Lots of good, accessible points here, but I think it's easy to overestimate the moat around [business] data. [Google] data includes pretty much every aspect of

Lots of good, accessible points here, but I think it's easy to overestimate the moat around [business] data. [Google] data includes pretty much every aspect of human activity, since they keep their platform in your pocket, in smart home devices, and somewhere in the path of so many online intents. Chinese surveillance and shared platform data is a similar asset - both are everything-adjacent for a massive population. And that's the big guys. A couple of years ago, this article might have said that MasterCard and Visa have all the spending data... But then Paribus proved that a scrappy startup with a free service could get tens of millions of people to share all their online receipts in record time, and give Capital One a great way of catching up through acquisition. That's not an equivalent dataset, but it's good enough for a lot of applications. I definitely think proprietary data exists, and that companies will benefit be exploiting it with ML. But they should be very careful about assuming their data will uniquely cover an industry, or even a wide swathe of applications, for long. And they might not have to simply leverage ML, but actually reorganize their business around what can remain unique about their data (like they do about every other asset). And for VCs evaluating new data-oriented startups, I wonder if they will need new thinking about the time horizon on which investment pays off. Once an application for a new data asset proves valuable, it may turn out to be much more replicable than expected.