I think one could describe LLM optimization as "hard but not a moat". Years ago, optimizing neural nets was described "graduate student descent" - it's tricky but throw enough conventionally smart people at it and it will happen. It's like tuning a hot rod and finding a reproducible bug in a large code base. It's hard and there are tricks but not absolute hurdles, no problems waiting for a conceptual breakthrough (and at today's scales, are there any problems waiting for an Einstein to solve? That's an open (AI) question).