Financial Modeler's Corner
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.
00:15:17 · Ian Schnoor, Paul Barnhurst
[00:15:17] S3: Oh, I love... Listen. I love that. I I was not expecting that response, but listen. I I applaud the fact that you have a plan and a strategy, and you and you are at least for the moment, not that it can't change, but that you are intending to take advantage of what you believe as weaknesses in in the alternatives. I mean, good for you. That's... I love that. We'll be cheering you on from the sidelines here. [00:15:43] S2: And so just just for our audience, think, to step back for a minute, you know, how would you describe Tracelight's core customer? I know you've done modeling. I know you help a lot in modeling and consulting, but you also now have a word app, a PowerPoint app. So you you mentioned the 10,000,000 as an example. Right? You're going after a very small subset. You're not going after the billion. So I know modeling's a big part of that, but what within modeling? Like, how would you describe your ideal customer? Someone's listening and say, hey. I wanna try Tracelight. Who's it really for?
00:15:43 · Paul Barnhurst, Peter Fuller
[00:15:43] S2: And so just just for our audience, think, to step back for a minute, you know, how would you describe Tracelight's core customer? I know you've done modeling. I know you help a lot in modeling and consulting, but you also now have a word app, a PowerPoint app. So you you mentioned the 10,000,000 as an example. Right? You're going after a very small subset. You're not going after the billion. So I know modeling's a big part of that, but what within modeling? Like, how would you describe your ideal customer? Someone's listening and say, hey. I wanna try Tracelight. Who's it really for? [00:16:14] S1: Our customers are primarily in management consulting and high finance. And the... I think it's... Yeah. That's fair to say that, you know, the the big early share of those customers work in areas related to private market transactions. Right? So that's valuations and modeling practices in in management consultancies. It's financial due diligence, commercial due diligence, as well as the investors and investment bankers who are kind of working on the other side of the deal. Right? These are very, very high stakes, very schleppy as you guys know. Yeah. They're just just a very important work. Right? And those users benefit a lot from a from a product that is better than the kind of basic horizontal products you can you can buy anywhere else. So, yeah, that's where we focus.