The Exchanges

Q And then that seemed to have evolved towards a greater emphasis on networks. Um, and network effects. Uh, how do, how do you think about, about those, uh, you know, what are some examples of proprietary data assets, and why do they matter?

A So, we came to realize that The only defensibility that you can get with data is defensibility around your own data. If you're getting data from somebody else, it's not defensible, because they can give it to somebody else as well. And even if you have an exclusive contract with them, that exclusive contract's gonna come up at some point, and you're gonna have to, if you've built a valuable business, you're gonna have to pay through the nose for it. So, um, we, we sort of realized that unless you Own your data. Um, you don't really have a defensible data asset. And then we started to look around and, and figure out where the most valuable data sets came from, and we saw these large networks, um, that have gotten built up on top of the internet that, um, create tremendous amount of data. And, uh, and so that, uh, to us started to become You call it an evolution. And I think that's probably the right way to put it. An evolution of our theme. We still very much believe in, in data. Um, you know, let's take Twitter for example. The, the stuff that Twitter's doing around discovery and, um, and who to follow and, and all those search, all those things leverage the data assets that they have. And, um, And they are making the service better and better and better because of that, but that data exists because they have this massive network of people who are contributing to their system. …

AI assessment note: “let's take Twitter for example. The, the stuff that Twitter's doing around discovery”

Q So, you know, I fully realize it's not your investment thesis, but any thoughts on, on big data in general, right? This getting, you know, tons of hype, uh, lots of exciting things, lots of smoke and mirrors to some extent. What, what, what do you think?

A Um, I think that machine learning is, has hit an inflection point, uh, in the past few years, um, artificial intelligence, machine learning, whatever we want to call it, um, To the point where, ah, we're starting to see, ah, these, ah, dreams that we've had for 30 plus years in terms of the potential of AI finally starting to get realized. And I think a lot of that has to do with the fact that we, you know, machines learn from data. And we have so much data now, um, that's available for the, to train these machines Um, that, uh, that we're, I think we're starting to see, you know. I think, like, the, the AI machine learning curve, you know, kind of looked like this for a long time, and maybe even like this. It was, like, very slow and frustrating, but I think it's, like, going like this now. And I think some of that is, um, because of, um, uh, just the massive data sets that are now available. Some of it is because some big companies like Google, you know, have, have just made massive investments in it, and the, And the returns on those investments are now abundantly clear to, you know, investors and the financial markets, and, you know, we're seeing that, you know, um, uh, we're seeing self-driving cars come because Google was able to amass, you know, all these data sets that were required to essentially make it so that you can have a self-driving car. And all of a sudden peop…

AI assessment note: “we have so much data now, um, that's available for the, to train these machines”

Q Um, what, you, you, you've, you know, invested in the companies that have had the most massive, ah, network effects. Any, any lessons learned there that could apply to data networks in terms of, like, how you, how you build those? Do they happen? How do, how do you start them, defend them, build them further?

A Well, I think free is really important. We have a company in our portfolio called Edmodo, which is a real, um, uh, phenomena, and what they did was, ah, it was two, Two guys who worked in just IT group inside a school system, and the teachers were complaining that, you know, the software that the school system was using was horrible. They were using Blackboard or Moodle or something like that, and they just wanted something light and easy to share with their students, share their homework assignments, practice tests, um, lesson plans, reading material and such. And so they basically knocked off Facebook. They literally copied the Facebook newsfeed UI, and they gave it to teachers for free. But they did one other thing that I think was really, really powerful, which is that they allowed teachers, I, I, I think of the architecture as like this and like this, so they allowed teachers to connect to each other, and so eighth grade math teachers would connect to each other in groups, and they would share lesson plans and homework assignments and practice tests and, and those kinds of things, and then they would Drop them down into their class, classrooms. And, ah, all this was free. So today, four years later, five million teachers, seventy million students are using it in the K through 12 system here in the United States. Um, and what's amazing about it is that, ah, they don't charg…

AI assessment note: “Well, I think free is really important.”

Q pitch, and the pitch goes somewhere like this. You know, we're building this great consumer product. It's going to do this and that. It's going to be free. We're going to turn it into a massive network. We're going to collect plenty of data, and then we're going to turn around and sell that data to Hedge funds, advertising companies, any of these. Have you ever seen this actually work?

A Um, I, I, I, I don't think selling the data is the right thing to do. I think the right thing to do is to build services on your platform that take advantage of the data that let the people who might buy the data from you instead come and build businesses And transact on your platform. Good example of that is Facebook doesn't really sell their data, but they provide an advertising system that's powered by their data that lets people, third parties, come onto the system and, and market to their users in hopefully highly targeted, effective ways. I think Twitter's doing the same thing. So that's an advertising version of that model, but you could also create e-commerce versions of that model. You could create, ah, financial services versions of that model.

AI assessment note: “I don't think selling the data is the right thing to do.”