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I agree, but I would say datasets increasingly trumps infrastructure in the moat department. The infrastructure (sw and hw) that DeepMind had wasn't anything the academic team couldn't reproduce. For datasets, the most defensible moats will be data that others can't easily obtain
I don't mean so much infra as in "ability to run experiments with a research paper as ultimate deliverable" (which is accessible), more like infra for production MLOps, which is hard and requires hard-won know-how
It's very much the same dynamic as software development in general -- no specific technology is a moat, but solid processes/culture are hard to copy and provide a real differentiating advantage
I am not sure I fully agree... yes culture and processes are important, but the moats often have more to do with business advantages. Google has great processes and execution, but holding and viciously defending a stranglehold on the advertising market is key.
Yeah, I get it, but that's what (if anything) is a little surprising here to me. A team of academic rabble had sufficient know-how to successful reproduce what DeepMind did. I don't doubt that know-how and MLops important, but it wasn't moat in this case.
Still, for businesses deploying AI, it's a barrier, but not one that will last too long, I don't think. Not moreso than for other sw engineering tasks
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