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Chapter 1: What problems did Model ML aim to solve for investment firms?
You built Model ML 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 maybe like 26, turning 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 spend 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 and before we know we just had too many people asking us whether they could use it and they're willing to pay for it um 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
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. Even when I think about this back at the family office, we were making 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 a lot of these tasks are still being done manually, frankly. That is a classic example of something that should be automated. We're not in the world of 100% automation. I think we'll get there, probably 12%.
sort of 24 months away and of a single task being close to a hundred percent, you know, anything above 60% 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 a classic one is something like 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 a hundred 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 ChatGPT and Anthropic and others, they're great, don't get me wrong. They're absolutely fantastic. They're very much gonna change the world.
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Chapter 2: How can investors use Model ML to improve reporting and monitoring?
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. These application layer products, whether that's in finance, legal, healthcare, whatever, they are productivity tools for the most part.
They're giving you an additional layer of efficiency in your businesses. 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 are actually going to start to see these systems deliver insight that wasn't possible pre-AI. Now,
If we gotta play that back a little bit, that's not gonna be today no insight, tomorrow insight. It's gonna be slightly more incremental than that. And I'll give you a quite 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.
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 from. Going to the data room and going to CapIQ 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. And these are things like 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 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, 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 gonna 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, 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.
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Chapter 3: What limitations do chat-based AI tools have in professional workflows?
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.
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, 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, 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 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 and we don't really look at workflows unless it's a 60 plus percent efficiency gain 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 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, you know, 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 fact 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.
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Chapter 4: How does automation shift productivity in investment firms?
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Chapter 5: What role does agentic AI play in generating investment insights?
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They're the largest registered agent LLC service in the US with over 1500 corporate guides, real people who know your local laws and help you every step of the way. What I love is how fast you can build your business identity with their free resources. You can access thousands of forms, step by step guides, and even lawyer drafted operating agreements and bylaws without even creating an account.
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So when I talk about last mile delivery, I'm sure you've heard of this concept of FDEs, forward 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.
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 you look at our nearest competitors, we're maybe, you built a better product in a quarter of the time with a tenth of the capital. Now, I know that we've raised a lot recently, but before that with a tenth of the capital.
I think that's really, really important because if you look at the 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 months time.
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Chapter 6: Why is capturing data crucial for future AI applications?
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. 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 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 you'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, 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, hopefully it works out. 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. No one else sees that.
That's basically the 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. 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 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. Pass it in.
Well, Chas, this has been an absolute masterclass. Thanks so much for taking your time and thanks so much for jumping on the podcast. David, thanks so much. 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.
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