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Adding to this, inference is getting cheaper and more efficient all the time. The investment bubble is probably the biggest reason why inference hardware is so expensive at the moment and why startups in this sector are only targeting large scale applications.

Once this bubble bursts, local inference will become even more affordable than it already is. There is no way that there will be a moat around running models as a service.

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Similarly, there probably won't be a "data moat". The whole point of large foundation models is that they are great priors. You need relatively few examples to fine tune an LLM or diffusion model to get it to do what you want. So long as someone releases up to date foundation models there is no moat here either.