E326: What Happens When AI Starts Replacing Analysts?

Will AI soon write investment memos, analyze deals, and run workflows inside investment firms? In this episode, I speak with Chaz, founder of Model ML, about the rise of agentic AI and how investment firms are beginning to automate complex workflows across private markets. Chaz explains how Model ML originally started as an internal tool built inside his family office to manage investments more efficiently — before evolving into a fast-growing AI platform used by asset managers, banks, and consulting firms. We discuss why chat-based AI tools have limitations for professional workflows, how firms can achieve major productivity gains through automation, and why the next phase of AI will shift from productivity toward generating investment insight.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00David Weisburd?

  You built Model ML to solve your own problems within your family office. What were those problems?

• 0:04Speaker 01

  So when Anz and I, who's my brother, we sold the second company. We were still pretty young. Anz was, I don't know, twenty one, twenty two. I was maybe like twenty six, twenty, twenty seven. We made a bit of money and we decided that we wanted to invest our own money for a while. So we decided on a few kind of asset classes, strategies. We hired a few people to predominantly do the investing. And then we spent a lot of our day writing software to make that process better. Before we knew it, that was maybe twenty one, coming into twenty twenty two, you know, twenty twenty three, coming in twenty twenty four, as LLM started to really be useful in these sort of environments. Frankly, the product just got better and better and better. And before we knew it, we just had too many people asking us whether they could use it and they're willing to pay for it.

• 0:48Speaker 01

  So the story goes that we called my mom, asked my mom if we could build a third startup and she said no. And so we started it the next day.

• 0:55David Weisburd?

  Give me a specific low hanging fruit that investors are using Model ML in order to solve their their everyday problems.

• 1:01Speaker 01

  A classic is just like reporting and monitoring in general, right? It's the classic problem, you know, you're, you know, even when I think about this back at the family office, you know, we were making, you know, on the venture startup side, maybe like twenty five investments a year, right? And we would receive updates in like every single format. You know, sometimes even now, it's like a website link, you know, it's like you got a website link, you got a notion page, you've got just a bunch of documents. You've got it in the body of the email, you've got it in an Excel file with multiple tabs, et cetera. And really what you want to do is you want to consolidate that down and bring that into your systems in your format. It's it's somewhat baffling to me that, you know, a lot of these tasks are still being done manually, frankly. You know, that is a classic example of something that should be, you know, automated.

• 1:45Speaker 01

  We're not in the world of like a hundred percent automation. I think we'll get there, you know, probably twelve, sort of twenty four months away and of a single task being close to a hundred percent. You know, anything above sixty percent automation, we really focus on. So, you know, it's not about getting, you know, to pixel perfect to the end. It's really where can AI be most applicable in that specific workflow today? And the classic one is something like reporting.

• 2:07David Weisburd?

  And twenty twenty five was supposed to be the year of Agentic AI. Now people are saying twenty twenty six, you're one of the only Agentic AI companies on the planet that have scaled to use over a trillion tokens with OpenAI. Why have you been able to solve agentic AI in a way that others have not?

• 2:23Speaker 01

  We really launched the end of 2024 and we raised about a hundred million across a couple of rounds in our first 12-ish months. The business has been growing, right? And looking back on it, you know, we often think, okay, where did we differentiate? And to us, it's quite clear. These chat type interfaces, think, you know, chat GPT and anthropic and others, they're great. Don't get me wrong, they're absolutely fantastic. They're very much going to change the world. But if you think about the complexity of work that actually goes on in, you know, these types of organizations, they're going to have a ceiling. So if you think of any relatively complex workflow that you've done, you know, in the last few months, can you solve that in a chat or Q and A type environment?

• 3:05Speaker 01

  You know, if that's resulting in a 200 page PowerPoint or a 50 tab wide Excel workbook, are you going to be able to solve that as of today in a chat type interface? The answer is almost certainly no, right? And we kind of knew that upfront. And so we came into the market with a slightly different perspective where it's like, look, the chat type interface is great and we have that and it's useful. But really, I think what firms want is they want true workflow automation, right? But also it's much easier for us to sell to them and easier for them to procure as well, right? Because they can actually say, okay, well, here are the three or four things that we want to automate. Can you automate those things? Well, if we can, yes, and it's very clear. The difficulty with a chat type interface is it's very hard to quantify, you know, whether that's delivering additional insight or productivity.

• 3:49Speaker 01

  It's very difficult to quantify with workflow automations, it's just a lot easier.

• 3:53David Weisburd?

  How should investors, GPs or LPs think about agentic AI? And where could they apply it in their day to day?

• 3:59Speaker 01

  The important thing is, is where are we today? But where is this heading, right? So today, let's make no mistake about it. You know, these application layer products, whether that's in finance, legal, healthcare, whatever, you know, they are. Productivity tools, for the most part, they're giving you, you know, an additional layer of efficiency in your businesses, right? The question is though, particularly if we think about this from an investing standpoint, is when is this going to be able to deliver a level of insight? That wasn't possible pre-AI. That's really the question, you know, because at the end of the day, productivity is great. But but it's all about that alpha from an insight perspective.

• 4:38Speaker 01

  And in our view, there is absolutely no doubt that 2026 is going to be that year. We think 2025 is the productivity year. 2026 is the year where we're actually going to start to see these systems deliver insight that wasn't possible pre-AI. Now. If we got to play that back a little bit, that's not going to be today, no insight, tomorrow insight. It's going to be slightly more incremental than that, right? And I'll give you a quite a specific example. One of our middle market private equity clients, it's a European client, they're absolutely fantastic. They've pretty much automated 80% of their IC memos, their IC paper, right? A lot of that is going to different data sources and really just data retrieval, a bit of reasoning and producing that in a format that they're used to digesting.

• 5:23Speaker 01

  Graph, tables, charts, logo in the same formats that they would digest the information from. You know, going to the data room and going to Q and going to pitch book and all the areas of information that you would normally go to. But there's two or three pages in that now that are not just generated by AI, but it's kind of like the AI's opinion, right? And these are things like, you know, the AI's opinion on the overall management team, based on your historical investments. We've noticed that there's a lack of experience over here. There's a lot of experience here, for example. Now, as of today. They glanced over that in their IC meeting. It's kind of like, this is interesting. We spend five minutes on it and we move on. But one of the things that's clear to them, or clear to us, is the importance of those two or three pages is only going one way.

• 6:04Speaker 01

  In other words, the AI opinion is only becoming stronger and stronger and stronger. And so I think that's why it's super important. That firms, you know, it might not be perfect today, right? But you've got to embed this into your culture and these systems into the way that you think as soon as possible. Because that future insight is only going to be unlocked by doing this today.

• 6:23David Weisburd?

  Tell me about that. Why do you have to prepare today for insights in the future?

• 6:27Speaker 01

  I was thinking coming into this conversation, what would I advise? You know, irrespective of what we do, what would I advise firms to think about today? I would really think about data. I think things like trying to transcribe calls is a great example of like, you know, if you think of what these systems are going to need in future, the more data that they have, particularly now because they are a sort of data structure agnostic, whether they're calls, emails, files and folders, structured data, it doesn't matter, right? You want to try and capture as much data as you possibly can as part of the investing process. I think that's important. The second part of that question is, you know, this is more of a cultural shift than anything else. I think as we've thought about, so I should say, you know, our customers are about

• 7:07Speaker 01

  a third, I'm going to say asset management in general, a third in, you know, the largest, you know, consulting firms, a third in banking. There are thereabouts, you know, there's a few other fellows in there now, but there are thereabouts. Now, the consistency across all of them, right? So not just on the buy side, consistency across all of them is this is clearly not a technology problem anymore, right? This is becoming more and more of a cultural change and a structural change. You know, as to how you think about the organization, how you think about AI from a cultural perspective. But that takes time and, and, and, you know, I really would encourage firms to just start, not overthink that initial process and, and start.

• 7:42David Weisburd?

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• 8:21David Weisburd?

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• 9:03David Weisburd?

  Check it out for yourself at alphascense.com/HowIInvest. It's interesting because a lot of people use this analogy of data is the new oil, but no one goes upstream of that and thinks about how do we capture more data? You drill in the ground to get more oil, and you have to capture the data. If it's truly oil, why aren't you capturing more of it? And why aren't you creating the schema, the processes, and the cultural aspects of becoming a data-driven organization?

• 9:27Speaker 01

  The reason I always use in that example, sort of call transcriptions, is because it's kind of in a way obvious but less obvious, right? You know, these models, they're incredible at interpreting videos, incredible at interpreting audio, text, it doesn't matter, right? And so it's almost like regardless of how you're thinking about AI in the short, middle, or long term, start capturing that data. We did some analysis around, you know, what actually goes into the decision-making process and where that information comes from. It's something like 70% of all information is coming from calls and meetings, right? So you want to try and capture as much of that as you possibly can. Again, this is, this is less relevant to us. It's more just our view on like, okay, outside of adopting a vertical application, which clearly seems like something that firms should do, what else can they start thinking about

• 10:11Speaker 01

  doing, sort of agnostic of that choice? And it's, it's just capturing more data where they can.

• 10:16David Weisburd?

  Last time we chatted, you gave me this shocking statistic that you believe that companies will experience 55% productivity gains in the next 12 to 18 months.

• 10:27David Weisburd?

  On the backs of AI, where are these productivity gains specifically going to come from?

• 10:30Speaker 01

  So I was saying, well, what even qualifies us for making that statement? A lot of what we're doing as we're selling into firms is we're building business cases, right? So we have to deeply understand where AI is applicable today. The big thing that really no one saw is the progress that happened with reasoning models. Reasoning is effectively a technique. It's very difficult to forecast progress in underlying techniques. Now forecasting progress in the underlying models, you know, is theoretically, you know, that's a lot more predictable, but techniques are more difficult to predict clearly. And so as we look at that and we look at all the data and really looking at where the time is going more importantly,

• 11:12Speaker 01

  particularly the more junior members of the team, you know, we're looking at a 50% efficiency, certainly before the end of 2027, but that could be a considerable amount of time before that. And I don't think that's because of the technology. I think that's because of the adoption.

• 11:29David Weisburd?

  Part of your business processes is you go into organizations and you look for productivity gains that they could have using Agentic AI. What's the exercise that you go through? How can an organization figure out where they have the most productivity gains?

• 11:42Speaker 01

  We do an entirely free, you don't have to sign up, discovery phase, right? So, so, you know, we will work with a customer and we will identify where we think AI is most applicable today beyond just a chat-based interface, you know, as I said, workflow automation. So, so we do that over a two, three or four week period. And really come the end of that period, you know, we're targeting a number of, and we don't really look at workflows unless it's a 60% plus percent efficiency gain on a, on a single workflow level. The reason we do that is because we really want to focus on sort of short-term ROI to deliver value, you know, quickly. But we do that now with, with all of our customers.

• 12:19David Weisburd?

  Perhaps this is a dumb question, but is this a software process that's running on machines? Are these virtual machines? How do you actually implement these systems? The user just logs in, presses a button, and the report is generated? How does that mechanically work?

• 12:33Speaker 01

  So if you kind of picture it, the way it would work is we have an Excel-type interface. So think of just Excel but powered by AI. You upload a document, and what we're doing is we're deconstruction that document into its individual components. And we're making, you know, the model is making the best guess as to where you would get that information from, from documents, from FAQ set, from filings, from, you know, wherever you may normally get that information from. The user would come in and confirm, they would click save, and then that workflow would exist, right? And they can deploy that workflow to just themselves, to the rest of the team, to the whole group. And then they can, obviously, as you go, you can make alterations, and then someone can make a copy and, you know, adapt that workflow to them as a specific user if they want to.

• 13:06David Weisburd?

  Is it fair to compare you as an enterprise open claw of sorts? And how do you look at open source projects like Open Claw competing with what you're doing?

• 13:16Speaker 01

  Quite important. That has become very clear in the legal AI space, I think. You know, I think it's like eighty, ninety percent of the top one hundred law firms now use either Legor or Harvey, and I think finance is about twelve months behind. So I think we can learn a lot from what happened in that market. And if you look what happened there, it's really in similar with us is the entire product, you know, that's the bottom of the agentic system through to the UI is designed with the customer in mind, right? And that last mile delivery to the customer, really from what we're seeing is where the impact is.

• 13:46David Weisburd?

  So it's not what the agents are necessarily going and gathering. It's how that's presented to the customer, and it's very specific to that customer base.

• 13:54Speaker 01

  Exactly, and it's just, it's the design of the overall system, right? So it's like, you know, if you think about the agents, it's what data you integrate with, the way that the agents communicate, what language do they communicate in? Do they understand financial concepts well? Where is the context coming from? So this, you know, from an agentic perspective, there's lots of finance-specific things that go on that are really, really important. Again, you've seen that in the legal AI world. But then it's the UI. How is a user interacting with these systems? You know, coming back to this point of can you build a two hundred-page PowerPoint presentation exactly as you would have done before, graphs, tables, charts, logos? You know, think about the complexity that goes into these types of outputs, think FDDs and CDDs and so on, right? Can you do that on your phone and ChatGPT or in Claude? Well, no.

• 14:37Speaker 01

  And so you have to, it's the agent system all the way through to the UI and with the user interacting with these systems that becomes really, really important.

• 14:45David Weisburd?

  You mentioned it, legal tech really pioneered this professional use of agentic AI. What can you learn from the Harveys, the Goros of the world? And how do you apply that to the fintech use case?

• 14:59Speaker 01

  That last mile delivery we talk about is really, really important. Right? So when I talk about last mile delivery, I'm sure you've heard of this concept of FTEs, forward deployed engineers that's like booming. That's, that's the concept of, you know, having people, you know, literally work, you know, in the offices or from the offices of, of your customers for a period of time to not just drive adoption, but to deal with onboarding and also to innovate really, really quickly. I think that you saw a lot of that in legal. I think that is just as, if not more important in finance again, because it's not about just the adoption and driving the cultural chains. It's those really, really quick feedback loops. You know, one of the things that we've become very well known for, if we look at the competitive landscape is, you know, if, if we look at our nearest competitors, we're maybe, you

• 15:43Speaker 01

  know, built a, a better product in a quarter of the time with, with a 10th of the capital. Now, I know that we've raised a lot recently, but before that, with, with a 10th of the capital. I think that's really, really important because if you look at the legal, legal AI world and our world, you know, our customers are not betting on our product today. You know, they're betting on where our product is in 12 month time. So, you know, for all intents and purposes, they're, they're betting on the team and the team's ability to ship, you know, the best of the best and be at the forefront consistently. Right? And so I think, you know, the, with this forward deployed model that you saw in the legal AI space a lot, you know, we're doing it. What, what's actually happening there is we're taking feedback in real time and we are altering and improving the products in real time. And let me be specific, you know, we have scenarios where someone will be at a customer's

• 16:26Speaker 01

  office, they will receive product feedback in real time, they will write all to the code base in real time, make that alteration, do the, do the pull request and it'll be in production, you know, within half an hour. And, you know, some may say, well, that doesn't, okay, that's great, but is that like really needed? What about like the day after? But the thing is in, in this world is, you know, these systems are improving so quickly and the competitive landscape is changing so quickly that really, you know, our moat as a business is the speed at which we can learn and therefore ship products.

• 16:55David Weisburd?

  Does that not limit your scale? How do you go about scaling a services-based?

• 17:00Speaker 01

  Maybe this is Arnie and I being a little bit English, or maybe this is the Y Combinator way kind of installed in our mind. But our motto here is like fewer happier customers, right? We're not a business that's going to over promise and under deliver, right? You know, we're a business that when we say we're going to do something for a customer, we do it. And if we don't, I think it hurts us emotionally, right? So I think like our view on the market is, you know, these demos that you see all over LinkedIn and so on, they're great. But the things that really matter, you know, if we fast forward 12 or 24 months' time, you know, it's going to be that sticky revenue. Sticky revenue is ultimately going to come from, you know, how happy your customers are with your products. So sure, look, we can't take it to the extreme, you know, do things that don't scale.

• 17:43Speaker 01

  But at the same time, we just believe that if we deliver to our word in a market where reputation frankly is, is everything, is the single most important thing. Now, you can still scale quickly. We've raised, you know, about, as I said, about a hundred million in our first 12 months. So we're very well capitalized to do so. You know, the team's gone from 20 people to 100. I'm sure we'll add another 100, you know, in the next few months. So we're scaling to accommodate for that. And sure, it might mean in the shorter term, our margins are slightly less as a business. But ultimately, we think if we do well in that first month, three months, six months and 12 months, you know, we can have these customers for a very, very long time.

• 18:16David Weisburd?

  Talk to me about the early adopters in the GP and LP space. What are the characteristics of these organizations? And where are they adopting it internally?

• 18:26Speaker 01

  It's interesting you say about characteristics because I think that comes back to this thing of

• 18:33Speaker 01

  a cultural shift, right? So I think it's really important that there is top-down buy-in, you know, CEO level or CIO level buy-in of these sorts of products or AI in general, which

• 18:49Speaker 01

  Now, I think is almost a bit outdated. I think that statement was relevant, maybe six months or so ago. I don't know of a single firm where this isn't like number one on, on the agenda, or maybe number two, but probably number one. So, so generally speaking, the, the best early adopters that we've seen where there is like true kind of top down buy. I think that's the first thing. The second thing is, we've also noticed it's the firms that think about how this is going to alter the organization structurally. Because clearly it will, like, you know, it, I don't think it takes a rocket scientist to figure out, you know, you go on chat G P T and do anything around, you know, and any of these AI products. You know, there, there's a huge efficiency game that's happened and happening, right? So there has to be a structural change. So the ones that are really thinking about things structurally as an overall organization, I think are the ones that are the best adopters.

• 19:32Speaker 01

  Being, if you want me to be specific, that I really mean, well, if, if you take your, your, your more junior members of the team. And you're seeing, even if we're pessimistic and we're seeing a ten, twenty percent overall possible efficiency gain. Well, where is that ten, twenty percent going? It needs to be reallocated. And, and the best firms and the best leaders, the ones that are thinking ahead and they're saying, okay, well, we're going to take this ten, you know, this pool of, you know, say ten percent of the overall team. And they're going to become our AI experts. A lot of time they're self identified, you know, we all have friends or, you know, people that naturally in their roles are like super interested compared to their peers at AI. They're probably the ones that you want to use because you don't need to write code in this sort of environment, right? So the ones that are actually saying, okay, we need to move these people and your job is to either assess or, you know, AI tools or build AI workflows in a, in a product like Model ML.

• 20:18Speaker 01

  They're the ones that I think are doing incredibly well. The last part of your question is, you know, where do you start? You know, what, what areas of the business do you focus on? I think this is, you know, can be on a case by case. And so that's where we've been very value add in this discovery phase. So I, I really think like we, we do an incredible job at working closely for, you know, during this free discovery phase, helping, you know, whether that's G P S, wherever it might be, you know, identify areas that they should immediately be thinking about applying artificial intelligence. That's what we've become very good at. Now, what does that actually look like in terms of use cases? Well, for now, it's more of those, those, you know, inefficient, you know, areas of the business that you can have these productivity gains. As I said, I think in future that'll become more how you're able to drive insight that wasn't possible pre-A I, but for today it's productivity.

• 21:04David Weisburd?

  What are we talking about budget wise? What size of firm do you need to be to hire firm like Model ML? Or maybe, how much do these projects cost?

• 21:13Speaker 01

  We as a business don't do many kind of like five, 10, 20 seater type deals. We're really looking at, you know, in the hundreds of seats at the minimum, if not the thousands, right? That's changing and quickly as we're scaling the team, it's enabling us to work on those smaller deals. I really, that's just a question of focus, right? Again, we want to make sure we over deliver when we're working with our customers. But, but it can, at the moment, it's in the hundreds of seats. I think that's going to come down to the five and 10s within the next few months. Pricing wise, depending on whether you're buying data through us, et cetera, you should really be budgeting for true AI tools. You know, anything from a hundred bucks a month, maybe 300 bucks a month, you know, per seat. But again, it just depends on scale.

• 21:55David Weisburd?

  Chaz, I wanted to do a live discovery. So we have a media side of our business. We have an asset management side of the business. So what, what questions would you typically ask a client? And maybe we could role play that.

• 22:05Speaker 01

  I always ask, this is a question that I think is important is I ask people about their own AI journey as, as, as an individual, right? You know, how, what are they using AI for inside and outside of work, right? And the reason I ask that is I like to understand their appetite towards AI in general.

• 22:20David Weisburd?

  I do kind of look like AI as a person. Sometimes they disappoint, he or she disappoints me and sometimes it comes through. I found, so we have on the media side of our business, we have a lot of our podcasting, the production, the editing. But we also have YouTube and thumbnail and packaging and all this. And I have found very specific cases where AI is very good and certain specific cases where it's extremely bad. And even worse than it being extremely bad, it's extremely confidence in those cases. And whenever I ask it how confident it is, it gives me like full confidence and then it's completely off. So that's been kind of my downside of working with AI where it not only gives me poor work, work output, but also tells me with very high confidence.

• 23:00Speaker 01

  If I would say to you areas that frustrate you, that you feel are inefficient, but you don't necessarily know how and where to apply artificial intelligence, like what are the things that springs to mind?

• 23:11David Weisburd?

  I think video production, editing the first part of our, the podcast. So we have an editor that does a bunch of fancy stuff, but just like the pure editing, the platforms haven't been able to do that. Terms of thinking strategically, like mapping guests, mapping outreach, all of that, that would be a big help. Also, obviously on the asset management side, figuring out which companies we would want to, like what should be on our target list, mapping the network of how to get to those people and how to get to those companies. These are the things that really take a lot of man hours.

• 23:43Speaker 01

  Okay, so that's great. And then what I always ask as well is to what extent across those, I'm particularly interested in the kind of more prospecting one, as you think about the managers, what tools of a tool have you used to kind of think about or look at the overall use case?

• 23:59David Weisburd?

  We're old school. We talk to a lot of other GPs, figure out which companies they like. We don't have a great process for prospecting. We're very much reactive in terms of the conversations that we have. I love to be much more proactive. So the answer is we don't really have a process for that outside of just gathering information old fashioned way, meetings, Zoom meetings, in-person meetings and building relationships with co-investors.

• 24:21Speaker 01

  And then one last question is how do you get the feedback loop of like the areas or the nuggets of things that your listeners are interested in other than people?

• 24:29David Weisburd?

  You're asking for my secret sauce. So I'll give you, I'll give you some secret sauce. So we get analytics from the audio, but the richest analytics we actually get from YouTube. And what I started to about two months ago is we have the retention curves. I take the retention curve, I upload that to AI, I take the transcript, I upload that to AI and I have it give me feedback in terms of like, where did people drop off? So we kind of do this recursive improvement.

• 24:55Speaker 01

  Yeah, nice, nice. Well, and then what we try and do if I was working with you as a customer is like, we try and at least kind of whiteboard out, it wouldn't be as quick as this, but we would whiteboard out a couple of use cases and try and get feedback on them in real time, mainly because I think that's what kind of gets the both the creative juices flowing of what's possible, but directionally where we think AI is good at. So the things that were springing to mind as you were speaking, by the way, are things like, okay, well, in the comments on YouTube, how are we thinking about categorizing those comments, right? And how are we then thinking about, you know, the different nuggets of content off the back of that? So by way of example, have you thought about if I said to you, you could have a workflow that the second that a video is published, it's monitoring those comments. So it's actually a workflow that's connected to that video and it's in real time grabbing those comments and it's categorizing them.

• 25:41Speaker 01

  With a view that is consistently updating this output, sort of summarizing where it feels that thematically everyone is interested in, or the main areas of interest.

• 25:50David Weisburd?

  Where my mind went, the two really leverage points for us on the podcast side, it's actually the guests. The guests is 80%. If I have Ben Horowitz, Cliff Asness, Balaji Shrinivasan, if they come on the podcast, yes, the conversation could be good or bad, but you're more or less know how it's going to do. So guest booking and how do you get to the, mapping the relationship graph, because I'm connected to pretty much everybody, just a matter of like how to trace that. And then the same thing on, on the company side, on the investment side, like which company should we be talking to? So the relationship graph is kind of where I went to, which is, how do we get the information of what the best companies are? And how do we get who we should have on the podcast and then mapping to them through the social networks?

• 26:28Speaker 01

  Obviously, we regularly connect, you know, pretty the most part of our customers are connecting our product into their, you know, CRM, for example. So, you know, clearly anything in the CRM, you know, we can tap into. But I think there's a lot of work to be done, you know, your exact point, it reminds me kind of when the business was started around one of our first use cases is, you know, we wanted to understand that relationship instantly. If there's a mutual connect with, you know, we're looking at opportunity and there's a mutual connect with the management team, we're looking at fun, you know, someone knows the GP, except whatever it might be, that's very much untapped. We've actually, we're looking at bringing on a product team specifically focused on that area. And that's mainly just because, you know, our customers are slowly moving in, in the direction of, you know, GPs, OPs, etc. So I think in general, I think it's going to be quite popular.

• 27:14David Weisburd?

  One of my oldest friends, John Somkim, he started Five9, this public company, and he always, he hammered this into me. He always hammered into me, you have to be close to cash. Companies die because they're not close to cash. And when I think, that's why my mind went to, well, what's driving our revenue? Advertising and more deals. So what should we be solving? Guests and more deals. That's kind of why my head just immediately went to sourcing, I guess sourcing on both sides.

• 27:39Speaker 01

  One of them, Y Combinator mottoes, which I think was Michael Seibert that said it most, was, you know, the idea of a growing business is just don't die, which basically just means, like, don't run out of cash. So I'm very aligned with that.

• 27:52David Weisburd?

  If you could go back to when you were just starting your first company, your first YC-backed company, and you could give yourself only one piece of timeless advice, what would that advice be?

• 28:02Speaker 01

  Definitely perseverance. You know, I've mentioned this, we were on the Y Combinator podcast recently and I said this on there. Not blind perseverance, but perseverance. I think if something makes sense to you and you are passionate about it. And it makes sense in itself. You should probably continue doing that thing and persevere at all costs. I think across all the companies, there's ups and downs, these economic cycles again. If you believe in something, I think the key is to just persevere.

• 28:32David Weisburd?

  It goes back, I've now interviewed nine billionaires. Hopefully it'll be my 10th billionaire, you'll let me know in a few years. And they all have one and a half things in common. The first thing is they're all compounding something. None of them are building linear businesses. You don't have enough life to become a billionaire linearly. It must be compounding. Sometimes it's literally network effects. Sometimes they're compounding brand. There's different ways to compounding, but they're all compounding. This is universal. And the second thing, almost all of them, I can't actually think of a counterfactual, is they're all not only walking in the opposite direction of the market. Oftentimes they're running in the opposite direction. I had the CEO of iCapital, he was a decade earlier to the retail trade. When he was doing it, people, you know, no institutional investor wanted to take retail capital. And he took a bet and not only did he say, I'm going to do this, you know, hopefully it works out.

• 29:17David Weisburd?

  He was just running there and now he's built a $7 billion company or so. Another example, Ryan Serhant, he was doing social media back in 2014. Every real estate agent was ridiculing him. You look like a clown. I think he literally jumped in pools, maybe even literally dressed like a clown. And he didn't care because he had that conviction. In 2014, he sold, I believe, like a $15 million penthouse through YouTube. And that's when he knew kind of he had that proof. So having this conviction, and if you have this conviction, you're going in the opposite direction and no one else sees that. That's basically a sign that there's these signs that you get in startups. We had, when we started the podcast three years ago, every single institutional investor that I had to go to, we had to create a compliance call. We knew that we were too early.

• 30:00David Weisburd?

  Thankfully I was a VC and understood that if you, if it felt too early, you're probably on time. Having this contrarian insights where everybody thinks, most of the time you have a contrary insight. Everybody thinks that you're wrong, you actually are wrong. But once in a while, you just keep on going back to first principles. What am I missing? What am I missing? And if you're not missing, you better run because people are going to catch up.

• 30:20Speaker 01

  That's it, persevere.

• 30:22David Weisburd?

  Well, Chaz, this has been an absolute masterclass. Thanks so much for taking your time and thanks so much for jumping on the podcast.

• 30:28Speaker 01

  David, thanks so much.

• 30:29David Weisburd?

  That's it for today's episode of How I Invest. If this conversation gave you new insights or ideas, do me a quick favor. Share with one person in your network who'd find it valuable or leave a short review wherever you listen. This helps more investors discover the show and keeps us bringing you these conversations week after week. Thank you for your continued support.