Data Moat
topic on 3 shows · 7 statements across 6 episodes
the Y Combinator Startup Podcast
More or Less
20VC
7 statements about Data Moat, every show
Green: AI continuous learning loops create stronger user retention moats
“I do think that data has long been talked about as a moat of sorts, but I think it's even more so in this area where you do have the continuous learning loop, where it's not just a data point that I, Share that then gets reflected back in a specific case. It's…”
Levie: Internet data moats differ from historical monopoly dynamics
“The data moats that, that exists now in the internet are just totally different than almost any other form of antitrust, you know, kind of monopolistic dynamics that I think we've ever seen in history. Like, you know, clearly, obviously if you're building what…”
Guo: Incumbent data moats are overblown in the AI race
“Much ado has been made about this idea of a data moat, but honestly, there's a lot of data out there, and entrepreneurs are incredibly creative about collecting it, and increasingly about generating it, and I don't think it's, ah, the incumbents are gonna win …”
Guo: Incumbent data moats are overrated against creative founders
“Much ado has been made about this idea of like a data moat. I'm sure you've heard this term, but honestly, there's a lot of data out there and entrepreneurs are incredibly creative about collecting it and increasingly about generating it.”
Vernal: AI data moats are overrated due to rapidly declining data requirements
“I personally am pretty skeptical. The reason is multifold. One is, I just think The technology and the research here is evolving so quickly that I think the number of data points you need to train a model is decreasing at, you know, some fixed efficiency level…”
Vernal: Workflow stickiness creates a stronger moat than data volume
“The mode to me is not really based on the data and the labels. It's more based on, well, now we have some specialty function. Let's say it's like radiology or dermatology, and you're labeling medical images, and now they have been trained to use our software f…”
Gil: Data moats rarely succeed as standalone startup assets outside genomics
“Yeah, the most common miracle that's quoted today is a data moat. People say, well, we'll generate tons of data, and then we'll be differentiated, and that, I think in genomics something like that could work, but outside of that, I actually think I've never se…”