Investing Billions - E326: What Happens When AI Starts Replacing Analysts?

Episode Date: March 17, 2026

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.

You built Model ML to solve your own problems within your family office. What were those problems? So when Anz 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 maybe like 26, 10, 27. 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, so that was maybe 21 coming into 2022, you know, 2020, 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. And 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, asked my mom if we could build a third startup and she said no, and so we started it the next day. Give me a specific low-hanging fruit that investors are using,

model ML 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, even when I think about this back at the family office, you know, we were making, you know, on the venture or startup 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've got a website link.

You've 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 times. etc. 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 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 100% automation. I think we'll get there,

you know, probably 12, sort of 24 months away and of a 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 in that specific workflow today? And the classic one is something like you're reporting. And 2025 was supposed to be the year of agentic AI. Now people are saying 2026, you're one of the only agentic AI companies on the planet that has scaled to use over a trillion tokens with open AI. Why have you been able to solve agentic 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, chaty 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 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 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 up front. 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 interface is it's very hard to quantify, you know, whether that's delivering additional insight or productivity, it's very difficult to quantify. With workflow automations, it's just a lot easier. How should investors, GPs or LPs think about agentic AI and where could they apply it in their day to day? The important thing 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 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'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. And I'll give you a quite a specific example. One of our middle market, proud equity clients, it's a European client, they're absolutely fantastic. They've pretty much automated 80% of their I see memos, their I see 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 to the data room and going to Cap IQ and going to pitchbook 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 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 very clear 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 insight is 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 in, a third in, you know, the largest, you know, consulting firms in a third in banking. There are their values.

There's a few other photos in there now, but there are their balance. Now, the consistency across all of them, right? So not just on the bisad, this is 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, you know, I really would encourage firms to just start not overthink that initial process and start. of the hardest things of investing is seeing what's shifting before everyone else does. For decades,

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of data as the new oil, but no one goes upstream of that and thinks 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'm always using that example, sort of cool transcriptions, is because it's kind of in a way obvious, but less obvious, right? You know, these models, they're incredible interpreting videos, incredible editing audio text. It doesn't matter, right? And so it's almost like regardless of how you're thinking about AI in a short, mid, 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 as you possibly can. 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. The last time we chatted, you gave me this shocking statistic that you believe that companies will experience 55% productivity gains in the next 12th 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. 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. No, 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, 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. 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? organization figure out where they have the most productivity gains? We do an entirely free, you don't have to sign up, discovery phase. Right.

So, you know, we will work with a customer and we will identify where we think AI is most applicable today beyond just a, you know, 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 or an. on a single workflow level. The reason we do that is because we really want to focus on sort of short-term R or why to deliver

value quickly. But we do that now with all of our customers. 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? 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 deconstructing 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 faxed, from verbal 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 their 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. Is it fair to compare you as an enterprise open claw of sorts?

And how do you look at open source projects like OpenClaught 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, 90% of 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 what happened there, it's really interesting with us, is the entire product, you know, that's the bottom in the Agentsic 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. 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. 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 agent, 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 from an agenic 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 200-page, you know, 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 FDs and CDDs and so on, right?

can you do that on your phone in chat GPT or in Claude? No. 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. You mentioned it. Legal tech really pioneered this professional use of agentic AI. What can you learn from the Harvey's, Ligoras of the world,

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See Experian.com for details. Use case. That last mile delivery we talk about is really, really important. So when I talk about last mile delivery, I'm sure you've heard of this concept of FTEs for deployed engineers that's like 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.

think that you saw a lot of that and legally 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 feedbacks 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 we look at our nearest competitors we're maybe you know built a better product in a quarter of the time with it with a 10th of the capital now I know that we've raised a lot recently but before that we're the 10th of the capital I think that's really, really important because if you look at the legal eye 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 purposes, they're betting on the team and the team's ability to ship, you know, the best, the best and be at the forefront consistently, right? And so I think, you know, with this forward deployed model that you saw in the league, I space a lot, you know, we're doing it. 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 be specific, you know, we have scenarios where someone will be at a customer's 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 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, it's great, but is that like really needed? What about like the day after? But the thing is in this world is, you know, these systems are improving so quickly and the competitive landscape is changing so quickly that really, you know, are moats 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 Arne 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 overpromise 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. 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 product. So sure, look, we can't take it to 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. We've raised, you know, I said about 100 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're allowed 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. Talk to me about the early adopters in the GP and LLP space. what are the characteristics of these organizations and where are they adopting it internally?

It's interesting you say about 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 buying, you know, CEO level or CEO level buying 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 of a single firm where this isn't like number one on the agenda or maybe number two, but probably number one. So generally speaking, the best early adopters that we've seen are 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, I don't think it takes a rocket scientist to figure out, you know, you go on chat, GPT and do anything around, you know, in 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, as an overall organization, I think are the ones that the best adopters. Being, if you want to be specific that, that, I really mean, well, if you take your, 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 the 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 Model ML. 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 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, whether 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 that actually look like in terms of use cases?

Well, for now, it's more of those, those inefficient, 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 size of a firm do you need to be to hire a firm like MonoML? Or maybe how much do these projects cost?

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 at the moment, it's in the hundreds of seats. I think that's going to come down to the five and tens within the next few months. pricing wise, depending on whether you're buying data through us, etc., you should really be budgeting for true AI tools, you know, anything from $100 bucks a month, maybe $300 a month, you know, per seat. 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 a bit. I always ask, this is a question that I think is important is I ask people about their own AI journey as a, as a, as a, as an individual, right? You know, how, what are they using AI for inside 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, 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 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 also tells me with very high confidence. 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 spring to light? I think video production editing the first part of 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 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. I'll get excited now. 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 that be 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, 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 old-fashioned way, meeting, Zoom meetings, in-person meetings, and building relationships with co-investors.

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... You're asking for my secret sauce. So I'll give you some secrets off. 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. 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. 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 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, 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 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, in those comments and it's categorizing them 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. Where my mind went, the two really leverage points for us on the podcast side, it's actually the guest. The guess is 80%. If I have Ben Horowitz, Cliff Asnes, Bollajus, 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, on the investment side, like which companies 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? Obviously, we regularly, a connector, you know, pretty the most part of our customers are connecting our product into that, you know, CRM, for example. So, you know, clearly anything, you know, 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 an 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 you on a product team specifically focused on that area. And that's mainly just because our customers are slowly moving in the direction of, you know, GPs, LPs, et cetera. So I think in general, I think it's going to be quite popular. One of my oldest friend, John Sump Kim, he started 5'9 in this public company. And he always hammered this into me. He always hammered into me.

You have to be close to cash. Companies die because they're not close to the 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? guess and more deals. That's kind of why my head just immediately went to sourcing, I guess sourcing on both sides.

One of the Y Combinator mottoes, which I think was Michael Seibel that said it most, was 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. 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? definitely perseverance. You know, I've mentioned this. We're 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 ago. If you believe in something, I think the key is to just persevere. it goes back i've now interviewed nine billionaires hopefully you'll be my 10th billionaire you know 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 I Capital. 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. He was just running there. And now he's built a $7 billion company or So another example, Ryan Surnat, 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 pulls, 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 penhouse 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.

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. Thankfully, I was a VC, and I understood that 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. That's it. Pussed in. 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 I Invest.

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