Why I'm still bearish on LLMs after Navier-Stokes

Jay Kruer argues frontier models are nowhere near replacing most knowledge workers. The Navier-Stokes proof is a best-case scenario: the theorem is its own rigorous spec, and Lean has been audited for years. Most knowledge work lacks this setup. Models generalize only within a small neighborhood of trained tasks; small perturbations cause failure or reward hacking. Rigorous specification demands domain experts who are rarely also spec experts, and the labor cost often exceeds direct implementation. Human review doesn't scale to model output volumes—the xz backdoor shows how vulnerable it is. LLMs remain a cracked intern: useful under supervision but not autonomous. Only three firm types can adopt fully autonomous LLMs: those that tolerate cheap failure, those with narrow well-guarded tasks, and those like chip design where rigorous validation is existential. The first two are price-sensitive and better served by cheap open models running locally. The third may use frontier models, but swarm width matters more than reasoning quality, so cheaper models in wider swarms may win.

Why it matters: A contrarian piece with concrete arguments. The author uses the Navier-Stokes proof as the 'best case' to highlight the gap for ordinary knowledge work, proposes a 'small neighborhood generalization' framework, and points out that rigorous specs require expensive domain expert...