The Exchanges

Q So let, let, let's dive into case text a little bit. What, what do they do?

A So what case text Does, um, let me just give you an example of what it can do. So let's say you're a lawyer and you get a brief from opposing counsel and your client's getting sued. You basically upload that brief into case text and it will tell you every single case that, you know, it'll cite the cases and all the cases that opposing counsel missed, which more than likely are the ones you're going to want to cite, right, in your case. And they also, it was also crowd-based and crowd-generated, so they took the cases and all the citations and the references into those Cases were done really by crowds of students to do what we call a shepardizing in the, in the legal world, to make sure that the reference that they're citing to is the most valid thing that you can actually cite to. So, you know, if I was an attorney today, there's no way I would go to court or, or file a brief without checking it through case text, because as an associate, it makes you look like a rock star to your partner. You know you didn't miss anything, and as a partner, you know your associate didn't miss anything, and you won't Uh, you won't, you won't, you know, sort of be, you know, be disbarred, which in, in law you, that really is a concern. Like, you miss some important things, and unlike other professions, you can actually, like, lose, lose your license. So, um, so CaseX is really great, a phenomena…

AI assessment note: “You basically upload that brief into case text and it will tell you every single case”

Q Great. Um, so K-Stakes is an example of a vertical application of AI, and I know that you have made other investments, like Crowdflower, which are more horizontal, meaning that they apply to lots of different industries. Is that partly how you think about the world, or you're more opportunistic in terms of, okay, those are great entrepreneurs doing something interesting?

A No, I'm pretty thesis driven. So Crowdflower was an earlier investment in AI, and that was really where, Yeah, I did a lot of data modeling in my prior life at P&G. Believe it or not, at Procter & Gamble, the marketing, 80% of the marketing people are engineers, right? So it's a very different world, I think, than at least in the Valley. So we did a lot of modeling, and it was sort of what Trifocta was talking about, where the cleaning and the appending and the adding of data is really the hardest part about writing those models. And so at Crowd, we invested in Crowdflower because we thought, well, this is really this approach where everyone can benefit. And what they're doing is a human in the loop. So no matter which model you're using, they're not 99% .99%, you know, right at this point in time. Really, if you're approaching the high nineties, you know, you're, you're doing pretty well, but what happens to that other 10% that you have to figure out what the answer is? And so an example is like an Uber ticket. For example, a customer complains, they run it through their model, but then 10% of those go directly into Crowdflower, and the crowd figures out how to allocate Um, the decision that needs to happen, and then it feeds it back into the model and makes the algorithm smarter. So in effect, it's actually providing training data for the algorithms, which is what, you know, …

AI assessment note: “No, I'm pretty thesis driven.”

Q One last question from me. You, you mentioned a couple of investments in, in New York. Uh, any thoughts on, you know, are, are there now multiple places where you can start a great company? Uh, does being in the valley, uh, is that still a major advantage?

A I actually think maybe being in New York is an advantage at this point because the noise levels are reduced, right? And, ah, and it's really interesting. I mean, I think we're seeing higher and higher quality companies out of New York. The technical talent is here as well. And so I think, I think it's a great place to be actually. And I just, I lost one of my, I have a, Priyanka is at a company here. I don't know if you met her out in the hallway a bit. She was, she worked with me as a Stanford liaison and, you know, she came out to New York. Seems like all the cool kids are coming out this way. So, so it's a good place to start a company.

AI assessment note: “I actually think maybe being in New York is an advantage at this point”

Q Yes, and so Lending Club was your first investment?

A It was actually, yeah, it was my first investment, so I looked at a couple of hundreds of companies before that, and I kind of what happened is I came on board at Morgenthaler at the time, and that's where we, I started up, And, ah, it was the summer of, of seven, and then I kind of helped them out through my last year of school. Well, then I took the bar for some ungodly reason, so if I ever wanted to be a lawyer in California, I probably could, I guess. And then, you know, came on board after I got done with that, the VC firm planning on starting my own company. I was just going to figure out how the sausage was made and then get them to fund my company. I was working on a couple things. And then Lehman crashed, right? And we had a four hundred million dollar fund, and I'd seen the game before, right? I'd been through the first dot com, and I thought this is the most fabulous time in the world to start in venture capital. We have four hundred million dollars of fresh capital, let's go, right? And we did some great investments that year. So Lending Club, I met Reneau through a friend of mine at Silicon Valley Bank, Shai Goldman, who's also here in New York.

AI assessment note: “It was actually, yeah, it was my first investment”

Q few investments you've made are the strong, uh, data and AI components. I'm curious, um, you know, from the perspective of an investor, especially in the, in the Bay Area, where are we in that cycle of, you know, big data was hot and now people talk about it less, although The reality is probably happening right now. AI is super hard. How do you look at all of this?

A Yeah, I, I'm pretty skeptically, honestly. I, I, I joke with my partners that AI means algorithm involved, right? And so, uh, and so everything's AI right now, and, and some things truly are, right? So Siri, the voice, the voice is definitely the platform for the future. I look at how, I have three little kids, and I look at how they interact with Alexa, and literally we went from an Alexa in one room to everyone got the little dots. And it's in every single room, and they listen to their books, and they ask it questions, and it helps with homework. And it's just, it's phenomenal. And how quickly the kids picked up on that. Like, it was too loud. I'm like, how do we turn that down? And my son Zane's like, Alexa, turn it down. And I'm like, oh, of course, right? And so it's really amazing to how those, how the children just, they just assume, and, you know, the computer's broken if they can't touch it, and it doesn't, it doesn't do something for them. So I do think there are cases like that. Case text was a more recent investment that I had done, um, and that is applying, so my, my legal background coming in finally, um, but really applying AI into the legal world, and so helping lawyers be smarter and get their job done faster, and helping their clients spend a lot less money on legal research that's done over and over and over again. So I do think.

AI assessment note: “I'm pretty skeptically, honestly. I, I, I joke with my partners that AI means algorithm involved”

Q Great. So we talk about, um, law and legal, the legal industry as a vertical. I know you spend a bunch of time in healthcare as well. Any thoughts on data, AI, as applied to healthcare?

A Yeah, probably our biggest data science team at any of my companies is Doximity. And I don't know how many of you all know Doximity. It's, um, it's essentially LinkedIn for doctors. And the way they apply data and their testing and how they message and how they build a network effect and how they build that community is Is pretty amazing. Even, even, even down to just, you know, what the email actually should say and how they figure that out. I mean, one word really does matter. And they have about, I think it's like, 73% of all the doctors in the U.S. are on their platform today. And they're expanding into, like, NP, nurse practitioners and physician assistants and people of that nature. And, and that thesis was really around, um, if you're really going to try to change healthcare. And we have, we sort of, four different healthcare investments that have come through time. It really, you have to be able to access the doctors, and the doctors have to have a platform where they're really talking and sharing ideas and data, and so that was where Doximity came out of.

AI assessment note: “probably our biggest data science team at any of my companies is Doximity.”

Q And especially for data and AI businesses, do you look for certain types of teams? Do, do founders need to be technical, for example?

A You know, I think for data and AI, it's not that they need to be, but they often are technical, like technical or product. I really like, you know, especially the product founders, but I also like founders with a certain degree of humility who know what they don't know and know who they need to hire, right? And so, you know, one of the harder things is getting founders to really reach out and hire those A-plus people and really go for it and have the confidence to go and do that. So I look for founders who have, like, Aimed high and surrounded themselves with amazing team members and, and talent. And, and advisors as well, who, they've really reached out to figure out, you know, who can help them and who can help them get, you know, the, you know, get further to the fastest, essentially.

AI assessment note: “it's not that they need to be, but they often are technical”