# Who Wins in AI; Startup vs Incumbent, Infrastructure vs Application Layer, Bundled vs Unbundled Providers From 150 LP Meetings to Closing $230M for Fund I; The Fundraising Process, What Worked, What Didn't and Lessons Learned with Tomasz Tunguz 20VC · Apr 21, 2023 · 53 min · 11,981 words Speakers: Tomasz Tunguz, Harry Stebbings Source: https://www.996.fm/episodes/20vc--ep-d855aad3/ ## Cold open **Tomasz Tunguz** [0:00]: I think at the foundational model layer, that's a big boys game or a big girls game. The odds of success are gonna be significantly higher at the application layer because the diversity of needs there is greater. We're faced with a technology that could actually replicate the postwar surplus out of World War two. I think Google had a rude awakening where, to some extent, they developed in house but ignored. So it's a classic innovator's dilemma. **Harry Stebbings** [0:21]: This is 20 VC ## Intro **Harry Stebbings** [0:22]: with me, Harry Stebbings. Now the last time I had Tom Tunguz on the show was seven years ago Since he's become a dear friend, and last week, he announced his new $230,000,000 fund, Theory Ventures. No one deserves this more than Tom, and it made me so, so happy to see. Prior to founding Theory, Tom spent fourteen years at Redpoint as a general partner, where he made investments in the likes of Looker, Expensify, Monte Carlo, Dune Analytics, and Kustomer to name a few. But before we dive into the show today, ## Sponsor read **Harry Stebbings** [0:48]: you've heard me talk about Coda. Coda is the doc that brings it all together and how it can help your team run smoother and be more efficient. I know this because Coda helps me. At twenty VC, we use Coda for all of our research for every episode. So all team members essentially can work on the schedule all in one doc seamlessly built by Coda. And here's how Coda can help your team run smoother and be more efficient. 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I get to speak to founders all day, and I know how crucial it is for them to have the right financial stack. Brexit gives you fast access to a high yield business account where you can safely store and move your cash while getting up to 6,000,000 in FDIC protection. Protection. Lately, it's been all too clear how important that is. Plus, you get high limit corporate cards, easy expense tracking, and automated bill pay. To learn more about the all in one financial stack for startups, visit brex.com/20vc. That's brex.com/20vc. 3210. **Tomasz Tunguz** [3:30]: You have now arrived at your destination. ## Conversation **Harry Stebbings** [3:34]: Tom, it is such a joy to have you on the show. I just checked, and it is 2016 when you were last on the show, so seven years ago. I've missed you dearly, my friend, but thank you so much for joining me today. **Tomasz Tunguz** [3:45]: Thanks for having me back, Harry. I can't believe it's been seven years. Time flies. Look at you. Huge audience. New funds. Look how far you've come. It's incredible. **Harry Stebbings** [3:52]: That is so, so kind. But I wanna start with obviously, we recently founded Theory. Such an exciting time. I wanna dive in. First, why did you decide to leave Redpoint, and why did you decide to start on your own? **Tomasz Tunguz** [4:03]: Yeah. I had a great time at Redpoint. I was there for fifteen years, learned from many wonderful people. And after that amount of time, I decided that after seeing so many founders start companies that I really wanted to start one of my own, when I was about 17, I started a little company. And over the last fifteen years, maybe more, twenty years, I've watched all these startups grow. I wanted to have that feeling for myself, and I also wanted to experiment a bit more. You know, everybody has an idea about how they wanna create their own business, and I've been a student of startups for a long time. And so I really wanted to build a venture firm in a slightly different way. In September of last year, jumped in, and then we were off to the races. **Harry Stebbings** [4:37]: You mentioned that, like, the learnings in the fifteen years. If there are one or two big takeaways for you from your time at Redpoint, I'm asking you to distill fifteen years of lessons into a short sound bite, but what would they be, and how does that influence how you think about building theory moving forward? **Tomasz Tunguz** [4:53]: Yeah. So I really believe in thesis driven investing. And what that means is going deep in a space and spending six, nine, twelve months researching it and really understanding it. As a board member, I will never know about as much about a space as a founder. But if I can deeply understand the space, then I think I can be a very helpful board member. That's one of the reasons why theory is called theory. I really believe in concentration. The industry is governed by a power law. And the more dollars you can have closer to the y axis so to speak on the power law, the better your returns will be. And so I wanted to set up a firm that was set up for thesis driven concentration. **Harry Stebbings** [5:27]: That was the whole idea. Listen, portfolio construction is what gets me out of bed in the morning. So when we're speaking my language, I wanna start there. This is like a process that's shrouded in much opacity, and we just see fundraisers announced. So I wanna talk about the fundraise. How many meetings did it take to close out the fund, my friend? **Tomasz Tunguz** [5:44]: It took about a 150 LP meetings. The fundraising market was a very challenging one over the last couple of months, I'd say. But it took about meeting about a 150 LPs. I I thought about it just like a regular software sales process, right, where sales assisted 15% close rate, so built up a funnel and a pipeline that was large enough with 15 close probability that we can hit our target. And we were very lucky where we exceeded the target. We raised the hard cap and ended up at about 230. **Harry Stebbings** [6:11]: So a 150 meetings. I'm fascinated. How many of those did you know before the raise itself? **Tomasz Tunguz** [6:16]: I probably knew about 40 of them. 40 to 50. **Harry Stebbings** [6:20]: How many of them committed having not known you before? Because I always say invest in lines, not dots, and the importance of building that relationship outside of the fundraise. How many committed having not known you? **Tomasz Tunguz** [6:31]: So about 50% of the capital was from new relationships, about half of the capital. **Harry Stebbings** [6:36]: Can I ask, for the starting checks, they're the hardest to get? When you think about the strategy there, did you go for large institutional anchors first, or did you go for the friendlies who are much more likely to say yes? **Tomasz Tunguz** [6:48]: I went for the large institutional anchors. I had some relationships there. So the LPAC has five members, if I could fill two members of the LPAC right out of the gate, then that would assuage a lot of concerns from newer LPs because they were institutional backers from the beginning, and they were wonderful. They took many reference calls on my behalf and gave me a lot of advice through the process. It's kind of like if you think about finding a lead for a series A or a series B, if you can find that lead who then does the diligence and then will talk to everybody else who comes in and guides you, It sets up the process really well. It's a little bit different because most venture firms are basically large party rounds. It's just the number of investors you're talking about 15 to 25, 30 sometimes. And so it doesn't really have that dynamic of a lead, but the LPAC is basically the limited partner advisory council. The board is the closest thing that I think you can get to, unless you have a super concentrated LP base. **Harry Stebbings** [7:36]: Did you have a limit on check size? Often when I was raising, people were like, oh, don't let them invest more than 20% of the fund. Did you have a limit on how much they could invest as a percent of the fund? **Tomasz Tunguz** [7:46]: I did. Yeah. I think the largest LP is no more than 12% of the fund. So there's different theories here. So the one of the wonderful things in raising this fund was I got to see startup land in all its beauty and glory. The number of people who reached out who I wouldn't have thought to help and talk about their journey and their experience and their fund construction was amazing. And so I learned about funds where a $200,000,000 fund with 80% of the capital from two LPs was super concentrated. And then there are other funds that are the opposite where it's just lots of small checks, and then you have people in the middle. Having that breadth, it was just absolutely eye opening to understand that there are many different ways of creating a venture firm, at least on the capital formation side. **Harry Stebbings** [8:22]: I'm glad you went with the 12%. I think diversification is important. So I'm thrilled that you went for the latter. What did you go for on the materials side? Did you send pitch decks to everyone beforehand? Did you have data rooms? How did you think about getting the right materials in place for the race? **Tomasz Tunguz** [8:38]: I prepared a data room. There was a track record in there, a pitch deck, a bio, some of the blog posts that I had written, some metrics that I put together. So I I set up a, like, a brief and a bio, and that was theory one. That was the outbound email. If I had been introduced, I would send them my bio and then the deck. The idea was to use those materials as prequalification. So some LPs prefer not to invest in solo GPs. Different LPs have different mandates. They might only invest in US. They might prefer early stage, later stage. And the idea was to qualify just like an SDR would. And so those briefing materials, that was the entire purpose. And I used the Docssend, and I didn't allow downloading because I wanted to understand where people were in the pitch deck, where they were stopping. And that informed the way that I would pitch them if they decided that they wanted to meet. **Harry Stebbings** [9:22]: That's so interesting. So I always have this contrarian view, and I always advise people to not send the deck beforehand. And the reason I say that is because they look for a reason to say no. They'll go, oh, well, actually, he's thesis driven and we want a generalist. Oh, he's only focused on North America. The truth is they might have those preconditions before, but when they meet you and they see how brilliant you are, all of those can go away. And so don't give them a reason to say no before they have the chance to hear how inspiring and brilliant you are. **Tomasz Tunguz** [9:49]: I think that's brilliant wisdom that I didn't follow. **Harry Stebbings** [9:53]: Do you know what I mean? And then afterwards, once we get them brought in, you can be like, hey, I'd love to show you more. I'm gonna send you our deck as a follow-up. And it also gives you a reason to follow-up more than one would normally have. **Tomasz Tunguz** [10:05]: There's a lot of wisdom there. The other reason is if you're raising a first time fund, it's really a bet about the individuals more than it is anything else. And so to send the materials ahead of time, maybe you don't have the opportunity to let yourself shine. So I could see that. **Harry Stebbings** [10:16]: Tom, what was the biggest reason that people said no? You mentioned the solo GP element there. What was the biggest reason people were like, not for us? **Tomasz Tunguz** [10:23]: Yeah. So solo GP is one. There's key person risk, and so some LPs are just not comfortable having a single person be the general partner. The other challenge was timing. I was raising during a time when the public markets had been down, but the private market valuations had remained elevated. And so the combination of those two put a lot of LPs in a place where they didn't really understand the nature of their portfolios. If you thought you were fifty fifty public private and then the publics fell by half, all of a sudden you were three quarters private, one quarter public. But the private market was going to be written down but hadn't been written down. And so one of the biggest reasons was just need more time in order to understand where my portfolio is so that I can figure out allocation in the future to this asset class. **Harry Stebbings** [11:02]: Which LP class did you predominantly raise from? Because if you enter, like, endowment funds in particular, that was a big scene that was troubling for a lot of them. And, also, they have mandated net outflows because they have scholarships and maintenance for sites. Was it predominantly endowment funds? How did you think about the different LP classes? **Tomasz Tunguz** [11:19]: Barry Eggers from Lightspeed, he has a great blog post on the ideal construction of a venture fund. And he talks about, ideally, you wanna have about 30% fund of funds, ideally 30% endowments and foundations, and then 10% pension funds, health care plans, etcetera. That was the advice that I got from a handful of other LPs. And so that was my sort of mental model when I went out. And the ultimate LP base is roughly that. It's predominantly US. **Harry Stebbings** [11:43]: First close, second close, final close. How did you approach the closing mechanisms? **Tomasz Tunguz** [11:48]: There is this sort of aura around a first and only close. No one really explained it to me. I talked to one friend who is an executive at a publicly traded company raised a venture fund, and he said he had 15 closes. And so every time an LP would commit, he would have a close. And his perspective was, somebody signed up. It doesn't cost me anymore to to close them and have them wire, and we'll just keep going. And then there other investors who said, ideally, having a single close means you raise from a position of strength. And we had a single close, but the purpose of a close date is just to drive people in unison to a cadence that you're trying to set. There's nothing magical about it. There's nothing terrible about having multiple closes. It's just a way of organizing a particular process. That close date is drawn out of thin air, and it's driven by how strong of an auction you can develop and what your LP relationships look like. But it's a vanity metric for VCs. **Harry Stebbings** [12:38]: I think one of the biggest mistake managers make actually is they get a load of people who say yes. They don't close them and they leave them hanging. And then a month later, they come back and say, hey, Tom, you committed to my fund, and you were like, oh, I forgot about that. I actually kind of allocated the money elsewhere, and it was actually two months ago now. That happens a lot, which is don't let it go stale. I would say close as soon as you can, as fast as you can to not let it go stale. So totally with you. And it shows momentum. **Tomasz Tunguz** [13:05]: Right? If somebody asks, like, how much have you closed? Do you wanna show a level of progress? And it gives you a reason to come back to people, or or like you said before, a reason to email people. So I think this mystique around the single close is completely misplaced. **Harry Stebbings** [13:15]: Did you do anything to drive urgency in the LP base? Because as you said, the single close is one way to do it in terms of ensuring that people move faster downpipe. Did anything else work for you in terms of just ensuring there was efficiency and urgency in the process? **Tomasz Tunguz** [13:30]: Every time I had another verbal commit, I emailed the LP base, and I I gave them the update. I wrote this blog post a long time ago talking about when you're fundraising, what you wanna convince people of is inevitability, that the company or the fundraising round, its positive conclusion is inevitable. And so any data point that you can provide to investors that supports that is hugely helpful. **Harry Stebbings** [13:51]: When you say, hey, this person just committed or we just got another great institution. For me, as someone who's already committed, I'm like, oh, actually, Tom's gonna be a winner. I should introduce him to more people. Do you see what I mean? Totally. Absolutely. Exactly. So that was what I did. I was very deliberate about that. On review, what would you say worked with your race that you'd do again? And what would you say did not work and you would change for the next time? **Tomasz Tunguz** [14:15]: Yeah. So after I raised capital, I went and I talked to I should have done this before. But I went and I talked to some of the most sophisticated capital formations people. I just asked them how they do their job. And it was really interesting because the really sophisticated fundraisers, they're always in market. They're referencing LPs. They are building pipeline, and that's a full time job. And so I think one of the things that's really important is building long term relationships that worked really well for me. So I was really happy to have a lot of long term relationships that I could lean on. That was a really big deal. Things that didn't work so well, there are different geographies where LPs as a whole are more conservative, and it took me a while to appreciate that. And so I probably spent more time traveling than I should have. And so next time, there'll just be longer lead times on some of those geographies. **Harry Stebbings** [15:00]: Did you find in person worked much more effectively than remote calls in terms of conversion and closing or actually not? No. **Tomasz Tunguz** [15:09]: No. There's no correlation. Boy, I'd have to go back and look, but maybe I wanna say, like, a quarter to a third of the LPs I only met after they committed in person. And that's probably an overhang from COVID where a lot of funds were raised entirely virtually and people are comfortable. **Harry Stebbings** [15:23]: I actually the thing that I do is every week I meet two new LPs, and with each LP meeting, I say, hey, Tom. I love this discussion. And they're one to two other great LPs that think like you and you think I'd have a great discussion with. And they go, oh, you've gotta speak to Satish and Logan. Oh, that'd be fantastic. Would you mind making the intro? I'll send you a blurb now that you can forward. Oh, of course. Super happy to. And the flywheel is self fulfilling. You're a machine, Harry. You now have this breadth of experience raising Theory in a pretty freaking hard market. What would you advise managers going out to raise, having had the experience you have done with Theory? **Tomasz Tunguz** [16:01]: The point of LP diligence that was new for me during the process was the business model. I think in the last eleven, twelve years where we've had this incredible bull market, the portfolio construction hasn't mattered as much. But the volume of questions that I received consistently from LPs about what are your assumptions on number of seats, number of a's, the fatality rate, the multiples on those, like, how does that compare to the standard venture capital distribute? Like, the number of questions that I got about that, I think, suggests that it's really important to have a business model in your deck. That that, I think, has changed just as a result of the cost of capital increase. **Harry Stebbings** [16:33]: I mean, it's sad that it wasn't always there given the fact that Well, well, it is. **Tomasz Tunguz** [16:37]: It's reflecting on the broader venture capital ecosystem. In 2008 or remember whenever we were met, I mean, at the series a, we were looking at financial plans, and we were putting together financing rounds that were a function of the capital into the business. And then, whatever, in 2012, all of a sudden, it was not about fundamentals anymore, and it was really just about access. **Harry Stebbings** [16:55]: I love that discussion. And so I wanted to dive into it because $230,000,000, okay, that's the fund size. Why did you decide $230,000,000 was the right amount to raise? **Tomasz Tunguz** [17:04]: It was all about portfolio construction. I ran a lot of math in order to figure out using historical venture data. I ran Monte Carlo simulations for optimal portfolio construction. **Harry Stebbings** [17:12]: Help me understand. How many companies, how much for initial, how much for reserve? It's **Tomasz Tunguz** [17:18]: about 12 to 15 portfolio companies, significant concentration, so you probably have 40 to 50% of the fund in the top three holdings, maybe more. It's an unusual portfolio construction. Monte Carlo Simulations spits out a couple of different dominant strategies, and this is one of them. And this is the one that aligned with when we talked at the beginning about the way that I like to invest about being thesis driven and really understanding the space. If you go deep into space and you can understand it, then ideally, you're in a place where you have a lot of conviction and you can keep investing and keep supporting a company. I also think in in a venture environment that's going to be significantly different this ten years, over the last ten years, setting up a venture firm to be able to consistently invest behind its companies should provide founders a bit more comfort from their financial partner. **Harry Stebbings** [17:59]: So I similarly did the math on Monte Carlo's, and I found that at 23 companies, you get something like 82 to 84% of the benefits of diversification. What you're saying is essentially with deep thesis and deep thinking and a lot of time, you need less diversification because your ability to pick is significantly better. Correct? That's exactly right. And so we're doing series A's. What's the check size per company estimate? **Tomasz Tunguz** [18:24]: Yeah. It's about eight to 12 initially. **Harry Stebbings** [18:26]: Eight to 12 initially. Is the fund big enough given how large AI machine learning rounds are today, being 30 to 50,000,000 on a pre seed or a seed as we're seeing quite often now? **Tomasz Tunguz** [18:40]: We can flex. We're not in a position to be able to lead a $50,000,000 series A. We could co lead, so that's one way of doing it. And if you're raising a $50,000,000 series A, you probably do want it from I would say you probably do want it from two different VCs. Because we're so concentrated, we can focus our resources where we have the most conviction. **Harry Stebbings** [18:55]: How do you think about the decision on doubling down? You said there are about kind of three companies could be, say, 50% of the capital base. What does that conviction building process look like to putting that much capital behind one of the three? **Tomasz Tunguz** [19:08]: It's a lot of diligence. We'll spend six, nine, twelve months researching a space. Like, one of the themes that we have is the decade of data. So I've been investing in lots of different data companies for a long time. So one component is just really understanding the market, understanding the buyer base, the different segments, their needs. Another component is benchmarking companies. I've been doing that for more than ten years. I've got a pretty significant database of data points there. So just understanding on a relative basis what is the ultimate performance. A third part is understanding what the exit markets look like in the entry prices and what is a reasonable multiple expectation over what period of time. **Harry Stebbings** [19:37]: On the exit market analysis, I go back and forth on, is it worth doing because it's so variable? You could look back on the prior twenty four months and say, it could be that or it could be today or it could be way, way worse. We can't project out seven, ten, twelve years. Is it valid doing it? Okay. So the historical forward multiple is about **Tomasz Tunguz** [19:56]: five x. It's a little higher than us, about 5.5. And in the heyday of quantitative easing, the top quartile companies are trading at 40 times. And today, it's about maybe six. And so you can't go into a company today and say, okay. I'm gonna project a 20 times forward multiple on this company at the time of IPO if it's at a 100,000,000 growing at 70%. You just can't. I would say that's irresponsible because it's just completely unrealistic. If you spent the majority of your time in venture during a time when you've had those kinds of multiples, you need to say check on what do you think your return expectations are going to be given that it is a 4% ESOP employee stock option pool dilution by year and the dilution created by other venture rounds. Going through that discipline, I think, is as much just like I said, particularly for working in a team, it's just a really important discipline step. There's this awesome book called Super Forecasters that, Gadam Tedlow wrote, and he talked about Enrico Fermi who created the atomic bomb, or was one of the team for the Manhattan Project. And Fermi had this way of thinking, which was all about conditional probabilities. It's called Fermization. The idea is, like, just enumerate the conditional probabilities. In order for this generative AI company to succeed, the first thing it needs to do is hire a PhD team. Okay. What are the odds I think they can that? Then they need to raise a series A. Okay. What is the the base rate for raising a series A from seed is about 60%? Then they need to raise a series B. Base rate is 50%. Then they need do this and this. And you put it all together, then you tie it to your expected value, and you come out with a big range of what you think the ultimate outcome can be. And each company, it's going to be different. A marketplace has to do supplier acquisition, supply side acquisition, and demand side acquisition. And so, like, that framework, at least for me, really helps me to think about what are the two or three key issues or questions that a company needs to answer over its timeline or its or it's lifetime, how do those odds change? And ideally, they improve, and the more that they improve, the more comfortable one ought to be in concentrating. **Harry Stebbings** [21:40]: I think it's Philippe Blason or one of the Lafonts says that if you know a market better than anyone else, you can pay a higher price than anyone else because you know more about it than anyone else. My question is to you, do you agree with that statement? And how do you think about your own price sensitivity? **Tomasz Tunguz** [21:57]: I think it's true because if you know more about a market, the range of expected outcomes is far more narrow, which means your certainty in making a bet is better. The result of that should be you should be willing to pay a higher price. The more you know that an option is in the money, the more valuable it is. I agree. Up two point. **Harry Stebbings** [22:12]: How do you think about your own price sensitivity, mate, ownership wise? Do you need 10%? Do you need 15%? How do you think about ownership sensitivity on a per company basis? **Tomasz Tunguz** [22:21]: Yeah. So I think the way I put it is it's important for us to have meaningful ownership because we're so concentrated. The idea is because we have such a small portfolio, we can spend a significant amount of time with each portfolio company. So ownership matters. Ownership also matters, I think, if with a form of returns. We wanna have significant ownership. And the idea with the firm is that you don't need to have significant ownership out of the gate, but you can build a position over time. **Harry Stebbings** [22:43]: And in terms of, like, multi round investing, how do you think about it? Can you do a 5% ownership on a and then get five more at the b, five more at the c? Like, how do you think about cross cycle investing as a bit **Tomasz Tunguz** [22:54]: of Yeah. So that's a tough configuration just because the dollar amounts that you're talking about just go up so significantly. Right? 5% at the a, it's a hot a. So you're probably talking whatever it is, five at a 100. And then the next round might be 200 or 300 and sort of buy another 5%. You can do the math. I think about it a bit more as, can you get 10 at the seed? Maybe you can buy another 10 to 15% at the a, and then buy another, say, 5% at the b. **Harry Stebbings** [23:16]: With the concentration on a per company basis, to have such concentration, you also have to not do pro rata or not concentrate capital in a lot of companies too because you have to preserve dollars for the best. How do you think about that aspect of bluntly being a little bit more disciplined around reserve dollars and not allocating to anything in the middle or underperforming? **Tomasz Tunguz** [23:37]: The business model of the firm affords both. So there's reserves for every company. The idea is with every business, there's a very sort of blunt instrument, which is with every stock position that you have, if you're any kind of investor, you either you should either be a buyer or a seller. And if you're in the middle, you probably don't know enough about a business. The idea behind the concentrating reserves is we will run diligence processes on those existing portfolio companies in order to understand where to concentrate, but we also have the capital to be able to support. Like, companies go up and down. One of the stories that I think hasn't been told enough is, like, the Snowflake series c and the series d almost didn't happen because the company was burning so much. The gross margins were in a really rough place, and and there was a flat round in there somewhere, and then it becomes the fastest growing software company in history. So one of the reasons for this portfolio construction is if you're in a radically different capital markets environment, you want a financial partner who has the wherewithal to be able to support you across multiple rounds. And that snowflake round multiple on that finance. That was a big lesson that I learned at Redpoint. The multiple on that financing is legendary. I wanna dive deeper on that. What happened, and what was the lesson for you? Snowflake at the time was competing with giants. So there was Redshift and there was GCP, and the the company was growing very quickly, and the market was there. The company had a really tough time, and I can't remember exactly if it was a series b or the series c, but it was this middle round. Maybe it was a series c. The company was burning a ton of capital and couldn't raise money from the outside, and it was the insiders that stepped up, led that round because they believed in the business. And so the ultimate result was, you if have an accurate thesis and you can find the right company and you have the wherewithal to be able to support that business through good times and bad, you can be disproportionately rewarded **Harry Stebbings** [25:10]: for **Tomasz Tunguz** [25:10]: it. **Harry Stebbings** [25:10]: I love that, and I agree with it. I also didn't know actually that in terms of the series c. I do have two questions on like thesis driven investing which is I always worry about confirmation bias which is you develop the thesis and then you find something that aligns to it and you're like this is it. And actually thesis can be wrong. How do you think about the dangers of confirmation bias and not just falling victim to your own predictions of the future model of the world? **Tomasz Tunguz** [25:35]: Yeah. Totally. You could become enamored with a particular view of the world. And in order to mitigate confirmation bias, you need to have many conversations. You just need to keep testing and keep pushing. And at the end of the day, the great part about investing in b to b software is there's a buyer, and either they buy the software or they don't. And so the greatest sort of foil to confirmation bias is a lack of customer demand. I have this view around the future of the marketing ecosystem being tied to the blockchain and this decentralized infrastructure, and I've been working on it for nine months. And the thing that I consistently look for is, okay, where's the pipeline? Who's the buyer? Who's willing to spend? Where are the experimental dollars? What are the advertising agencies saying? And so I can come up with that idea consistently. But if I can't find a buyer for it, I can still have this beautiful vision glass pyramid, so to speak. But if I can't find a buyer, then I need to abandon the thesis or at least set it aside for now. **Harry Stebbings** [26:22]: The other question, for lines to what you just said there about blockchain applied to marketing, which is timing. A lot of these can be right, but can just be too early, as I know. How do you think about bluntly market timing, especially on thesis where you can know too much ahead of market? **Tomasz Tunguz** [26:38]: You have to look for pipeline. So it all comes down to customer need. Right? So pez.com versus Instacart or Peapod versus Instacart, I think is kind of the canonical example. The market timing, as long as you have a strong pipeline, you can have a lot of confidence. As long as you can extrapolate the needs of one buyer to another. And so that's why spending time and trying to get as broad of an understanding of as broad a cross section of the customer buyer population is absolutely essential in developing these thesis because somebody has to buy it at the end of the **Harry Stebbings** [27:04]: I do wanna move to the future and discuss a way you're investing, but also some of the broader topics around it. A question that I have which I can't really find an answer to, but it's like when we think about the future, especially in terms of AI models, does the rise of large AI models mean the future of AI as an ecosystem is dominated by a single general model or one or two single general models? Or will we have a decentralized fragmented ecosystem? **Tomasz Tunguz** [27:29]: I think you have both. I think the analogy of Apple and Linux is really useful here, Apple and Windows, where you'll have one system that is basically fully integrated and closed, and then you'll have another world where people are are building little open source models. And some people believe that there's going to be a single dominant model. I'm of the mind that there's probably an interface that if you look at Microsoft Jarvis, you look at LangChain or Fixie or any of these companies where they take an input and then they are basically mediator across a bunch of different models for different purposes, I think that's probably going to be the dominant model, at least in the consumer world. And then in the enterprise world, you'll have the Stripe Twilios who are creating platforms where it's very simple for developers to get started with large language models, and then you'll have, like, full enterprise services firms where a big Fortune 500 just wants a problem solved. Pepsi needs a generative model for whatever reason. They don't have the discipline. They want the whole thing in a box. And so, you know, this really nice spectrum. I think at the foundational model layer, that's a big boys game or a big girls game just because of the capital intensity required both for training and the GPU access and all those kinds of things. So maybe there's a startup or two that's able to raise a couple billion dollars in order to compete. I think it's more at the application layer. I ran this analysis. So in in web two, if you take the top three clouds and you look at their market cap, so AWS, GCP, and Azure, it's about a $2,100,000,000,000 market cap just for the cloud businesses. And then if you take the top 100 publicly traded cloud companies, both on b to c and b to b sides of Netflix and ServiceNow, they have equivalent market cap, about 2,100,000,000,000 for both. So one's at the infrastructure layer, one's at the application layer. Market cap is basically equivalent. The difference is the infrastructure layer, there are three businesses, and at the application layer, there are a 100. If the analogy holds as an investor, the odds of success are gonna be significantly higher at the application layer because the diversity of needs there is greater. **Harry Stebbings** [29:11]: You mentioned kind of enterprise usage there. The thing I can't get my head around is, like, some of the biggest companies in the world will not allow the majority of their data to be put through a different solution stored in some cloud infrastructure they've got no idea about. This is some of the most sensitive data they have. If they won't wanna run any form of queries or models on it, it will need to be on prem in their HQ under lock and key. How do we think about, like, enterprise access when data access is so core to their needs? **Tomasz Tunguz** [29:40]: So the first generation of software, all the software was run on an enterprise's machines. And Salesforce said, let's move it to the cloud. And we convinced, as an ecosystem, everyone that the cloud was safe. And the cloud is also expensive, as we're starting to realize. And so now there's a bifurcation where data remains in the customer's account. The application is being run by the software company. So then you have a separation of the application from the application plane from the control from the data plane. I think we'll see a very similar architecture where the model actually goes to the data and then comes back out with the result. So the data is actually within the customer's account. There's some compute that's input next to the data. The model is executed, and then it goes away. And that way, whoever's managing the model can update the model, modify it, do whatever they need to. And then at the time the model is needed, it's then deployed and pulled back. So I think that's probably a dominant architecture. I think if you're in, like, finance or health care, you'd probably be completely on prem for the foreseeable future. There are other kinds of issues. Like, if Copilot produces a bunch of code and you're a global 2,000, and that code is actually copyrighted by somebody else, what do you do? If a model produces a bunch of PII that's, like, quasi related to somebody else I put together this presentation on the opportunities for AI startups, and one of them is this whole bucket of enterprise readiness, like SOC two compliance, legal shielding, data security. There are all these kinds of deployment models. There are all these kinds of challenges and issues that are associated with them, and there's a big business there, many big businesses to be built there. **Harry Stebbings** [30:56]: That was the question. Did you like, do you think this is a bundled environment? I always think about the quotes, Jim Bost deal, bundling or unbundling. As you said that, there's many different big businesses to be built there, but they could also be bundled into an enterprise software suite. Do you think it's a bundled or an unbundled world in that envisioning? **Tomasz Tunguz** [31:12]: My learning has been that in early markets, people want bundling. And they want bundling because they don't yet understand the technology moving so fast that most people don't really understand it end to end, but they want the technology to solve a problem. Like, for your Global 2,000, you want a generative model, you're not yet in the place where you can, most people aren't, say that these are the five different layers, these are the best of breed across the five different layers, and these are the parameters upon which I'm going to choose best of breed. So the embeddings layer, the two most important things are, I don't know, x and y. Right? And at the model serving layer, the latency versus cost. Most people aren't there yet in their level of sophistication because they don't have enough experience with it. So my sense is in the beginning, people want an end to end solution. Just give me a thing that works, that's simple. And then as I learn what my needs are and what my customer needs are, what I need the software to do, I will break it in a particular way, then I will go and look for best of breed in the market, and I will swap out that layer. **Harry Stebbings** [32:00]: My my question to you is I'm worried about this asymmetry of knowledge. We mentioned kind of enterprise buyers there and then the providers. Tom, I'm European. I know how some of these large enterprises think, especially in Europe. AI, it's kind of like you need to remind them it's artificial intelligence. LLM's is you're gone. You've lost me already. How ready do you think enterprise buyers actually are? Do you think the hype cycle is ahead of the enterprise propensity to buy? **Tomasz Tunguz** [32:27]: This is a technology that the most buyers won't need to understand how it works. It's like a database. How does Snowflake work? I bet most people who buy Snowflake don't know. I don't know if you've ever seen the story, but there's this Italian artist, and he was exploring this idea. It's called the the illusion of explanatory depth. So he found a 100 people in Milan, and he asked them to draw a bicycle, and then he three d printed all those bicycles. And out of the 100 bicycles, how many do you think worked? 10. Two. So just because we're very familiar with the technology or an innovation doesn't necessarily mean that we understand how it works. And so I think in the case for most enterprise buyers, like I said before, I think they want an end to end solution that will just work and will work in 85 to 90% of the time, and that'll be good enough. And if in those 80 to 95% of the time, it can save you half of your time, just Copilot does, then that's good enough. And as long as it checks all the boxes for my security team, my IT team, and my compliance team, then that's good enough. **Harry Stebbings** [33:20]: You mentioned that Copilot, you mentioned it quite a few times. Today, I think it's cogeneration. 40% of new code generation is artificially intelligent code generation. What do you think that will be in ten years' time? **Tomasz Tunguz** [33:31]: I think it'll probably be 70 to 80%. And the reason I say that, I bet that 40%, a lot of it is what's called boilerplate code. A lot of it is standard code or code that's been slightly modified. Right? Like, I'm creating an HTML page. I need the HTML thing and the header and then the title. And so that's probably 40% of the content of an HTML page. It's probably the same for a Ruby file or a Python environment. And so we're at 40% today, and I bet we're at 75 to 80% because most of the code that's written is slight modifications of existing code. One, Pepsi's website is not that different to Coca Cola except for the underlying assets and the text. And so we'll get there. And so then the question is, okay. Goldman projects a 7% reduction in the labor population as a result of artificial intelligence, but overall, a two and a half percent increase in GDP. And so that's massive. Right? The US GDP is growing at about two and a half. Over the last twenty years, about two and a half percent a year. And so you have this impact where you could literally double the GDP growth of The US as a result of AI. And so the reason I think a lot of people are super excited about it, the reason I'm so excited about it is macroeconomically for The US. We're in a hole where we've printed way too many dollars for the GDP that we're producing, but now we're faced with a technology that could replicate the postwar surplus out of World War two that drove the next forty to sixty years of prosperity, but you've got a technology that's not really a wartime technology that it could do it. That's the reason I think so many people are so excited about it and that evaluations are as astronomical as they are. **Harry Stebbings** [34:51]: The one concern that I have is, like, when you look at it, it does it does bring about a concern on, like, distribution of wealth and the concentration of income. How do you think about wealth inequality over the next few years and the dangers of it actually concentrating wealth further into the hands of fewer? **Tomasz Tunguz** [35:07]: Getting into politics, I think it's the role of the private markets in order to drive innovation forward, and it's the role of government in order to encode the values of a population into its laws. So I think those are the forces that exist in tension, the network effects and the power laws that we are all chasing definitely create those dynamics when it comes to wealth, but it's not a new problem. You look at railroads or telecommunications or whaling. It's been around for forever. **Harry Stebbings** [35:30]: Final one on this. How do you think about regulation? I'm concerned about the asymmetry of knowledge between private and public. We're very fortunate to spend time with some of the most brilliant entrepreneurs in the world. And then you go and speak to regulatory bodies, bloody just don't have the same level of information and knowledge, and they're setting the regulation. It's concerning. How do you think about that chasm of knowledge between those two bodies and where it means we'll shake out from a regulatory standpoint. This **Tomasz Tunguz** [35:57]: is a longer conversation. I think regulation on the whole, one, benefits incumbents because the cost of adhering to regulations are significant. You take a look at in the mid nineties, you could have 25,000,000 in revenue and go public. Today, if you have a 100,000,000 in revenue, then it it costs you $15,000,000 in your first year to go public, and that's just a byproduct of regulation. So regulation benefits the winners or the bigger companies. I think the second thing is a lot of the times when regulation is imposed, people don't anticipate the second order effects. You look at real estate prices in California as they are three to four times what they are in the rest of the country because of the law that was passed in the nineteen seventies called prop 13. There are all these sort of, like, second and third order effects that a lot of regulation doesn't anticipate, and the legal process doesn't move fast enough. You look at crypto. Right? It's taken the US government ten years to catch up to what's going on, and now with Operation Chokepoint, it's starting to really regulate that ecosystem, and they finally gotten around to it. So I think the system maybe I'll indulge in another example. Like, if you think about airplanes okay. Post World War two airplanes, we were just invented the jet engine, and the commercial airlining business was growing, but it was still really risky. The FAA put a bunch of regulations around planes, and we kept flying planes. And then one day, we realized that planes with square windows crash more because they create stress fractures along the points of the squares. And so we regulated that out. What does that teach you? We don't know what we don't know, and so the best path to regulation is incremental when we identify that there's something wrong. Will bad things happen long way? Yes. There's no doubt. And this might sound callous, but that's the path of at the price of progress. **Harry Stebbings** [37:21]: Final one. You mentioned it sometimes benefiting larger companies and incumbents. This is my also big question, which is like startups versus incumbents. Alex Rampell of Andreessen says a brilliant one, which is, will the incumbent acquired innovation before the startup acquires distribution? When we look at kind of the two ends of the spectrum for the next generation of AI and LLM's, which is will existing incumbents integrate it well enough into their distribution channels to be highly effective and continue their dominance? Or actually, as startups with agility, flexible code bases, much better place to win in this next generation. How do you think about that kind of startup versus incumbent mega war? **Tomasz Tunguz** [37:58]: My thinking's evolved here. In the beginning, I thought the incumbents were gonna win the whole thing. And I thought that because the incumbents have far greater distribution. Microsoft has an incredible channel. Microsoft has a special relationship with OpenAI. The pace with which Microsoft is injecting its products with LLMs is astounding. Right? And so startups are in in this unusual position where they have negative time to launch. They're actually behind the market, which is unusual. Think about mobile apps and the launch of the Apple Store. Startups were the first ones to understand how to write mobile apps with Objective C. But I think anytime we talk about machine learning, there's always this question around what is the moat? And I have this this reaction, which is, like, the data moat is the data moat. And I think the answer is the one that it's always been, which is better execution is the moat. If you can build a better CRM and get it into market, you can win. Right? You take a look at what Notion has done with documents or what Snowflake did with databases facing two big incumbents. There are these stories. They're all over. They create this beautiful constellation within startup land of the David versus Goliath story. I think if you're a venture capitalist or if you're a startup founder, you have to believe. I think it's in your fabric that no matter how big the incumbent is or the advantages that they have, that if you have really great execution, you can still win and you can win big. **Harry Stebbings** [39:07]: I totally agree. I always say the speed of execution is the biggest determinant that I see in the differences between achieving and not achieving product market fit. Tom, I think we both agree that Microsoft's absolutely killed it in terms of their embracing and approach to this next generation. Who's done really badly? Which one of them is oh, you really missed the beat on this one, guys. **Tomasz Tunguz** [39:25]: Oh, it's gotta be Google. I had it's my former employer, so it pains me to say it. And I didn't believe that chat would replace search, but I think it for many use cases, it will. And I think Google had a rude awakening where, I don't know, for twenty twenty five years, they were uncontested. And now all of a sudden, there's a disruptive technology. To some extent, they developed in house but ignored. So it's a classic innovator's dilemma. And so this technology went to other places and now is challenging the hegemony, the monopoly power. And that is so exciting if you think about, like, the ads ecosystem, like, the b to c ecosystem has been relatively quiet over the last ten years because of that dominance of Facebook and Google. And now all of a sudden, you have technology and a replatforming where all that market share is conceivably up for grabs. You could create a new travel agency. You could create a new shopping experience. You could create a new stack overflow. You could create a new social experience based on chat. And so it's wide open. **Harry Stebbings** [40:14]: They were so strategically ahead of the game acquiring DeepMind, an amazing team there. What went wrong with that? I think **Tomasz Tunguz** [40:21]: it's a classic thing that when you have a golden goose, when you have an incredible business model, you're always faced with the choice of disrupting yourself and destabilizing the ship or waiting until somebody destabilizes it for you. And I think as a leadership team, it is so difficult to have the discipline to say, we are going to destabilize this ourselves. That's what happened. **Harry Stebbings** [40:39]: Do you think they knew? And what I mean by that is, like, Netflix did destabilize the golden goose. They took their mail order business and put it online because it was obvious. It would lose revenue in the short term, but it was obvious. The move from search to chat still isn't actually obvious. It's potential, but it's not obvious. Do think that's why? I think it's **Tomasz Tunguz** [40:59]: part of it. So if you think about the cost to produce a g b t four query versus the cost to produce a Google query, I bet it's, like, a 100 or a thousand or 10,000 times different. And Chris Dixon had this post that every major innovation starts out looking like a toy. I but the chat, the Google, or anybody working in search is looking at those technologies. Then we had conversations with friends talking about, like, the cost per query on this stuff. You just can't get the economics, but you can't look at it at a point in time. You've gotta look at it on some geometric curve or some logarithmic curve where you've got Moore's Law happening for you. So I think that was definitely a mistake that I made in anticipating the technology. I think the other thing that I didn't really appreciate until some of the later models came out was just how sophisticated the emergent behavior can. So there's this paper that talks about the how these LLMs learn, and the analogy is like humans. Right? So I can learn math by reading a book. Right? I can learn addition. I can learn division. In that way, like, the next time I see four plus four, know what the answer is or what a cube root of 27. I also learn how to swim. And in order to learn how to swim, I can read a book about the physics, fluid dynamics, and I can understand, like, what's happening with the vortices and where my arms need to be and what my legs need to do. But after reading that book, you throw me in the pool, I will drown. There's no question. I can read all the theory in the world. And so there's two different ways that we learn. Like, we we learn by effectively memorization, and we learn by doing. And what we thought at the beginning with these LLMs was that they're primarily memorization systems, and that that's why there's improvements in the GMATs and the LSATs and the AP tests just because they have more and more exposure to those questions. What we're starting to realize is they learn also by doing. And so there are these sort of what they are called emergent properties where the more questions that they're asked, the more they figure out how to answer those questions in a better way. There's this beautiful feedback loop that exists that only happens when they swim more. They swim more, they learn how to swim faster. They ask more questions. They learn how to answer questions better just like a human would. And not to say that they're humans, set that whole thing aside, but that I think has a compounding benefit that is really difficult to appreciate. Humans are very good at linear stuff, and they're terrible at geometric stuff. And I think what happened is that the quality of the answers and the breadth of the knowledge and some of these emergent behaviors, like the models learning how to swim, all of a sudden snuck up on everybody. And now the pace of innovation in this space is so fast. You wake up every morning and there's a new model, there's a new way of putting it together, there's a new application. It's just it's really hard to stay on top, and that's because we're on the steep part of this geometric curve for the sophistication of these models. And at some point, it will make the shelf of an s, but **Harry Stebbings** [43:16]: it doesn't feel like we're near close. A final one, I promise. The one thing that as a European, I'm like shocked that no one else is thinking about or seems to be no one thinking about is, like, the data or content ownership. And what I mean by that is Google will redirect you to a newspaper website where the cool page is, where the original post is. ChatGPT will leverage the internet and the world's content base and retain you on their website and simply scrape the information to theirs. Content providers will not be able to build a business when ChatGPT just scrapes all of their content and they have no way to monetize in any way. But how do we think about the future of data attribution and content attribution in that model? **Tomasz Tunguz** [43:54]: So Google has had this problem for forever with snippets. You ask it, like, who is Harry Stebbings? And it puts the three paragraphs about how amazing you are on those search results page. Right? And that could come from the New York Times, and the publishers and Google have been fighting back and forth in Europe and other geographies. It definitely exists here. Who owns that content? The notion of fair use, if I take two music tracks and put them together, that's a new product, and so I have that copyright. If I take the New York Times article about the events in Taiwan and I mix it with a CNN article and it produces a new article, is that a new thing where I should have copyright? And so, basically, the Internet becomes one huge walled garden that's just summarized by. I don't think any large language model operator wants to see that world because the reality is you need CNN and New York Times or any of the content producers to have a viable business model in order to put into the system. And the large language model companies probably do not wanna get into that business. And so what does the revenue share look like and what those arrangements and features TBD? I wonder if you can look at, like, the Mozilla Google deal or the Google Apple deal or some of the publisher contracts or even, like, distribution agreements across media companies today. We'll probably get to something like that. Be my guest. **Harry Stebbings** [45:00]: I could clearly talk to you all day, but I wanna move into a quick fire. I say a short statement, and then you give me your immediate thoughts. So will we be in a better or a worse place macro wise by the end of twenty twenty three? **Tomasz Tunguz** [45:11]: I think we will probably be in a worse place by the end of twenty three. I think the Fed has overcorrected on rates. The rate of money production m one and m two is decreasing faster than anyone expected. I think there's just, a human psychology to wanna over rotate on things and be slow. And then I think the risk of conflict in Taiwan is significant. So the combination of those four risk factors, I think, puts the odds of a US recession meaningfully higher than I think a lot of people appreciate. **Harry Stebbings** [45:35]: We mentioned Microsoft is, like, the leader and Google is plenty behind. Who's, like, second to be chasing Microsoft? Who are you, like, they have a shot at chasing them? **Tomasz Tunguz** [45:44]: Adobe. Doesn't have the recognition it deserves when it comes to using generative. I think about the applications in Photoshop. They launched a product called Firefly. I think they're right there. **Harry Stebbings** [45:52]: What trend in AI and the next generation of AI do you see that you don't think others are spending enough time on? **Tomasz Tunguz** [45:58]: Enterprise readiness. I think if there's one big market opportunity that people haven't focused on, it's how do you bring this to the global 2,000 in a way that they will accept and buy that's consistent with ways that they bought software in the past. **Harry Stebbings** [46:09]: You can invest in and you can short one multistage firm. Which firm do you invest in and which do you short? **Tomasz Tunguz** [46:16]: I would invest in Founders Fund, and I don't wanna say on the shorting side. **Harry Stebbings** [46:24]: Okay. On the seed fund boutique side, pure play seed fund, you can invest in one fund, and I guess you don't want a short one, so we can just say invest in. Which one would you invest in on that side? **Tomasz Tunguz** [46:35]: On the seed stage, I'd invest in Goodwater. I'd invest in Goodwater because it's completely orthogonal to b two b. I really respect what Chihuah is building with his huge, pretty significant engineering team to identify b two c opportunities all over the world. The opportunity for LLM's to destabilize the existing b two c Internet is really huge, and so I think he's got a nice market opportunity in front of **Harry Stebbings** [46:55]: What's your biggest investing miss, and how did that impact your mindset? **Tomasz Tunguz** [46:59]: Yeah. I've missed so many companies, Datadog and Twilio and many others. The thing that I've learned is that the startups are the ones who create the markets. And so if you have a rabid user base in a really early market, it will most of the time surprise you on the upside. What would you most like to change about the world of LPs? I think the thing that I'd love to see happen in the LP base is LPs educating VCs on their goals. This sort of happened in venture where venture capitalists explain their business models in really clear ways about, like, fund construction. And I think the most impenetrable part about the LP in a lot of cases is just understanding what drives them, what's their portfolio construction, and then figuring out how to map that to a fund. That's been the hardest part for me. **Harry Stebbings** [47:39]: I think it's really hard also for me. I agree with you, and I asked many LPs to come on the show, but a lot of them really don't like to be public. About ten years ago, Venture was not nearly as transparent, and I hope we bring in level of transparency to the LP market that we haven't had before. Will Trump win the election, Tom? **Tomasz Tunguz** [47:55]: I don't think so. I bet DeSantis wins. I think it will be tough for him to circumnavigate all the legal troubles, and I wonder if the RNC doesn't get involved. Would DeSantis be good for **Harry Stebbings** [48:04]: our business? **Tomasz Tunguz** [48:05]: He's a very complicated person. I think the Republican Party is still the party of business and capitalism. And so I would say yes, put it a different way, which is the entitlement spending in The US over the next ten years is projected to consume something like 95% of tax receipts. And so we need and I don't know who it will be, but we need some reform on the entitlements. And France is going this is funny. France is going through this now, and it's you can see it's extremely painful. And there's strikes in The US and there's strikes in France. And I think we're looking at a long period of time where the relationship between governments and people are going to change over the next ten years pretty meaningfully. And so we need a leader who can guide us through **Harry Stebbings** [48:40]: all **Tomasz Tunguz** [48:41]: that. **Harry Stebbings** [48:41]: Tom, penultimate one, who's your favorite angel to work with, and why them? So I I love to work with Guy Pajani, who's the founder of Sneak. Just fantastically insightful, really helpful, very granular advice. It can be a founder that you bring in. **Tomasz Tunguz** [48:55]: One of the angels I really like to work with is a man named Alan Black. And Alan was a CFO at Zendesk, and he was on the board with me at Looker. And he took Zendesk public during the crash of o eight. And so his experience going through financial carnage is just awesome. Just to have that story to have lived it, I really respected it. I think he's got a really great world view as a result of that. **Harry Stebbings** [49:16]: Tom, final one, my friend. What's been the biggest home run cash generative investment that you've made from a DPI perspective? And how did it come to be? It was Looker. The story **Tomasz Tunguz** [49:26]: there was in 2012, Redshift was the fastest growing product inside of AWS and Tableau was the dominant BI product. And there was a thesis that there would be a new BI product that would be architecture for the cloud. And a friend of mine from Google introduced me to Lloyd, the founder, and we clicked. And I loved the technology that we had built. And there was a post that, I think it was Josh Kaufman or Finn Barnes wrote, and the question was, who took a bet on you when you were young in your career? And Lloyd took a bet on me and brought me in a DA, forever grateful for it. **Harry Stebbings** [49:52]: Tom, listen. I've loved doing this. I hope my interviewing style has changed a little bit over the years. Is this so much fun. Thank you **Tomasz Tunguz** [50:00]: so much for doing it, my man. Thank you so much, Harry. I really appreciate it. Congratulations on all your success too. I **Harry Stebbings** [50:06]: just loved doing that episode with Tom, and I implore you check out his writing. It is fantastic. Find it at tomtunguz.com. You can also find us on YouTube by searching for 20 v c, where you can watch the full interview today in full. But before we leave you today, ## Sponsor read **Harry Stebbings** [50:20]: you've heard me talk about Coda. Coda is the doc that brings it all together and how it can help your team run smoother and be more efficient. I know this because Coda helps me. At twenty VC, we use Coda for all of our research for every episode. So all team members essentially can work on the schedule all in one doc seamlessly built by Coda. And here's how Coda can help your team run smoother and be more efficient. Coda allows your team to operate on the same information and collaborate like my team does all in one Coda place. 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