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TUNNEL VISION

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Summary

Raised by elite nuclear immigrant physicists, Wang was groomed to follow his parents' footsteps into traditional, government-backed academia. Instead, the coding prodigy rebelled. At just nineteen, he used the guise of a "summer break" to drop out of MIT, defying his family's expectations to conquer the real world. By founding Scale AI, he built the data infrastructure powering global tech giants and defense intelligence, becoming the world's youngest self-made billionaire. Now, as Meta’s Chief AI Officer leading Superintelligence Labs, he isn't just following a scientific legacy, he is redefining it by engineering the birth of Artificial General Intelligence.

Status
Ongoing
Chapters
1
Rating
n/a
Age Rating
13+

Chapter 1

The Summer Break

The desert air in Los Alamos didn’t circulate; it compressed. Growing up in a town built on secrets, under a roof shared with two senior nuclear physicists, meant that silence was rarely empty. It was heavy with the weight of unshared data and a clear, unspoken expectation: you do not build things for profit; you decode the universe for the state.

Alex sat on the edge of his twin bed in his MIT dorm room, staring at a terminal screen flashing with unannotated computer vision data. His phone buzzed against the particle-board desk.

“Did you secure the research assistantship with Professor Gallagher?” his mother’s text read. “The computational physics lab needs someone with your Olympiad record.”

He didn’t reply. Instead, he pulled up the flight confirmation template from Y Combinator. He had forty-eight hours to get to San Francisco.

Downstairs in the lobby of his dorm, his suitcase was already packed, stuffed with three pairs of jeans and a single tailored blazer he’d bought for a high school awards ceremony. The narrative his parents expected was linear: the USACO finals, the national Math Olympiad, the MIT degree, and eventually, a security clearance at a national lab. A legacy forged in the same quiet, highly structured isolation that defined their own lives.

But the real world was moving too fast. The university curricula were lecturing on algorithms that industry giants had discarded six months ago. The bottleneck holding back true machine learning wasn't a lack of theory; it was a lack of raw, perfectly labeled data. And the academic elite were too busy writing papers to notice the infrastructure was crumbling.

He picked up his phone and typed a message to his father.

“Flying out tomorrow. Just taking a summer break to work on a small data project with a friend in California. Will be back for the fall semester.”


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