 from our studios in the heart of Silicon Valley, Palo Alto, California. This is a CUBE Conversation. Hey, welcome back, everybody. Jeff Frick here with theCUBE. We're in our Palo Alto studios for a CUBE Conversation. We're excited to have a many-time CUBE alumni. He's been at all types of companies. He's moving around. We like to keep him close because he's got a great feel for what's going on. And now he's starting a new adventure. So I'm really happy to welcome Alan Cohen back to the studio. Alan, great to see you. Hey Jeff, how are you? In your new adventure, let's get it right. It's the DCVC, your partner. So this is a... I'm on the venture side. I've gone to the dark side. You've gone to the dark side of the money side, but this is not a new firm. Light side of the dark side, yeah. So what's special about this? A ton of money in venture right now, but you guys kind of have a special thesis. So tell us about it. Yeah, and I think you've spoken to Matt and Zach, my partners in the past. So DCVC has been in the venture business for about a decade. And the first five years of the fund was very much focused on building a lot of the infrastructure that we kind of take for granted. Now, things that have gone into VMware and into Citrix and into AWS and hence the data collective, the DC side of DCVC. Really, the focus of the firm in the last five years and going forward is in an area that we call deep tech, which think about more about the intersection of science and engineering. So less about how do you improve the IT infrastructure, but how do you take all this computational power and put it to work in specific industries, whether it's addressing supply chains, new forms of manufacturing, new forms of agriculture. So we're starting to see all that, all the stuff that we've built out for the last 20 years and really apply it against kind of industrial transformation. So, and we're excited. We just raised the $725 million fund. So we've got a little bit of ammunition to work with. Congratulations. So it's fund five, it's your eighth fund. And really it's consistent with what we're seeing all the time about applied AI and applied machine learning. It's not the generic AI company that's going to build AI. It's more the where are you applying AI within an application? Where are you applying machine learning within what you do? And then you can just see the applications grow. Exactly right. So are you targeting specific companies that are attacking a particular industrial focus and just using AI as their secret sauce or using deep tech as their secret? All of the above, right? So like when I think about DCVC, so don't think about IOPS or throughput or bandwidth. Think about rockets, robots, microbes, building blocks of effectively of human life and of materials, and then applying computational power and AI against those areas. So a little bit different focus. So it's the intersection of really smart computer science. But I'll give you a great example of something that would be a little bit different. So we are investors and very active in a company called PivotBio, which is not exactly a household name. PivotBio is a company that is replacing chemical fertilizer with microbes. And what I mean by that is they create microbes. So they've used all this big data and AI and computational power to construct microbes that when you plant corn, you insert the microbe into the planting cycle, and it continuously produces nitrogen, which means you don't have to apply fertilizer, which fertilizer today in the US is $212 billion industry. And two things happen. One, you don't have all of the runoff doesn't leach into the ground, the nitrogen doesn't go into the air, and the crop yield has been between about 12% and 15% higher. So it's interesting though, the food industry is such a great place, and there's so many opportunities both in food production. This is like beyond chemical fertilizer instead of being on meat. Oh, but it's great, but it's funny because you think of GMO, right? So all food is genetically modified. It took a long time in the past because you had to get trees together and you had to replant the pretty apples and throw the old apple trees away. Because if you look at an apple today versus an apple 50 years, 100 years ago, they're very, very different. And yet when we apply a man-made kind of acceleration to that process, then people kind of push back, whoa, this is not nature. So I'm just curious, and it's like the microbe, and actually it is nature, right? So it is nature, but there'll be some crazy person who says, wait, this is not, you're introducing some foreign element into this process. You could take potash and pour it on corn, or you could use a microbe that creates nitrogen. So which one is the chemical and which one is nature? Right, that's why I think it's a funny part of that conversation. But it's a different area. So you guys, look, you guys spent a lot of time in the road, you talked to a lot of startups, you talked to a lot of companies, you actually talked to venture capitalists, and most of the time we're working on the $4 trillion IT sector, not an insignificant sector, right? So that's globally, that's about the size of the economy. Manufacturing, agriculture, and healthcare is more like 20 to $40 billion of the economy. So what we've also done is open the aperture to areas that have not gone through the technical disruption that we've seen in IT now in these industries. And that's why I joined the firm, and that's why I'm really excited, because on one hand, you're right, there is a lot of capital, you mentioned what we were talking about before, there is a lot of capital in venture, but there's not as much targeted at these areas. So you have a larger part of global economy and then a much more specific focus on it. Yeah, I think it's such a, it's kind of the features here, kind of the concept, because no one knows the rate of which tech is advancing across all industries currently. And so that's where you wake up one day and you're like, oh my goodness, look at the impacts on transportation, look at the impacts on construction, look at the impacts on healthcare, look at the impacts on agriculture. So the opportunity is fantastic and still following the basic ideas of democratizing data, not using a sample of old data, but using real-time analytics on whole data sets, all these kind of concepts that come over really, really well to a more commercial application than an IT application. Yeah, so Jeff, I'm kind of like looking over your shoulder and I'm looking at Tom Friedman's book, The World is Flat. And if we think about all of us who've been kind of working on the internet for the last 20 years, we've done some amazing things like we've democratized information, right? Google is a fairly powerful part of our lives. We've been able to allow people to buy things from all over the world and ship it. So we've done a lot of amazing things in the economy, but it hasn't been free. So if I need a 2032, CR 2232 battery for my key fob for my phone and I buy it from Amazon and it comes in a big box, well there's a little bit of a carbon footprint issue that goes with that. So one of our key focuses in DCVC, which I think is very unique, is we think two things can happen is that we can deal with some of the excesses of the economy that we've built and as well as unlock really large profit pools at the end of the day. It has the word venture, but it also has the word capital. So we have limited partners, they expect returns. We're doing this obviously to build large franchises. So this is not like this kind of political social thing is that we have large parts of the economy that are not sustainable. And I'll give you some examples. Actually, Jeff Bezos put out a pledge last week to try to figure out how to turn Amazon carbon neutral. Pretty amazing thing from the was the richest person now the half the richest person in the world, right? But somebody who's completely transformed the consumer economy as well as computing economy. And soon transportation. So people like us are saying, hey, how can we help Jeff meet his pledge? And there are things that we work on like next generation of nuclear plants. Like, we need renewables, we need solar, but there's no way to replace electricity. The amount of electricity we're gonna need to run our economy and move off of coal and natural gas, right? So being able to deal with the climate impacts, the social impacts are gonna be actually some of the largest economic opportunities. But you can look at it and say, hey, this is a terrible problem, it's ripping. People across, I got caught in a traffic jam in San Francisco yesterday up on the top of the hill because there was climate protests, right? And so I'm not kind of judging the politics of that. We can have a long conversation about that. The question is how do you deal with these real issues? And obviously how do you deal with them profitably and ethically? And I think that's something that's very unique about DCVC's focus and the ability to raise probably the largest deep-tech fund ever to go after it means that a lot of people who back us also see the economic opportunity, right? At the end of the day, they're a lot of our limited partners, our pension funds in universities, like there was a professor who has a pension fund who's gotta retire, right? So a little bit of that money goes into DCVC, so we have a responsibility to provide a return to them as well as go after these very interesting opportunities. So is there any very specific kind of investment thesis or industry focus or kind of subset within heavy lifting technology and science and math? That's a real loaded question. I'll try to unpack it a little. So we like problems that can be solved through massive computational capabilities. And that reflects our heritage and where we all came from, right? You and I and folks in the industry. So we're not working at the intersection of lab science at a university, but we would take something like that and invest in it. So we have a lot of investments in agriculture and healthcare. We are surprisingly one of the largest investors in space. We have investments in rocket labs, which is the preferred launch vehicle for any small satellite under two and a half kilograms. We are large investors in planet labs, which is a constellation of 200 small satellites. Our investors in compel of space. So we like space and it's not space for the sake of space. It's about geospatial intelligence, right? So planet labs is effectively the search engine for the planet Earth, right? I mean, it's effectively Google for the planet Earth. And all that information could be fed to deal with housing with transportation, with climate change. It could be used with economic activity, with shipping. So we like those kinds of areas where that technology can really impact an industry. So we're not limited, but we also have a bio fund. So we have, we like agriculture instead of synthetic biology types of investments. And we've still invested in things like cyber. We invest in physical security. We're investors in evolve, which is the lead system for dealing with active shooters and venues, as well as Fordham, which is a drone security company. But they're all built on AI and mass computational power. I'm just curious. You probably don't have an investment in it. If I'm sure you have a point of view, because you got a point of view of most everything on quantum. You know, we just hear all this little buzz about quantum, you know, Accenture opened up their new innovation hub in the Salesforce Tower of San Francisco and they've got this little dedicated kind of quantum computer space. And, you know, regardless of how close it is, you know, there's some really interesting computational opportunities slash challenges that we think will come with some period of time. So we have quantum encryption and, I'd love to hear about that. So we have multiple quantum investments. We're one of the lead investors in Rigetti computing. Okay. And in control Q down in Australia. No, we like quantum. Now, quantum is a emerging area. Like it's, we're not quite at the x86 level of quantum. We have a little bit of work to get there, but it offers some amazing, you know, capabilities. One thing that also I think differentiates us, and I was listening to what you were saying, is we're not afraid to go long. You know, a lot of our investments are going to be between seven and 15 years. And I think that's also, it's very different. If you follow the basic economics adventure, most funds are expected to be about 10 years old, right? And in the first three or four years, you do the bulk of the preliminary investing, and then you have reserves for additional, you know, the big winners emerge so you can continue to support the companies. Some of ours are going to go longer because of what we do. And I think that's something very special. Now look, we'd like to return in the life of the fund, of course. I mean, that's our fiduciary responsibility, but I think things like quantum, some of these things in the environment, they're going to take a while, and our limited partners want to be in that long ride. Now we have a thesis that they will actually be bigger economic opportunities, they'll take longer. So by having a dedicated team, dedicated focus in those areas, that gives us, I think, a unique advantage. One of the things when we were launching the fund that we realized is we have more people that have published scientific papers and started companies than MBAs in the firm. So we are a little bit, you know, we're a little geekier than that. That's good. I was at a party one time when I was talking to this guy. Yeah, we're not the best people at parties. We know, but it's funny, the guy was, he was a BC in medical, medical tech, and I just asked him, like, so are you like a doctor? Did you work at a hospital, or you worked at a university that doesn't, you know, I was an investment banker on Wall Street. I'm like, oh, that's something to make money move, but do you have the real world experience of being in the trenches where some of these applications are being used? But I'm also curious, where do you guys like to come in? A, B, C, what's your sweet spot? Well, traditionally we have been a C in series A investor. We like to be early. Okay, and you like to lead or follow on? Everybody likes to lead, right? Right, right, right. You know, right, and you have to learn how to, sometimes you lead, sometimes you follow, so we, you know, we do both. Okay. There are increasing, because of the size of the fund, we will have the opportunity to be a little bit more multi-stage than we traditionally are known for doing. Like, for example, we were seed investors in little companies like Confluent and Elastic that worked out okay, but we were not later stage investors in companies like that. With the new fund, we're more likely to also be in the later stages as well for some of the big banks. But we love seed, we love pre-seed. We like three guys and a dog, right? If they have a brilliant- It's tough, though, to put 750 to work when you're investing in the three guys and a dog, unless it's the one that runs and runs and runs again. You know, we do things we call experiments. We'll just, you know, we also have a very unique asset. We don't talk a lot publicly. We have a lot of really brilliant people around the firm that we call Equity Partners. So there's about 60 leading scientists and executives around the world who are also attached to the firm and actually have a financial stake in the firm who work with us. That gives us the ability to be early. Now clearly, if you put in a $250,000 seed investment, you don't put in the same amount of time necessarily as if you just wrote a $12 million check. What's interesting, that's the traditional wisdom. I think we actually work out just as hard on those. And do you have any formal relationships within the academic institutions? How does that work? Well, I mean, like we work like everybody else with Stanford and MIT. I mean, we have many universities who are limited partners in the fund. You know, I'll give you an example of, so we helped put together a company in Canada called Elliman AI, which actually just raised $150 million. And they, the founder of that company is a co-founder, is a fellow named Yashua Benjio. He was Jeff Hinton's PhD student, him and Eva Lacun. These are the guys who invented neural networks in AI. And this company was built at a Yashua's position at the University of Montreal. There's 125 PhDs in AI that work at this firm. And so we're obviously deeply involved now with the Montreal AI scene. By the way, Montreal is one of the best AI scenes in the world and cool food. Didn't know that. Yeah. And well, because of Yashua, because everybody came out of his lab. So I think, yeah, I think so, you know, we've worked with Carnegie Mellon. So we do work with a lot of universities. What I would say is universities work with multiple venture firms. Such an important pipeline for really smart, heavy duty. Totally. Math and tech guys. All right, smarter than me, that's for sure, yeah. You always want to, you never want to be the smartest guy in the room, right? Or you're in the wrong room is what they say. You know, you said there's probably an equivalent of venture, they always say you should buy the smallest house in the best neighborhood. Exactly. I was able to squeeze in DCVCs. I'm like the least smart technical guy in the smartest technical neighborhood. There you go, that's the way to go. All right, Alan, well, thanks for stopping by and we look forward to you bringing some of these exciting new investment companies inside the Cube. Great, thanks for the time. All right, he's Alan. I'm Jeff, you're watching theCUBE. We're in our Palo Alto studios. Thanks for watching. We'll see you next time.