Data moats for startups
I've heard a lot recently about how the moat / defensibility for a particular startup building in AI largely centers around data, with the thinking being that as they grow, they'll leverage their expanding dataset to train their model better, hence reinforcing their superiority.
This seems like really circular logic to me. To have a better product than your competitors, you need to have better data. But your data comes from selling your product and winning customers. A competitive advantage that's derived from being a large company seems to be skipping a couple steps.
Data moats are very real in AI though - model quality can be the difference between a good vs a great product. Hence why incumbents / later stage tech companies who've already solved for distribution and have proprietary data have the potential to create the most relevant models for given use cases.
That said, if folks can start to build a data moat right out the gate because of some unique access, this becomes really interesting.
We've seen this take place by paying for unique datasets, leveraging networks to access incumbent data, creating large synthetic datasets, or even by acquiring other businesses with extensive operating histories. But having this data upfront that other people don't have widespread access to represents a real competitive advantage early on for AI-first products.
Now, there are many companies out there leveraging AI in some capacity for whom model quality isn't the core defining variable. Even wrappers around off the shelf LLMs targeted at particular use cases can still deliver immense value given how transformative the underlying infrastructure is. If having the best model does represent a key variable to winning a space though, finding ways to accelerate unique data access seems fundamentally important to me.