The Exchanges

Q So can we just unpack this? You said about the Oracle credit default swaps. Can you help me understand what's going on there and why that's important?

A Okay. Oracle has a big deal with open AI. Oracle needs to build lots of data centers to build those data centers. They borrow money like a mortgage and they've borrowed money. And there's a thing called a credit default swap, which you may remember from the great financial crisis, which is the odds that Oracle defaults on its debt that they cannot pay their mortgage. Google and Microsoft and other major technology companies are, um, at a certain level, which is basically the same rate as the federal government, right? So government grade and Oracle is three times that in the last three or four days. So it's a big move. It's the, the risk is still quite small. So the overall probability of an Oracle default is small. The magnitude of the move suggests a meaningful repricing of risk.

AI assessment note: “The magnitude of the move suggests a meaningful repricing of risk.”

Q Do you think there are any screaming flags from the last week?

A I don't think so. I mean, most of the hyperscalers GPU capacity is sold out for the next two years. Um, they generate cash. The debt as a percentage of free cash flow is really small. I think the major red flags for me are customer concentration risk is higher than it's ever been. So Nvidia, two customers for Nvidia represent more than 40% of revenues. Four percent represent more than 50% of revenues. I went back and looked at the dot-com era, the networking companies. NVIDIA is 10 times more concentrated in terms of revenue than Lucent was. So I think that's an issue. But most of NVIDIA's customers are super cashflow positive, right? Google and Meta and others are spitting out cash, and they can decide to stop at basically whatever point. Um, so I think it's all okay. How does this merry-go-round stop? Like, if the game of musical chairs were to collapse and everyone falls on their But, uh, in the seat, what happens it's inference demands slows. And if there's a hiccup, if Google says we built this amount of capacity and we can only fill 80%, if that happens, then you see a.

AI assessment note: “I think the major red flags for me are customer concentration risk”

Q So can we just unpack this? You said about the Oracle credit default swaps. Can you help me understand what's going on there and why that's important?

A Okay. Oracle has a big deal with open AI. Oracle needs to build lots of data centers to build those data centers. They borrow money like a mortgage and they've borrowed money. And there's a thing called a credit default swap, which you may remember from the great financial crisis, which is the odds that Oracle defaults on its debt that they cannot pay their mortgage. Google and Microsoft and other major technology companies are, um, at a certain level, which is basically the same rate as the federal government, right? So government grade and Oracle is three times that in the last three or four days. So it's a big move. It's the, the risk is still quite small. So the overall probability of an Oracle default is small. The magnitude of the move suggests a meaningful repricing of risk.

AI assessment note: “The magnitude of the move suggests a meaningful repricing of risk.”

Q there in terms of open versus closed. I think the next big debate is Where does the actual value accrue? Is it in the infrastructure layer or is it in the application layer? Now, Tom Tungus, you know, you founded Theory Ventures, which is one of the leading AI investors. When you think about infrastructure versus application layer in terms of where value accrues, how do you think about that?

A I ran this analysis. So 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 2.1 trillion dollar market 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 2.1 trillion for both. So once at 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 a hundred. If the analogy holds, as an investor, the odds of success are going to be significantly higher at the application layer because the diversity of needs there is

AI assessment note: “market cap is basically equivalent. The difference is the infrastructure layer, there are three”

Q Totally many different, many different ways. I'm glad you went with the 12%. I think diversification is important. I'm thrilled that you went for the latter. Kamal, on the materials side, what did you go for on the materials side? Like, 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 raise?

A I did. So 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, I put together and what I would do. So I, I set up a, like a brief and a bio, and that was sort of like theory one. Um, that was the outbound email. So if I had been introduced, I would send them my bio and then the deck. And the idea was. To use those materials as pre-qualification. So some LPs prefer not to invest in solo GPs. Different LPs have different mandates. They might only invest in the 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's, that was the entire purpose. And I, I used a doc send 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, the way that I would pitch them if they decided that they wanted to meet.

AI assessment note: “I prepared a data room. There was a track record in there, a pitch deck”

Q I absolutely love that narrative. Uh, but I, I do want to deep dive on some elements today then. Uh, and so starting with a simple one, we said about data-driven teams there. Uh, and ones that really know the power of data. So what are the best in class doing to operationalize their data today? What have you found that they're all doing in terms of commonalities?

A Yeah, there are three things they do. The first is they have a data team, and there's this data, and that, this exists at Facebook, it exists at Zendesk, and these data teams, they, they're structured a little bit differently, but they all basically do the same thing. They're, they're the experts. They know where the data is stored. They understand how to perform analysis, but their main function is really education. They go around educating people with, like, office hours and tech talks and that kind of thing. The second thing is a product of the data team. It's this data dictionary, and it was the head data scientist at Warby Parker that came up with this idea, and he found when he was doing a bunch of analysis internally, two different teams internally, they had the same metric, but they had defined it differently. In talking to a bunch of different people, we found that this was a really common problem. Like, in The software world, a marketing qualified lead and a sales qualified lead. Well, the sales team might define each of those differently than the marketing team. And so you have a communication breakdown. So the second common characteristic is there's a single person or team that, that creates this data dictionary that defines all the key metrics for the business once and for all. And then the third thing is, uh, what we call in the book, a data pipeline, which is a w…

AI assessment note: “there are three things they do. The first is they have a data team”

Q SaaS landscape firstly, and today where we're at and how it's developing, and then talk about the incredible process that is your blog and your writing. So, so let's start by taking a macro view then of the SaaS landscape, and I'd love to hear your thoughts on what the huge drop in late stage SaaS valuations really means for the early stage guys. What do you think of this?

A It means relatively little at the Series A. You know, I think you'll probably see, you know, seat, well first, let's go stage by stage. Seat valuations, from what I've seen, are falling. I remember maybe like a year ago, you know, the high watermark for like Y Combinator companies was something like a twenty million cap or an unlimited cap, and I think you'll see maybe one or two of those, but I think on the average you'll probably see five to seven million caps for a lot of the companies, and that's a pretty meaningful drop. At the Series A for SaaS companies, I suspect you might see like a 10 or 15% reduction. I mean, I ran this analysis for SAS in early 2016. The average Series A for like a premium SAS company was about ten million dollars. And I suspect that will probably fall to seven or eight on average. And valuations will kind of fall with it. At the Series A, it's a meaningful drop, but it's not a huge drop. It's nothing like we've seen in the public markets. Where we are seeing a lot of contraction or compression is in the B rounds and later. I think the major difference, or the reason you're seeing a greater drop there is Bs, Cs, and D rounds, they're all priced the way that public companies are priced. They're priced as a function of a forward revenue multiple, right, um, which is, let me take the sum of the revenue over the next 12 months, multiply that by a number…

AI assessment note: “It means relatively little at the Series A.”

Q SaaS landscape firstly, and today where we're at and how it's developing, and then talk about the incredible process that is your blog and your writing. So, so let's start by taking a macro view then of the SaaS landscape, and I'd love to hear your thoughts on what the huge drop in late stage SaaS valuations really means for the early stage guys. What do you think of this?

A It means relatively little at the Series A. You know, I think you'll probably see, you know, seat, well first, let's go stage by stage. Seat valuations, from what I've seen, are falling. I remember maybe like a year ago, you know, the high watermark for like Y Combinator companies was something like a twenty million cap or an unlimited cap, and I think you'll see maybe one or two of those, but I think on the average you'll probably see five to seven million caps for a lot of the companies, and that's a pretty meaningful drop. At the Series A for SaaS companies, I suspect you might see like a 10 or 15% reduction. I mean, I ran this analysis for SAS in early 2016. The average Series A for like a premium SAS company was about ten million dollars. And I suspect that will probably fall to seven or eight on average. And valuations will kind of fall with it. At the Series A, it's a meaningful drop, but it's not a huge drop. It's nothing like we've seen in the public markets. Where we are seeing a lot of contraction or compression is in the B rounds and later. I think the major difference, or the reason you're seeing a greater drop there is Bs, Cs, and D rounds, they're all priced the way that public companies are priced. They're priced as a function of a forward revenue multiple, right, um, which is, let me take the sum of the revenue over the next 12 months, multiply that by a number…

AI assessment note: “It means relatively little at the Series A.”

Q your punctuation next. Um, but I want to start on some very exciting news for Cursor. 2.3 billion dollars at a 29.3 billion dollar valuation. Andreessen, Thrive, Koto, DST, Excel, all the big players all involved. Chaps, how did we analyze this? And I, I look at this and honestly feel more irrelevant than I've ever felt. How should we look at this? And this is a free for all.

A Look, I think product market fit for agent decoding is probably the best of any use case aside from search. Uh, and so, and then you have this just like massive growth. So I think the bull case is the productivity gains for software engineers here are pretty enormous, 30 to 70%, kind of depending on what, which company you're looking at. You have pretty significant multiple expansion. I'm not sure if you guys have played with the new cursor model, but it's phenomenal. It's unbelievably fast, four or five times faster on a tokens per second basis. And that allows them to capture a whole bunch of margin. And so, and then on a multiples basis, it's actually not that wild. You put all those things together, plus the, the bullions in the market. And so you see evaluation here. I mean, can you see a three X? You don't have a lot of ESOP dilution because total employee count is 30. And so it's still 30. Yeah. It's the third. They just hired a PM four months ago. So, you know, they increased headcount by five percent, but you don't have a lot of the capex dilution that you'll see within the foundation models. And so does it, you know, does it go public? You have Massive revenue growth, increasing margin, pretty attractive financial profile. We can debate the entry price, but I think bull market bet, classic bull market bet. Big question. I mean, we were looking at a bunch of the vibe c…

AI assessment note: “I think the bull case is the productivity gains for software engineers here are pretty”

Q Do you think there are any screaming flags from the last week?

A I don't think so. I mean, most of the hyperscalers GPU capacity is sold out for the next two years. Um, they generate cash. The debt as a percentage of free cash flow is really small. I think the major red flags for me are customer concentration risk is higher than it's ever been. So Nvidia, two customers for Nvidia represent more than 40% of revenues. Four percent represent more than 50% of revenues. I went back and looked at the dot-com era, the networking companies. NVIDIA is 10 times more concentrated in terms of revenue than Lucent was. So I think that's an issue. But most of NVIDIA's customers are super cashflow positive, right? Google and Meta and others are spitting out cash, and they can decide to stop at basically whatever point. Um, so I think it's all okay. How does this merry-go-round stop? Like, if the game of musical chairs were to collapse and everyone falls on their But, uh, in the seat, what happens it's inference demands slows. And if there's a hiccup, if Google says we built this amount of capacity and we can only fill 80%, if that happens, then you see a.

AI assessment note: “I don't think so. I mean, most of the hyperscalers GPU capacity is sold out”

Q look at kind of the two ends of the spectrum for The next generation of AI and LLMs, 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?

A My thinking's 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 open AI. The pace with which Microsoft is injecting its products with LLMs is astounding. 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. And so startups are in this unusual position where they have negative time to launch. They're actually behind the market, which is unusual. I 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 and, 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 to 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 too big incumbents. There are these stories. They're all over. They create this beautiful constellation within start…

AI assessment note: “no matter how big the incumbent is... if you have really great execution, you can still win”

Q 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?

A It was Looker. And, uh, you know, the, the story 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, um, 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 Koppelman 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 into DA, and forever grateful for it.

AI assessment note: “It was Looker.”

Q That's pretty incredible, because that's quite rare. So, I mean, well done to you there. Can I ask, for the starting checks, they're the hardest to get. When you think about, like, 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?

A I worked for the large institutional anchors. I had some relationships there and, uh, you know, I figured if I could get, so the LPAC has five members and 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 there were institutional backers, uh, from the beginning and, uh, 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, uh, if you think about Finding a lead for a series A or a series B. If you can find that lead who then, um, Does the diligence and, and then we'll talk to everybody else who comes in and sort of guides you. Uh, it sets, it sets up the process really well. It's a little bit different because LP, most, 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 it's close, like the LPAC is basically the limited partner advisory council, like the board is the closest thing that I think you get to, unless you have a super concentrated LP base.

AI assessment note: “I worked for the large institutional anchors. I had some relationships there”

Q Totally many different, many different ways. I'm glad you went with the 12%. I think diversification is important. I'm thrilled that you went for the latter. Kamal, on the materials side, what did you go for on the materials side? Like, 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 raise?

A I did. So 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, I put together and what I would do. So I, I set up a, like a brief and a bio, and that was sort of like theory one. Um, that was the outbound email. So if I had been introduced, I would send them my bio and then the deck. And the idea was. To use those materials as pre-qualification. So some LPs prefer not to invest in solo GPs. Different LPs have different mandates. They might only invest in the 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's, that was the entire purpose. And I, I used a doc send 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, the way that I would pitch them if they decided that they wanted to meet.

AI assessment note: “I prepared a data room. There was a track record in there, a pitch deck”

Q Just questions on like thesis driven investing, which is I always worry about confirmation bias, which is, you know, you develop the thesis and then you find something that aligns to it and you're like, this is it. And actually theses 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?

A Yeah, totally. I mean, you could become enamored with a particular view of the world. And I think in order to mitigate confirmation bias, you need to have many conversations. You just need to keep testing and keep pushing. And, and at the end of the day, the great part about investing in B to B software is there's a buyer that is buying a product 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. You know, if you're so enamored, like 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 have, I can still have this beautiful visions, you know, 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.

AI assessment note: “the greatest sort of foil to confirmation bias is a lack of customer demand”

Q I mean, listen, portfolio construction is what gets me out of bed in the morning. Speaking my language. Um, I want to start there. This is like a process that's shrouded in much opacity, and we just see fundraisers announced. So I want to talk about the fundraise. How many meetings did it take to close out the fund, my friend?

A So it took a bit more than you, uh, it took about a 150 LP meetings. So the, the fundraising market was a very challenging one over the last couple of months, I'd say, but it took about a 150 meeting about a 150 LPs. And the way I thought about it was, I mean, you know me, I thought about it just like a regular software sales process, right? Where sales assisted, 15% close rate. And so built up, built 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, we exceeded the target, we raised the hard cap, and I ended up at two, about 230.

AI assessment note: “it took about a 150 LP meetings.”

Q That's pretty incredible, because that's quite rare. So, I mean, well done to you there. Can I ask, for the starting checks, they're the hardest to get. When you think about, like, 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?

A I worked for the large institutional anchors. I had some relationships there and, uh, you know, I figured if I could get, so the LPAC has five members and 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 there were institutional backers, uh, from the beginning and, uh, 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, uh, if you think about Finding a lead for a series A or a series B. If you can find that lead who then, um, Does the diligence and, and then we'll talk to everybody else who comes in and sort of guides you. Uh, it sets, it sets up the process really well. It's a little bit different because LP, most, 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 it's close, like the LPAC is basically the limited partner advisory council, like the board is the closest thing that I think you get to, unless you have a super concentrated LP base.

AI assessment note: “I worked for the large institutional anchors. I had some relationships there”

Q There's the exit market analysis. I, I go back and forth on, is it worth doing because it's so variable? You could look back on the prior 24 months and say, well, it could be that, or it could be today, or it could be way, way worse. We can't project out seven, 10, 12 years. Is it valid doing it?

A I think it is because Okay, so the historical forward multiple is about five X, let's just say, right? It's a little higher than that's about 5.5. And in the heyday of quantitative easing, the top quartile companies are trading at 40 times. And so you can't go, and today it's about maybe six. And so you can't go into a company today and say, okay, I'm, I'm going to project a 20 times forward multiple on this company at the time of IPO if it's at a hundred million growing at 70%. You just can't. That's irresponsible. I mean, I would say that's irresponsible because this is completely unrealistic. And if you've, you know, if you spent the majority of your time in venture during a time when you've had those kinds of multiples, you need to check on what do you think your return expectations are going to be given that, you know, whatever it is, a four percent ESOP employee stock option pool dilution by year and the dilution of created by other venture rounds and that. And so going through that discipline, I think is as much, just like I said, particularly for working in a team or. It's just a really important discipline step. The other thing that, that I really love to do is, um, there's this awesome book called Superforecasters that a guy named Ted Lowe wrote, and he talked about Enrico Fermi who created the atomic bomb. That was one of the team for the Manhattan Project. And Fermi…

AI assessment note: “I think it is because Okay, so the historical forward multiple is about five X”

Q So what, talk to me about that lesson. I want to dive deeper on that. What, what happened and what was the lesson for you?

A Snowflake at the time was, I mean, competing with giants, right? 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, maybe it was a series C. And the company was burning a ton of capital and couldn't raise money from the outside. And it was the insiders that stepped up and 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, you have the wherewithal to be able to support that business through good times and bad, you can be disproportionately rewarded for it.

AI assessment note: “the ultimate result was if you have an accurate thesis and you can find the right company”

Q Just questions on like thesis driven investing, which is I always worry about confirmation bias, which is, you know, you develop the thesis and then you find something that aligns to it and you're like, this is it. And actually theses 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?

A Yeah, totally. I mean, you could become enamored with a particular view of the world. And I think in order to mitigate confirmation bias, you need to have many conversations. You just need to keep testing and keep pushing. And, and at the end of the day, the great part about investing in B to B software is there's a buyer that is buying a product 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. You know, if you're so enamored, like 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 have, I can still have this beautiful visions, you know, 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.

AI assessment note: “the greatest sort of foil to confirmation bias is a lack of customer demand”

Q achieving and not achieving product market fit. Um, 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? I'm just like looking on the flip side incumbent wise. Is it Apple? Is it Amazon? Is it Facebook? Like which one of them is like, ah, you really missed the beat on this one guys.

A Oh, it's gotta be Google. I had my former employer, so it pains me to say it, but, and I, 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 a disruptive technology that Some extent they developed in house, but ignored. So it's a classic innovators dilemma. And so this technology went to other places and now is, is a challenging the hegemony, right? The monopoly power. And that is so exciting. I think if you are into the consumer ecosystem, if you think about like the ads ecosystem, like the BTC ecosystem has been relatively quiet over the last 10 years because of that dominance of Facebook and Google. And now all of a sudden you have a technology and a replatforming where all of that market share is conceivably up for grabs. Uh, you could create a new, uh, travel agency. You could create a new shopping experience. You create a new stack overflow. You create a new social experience based on chat. And, and so it's wide open.

AI assessment note: “Oh, it's gotta be Google.”

Q 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, ah, not for us?

A Yeah, so solo GP is one. There's key person risk, and so some LPs are just not comfortable having a single person, um, to be the general partner. The other challenge was timing. So, you know, 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. In other words, 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 VCs in the private market was going to be written down, but hadn't been written down. And so one of the biggest reasons was I 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 in light of that.

AI assessment note: “one of the biggest reasons was I just need more time in order to understand”

Q That's the question. Like, do you think this is a bundled environment or an, I always think about Jim Bottsdale, um, bundling or unbundling. Like, 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 bundle or an unbundled world in that envisioning?

A So my learning has been that in early markets, people want bundling. They want bundling because they don't yet understand the tech, 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. And so they'll get, if you're a 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 sort of the parameters upon which like I'm going to choose best of breed. So the embeddings layer, the two most important things are 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 and 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.

AI assessment note: “So my learning has been that in early markets, people want bundling.”

Q I absolutely love that narrative. Uh, but I, I do want to deep dive on some elements today then. Uh, and so starting with a simple one, we said about data-driven teams there. Uh, and ones that really know the power of data. So what are the best in class doing to operationalize their data today? What have you found that they're all doing in terms of commonalities?

A Yeah, there are three things they do. The first is they have a data team, and there's this data, and that, this exists at Facebook, it exists at Zendesk, and these data teams, they, they're structured a little bit differently, but they all basically do the same thing. They're, they're the experts. They know where the data is stored. They understand how to perform analysis, but their main function is really education. They go around educating people with, like, office hours and tech talks and that kind of thing. The second thing is a product of the data team. It's this data dictionary, and it was the head data scientist at Warby Parker that came up with this idea, and he found when he was doing a bunch of analysis internally, two different teams internally, they had the same metric, but they had defined it differently. In talking to a bunch of different people, we found that this was a really common problem. Like, in The software world, a marketing qualified lead and a sales qualified lead. Well, the sales team might define each of those differently than the marketing team. And so you have a communication breakdown. So the second common characteristic is there's a single person or team that, that creates this data dictionary that defines all the key metrics for the business once and for all. And then the third thing is, uh, what we call in the book, a data pipeline, which is a w…

AI assessment note: “there are three things they do. The first is they have a data team”

Q Now, now, Tom, for anyone that knows you, they know that you obviously write the incredible newsletter, which is really my must read every day. Um, so, so I have to ask, without being rude, why does it take so long to write the book? You've done incredible Incredible array of newsletters. Why now for the book, and why did you decide to write it?

A Yeah, we wrote it because, like you said before, this data-driven theme, I've really been excited about data for a long time, and one of the benefits of working at Redpoint is I've been able to see the differences between data-driven companies and not, and companies that are less data-driven. The biggest difference among them is that data-driven companies tend to iterate faster, like, you know, I was talking to an angel investor the other day, and I was asking him, this is a SaaS angel investor, and I asked him, if you had one question to determine Whether or not you would invest in a software company, what would it be? He had this amazing response. He said, I would ask the CEO how many meetings the sales team needs to set up so that the company hits its bookings number next month. He asked that question because it shows a really deep understanding of the way that a company works. That kind of insight, that's when you know you have a data-driven business, and so that's really attractive. And so in We were talking about this data-driven theme for a while, Next Generation BI, and we were lucky enough to partner with a company called Looker, which is a Next Generation BI company. It's growing incredibly fast, and because they work with hundreds of companies now, we hear all these wonderful stories about the way that different leaders transform their businesses using data, and so w…

AI assessment note: “when Wiley, which is the publisher of the book, reached out and said”

Q It's a great catchy headline. And on the hiring front, then, with regards to kind of data-driven professionals, uh, Adam Grant states that eight percent of job interviews are productive. Uh, love Adam Grant, by the way. Um, so talk to me. What structure can we use to ensure higher efficiency with regards to the archaic process of hiring these data-driven people? How can we source them?

A Yeah, so I was, I was stunned to hear this. I remember going for a walk in San Francisco, and this product manager at a SaaS company told me that only, that unstructured interviews, which is when we interview people by asking just kind of the typical questions, can only predict success in about eight percent of the cases, which is far worse than a coin flip. And so what Adam Grant, who's a professor in this field, recommends is the first thing you do is you perform a structured interview. Everybody asks the same set of questions. And those questions are based upon the characteristics that are going to find success in this role. So the right way to recruit somebody is first, you define the role, you define what success looks like, and then you create a set of structured questions that the four or five people are going to be interviewing this person, all ask, and all rank on the same scale. And then, uh, Professor Grant also recommends doing actually a, basically a standardized test, like an IQ test. If you do those two things, then you go from about eight percent predictive, predictive capability to more than I think it's a 54%, which is a huge increase. Also in the book, there's this company in New York called Greenhouse, which is a really fast-growing applicant tracking system.

AI assessment note: “what Adam Grant, who's a professor in this field, recommends is the first thing you do”

Q And we've seen a massive rise of this in the last probably five to 10 years. So, so why do you think we've seen this rise, and what do you think the benefits are of adopting this strategy and model?

A Yeah, so free SaaS enabled marketplace is when you have, there's a supplier of something who has a piece of software, there's a buyer of something who has a piece of software, and then there's a marketplace in the middle. You can have some of those SaaS enabled marketplaces that are Free. In other words, the software on each side is free, and the marketplace is taxed, or you can have, ah, everything being paid for. The attraction of a free SaaS-nable marketplace is your distribution, ah, mechanism is really disruptive, right? So, like, there's a company in Canada that's called Joyce that provides a free SaaS-nable marketplace for, ah, home remodeling, and, ah, there's an application for contractors, and there's an application for consumers to help them figure out what remodel they want to do, and then there's a marketplace in the middle So that you can instantly find the best contractor to solve your needs in your area. You know, historically, SMB acquisition is really expensive, and the payback periods don't work, don't work, don't work. They get around this by having three-year contracts with their SMBs, just to make sure that the United Economics work, but, um, you can't do three-year contracts everywhere. So the free model, uh, displaces the cost of the inside sales teams, and then, and so you can get massive amounts of distribution really quickly. You know, as software con…

AI assessment note: “The attraction of a free SaaS-nable marketplace is your distribution, ah, mechanism is really disruptive”

Q Okay, so Apple's in a very strong position looking forwards. Tom Tungus, they're your former employer. How do you see Google playing out over the next few years?

A 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 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 BTC ecosystem, it's been relatively quiet over the last 10 years because of that dominance of Facebook and Google, and now all of a sudden you have a technology and a replatforming where all that market share It's conceivably up for grabs. You could create a new travel agency, you could create a new shopping experience, you create a new stack overflow, you create a new social experience based on chat, and so it's wide open. 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 it's, 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.

AI assessment note: “Google had a rude awakening where, I don't know, for 20, 25 years”

Q look at kind of the two ends of the spectrum for The next generation of AI and LLMs, 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?

A My thinking's 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 open AI. The pace with which Microsoft is injecting its products with LLMs is astounding. 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. And so startups are in this unusual position where they have negative time to launch. They're actually behind the market, which is unusual. I 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 and, 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 to 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 too big incumbents. There are these stories. They're all over. They create this beautiful constellation within start…

AI assessment note: “if you have really great execution, you can still win and you can win big.”

Q Can I ask, how do you think about the decision on doubling down? You said there 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?

A Yeah, I think it's, it's a lot of diligence. I mean, it's, we'll spend six, nine, 12 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, have a pretty strong network there. So one component is just really understanding the market, understanding the buyer base, the different segments, their, their needs. Another component is benchmarking companies. So I've been doing that for more than 10 years. I've got a pretty significant database of, of, uh, 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.

AI assessment note: “one component is just really understanding the market”