Scaling requires depth: researcher argues reasoning efficiency drives model design
eliebakouch · x · 2026-09-03
Researcher elie bakouch argues that scaling model size inherently requires scaling depth (with or without recurrence), which makes models more reasoning-efficient. He notes OpenAI and, somewhat less clearly, Anthropic are openly training for greater reasoning efficiency rather than less—for obvious intelligence-per-cost reasons—while cautioning this doesn't justify building 1000-layer-deep networks we can't monitor or align. Models still burn millions of inference tokens to reason about complex problems.
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