I'm not sure this argument is in any way specific to LLMs, and the space for their application is still enormous. Search results, ad targeting, recommendation s

I'm not sure this argument is in any way specific to LLMs, and the space for their application is still enormous. Search results, ad targeting, recommendation systems, anomaly detection, content flagging, and so on, are all systems using machine learning with a high false positive rate. Up until fairly recently many systems used non-LLM models for making decisions based on natural language. Their performance would have been far worse but they still did useful work. Examples would include content policy enforcement, semantic search and so on. There are very many cases where a system will make an automated decision on a heuristic or random basis for lack of better options. ML improved those decision points and spawned new ones. LLMs improve a subset of those decision points and spawn new ones.