Meta’s clandestine $100 million AI talent hunt grabbed headlines. Sam Altman cried foul and claimed that Meta’s was running a vaccum through the limited AI talent available. Then there were posts from those hired. They denied million dollar sign on bonuses. I don’t know the truth about the pay checks of Meta but talent spotting is HARD.
Red Bull can teach you some tricks
Red Bull’s driver development program has a secret database tracking over 10,000 young racers globally. They monitor everything from lap times in obscure regional championships to how drivers handle pressure during rain-soaked qualifying sessions. Christian Horner, the boss of once revealed that they’ve spotted future champions based on a single qualifying lap in a junior formula race—the equivalent of Meta spotting an AI genius from a single GitHub commit.
F1 teams don’t just want race winners—they want drivers who show specific adaptability patterns. Similarly, Meta isn’t just hiring published researchers; they’re targeting minds that demonstrate particular thinking architectures.
The real kicker? Both industries have discovered that traditional recruiting methods miss their best prospects. In F1, some of today’s champions were initially overlooked by major teams because they didn’t fit conventional profiles. Lewis Hamilton was famously rejected by multiple teams before McLaren took a chance on him—not because of his karting record, but because of how he processed feedback during simulator sessions.
Thomas Keller’s Secret
The legendary Thomas Keller reportedly discovered several of his protégés not through résumés. How did he spot talent?
Read all about it https://abhijitbhaduri.substack.com/p/silicon-valley-is-doing-what-f1-and
