadership, emphasising a hands-on, detail-oriented style balanced by a commitment to fostering strong organisational culture. He also predicted the rise of an “agentic world” in which autonomous AI agents will conduct a growing share of economic, governmental, and military activity. This shift, he believes, demands robust infrastructure and thoughtful policy to ensure stability during a time of profound transformation.
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Despite growing automation, Wang insists that data annotation remains a critical, expanding part of the AI ecosystem. He argues that AI models inherently require continual human input to address persistent deficiencies. Scale’s success, he contends, comes from recognising data as a foundational pillar—alongside compute and algorithms—of modern AI. Rather than merely providing data, the company has positioned itself as a platform for building advanced applications, helping governments and businesses unlock value from vast datasets. As the race for superintelligence accelerates, Wang’s role may prove decisive—not only in shaping Meta’s future, but also in determining how the world manages the promise and peril of advanced AI.
