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...but this all does say something interesting about defensive moats in the AI age. It's very hard to prevent another group from replicating your work... indeed, just knowing that something is possible is often enough to enable it to be copied. 3/
There are moats in deep learning, but they're never models/algorithms, because they're always trivial and easy to duplicate (it's just gradient descent). The moats are datasets and infrastructure. See thread from 2017https://twitter.com/fchollet/status/892608470989459456?s=19 …
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
data that uniquely occurs as a byproduct of doing a particular kind of business, or which is ensnared in regulatory complications probably provides the closest thing to something defensible.
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