20VC: 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

21:51My question is to you, do you agree with that statement? And how do you think about your own price sensitivity? 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 How do you think about your own price sensitivity in my ownership wise? Do you need 10%, you need 15%, how do you think about ownership sensitivity on a power company basis?

22:21Yeah, so I think where 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, it can spend a significant amount of time with each portfolio company. So ownership matters, ownership also matters, I think, with a form of returns. We want to have significant ownership. And the idea with the firm is that you don't need to have significant ownership out of the but you can build a position over time. 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?

22:51Like how do you think about that cross -cycle investing? Is it a bit of a deal? 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 100. And then the next round might be 200 or 300 and so to 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 6 5 % at the b 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 under performing.

23:37The business model of the firm affords both, so there's reserves for every company. The idea is with every business, there's a very blunt instrument, which is with every stock position that you have if you're any kind of investor. 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 companies go up and down. One of the stories that I think hasn't been told enough is like the Snowflake series C in the series D almost didn't happen because the company was burning so much the gross margins were in a really rough place And there was a flat -round in there somewhere and then it became as fast as 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 to wear with all to be able to support you across multiple rounds and that's no Flake route multiple on that finance that was a big lesson that I learned at redpoint But the multiple on that financing is legendary I want to dive deep on that.

24:35What happened and what was the lesson fee? Snowflake at the time was competing with giants. So there was red shift and there was GCP and 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 if you have an accurate thesis and you can find the right company and you have the way with all to be able to support their business through good times and bad.

25:08You can be disproportionately rewarded for it. 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 following victim to your own predictions of the future model of the world. Yeah, totally. You can become enamored with a particular view of the world and in order to mitigate confirmation bias, you need to have many conversations.

25:41You just need to keep testing and keep pushing. And at the end of the day, the great part about investing in B2B software is there's a buyer, and neither 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 system 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?

26:10Where 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. The other question for lines to what you just said there about blockchain and 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? You have to look for pipeline.

26:39So it all comes down to customer need, right? So pest .com versus Instacard or Peapod versus Instacard I think it's 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 by our population is absolutely essential in developing these diseases because somebody has to buy it at the end of the day. I do want to move to the future and discuss A.

27:07where 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, but 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? 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 building little open source models.

27:43And 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, so you look at Lange and fix here. 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.

28:10And then you'll have like full enterprise services firms where 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 it's a foundational model layer. That's a big boys game or a big girls game. It's because of the capital intensity required both for training and the GPU access and all those kinds of things. So maybe there's a start up or two that's able to raise a couple billion dollars in order to compete. I think it's more at the application layer.

28:37I ran this analysis. So in Web2, if you take the top three clouds and you look at their market cap, so AWS, GCP, and Azure, it's about a 2 .1 trillion dollar market cap just for the cloud businesses. And then if you take the top 100 publicly traded cloud companies both on B2C and B2B side Netflix and ServiceNow, they have equivalent market cap about 2 .1 trillion for both. So I wanted the infrastructure layer once 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 100. If the ideology holds as an investor, it odds of success or can be significantly higher at the application layer because the diversity of needs there is greater.

29:11You mentioned enterprise usage there. The thing I can't get my head around is 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 want to 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 enterprise access when data access is so cool to their needs? So the first generation of software, all the software was run on an enterprise's machines.

29:45And 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, 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 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.

30:11There's some compute that's in put 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 to extend the point and pull back. So I think that's probably a dominant architecture. I think if you're in like finance or healthcare, it'll 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 2000 and that code is actually copyrighted by somebody else. What do you do?

30:35If 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 SOC2 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 people there. That's a question. Do you think this is a bundled environment? I always think about the quotes Jim Bortstale, bundling or unbundling. As you said that, there's many different big businesses to be built at, but they could also be bundled into an enterprise software suite.

31:09Do you think it's a bundle or an unbundled world in that envisioning? 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 2000 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 team bedding's layer the two most important things are I don't X and Y right and at the model serving layer that 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 and what I need the software to do I will break it in a particular way Then I will go and look for a best of breed in the market and I will swap out that layer My my question to is I'm worried about this asymmetry of knowledge We mentioned kind of enterprise buyers there and then the providers Tom.

32:07I'm European I know how some of these large enterprises think especially in Europe. Hey, I it's kind of like you need to remind them It's artificial intelligence. Al Alams 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? This is a technology that 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 ever have seen the story, but there's this Italian artist and he was exploring this idea, it's called the illusion of explanatory depth.

32:43So he found 100 people in Milan and he asked him to draw a bicycle and then he 3D printed all those bicycles. And out of the hundred bicycles, how many things worked? Dan? 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 what work can 85 % to 90 % of the time, and that'll be good enough. And if in those 80 % to 95 % of the time, I can save you half of your time, just co -bile it does.

33:13Then that's good enough. And as long as it checks all the boxes for my security team, my AT team and my compliance team, then that's good enough. You mentioned that co -pilot, you mentioned it quite a few times. Today, I think it's code generation, 40 % of new code generation is artificially intelligent code generation. What do you think that will be in 10 years time? I think it will probably be 70 -80 % And 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. I'm creating an HTML page, I need the HTML header and the title.

33:46And so that's probably 40 % of the content of an HTML page. It's probably the same for a Ruby file or Python environment. And so we're at 40 % today, and I bet we're at 75 % to 80%. Most of the code that's written is slide modifications of existing code. One Pepsi's website is not that different to Coca -Cola except for the underlying assets in the text. And so we'll get there. And so then the question is, OK, 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. Like the US GDP is growing in about two and a half, over the last 20 years grew in about two and a half percent a year.

34:21And 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 to 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 face with a technology that could replicate the postwar surplus out of World War II that drove the next 40 or 60 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 the evaluations are as astronomical as they are.

34:51The one concern I have is when you look at it, it does bring about a concern about distribution of wealth and the concentration of income. How do you think about wealth inequality over the next years, and the dangers of it actually concentrating wealth further into the hands of fewer? 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 the population into its loss So I think those are the forces that exist in tension and 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.

35:25You look at railroads or telecommunications or whaling that's been around for forever Final one on this but 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 someone most brilliant entrepreneur in the world and then you go and speak to the regulatory bodies Which Bloney 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?

35:56There's a lot of your conversation. I think regulation on the whole one it benefits and comments because the cost of adhering to regulations are significant You take a look at in the mid -90s you could have 25 million in revenue and go public today If you have a hundred million in revenue of an it costs you fifteen million dollars in your first year Go public and that's just a byproduct of regulation So regulation benefits the winners are 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 there are three to four times What they are in the rest of the country because of the law that was passed in the 1970s called prop 13 They're 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 a crypto, right?

36:37It's taken the US government 10 years to catch up to what's going on and now with operation choke point starting to really regulate that ecosystem And they finally gotten around to it So I think the system maybe a little bit of another example Like if you think about airplanes, okay post -World War II 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 plain to keep flying planes. And then one day we realized that planes will square windows crash more because they create stress fractures along the points of the squares.

37:07And 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. Well bad things happen long way. Yes, there's no doubt. And this is my town, Calis, but that's the path of the price of progress. Final one. You mentioned it. Sometimes benefiting larger companies and incumbents. This is my also big question, which is like startups first in Cummins, Alice Rampella Landryson says a brilliant one, which is, will the incumbent acquired innovation before the startup acquires distribution?

37:35When we look at the two -anceless spectrum for the next generation of AI and LLM, which is will existing incumbents integrate it well enough into their distribution channels to be highly effective and continue their dominance. Or actually a startup with agility, flexible code bases, much better place to win in this next generation. How do you think about that kind of startup versus income and mega wall? My thinking is evolved here. In the beginning, I thought the incumbents were going to 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.

38:14And so startups are 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 they're launched at the Apple store. It starts with the first ones to understand how to write mobile apps with objective seeing. But I think any time we talk about machine learning, there's always this question around what is the moat? And I have this as reaction, which is like the data mode to data mode. 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.

38:42You take a look at what notion has done with documents or what snowflake did with databases facing too big and combat. There are these stories. They're all over, they create this beautiful for consolation within startup land of the David versus Koliest 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. I tell you, Lager, 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.

39:14Tom, I think we both agree that Microsoft's absolutely killed it in terms of their embracing and approach to this netting generation who's done really badly. Which one of them is, ah, you really missed the beat on this one guys. How it's gotta be Google. I had my former employer so it pains me to say it. And I didn't believe that chat would replace search, but I think for many use cases it will. And I think Google had a rude awakening where I don't know, for 20, 25 years they were uncontested. And now all of a sudden there's disruptive technology. To some extent they developed in -house but ignored.

39:45So it's a classic innovator's dilemma. And so this technology went to other places and now is a challenging the hegemony, the monopoly power. And that is so exciting if you think about like the ads ecosystem, like the BDC ecosystem has been relatively quiet over the last 10 years because of that dominance of Facebook and well. And now all of a sudden you have a technology and a re -platforming where all that market share is conceivably up for grabs. You couldn't 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.

40:13And so it's wide open. They were so strategically ahead of the game acquiring DeepMind. An amazing team there. What went wrong with that? I think 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. 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. Do you think they knew? And what I mean by that is that Netflix did destabilize the Golden Goose.

40:44They took their mail all the business, put it online because it was obvious. It would lose revenue in a short time, but it would obvious. The move from search to chat still isn't actually obvious. It's potential, but it's not obvious. Do you think that's why? I think it's part of it. So you think about the cost that produce a GPT -4 query versus the cost to produce a Google query. I bet it's like 100 or a thousand or 10 ,000 times different. And Chris Dixon had this post at every major innovation start out looking like a toy. about the chat, the Google, or anybody working in search is looking at those tech, and then we had conversations with friends talking about the cost of career on the stuff, you just can't get the economics, but you can't look at it at a point in time, you've got to look at it on some geometric curve or some logarithmic curve where you've got effectively a Moore's Law happening for you.

41:26So 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 that how these LLMs learn and the analogies 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 I 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 initially a 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 learned by doing.

42:10And what we thought at the beginning with these LLMs was that the primarily memorization systems and 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. But we're starting to realize that they learn also by doing. And so there are these sort of what they're 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, or they're asking more questions, they learn how to answer questions better, just like a human would.

42:41And not to say that they're humans, said 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 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.

43:07It'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 they will make the shelf of an S But it doesn't feel like we're gonna be close The final one I promise the one thing that as a European I'm nice shot The no one else's thinking about or seems to be known thing 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 pages where the original post is Chagy Beauty will leverage the internet and the world's content base and retain you on their website and simply scrape the information to theirs.

43:40Content providers will not be able to build a business when Chagy Beauty just scrapes all of their content and they have no way to monetize it anyway. But how do we think about the future of data attribution and content attribution in that model? So Google has had this problem for forever with snippets. You ask it, like, who's Harry Stubbix and it puts the three paragraphs about how amazing you are on the search results page right and that could come from 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.

44:10The 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 wall garden that's that's just summarized by ChatGPD. 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 this system.

44:41And the large language model company is probably not going to 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 the Mozilla Google Deal or the Google Apple Deal or some of the publisher contracts or even like distribution agreements It's a great environment to cross media companies today. We'll probably get to something like that, be my guest. I could clearly talk to you all day, but I want to 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 worse place macro wise by the end of 2023?

45:11I think we will probably be in a worse place by the end of 2023. I think the Fed is over corrected on rates, the rate of money production M1 and M2 is decreasing faster than anyone expected. I think there's just like a human psychology to want to 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. We mentioned Microsoft as like the leader and Google is running behind. Who's like second to be chasing Microsoft?

45:42Who you like they have a short at chasing? Adobe doesn't have the recognition it deserves when it comes to using generative. I think about the applications in Photoshop, the launch of Protocol Firefly. I think they're right there. What trend in AI in that generation of AI do you see that you don't think others are spending enough time on? 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 2000 in a way that they will accept it by. That's consistent with ways that they've bought software in the past. You can invest in and you can short one multi -stage firm, which firm do you invest in and which do you short?

46:16I would invest in Founders Fund and I don't want to say on the shorting side. Okay, on the seed fund BT side pure play seed fund you can invest in one fund and I guess you don't want to short one So we can just say invest in which one would you invest in on that side on the seed stage? I'd invest in good water. I'd invest in good water because it's completely worth organelle to B2B I really respect what she was building with his huge pretty significant engineering team down in the five B2C opportunities all over the world The opportunity for LLMs to destabilize the existing BTC Internet is really huge And so I think it's got a nice market opportunity in front of them What's your biggest investing miss and how did that impact your mindset?

46:59Yeah, I've missed so many companies data dog and Twilio 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 rabbit 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 Alps? 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 fun Construction and I think the most impenetrable part about the LP in a lot of cases is just understanding what drives them What's the portfolio construction and then figuring out how to map that to a fund?

47:38That's been the hardest part for me And he'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 10 years ago, Vansha was not nearly as transparent, and I hope we bring a level of transparency to the LP market that we haven't had before. Will Trump win the election? I don't think so. I bet the Santa's 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 this antists be good for our business? 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 10 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 way France is going through this now And it's you can see it's extremely painful and the strikes in the US and the strikes in France And I think we're looking at a long period of time where the relationship between Government and people are going to change over the next 10 years pretty meaningfully and so we need a leader who who can guide us through all that.

48:41Tom, an ultimate one. Who's your favorite angel to work with? And why then? So I love to work with Guy Pajone. He's the founder of Sneak. Just fantastically insightful, really helpful, very granular advice. It can be a founder that you bring in. One of the angels I really like to work with is his name, 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 O8. And so his experience going through financial carnage is just awesome. them. Just to have that story to have lived yet, I really respected it. I think he's got a really great worldview as a result of that.

49:16Tom, final one, my friend. Well, it's been the biggest home run cash -generative investment that you've made from a DPI perspective. And how do it come to be? It was looker. The story there was in 2012, Ritchift, with the fast -screwing 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 the friend of mine from Google I was actually the loyal, the founder, and we clicked, and I loved the technology we had built, and there was a post that I think it was Josh Carpeman 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 to DA, and forever grateful for it.

49:52Tom, listen, I've loved doing this. I hope my interviewing style has changed a little bit over the year. It's, it's so much fun. Thank you so much for doing it, my man. Thank you so much, Harry. I really appreciate it. Congratulations on all your success, too. I just love doing that episode with Tom and I implore you to check out his writing. It is fantastic. Find about TomTongers .com. You can also find us on YouTube by searching for 20VC where you can watch the full interview to stay in full. But before we leave you today, you've helped 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.

50:27I know this because Coda helps me. At 20VC, we use Coda for all of our research for every episode, so all team members essentially can work on the schedule all in one dog seamlessly built by Coda. And here's how Coda can help your team run smoother and be more efficient. Coda allows you team to operate on the same information and collaborate like my team does all in one Coda place. By putting date in one centralized location, regardless of format, it eliminates so many road blocks that can just stop your team in their tracks. This is really what slows down productivity and collaboration. With Coda, your team can operate on the same information and collaborate in one place to get projects across the finish line faster, help your team run more smoothly, more efficiently with Coda.

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