Financial Modeler's Corner
00:06:22 · Paul Barnhurst, Giles Male, Peter Fuller
[00:06:22] S2: Alright. I'll let Giles are in, ask some questions so I don't hog all the mic. [00:06:25] S4: I got a... Just an initial thought when you were describing... Is it Yanik you're saying you're the other founder, machine learning at Cambridge. I can imagine somebody like that must look around now at all the AI experts popping up left, right and center and just be like, good God, you know, machine learning Cambridge. I said this some some point, like a year ago, was like, there is a world of actual, like, AI experts. He must be so annoyed with this. [00:06:55] S1: I don't think Yannick feels that way about you guys, but he definitely did about me, I think, at the at the beginning, for sure. I think I've learned this. [00:07:01] S4: You won him over. [00:07:03] S1: No. He's he's an incredible guy. Right? So he has this, obviously, deeply technical backgrounds. But, you know, he also loves, like, front end and, like, design. He's just, like, unicorn of an engineer who can do the kind of ins and outs of machine learning, but also loves an interface. And and also, you know, a lot of building with LLMs is is not very sexy. Right? Like, it's... You know, so it is a lot of prompt prompt engineering. Right? It certainly was. And it's a lot of evals and there's a lot of hill climbing and and you need a kind of tenacity and a certain mindset to be really, really good at that and and Giles has that. Right? So it's... Yeah. I think he's very, very well suited to to what we do. [00:07:50] S4: I've got another question, but I I don't wanna take over. Go for Ian.
00:07:50 · Giles Male, Ian Schnoor, Paul Barnhurst
[00:07:50] S4: I've got another question, but I I don't wanna take over. Go for Ian. [00:07:53] S3: No. Go ahead, Giles. You go ahead. You can get five in a row. The [00:07:57] S2: I'll take it. I'm gonna limit you. [00:07:59] S4: I don't know if this is jumping ahead to, like, the meat of what we're gonna get into, but, obviously, I guess what was interesting for us when we started last year with the Mod Squad, we tested Tracelight first and a bunch of other tools. And then suddenly, you know, Microsoft came out with their first agent in web and and Anthropic did all of this. I'd be really intrigued to hear what that journey's been like for you. And and I guess, like, for me, did you expect those, you know, the LLM providers like Anthropic and OpenAI and Microsoft to do what they did when they did it? Or was it a bit of a pride?
00:08:34 · Peter Fuller
[00:08:34] S1: Yeah. No. No. That's that's... I have to be intellectually honest about this because I actually went back to a memo that I wrote to raise our our seed round back... Way back in the day. And rereading that memo, it's clear that I didn't expect Anthropic and OpenAI to launch and add in to Microsoft x... Excel. Right? Of course, I talked about collapse, but I I I just didn't think that was a likely product direction. I was obviously completely wrong about that. Like, clearly, I expected Microsoft to. But, you know, like, there's a few things going on there. Like one, when big labs launch products like that, yeah, of course, it's it's easy to be scared. It's also very, very validating actually for what you do and you just get orders of magnitude more attention in your space. Right? So Anthropic in particular launching, Claude for Excel was a huge moment for all of us. Right? And it was actually, it was fantastic for sales and I, you know, it's not like everyone goes like, oh, okay, we'll just dump Tracelight and we'll go and use Anthropic. It was like quite the opposite. Everyone got interested in in Excel. Right? So that was really good actually. And like the core argument for us, which remains the case to this day and slightly expanded form is that the needs of the top 1% of users, right, are different from the needs of the other billion people using Excel. And that's that's still true. So that's very much us as a as a company. We have no ambition to go fight Microsoft and Anthropic for the for the billion users. We'll fight them on the 10,000,000.
00:10:17 · Ian Schnoor
[00:10:17] S3: That's awesome. And listen, we're... We love we love supporting the, the startup and the and the little guy as it will against the $5,000,000,000,000 market cap. And this... And we love Microsoft too. Right? I mean, at FMI, we've got a wonderful partnership with Microsoft, but we love the innovation that comes out of, nimble, creative, small, hungry organizations. I mean, we all work at... You know, and run small, nimble, hungry organizations. So we're... So we love that. But, you know, we've... We talked a year ago. I don't remember if you remember, guys, that it felt like at the time last year that we were in the early stages of the auto industry. We remember that in the early nineteen hundreds, there were... We didn't remember this. Paul might have remembered this, but Giles and I just just read the research on it that back in the early nineteen hundreds, there were about 3,000 carmakers in The US, and that quickly got condensed down either through consolidation or just, you know, companies just evaporating, and not making it. And we've already seen both extremes in the agentic AI space with spreadsheets. I mean, it's a couple of the companies that we interviewed and that we that we tested in early days are gone. They just they just threw in the towel. And others seem to have taken a more niche approach and said, you know what? We are gonna focus on this niche and this target and this particular subset that we don't think we don't think the big folks will either focus on or they can't do as good a job on. And I'm just wondering. I mean, as Giles... Just to build a follow on to Giles' question, what is your plan or strategy to, you know, ensure survival and growth? Because as you said, I mean, it... Most people have discovered it doesn't really make sense to go against head to head against Microsoft. So so what's what's what's the plan? I'd love to know how you're planning to develop something, you know, or what you're doing to develop some niche.
00:12:10 · Peter Fuller, Giles Male, Paul Barnhurst
[00:12:10] S1: Yeah. Well, call me an idiot, but I actually am fine going head to head against Microsoft. Like, in my my in my user base, I'm... I have vast respect for Microsoft. Do I think they're a good product organization? No. I do not. Do I think Copilot is a good product? No. I do not. I think it's actually... And I'm sorry if if if you guys have allegiances there, but I think it's Honor. [00:12:40] S4: Honor. Let it let it out. I I was what everything you're about to say. I [00:12:45] S1: remain shocked by how bad it is. Right? [00:12:50] S2: I've... Anyway, I won't
00:12:52 · Peter Fuller
[00:12:52] S1: I won't go on. So that like that's been number one. Right? Like, I think it's easy just to like roll over and say, oh, you know, these big companies are looking at it or it must all be gone. Now, I think there would be... There are countless entrepreneurs who if they're taking that approach, then massive companies would never have been built. So that's that's the first thing. The second thing, I... You know, when I when I... It's interesting when you said that this is like the beginning of the auto industry. I was like nodding along because I thought you were gonna say something else. I thought you I thought you were gonna say that the products are all, like, very rudimentary. Like, I... And and I I I still feel that way. I feel that there's enormous product overhang in the space now. Right? And by that, I mean, the capabilities of the models are are fantastic, but the products that we built around those models, the harnesses that we built around those models still to me feel inadequate, right, when I look around the the market. So I I I think there's an an just enormous amount of work to be done there. And then the the final thing is, like, you know, Tracelight, we start at the application layer. Right? We we we we aim to build the very best product for our users to do all of their work with AI, including their Excel models. But the way to emote in our space and in many spaces is by making your products get better with every single interaction a user has with it. Right? And so that means that you are not just building at the application layer. Right? You are building memory. Right? You are building... Like, you're taking all of the data that's generated there and you're helping your customers to to to... Or their instance of Tracelight to learn from that data and get better every single time they use it. And there's actually enormous entrenchment that you can build there if you if you get that loop right. Right? So this is what we're working on currently with with with customers, and it's it's really, really exciting. Right? And the needs of our customers in terms of not just the application level, so the kind of information architecture and everything that sits behind, the needs are very, very specific. Right? And the labs and and Microsoft definitely are not not building for them right now. So it's a very exciting time.