AI, VC, & Data Insights into Corporate Innovation with Thomas Thurston of Ducera Partners

On this week's episode of Inside Outside Innovation, we sit down with Thomas Thurston, Chief Technologist at Ducera Partners. Thomas and I talk about AI, venture capital, and some interesting data insights into what makes corporate innovation work or not work. Let's get started.  Inside Outside Innovation is the podcast to help new innovators navigate what's next. Each week we'll give you a front row seat into what it takes to learn, grow, and thrive in today's world of accelerating change and uncertainty. Join us as we explore, engage, and experiment with the best and the brightest innovators. entrepreneurs and pioneering businesses. Interview Transcript with Thomas Thurston, Chief Technologist at Ducera Partners Brian Ardinger: Welcome to another episode of Inside Outside Innovation. I'm your host, Brian Ardinger, and as always, we have another amazing guest. Today we have Thomas Thurston. He's the chief technologist at Ducera Partners. He was introduced to us from a mutual friend at Amazon, Kate Niedermeyer, who said you have a driving interest in helping corporate innovators and investors be more successful by unlocking insights from data. So welcome to the show Thomas.  Thomas Thurston: Hey, thanks. Great to be here.  Brian Ardinger: Hey, I'm excited to have you. As I've alluded to in the intro, you're a data scientist, a venture capitalist, focused on this particular space for a long time and a pretty varied background. So, tell us a little bit about yourself and what you do. Thomas Thurston: I like to think of myself as a data scientist who's been in the venture capital industry for almost 20 years now. The idea has always been how can you use. Data, AI, any, any quantitative tools to get insights into what's happening in private markets. So, what's happening with companies that aren't disposing a lot of data that are early stage or otherwise librarial shape environment.  Today at Ducera Partners, it's an investment bank, where I'm Chief Technologist, as you mentioned. The way I would explain to Ducera which may be a little different in that it's kind of a startup investment bank. And the idea is we want to be disruptive in investment banking and really use technology as a backbone to do that. So, through AI, through analytics we build in-house, can we do that? Can we really be disruptive in industry that hasn't seen that much change in its business model for a hundred years? And since the bank was launched about six, seven years ago, we've done over $750 billion in transactions.  We're averaging around a $100 billion a year in deals, and we've done that all with you know, somewhere around 50 people or so, although it's growing quickly. I really do think it's been the technology that's been able to enable us to really change the way we do things. So, I'm proud of that. My story really started a long time ago when I was at Intel in an incubation group just like everyone else. They had a new business incubator, about a dozen or so projects. We were one of those projects and we were starry-eyed, hoping to build a billion-dollar business for Intel. We got our blue badges ready to go every morning. And it's kind of what you might expect the first year or, so it was amazing. We were doing great, and then one year we were super strategic. We got this funding a few years later, we were no longer strategic, and it got shut down instead. Those decisions had nothing to do with us. So, one day someone up in top of the ivory tower thought it was optics for strategic, the next time it wasn't. And I'm pretty sure nobody was thinking about our project when that decision was made to shut it down and everything related to what we were doing. So, I think it just was demoralizing. You give your blood, sweat, and tears to a project. At end of the day, it didn't matter, right? Something completely random blew up your project. And I just remember looking at all these cubicles at Intel and just seeing all these projects just like ours, everyone's smart, everyone's doing their best, everyone's working hard to be innovative and just wondering, does this ever work? I mean, what? Because you know, at the beginning I thought we couldn't possibly fail. We've got this big, you know, like the best of both worlds. Big company, excitement with a startup. Couple years later, you're so grizzled and battle scarred. You're like, can this, this even possible? Because any of these projects ever work. And I wanted to know what percentage of the time projects like this succeed or fail at Intel. And of course, I realized that nobody actually knew. Because like every big company, things get started when shut down all over the place. And it's nobody's job to run around and track it and, and kind of make a database out of it. And if you think about the contrast at Intel, you can measure latency in picoseconds, right? They measure absolutely everything. But when it comes to all the money, I was finding in venture capital and M&A, and new product launches, kind of all this growth, money going to work, there just wasn't much in the way of quantitative metric. It was like every other company; people do their best deal by deal. You win some, you lose some and hey, and no one could say what percentage of the time it worked. So, I just decided to start studying it myself. Spent over a year collecting data on all the product launches, all the deals I could find, to see were any of the variable’s decision makers had in the beginning correlated at all with how those deals performed 5, 7, or even 10 years later. Brian Ardinger: Well, that's the first question I want to ask, because I think you're correct that most people don't track it or, or don't track it well. Why is it so hard to track new innovation and especially, you know, at the earliest stages? Is it because they're used to tracking different types of metrics or talk to me about that. Thomas Thurston: It's something they're all capable of doing. But also, yet I have yet to find a big company that's actually tracking this. Usually as you know, different groups are launching new innovations or products around the company. It's not centralized, so these projects kind of get funded. They come and go. Usually when they go, they go very quietly or they, the team gets reshuffled or something. So, there's no big announcement, and again, it's just no one's job to try to add them all up and track them. Everyone's doing it off in silos, and as you probably have experienced, there's a venture capital group. They're off doing God knows what in their thing and they're kind of behind some kind of closed door.  The M&A group is behind another closed door doing something else. They have different stakeholders, constituency. It's not centralized. But if somebody can kind of put their arms and call it data and actually start to mine it. That's the downside.  Brian Ardinger: And no one wants to talk about failure, especially with an existing company that's making money, et cetera. You know, the last thing you want to do is say, hey, my great idea was a bomb and now what do I do? Versus like in a startup world where it seems to be, if you fail, that's part of the natural process because there are things that fail when you start new things. Thomas Thurston: Yeah. In big companies, especially. We've...