A.I. researchers are negotiating $250M pay packages

Original Article ↗ Hacker News Discussion ↗

Scarce “genius” vs 250 solid hires

  • One camp argues AI breakthroughs follow a power-law: a few top researchers create orders of magnitude more value than hundreds of “merely very good” people, justifying 9‑figure packages.
  • Others counter that no individual is truly 1000× more valuable; this looks like panic hiring, brand/FOMO, or de‑risking future competitors rather than rational productivity math.
  • Several note you could run multiple full research labs for the same money.

Sports-star and superstar economics analogies

  • Many compare these packages to top athletes: rare talent in a global, winner‑take‑most market.
  • Critics reply that sports stars have clear, measurable revenue impact (tickets, TV, merch); AI researchers mostly ride speculative expectations.
  • Some see “superstar economics” at work: markets overpay the most visible names even when underlying contributions are hard to attribute in multi‑author research.

Bubble, markets, and AI race

  • Strong disagreement over whether this is a rational “existential race to AGI/ASI” or a classic bubble like dotcoms/crypto: massive capex, unclear business models, and VCs chasing hype.
  • Several expect an eventual crash or “AI winter” even if the tech itself persists; others insist the trajectory toward much stronger AI is obvious and capital flows are justified.
  • Some frame these hires as pre‑emptive acquihires or golden handcuffs to prevent rival startups rather than purely about output.

Meta, leadership, and motives

  • Meta is seen as both uniquely well‑resourced and uniquely tarnished: critics say pouring billions into attention‑maximizing AI is “diabolical.”
  • Zuck’s track record with the metaverse fuels skepticism that he can steer frontier AI effectively; others see this simply as him trying to secure legacy and avoid being outflanked.

Comp structure and role reality

  • Packages are described as mostly RSUs over ~4–5 years, often with milestones and big early grants, not pure cash.
  • Roles are nominally IC but viewed as quasi‑executive: deciding how to allocate extremely scarce GPU time and shaping large internal labs.

Inequality, politics, and social impact

  • Some left-leaning commenters say they should cheer workers extracting money from capital; others say this reinforces elite wealth and does nothing for the “floor.”
  • Broader frustration surfaces about tech’s vast resources going to ad optimization and stock pumping rather than public problems, deepening cynicism about capitalism and AI alike.