00:05:23 · Donald Muir, S1
[00:05:23] S0: I haven't left ARK, at least not in heart or in spirit. Actually, we share office space. Our teams are a 100% in person in San Francisco and New York. We have offices in the Financial District on both coasts. One side is F2. The other side is ARK. I'm on the board. I'm a large shareholder. I see the ARK team every day. So it still feels like one family. Now the teams are focused entirely on their own bookends of the market, but I haven't gone anywhere, and that made the transition easier. Not to mention my cofounder at ARK, who's now CEO. He'd been playing that role for the, you know, call it the last year before I left as I spent more and more of my time building the AI for the lenders. And that was a fairly natural transition. But ARK is always my baby, and 100% of my cash is stored with ARK. I'm still spending tons of time with all the founders, you know, who we bank at ARK and who are, you know, loyal customers of my first company. [00:06:13] S1: So F2 dot AI, saw that you have that domain. What's the story there? I have to imagine that that was not just sitting there on the beam chief ready to buy.
00:06:13 · S1, Donald Muir
[00:06:13] S1: So F2 dot AI, saw that you have that domain. What's the story there? I have to imagine that that was not just sitting there on the beam chief ready to buy. [00:06:21] S0: I went deep in my own pocket for that one. I maxed out all my personal credit cards. I just got my Chase turned back on this week. But I saw the domain. First, the name came to me. Maybe I'll start there. F2. Like I said, I grew up in the bullpen in private equity, where at three in the morning, I would be F2 ing through spreadsheet models, auditing my own work. When your glassy eyed have been staring at a financial model that you've been spending weeks building, you're on V 150. F2 is the keystroke you use to audit calculations in an Excel model. And so in the world of AI, one of the big gaps that we need to bridge, and again, I know this from personal experience, is reliability and trust. LLMs hallucinate. They get stuff wrong. They can't do math. We've developed technology that does not hallucinate, that can do math. We have a category leading Excel agent product. It's core to the platform. F two speaks to that core competency. It's auditability and trust for, you know, the financial buyers, for the private credit and private equity associates who are toiling in the bullpen late at night and long in the weekends. The domain, I scooped up and bought that before incorporating the company. Maxing out all my credit cards to do it.
00:07:31 · S1
[00:07:31] S1: This show is brought to you by Frontlines Media, podcast production studio that helps b two b founders launch, manage, and grow their own podcast. Now if you're a founder, you may be thinking, I don't have time to host a podcast. I've got a company to build. Well, that's exactly what we built our service to do. You show up and host, and we handle literally everything else. To set up a call to discuss launching your own podcast, visit frontlines.io/podcast. Now back to today's episode. And for you, how do you get past that, you know, general perception that AI does hallucinate? So, like, I think every consumer in The US at least is, like, using ChatGPT. We all experience that. How do you sell against that, and how do you make the case that, you know, your AI is not going to hallucinate and, you know, invent shit like the other ones do in our consumer lives?
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.