Two scaling laws for AI research: hiring Einsteins caps output, smarter models don't
menhguin · x · 2026-10-16
The author argues AI research progress follows two distinct scaling laws:
- Human-bottlenecked path: handing ChatGPT Pro to a pool of "Einsteins" yields faster upfront progress (and faster fundraising), but output is capped at Einstein-tier by how many such people you can recruit, hire, monetize, and extract working hours from.
- Model-bottlenecked path: not constrained by humans — you wait until models get smarter than Einstein, then simply spend more tokens.
Core claim: the human route pays off short-term but has a hard ceiling; the model route has a higher ceiling but requires waiting for capability to cross that threshold.
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