But the problem is the tight coupling of prompts to the models. The half-life of prompt value is short because the frequency of new models is high, how do you defend a moat that can half (or worse) any day a new model comes out?
You might get an 80% “good enough” prompt easily but then all the differentiation (moat) is in that 20% but that 20% is tied to the model idiosyncrasies, making the moat fragile and volatile.
