00:08:13 · Donald Muir
[00:08:13] S0: It hasn't ceased to stop amazing me how the foundation models have not yet been able to solve this problem that we did over a year ago now. Why is that? Is maybe where I'll start. Or how is that possible? I think it's because we developed it for our own organic internal use case. There's other kind of AI for finance applications, general purpose tools, foundation models. They're not focused on solving this one pain point that I know is mission critical for ICP. For me, we're laser focused on the hardest engineering problem first because we needed to do that in order to make the marketplace work at ARK. And so we cracked the code on financial analysis for unstructured XLS data before anyone else, and that continues to be the case as of this meeting today. Because we're focused on solving our own problem organically. And it was the moment that this technology, I need to breathe oxygen into it, and commercialize it as a standalone enterprise SaaS product for this market, to solve a pain point that I know intimately well. And so that's how we got there. In terms of building trust, or proving that out to our customers, that's really the next big hurdle. And I developed the go to market strategy to overcome this, and it's analogous to how I interviewed for these private equity roles back in the day. Private equity and private credit, talent is the product. Right? It's a business where you generate alpha, you outperform your peers based on the people you hire, and how competent they are, and how hard they work. So hiring is incredibly important in private markets investing. To the point that associates are being hired, giving offers to join their buy side shop two years before their start date. That's how competitive the talent market is in private equity and private credit. So you go to a super day at these funds. They call you. It's literally eighteen months before your start date. You go through the screens, and then you go to a super day, and they give you a laptop and a binder. And the binder has a bunch of docs on a company, probably a SIM, maybe some market study analysis, and then a computer with no access to the Internet. And your job is to effectively underwrite the business, come up with an LBO model, an operating model from scratch. You have three hours complete. You're on the clock. You have no access to Internet. You're on the phone.
00:10:27 · Donald Muir, S1
You need to put together an investment thesis on this business using your knowledge of financial analysis as a human. So the AI we're building is becoming a part of the deal team for these same funds who have been hiring this way for decades. And so I replicated that model. We now do timed tests under NDA on our customers' data. They're working on a live deal. They'll send us a data room under NDA for a live deal. They'll send us sample investment committee memos that they use on different deals, and we will create an AI generated template of that IC memo. We'll run it against their data room. We'll work with the F2 agent to clean it up, take it from 80 to a 100%, and they'll present it to their team typically same day on a call. And that's the wow factor. That's how we prove that we're not a vaporware company. We have real tech. We can deliver real value out of the box at the firm. [00:11:17] S1: That's a very clever go to market approach. Was there an inspiration there somewhere along the way of another company that's not in your space, but doing something similar? [00:11:25] S0: Just my own interview experience on the buy side. I just emulated the human interview experience for the AI. And so hold the AI to the same standard you'd hold your human analysts that you're hiring at your firm. [00:11:37] S1: Is there a segment of the population that's earlier in their career if you're gonna listen to this interview and think, like, you're the devil? Like, that this is going to eliminate future jobs for them?
00:11:46 · Donald Muir
[00:11:46] S0: I don't think we're... Eliminate future jobs. It's interesting framing. I think a couple of things on the topic. I get the question frequently from customers and investors. Two points to make. One, AI is catalyzing a economic super cycle, and I'm obviously bullish. I'm the CEO of an AI company, but I believe wholeheartedly that GDP growth will outpace historical averages, and that will create new industries and new jobs for those who lean in and adopt these tools and find the fast moving water. And so there will be some frictional changes in the labor market, but I genuinely believe that tons of new jobs will be created. I'm doubling my team. I have many AI, you know, founder, CEO friends who are doubling theirs. There's new industries coming up. So there'll be changes in the labor force, but net job loss, I'm not seeing that happening near term. There will just be some rotation in the job market. And that's especially the case for these super credible junior folks you're referring to who are going after PE roles. These are some of the highest caliber folks in the talent market. Those skills could easily be applied at other roles, including at F2. So if you're a top decile, a finance minded go getter, please reach out to me for a potential role at F2. In terms of how the role itself, yes, it's true that there will be fewer hires being made at these funds in the future. That's because the individuals at the funds are becoming 10 times more productive. Their role is evolving from what I did in my prior life, which is monotonous, brutal, menial, low leverage work of scraping data rooms, tying out financials, formatting PowerPoint slides, going through 10 k's and queues and pulling out organic growth rates, sanitizing numbers. That work could be fully automated with AgenTic AI today. And by the way, it should be. Because these investment professionals are being underutilized in that capacity. They will evolve from menial individual contributor to system operator, where they're managing the system. They're AI native, just like software engineers using cursor are. And they're using critical thinking and applying human judgment to make better investment decisions faster, which ultimately allow the firm at the fund level to scale AUM, deploy capital much more efficiently and precisely.
00:14:02 · S2, S1
[00:14:02] S2: This show is brought to you by the Global Talent Co, a marketing leader's best friend in these times of budget cuts and efficient growth. We help marketing leaders find, hire, vet, and manage amazing marketing talent for 50 to 70% less than their US and European counterparts. To book a free consultation, visit globaltalent.co. [00:14:22] S1: How are you approaching messaging? So I I think there's two brands that come to mind. I interviewed the founders of Augment Code. They're unicorn. You know, they're very much focused on this message of, like, AI can help you. AI is a buddy. It's, you know, a supporter. It's a copilot. And the other side you have, think it's Artisan who you may know, like, that AI s p r company that did this very controversial, but well done, Billboard. It's like I was talking about it. Like, where do you sit there in terms of those, like, two very different styles of AI messaging?
00:14:49 · Donald Muir
[00:14:49] S0: Yeah. I gotta figure out who did that marketing campaign and hire her or him. I am familiar with those billboards in San Francisco. They're catchy. Intentionally controversial. Look. I think where do I stand? I believe in the Y Combinator ideology of building a partially autonomous AI experience where the human works in harmony with the agent to produce better output faster and more efficiently. I'm building my platform that way. This is not a black box experience. You don't drop in a data room and output an investment decision. These are, on average, $500,000,000 transactions flowing through my platform. That requires an immense amount of human judgment. What's changing is that the human is working harmoniously with his dedicated AI agent to produce better insights more efficiently with more context. I'll give you an example. In my prior life, working in upper middle market private equity, I would get a new investment opportunity to come across my desk, and I need to produce a screening memo to determine whether we wanted to spend more time in the deal. I would spend hours parsing through that preliminary data room, trying to identify across thousands of Microsoft SharePoint folders precedent transactions to benchmark the deal against and pull down any type of institutional knowledge to inform the underwrite. And then I'd laboriously create a screening demo, which would take a dozen hours. You can get 90% plus time savings in all of those workflows today with AI. And more importantly, or what I'm even more excited about, is the ability to pipe in your institutional knowledge. Rather than combing through these thousands of forgotten folders in your SharePoint, you can now pipe in context via API and instantly benchmark all deals that you've done historically and drill down by vertical, by revenue sizing, by leverage levels, by gross profit margins, by quality of management team to create really crisp benchmarks, understand what the investment merits were, what the risks and mitigants were, how those vary by industry. You can get just much more informed underwrite retaining all of that institutional knowledge at your fingertips.
00:16:56 · Donald Muir, S1
So not only are you doing existing workflows faster, but they're actually much better because the AI has complete comprehensive context of the decades of work your firm has done in the past and could use that to inform the net new underwrite on a go forward basis. [00:17:11] S1: What other go to market decisions have you made? You know, like that example you shared with doing the demo approach, which is very, very clever. What other decisions have you made, or what decisions are you actively trying to decide between right now and trying to make on the go to market side?