Comment by w10-1

The question is not average cost but marginal cost of quality - same as voice recognition, which had relatively low uptake even at ~2-4% error rates due to context switching costs for error correction.

So you'd have to account for the work of catching the residue of 2-8%+ error from LLMs. I believe the premise is for NLP, that's just incremental work, but for LLM's that could be impossible to correct (i.e., cost per next-percentage-correction explodes), for lack of easily controllable (or even understandable) models.

But it's most rational in business to focus on the easy majority with lower costs, and ignore hard parts that don't lead to dramatically larger TAM.