E326: What Happens When AI Starts Replacing Analysts?

You built model and model to solve your own problems within your family office. What were those problems? So when Hans and I, who's my brother, we sold the second company, we were still pretty young. Hans was, I don't know, 21, 22. I was made like 26, 20, 27. We made a bit of money and we decided that we wanted to invest our money for a while. So we decided on a few kind of asset classes, strategies behind 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, so that was maybe 21 coming into 2022, you know, 2023 coming in 2024 as LLM started to really be useful in these sort of environments. Frankly, the product just got better and better and better. Before we knew we just had too many people asking us whether they could use it and they're willing to pay for it. So the story goes that we call my mom, ask my mom if we could build a third start up and she said, no, and so we started it the next day. Give me a specific low-hing fruit that investors are using model and now in order to solve their everyday problems. 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 in the family office, you know, we were making, you know, on the bench or start-up size, maybe like 25 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 exile file with multiple times, 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. We're not in the world of like a hundred percent automation. I think we'll get there, you know, probably 12 sort of 24 months away and over single task being close to 100%. You know, anything above 60% automation we really focus on. So, you know, it's not about getting, you know, to pixel perfect at the end. It's really where can AI be most applicable and that specific workflow today? And the classic one is something like your reporting. And 2025 was supposed to be the year of agente AI. Now people are saying 2026. You're one of the only agente AI companies on the planet that a scale use over trillion tokens with open AI. Why have you been able to solve agente AI in a way that others have not? We really launched the end of 2024. And we raised about 100 million across a couple of rounds in our first 12-ish months. So 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 with you 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&A type environment? You know, if that's resulting in a 200 page PowerPoint or a 50 tab wide at Excel workbook, are you going to be able to solve that as of today in a chat interface? The answer is almost certainly no, right? And we kind of knew that up from. 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. 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 the chat type interfaces, it's, it's very hard to quantify, you know, whether that's delivering additional insight or productivity, it's very different. It's quantified with workflow automations. It's just a lot easier. How should investors, GPs or LPs think about agentech AI and where could they apply it in their day to day? The important thing is, is where are we today? But where is this headache? Right? So today, let's make no mistake about it. And you know, these are application layer products, whether that's in finance, legal, health care, 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. 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 are actually going to start to see these systems deliver insight that wasn't possible pre-AI. Now, if we're going 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? 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 memo, so 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, graph tables, charts, logo in the same formats that they would digest the information for, you know, going into the data room and going to Kappa Q, I got a pitch book and so on. 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 glance 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 and it relates to us is the importance of those two or three pages is only going one way. 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 insights only going to be unlocked by doing this today. Tell me about that. Why do you have to prepare today for insights in the future? 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 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 a third, you know, say asset management in general, a third and, you know, the largest, you know, consulting firms, they're on a third and backing. There are their vows is a few other vows in and out, but there are their vows. Now the consistency across all of them, right? So not just on the bicep system, but 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 and all over thing, that initial process and start one of the hardest things of investing is seeing what's shifting before everyone else does. For decades, only the largest hedge funds could afford extensive channel research programs to spot in selection points before earnings and to stay ahead of consensus. Meanwhile, smaller funds have been forced to cobble together ad hoc channel intelligence or rely on stale reports from sell side shops, but channel checks are no longer a luxury. They're becoming table stakes for the industry. The challenges has always been scale, speed and consistency. 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It's interesting because a lot of people use this analogy of data as the new oil, but no one goes upstream of that and things about how do we capture more data? You drill it. 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. 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, advertising, audio, text. It doesn't matter. Right. And so it's almost like regardless of how you're thinking about AI in a short, middle, 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 as that. is you possibly can't. Again, 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 doing, sort of agnostic of that choice? And it's just capturing more data where they can. 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 on the backs of AI, where are these productivity gains specifically going to come from? 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 is theoretically, that's a lot more predictable, but techniques are more difficult to predict clearly. As we look at that and we look at all the data, and really looking at where the time is going more importantly, particularly the more junior members team, 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. Part of your business processes is you go into organizations and you look for productivity gains that they could have using a Gentech AI. What's the exercise that you go through? How can an organization figure out where they have the most productivity gains? We do an entirely free, you don't have to sign up, discovery phase. We will work with a customer and we will identify where we think AI is most applicable today, beyond just a chat-based interface, as I said, workflow automation. We do that over a two, three, or four-week period and really come the end of that period. We're targeting a number of. We don't really look at workflows unless it's a 60% efficiency gain on a single workflow level. The reason we do that is because we really want to focus on short-term or white. It's a little more value quickly. But we do that now with all of our customers. Perhaps this is a dumb question. 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? So if you kind of picture 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. The model is making the best guess is to where you would get that information from, from documents, from faxet, from local filings, from 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. 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 adapt that workflow to them as a specific user if they want to. Is it fair to compare you as an enterprise open-cloth of sorts? And how do you look at open source projects like open-cloth, competing with what you're doing? Quite important. That has become very clear in the legal AI space, I think. I think it's like 80%, at the top 100 law firms now use either Lego or Harvey. And I think finance is about 12 months behind. So I think we can learn a lot from what happened in that market. And if you look at what happened there, it's really in-simple with us is the entire product. That's the bottom and the agentsic system through to the UI is designed with the customer might. And that last mile delivery to the customer, really from what we're seeing is where the impact is. So it's not what the agents are necessarily going and gathering. It's how that's presented the customer. And it's very specific to that customer base. Exactly. And it's just the design of the overall system. So it's like, if you think about the agent, it's what they do integrate with the way that the agents communicate, what language do they communicate? Do they understand financial concepts? Well, whereas the context coming from this, from an agentsic perspective, there's also finance specific things that go on. And I'm really, really important. Again, you've seen that in the legal AI, well, but then it's the UI. How is a user interacting with these systems? Coming back to this point of, can you build a 200 page PowerPoint presentation exactly as you would have done before, graph tables, charts, logos? 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 chat you can see or enclaw it? No. 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. You mentioned it. Legal tech really pioneered this professional use of agentech AI. What can you learn from the Harvies, Legoras of the World? And how do you apply that to the FinTech curious about online trading, but haven't taken the first step yet? You're not alone. And plus 500 futures is a great place to start. The futures markets are moving fast. 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That last mile delivery we talk about is really really important. When I talk about last mile delivery, I'm sure you've heard of this concept of FDEs for the ploy engineers that's booming. That's the concept of having people literally work in the offices or from the offices 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 earlier. I think that is just as if not more important in finance again, because it's not about just the adoption and the driving, the cultural chains. It's those really, really quick feedback. One of the things that we've become very well known for if we look at the competitive landscape is if you look at our nearest competitors, we're maybe built about a product in a quarter of the time with a tent of the capital. Now I know that we've raised a lot recently, but before that, we're the tent of the capital. I think that's really, really important because if you look at the legally, legally, I, well, and all-world, our customers are betting on our product today. Their betting on where our product is in 12 months time. So if we're all in tents, purposes, they're betting on the team and the team's ability to ship the best of the best and be at the forefront consistently. With this forward, the ploy model that you saw in the league, I spaced a lot. We're doing what's actually happening there is we're taking feedback in real time and we are altering and approving the products in real time and let me do you specific. We have scenarios where someone will be at our customers office. They will receive product feedback in real time. They will write all to the code base in real time. Make that alteration the poor request and it'll be in production within half an hour. And some may say, well, that's just great, but is that really needed? What about the day after? But the thing is in this world is these systems are improving so quickly and the competitive landscape is changing so quickly that really, our most as a business is the speed at which we can learn and therefore ship products. Is that not limit your scale? How do you go about scaling a services based? Maybe this is only a night being a little bit English or maybe this is the y-combinator way kind of installed in our mind, but our motto here is fewer, happier customers. We're not a business that's going to over promise an underlever. 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. So I think our view on the market is these demos that you see all over LinkedIn and so on, they're great, but the things that we're really manner, if we fast forward 12 or 24 months time, it's going to be that sticky revenue. Sticky revenue is ultimately going to come from how happy your customers are with your products. So sure look, we can't take it to the the extreme, you know, do things that don't scale, 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, you've raised, you know, about a set of about 100 million our first 12 months, we're very well capitalized to do so, you know, the team's gone from 20 people to 100, I'm sure we're allowed to have another 100, you know, in the next few months, so we're, we're skating to accommodate for that. And sure, it might mean in the shorter term, our margins of society, less as a business, 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. Talk to me about the early adopters in the GP and LLP space. What are the characteristics of these organizations and where they adopting it internally? It's interesting, you, you, you serve our characteristics because I think that comes back to this thing of 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 now I think is almost a bit outdated. I think that statement was relevant maybe six months or so ago. I don't know, 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 are where there is like true kind of top down buy-in. 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, I don't think it takes a rocket scientist to figure out, you know, you go on chat, you be T and do anything around, you know, and any of these AI products, you know, there's a huge efficiency game that's happened and happening, right? So there has to be a structure change. So the ones that are really thinking about things structurally, has an overall organization, I think, of the ones of the best early adopters. Being, if you want to be specific that I really mean, well, if you take your, you know, you're more junior members of the team and you're seeing even if we're pessimistic and we're seeing a 10 20% overall possible efficiency game, well, where is that 10 20% going? It needs to be reallocated and the best firms and the best leaders are the ones that are thinking ahead and they're saying, okay, well, we're going to take this 10, you know, this pool of, you know, say 10% 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 product like ModernML, they're the ones that I think you're doing incredibly well. The last part of your question is, you know, where do you start? You know, 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 really think like we do an incredible job at working closely for, you know, during this free discovery phase, helping, you know, where that's GPs, 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 it actually look like in terms of use cases? Well, for now, it's more of 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 will become more how you're able to drive insight that wasn't possible pre-AI, but for today it's productivity. What are we talking about budget wise? What's has affirmed you need to be to hire firm like ModernML or maybe how much do these projects cost? We as a business don't do many kind of like 5, 10, 20, cedar 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, if we want to make sure we over-deliver when we're working with our customers, but at the moment, it's in hundreds of seats, I think that's going to come down to the 5 and 10s within the next few months. Pricing wise, depending on whether you're buying data through our success, 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 seats, but again, it just depends on scale. 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 questions would you typically ask a client and maybe we could roleplay with it? I always ask, this is a question that I think is important is I ask people about their own AI journey as an individual, right? You know, how, what are they using AI for insight and outside of work, right? The reason I ask that is I like to understand their appetite towards AI in general. I do kind of look like AI as a person. Sometimes they disappoint it. Here 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 in 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 it also tells me with very eye confidence. If I was say to you areas that frustrate you, that you feel inefficient, but you don't necessarily know how and where to apply artificial intelligence, like what are the things that springs to mind? 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 in terms of thinking strategically, like mapping guests, mapping outreach, all of that. That would be a big help. Also, obviously on the Asa 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. Okay, excited now. So that's great. And then what I always ask as well is to what extent across those are particularly interested in the kind of more prospecting one as you think about the managers, what tools if a tool ever used to kind of think about or look at that overall use case? We're old school. We talk to a lot of other GPs to 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'd love to be much more proactive. So the answer is we don't really have a process for that outside of just gathering information all fashioned way, meetings, Zoom meetings, and person meetings, and building relationships with co-investors. And then one last question is how do you get the feedback loop with like the areas or the nuggets of things that your listeners are interested in other than people you're asking for my secrets sauce. So I'll give you some secrets sauce. So we get analytics from the audio, but the richest analytics we actually get from YouTube. And what I started to do about two months ago is we have the retention curves. I take the retention curve. I upload that to AI. I take the transcript that I upload that to AI and I have a give me feedback in terms of like where did people drop off? So we kind of do this recursive improvement. 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 use is flowing of what's possible, but directionally where we think AI is good at. So the things that were spring into mind as you were speaking, by the way, 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 the different nuggets of content off the back of that? So by way of example, have you thought about AI? If I said to you, you could have a workflow that the second that a video has 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 with a view that is consistently updating this output, sort of summarizing where it fills that thematically, everyone is interested in or the main areas of interest. Where my mind went, the two really leverage points for us on the podcast side, it's actually the guest. If the guest is 80 percent, if I have Ben Horowitz, Cliff Asnis, Volga Shrinovassin, 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 the company side and 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 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 social networks? Obviously, we regularly connect, pretty much by the customers of connecting our product into that CRM, for example. So clearly anything in the CRM we can tap into. But I think there's a lot of work to be done. Your exact point, your own mind, we kind of when the business was started around one of our first use cases is we wanted to understand that relationship instantly. If there's a mutual connect with, we're looking at an opportunity and there's a mutual connect with the management team. We're looking at fun, someone knows the GP, except whatever it might be. That's very much untouched. We've actually, we're looking at bringing it on a product team, specifically focused on that area. And that's many just because our customers are slowly moving in the direction of GP's LPs, etc. So I think in general, I think it's going to be quite popular. When my oldest friend, Johnson Kim, he started 5'9 as public company and 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. One of them, why culminates a multi which I think was that like a cycle that said it most was the idea of a growing business is just dead die, which basically just means they run out of cash. So I'm very aligned with that. If you could go back to when you were just starting your first company or first YC back company, and you could give yourself only one piece of timeless advice, what would that advice be? Definitely perseverance. You know, I've mentioned this. We were on the YC 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 are going. If you believe in something, I think the key is to just persevere. It goes back. I've now interviewed nine billionaires. Hopefully, it'll be my tenth billionaire. 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 actually think of a counter factual 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 I capital. He was a decade earlier to the retail trade. When he was doing it, people, no institutional investor wanted to take retail capital. He took a bet and not only did he say, I'm going to do this, hopefully he works out. He was just running there. Now he's built a $7 billion company or so. Another example, Ryan Sirhant, 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 and pulled 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 pen house 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. 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 had to go to, we had to create a compliance call. We knew that we were too early. Thankfully, I was a VC and I 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 contrarian 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. That's it. Plus event. Well, Chas, this has been an absolute masterclass. Thanks so much for taking your time and thanks so much for jumping on podcasts. David, thanks so much. That's it for today's episode of How 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.