Baris Gultekin: belief

Transcript context

…Yeah. That's an interesting metric. I've seen a bunch of stuff recently over the last, even just the last week. It was really the Venezuela moment where all of a sudden people were bragging about how they had created these AIs that monitor the prediction market platforms and were looking for early signal and trying to capitalize on that. That is going to be a really interesting phenomenon. How about in terms of just, actually, let me go a different direction. I'll do maybe a little lightning round to close us out. I have this sense that Right now we're in this kind of expansionary phase. I'm no astronomer, but my experience with platforms in the past and definitely experiences with the Meta platform, formerly known as Facebook, where it was like they came on the scene, they opened up a ton of stuff. Everybody could tap into all these data and social connections. And there was, for a moment, there was like an incredible flourishing of a ton of different ideas. And then after that supernova came the black hole and it was like, actually, we're going to close all this stuff back down. And a lot of that value that entrepreneurs created on the edges, experimenting with different ideas. The things that really mattered mostly ended up getting sucked back into the platform. And there wasn't nearly as much value created on the margins as it seemed like there was going to be. And I, I might be just over indexing on this. experience of having lived through this pattern once before. But I feel like the AI moment is set up for that to happen to a lot of people again, where, for example, just this week, ChatGPT launches medical version, which is great. I think that's going to be awesome for a ton of people. And the fact that I can now just connect my ChatGPT into an EMR instead of having to go laboriously copy and paste or find some other third party thing to do that kind of connector work for me, like the consumer surplus of that is going to be amazing. That's like my strongest belief is we will see high consumer surplus. But for the businesses, it seems like it does create a very tricky balance where you're like, I want to go do a bunch of cool stuff, but how do I know which of these things will be durable over time? How do I know I'm not just doing R&D for the next generation of the mega platforms that are ultimately going to eat my lunch? How do you guys think about where you want to place your bets, but what is going to get absorbed into the models? versus what, you know, only you can do over a longer period of time. Yeah, I think we are in a fortunate place because ultimately data is an incredibly important asset for all companies. That is what kind of defines and differentiates them. And that is not getting commoditized anytime soon. So as a data platform, we sit in that layer in between the application and the model, if you will. So the way we think about this is how do we help our customers build very high quality products that are catered towards their use cases, which is all powered by their data. And whether any of that can be subsumed by these other platforms, I am sure the shape of shapes of products will continue to evolve. I think we're still in the very early innings of huge transformation. But I also believe intelligence is a... Once these models are out there and there is enough competition and we're seeing enough competition, The dynamics seem to be playing in such a way that is all pro customers and consumers versus these mega platforms. So I do believe the competition will keep things in check, and the opportunity is so massive that that growing pie will also create lots of new opportunities. Obviously, people have to have some place to keep their data. Tell me what's wrong with this theory. If I were to be a skeptic or if I took the perspective of the snowflake bear for a second. A recent experience I had was my company's been a customer of Intercom for a number of years. And I was trying to do just some basic analysis of recent tickets. So I, and they didn't have the dashboard to do what I wanted to do. So I went to their docs. And the docs were 100 pages of docs. It's a full-featured platform at this point, so a lot of docs. So the first thing I did was told a web agent, Hey, go compile all these docs. And it literally went page by page, copied them all into a Google doc in a browser. I ended up with some 600 pages of text. Then I took that to Gemini and said, Okay, hey, there's a lot of repetition in here, but can you streamline that down to what I really need to know? And so it did that fit in the million tokens. So now I've got my consolidated single view of the docs. Then I go to a coding agent. I say, here's what I really want to do is export all my data from Intercom. And that also ended up being just one prompt to work. And then it exported all my data from Intercom and it was able to do the analysis I wanted to do. But then the sort of eureka moment was like, wow, it's never been easier to unplug from Intercom if I want to take my data somewhere else. They didn't really anticipate it being that easy when they created all these APIs. So what prevents the data platforms of today from running into trouble there. In the past, presumably, like, somebody were to say, hey, I'm not happy with you, or I want a better price, whatever. You had some leverage of just, what are you going to do? You're going to pull out all your data? And I'm not saying that would be easy today because I know you guys handle vastly more data than I have in Intercom. But it does also strike me that it's become a lot easier to move things around. It's become a lot easier to understand what it is, especially once you've gone and done all this metadata layering on. So what are the moats? What are the sticking points? Has it changed or will it change?…

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