How I Invest with David Weisburd
00:00:00 · David Weisburd, Chaz Englander
[00:00:00] S0: You built ModelML to solve your own problems within your family office. What were those problems? So [00:00:05] S1: when Arnes and I, who's my brother, we sold the second company. We were still pretty young. Arnes was, I don't 21, 22. I was maybe, like, 26, 20, 27. We made a bit of money, 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 2021 coming into 2022, you know, 2023 coming into 2024 as LLMs 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 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. [00:00:55] S0: Give me a specific low hanging fruit that investors are using ModelML in order to solve their their everyday problems.
00:00:55 · David Weisburd, Chaz Englander
[00:00:55] S0: Give me a specific low hanging fruit that investors are using ModelML in order to solve their their everyday problems. [00:01:01] S1: A classic is just like reporting and monitoring in general. Right? It's the classic problem. You know, you're... Even when I think about this back at the family office, we were making on the venture of startup sites maybe like 25 investments a year, right? And we would receive updates in like every single format. Sometimes even now it's like a website link. It's like you got a website link, you got a Notion page, you got just a bunch of documents, you got it in the body of the email, you got it in an Excel file with multiple tabs, etcetera. And really what you wanna do is you wanna 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 a 100% automation. I think we'll get there, you know, probably 12, sort of twenty four months away and of a single task being close to a 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 a classic one is something like your reporting. And 2025 was supposed to
00:01:01 · Chaz Englander, David Weisburd
[00:01:01] S1: A classic is just like reporting and monitoring in general. Right? It's the classic problem. You know, you're... Even when I think about this back at the family office, we were making on the venture of startup sites maybe like 25 investments a year, right? And we would receive updates in like every single format. Sometimes even now it's like a website link. It's like you got a website link, you got a Notion page, you got just a bunch of documents, you got it in the body of the email, you got it in an Excel file with multiple tabs, etcetera. And really what you wanna do is you wanna 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 a 100% automation. I think we'll get there, you know, probably 12, sort of twenty four months away and of a single task being close to a 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 a classic one is something like your reporting. And 2025 was supposed to [00:02:08] S0: be the year of AgenTik AI. Now people are saying 2026. You're one of the only AgenTik AI companies on the planet that has scaled you use over a trillion tokens with OpenAI. Why have you been able to solve agentic AI in a way that others have not?
00:02:23 · Chaz Englander
[00:02:23] S1: We relaunched at the end of twenty twenty four, and we raised about a 100,000,000 across a couple rounds in our first months. So the the business has been growing. Right? And looking back on it, you know, we often think, okay, where where did we differentiate? And to us, it's it's quite clear. These chat type interfaces, think, you know, 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. But if you think about the complexity of work that actually goes on in, you know, these types of organisations, they're gonna 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 and A type environment? If that's resulting in a 200 page PowerPoint or a 50 tab wide Excel workbook, are you gonna 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 upfront. And so we came into the market with a slightly different perspective where it's like, look, chat 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 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 wanna 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 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 a lot easier.
00:03:53 · David Weisburd
[00:03:53] S0: How should investors, GPs, LPs think about AgenTeq AI, and where could they apply it in their day to day?
00:03:59 · Chaz Englander
[00:03:59] S1: 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, health care, whatever, you know, they are productivity tools for the most part. They're 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'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 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. Graph, tables, charts, logo in the same formats that they would digest that information from. You know, 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, right? 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, directly clear to us, is the importance of those two or three pages is only going one way.