How I Invest with David Weisburd
00:16:42 · David Weisburd
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00:18:21 · Chaz Englander
[00:18:21] S1: That last mile delivery we talk about is really, really important. Right? So when I talk about last mile delivery, I'm sure you've heard of this concept of of FDEs, forward deployed engineers, that's like booming. That's that's the concept of having people literally work in the offices or from the offices of your customers for a period of time to not just drive adoption, but to deal with onboarding and also to innovate really really quickly. I think that you saw a lot of that in LegalAI. Think that is just as, if not more important in finance. Again, because it's not about the adoption and driving the cultural chains, it's those really, really quick feedback loops. You know, one of the things that we become very well known for if we look at the competitive landscape is, you know, if if we look at our nearest competitors, we're maybe, built a better product in a quarter of the time with tenth of the capital. Now I know that we've raised a lot recently, but before that with tenth of the capital. I think that's really, really important because if you look at the LegalAI world and our world, our customers are not betting on our product today. They're betting on where our product is in twelve month time. So for all intents and purposes, they're betting on the team and the team's ability to ship the best of the best and be at the forefront consistently. Right? And so I think with this forward deployed model that you saw in the legal AI space a lot, we're doing it. What's actually happening there is we're taking feedback in real time and we are altering and improving the product in real time. And let me be specific. We have scenarios where someone will be at a customer's office, they will receive product feedback in real time, they will write all to the code base in real time, make that alteration, do the pull request and it'll be in production within half an hour. And some may say, well that doesn't... Okay, that's great but is that really needed? What about the day after? But the thing is in this world is these systems are improving so quickly and the competitive landscape is changing so quickly that really our moat as a business is the speed at which we can learn and therefore ship products.
00:20:17 · David Weisburd
[00:20:17] S0: Does that not limit your scale? How do you go about scaling a services based...
00:20:21 · Chaz Englander
[00:20:21] S1: Maybe this is Arnie and I being a little bit English or maybe this is the Y Combinator way kind of stalled in our mind. But our motto here is fewer happier customers. We're not a business that's going to over promise and under deliver. We're a business that when we say we're going to do something for a customer, we do it. And if we don't, I think it hurts us emotionally. Right? So I think, like, our view on the market is, you know, these demos that you see all over LinkedIn and so on, they're great. But the things that really matter, you know, if we fast forward twelve or twenty four months' time, it's gonna be that sticky revenue. Sticky revenue is ultimately gonna come from how happy your customers are with your products. So sure, we can't take it to the extreme, do things that don't scale, but at the same time, we just believe that if we deliver to our word in a market where reputation frankly is everything, is the single most important thing. Now you can still scale quickly. We've raised, I said, about 100,000,000 in our first twelve months, so we're very well capitalized to do so. The team's gone from 20 people to a 100. I'm sure we'll add another 100, you know, in the next few months. So we're scaling to accommodate for that. And sure, it might mean in the shorter term, our margins are slightly less as a business, but ultimately, we think if we do well in that first month, three months, six months, and twelve months, you know, we can have these customers for a very, very long time.
00:21:37 · David Weisburd
[00:21:37] S0: Talk to me about the early adopters in the GP and l's... LLP space. What are the characteristics of these organizations, and where are they adopting it internally?
00:21:47 · Chaz Englander
[00:21:47] S1: It's interesting you you you say about characteristics because I think that comes back to this thing of a cultural shift. So I think it's really important that there is top down buy in, CEO level or CIO level buy in of these sorts of products or AI in in general, which now I think is almost a bit outdated. I think that statement was relevant maybe six months or so ago. I don't know of a single firm where this isn't like number one on on the agenda, maybe number two, but probably number one. So so generally speaking, the the best early adopters that we've seen are where there is, like, true kind of top down buy. I think that's the first thing. The second thing is we've also noticed it's the firms that think about how this is gonna alter the organisation structurally. Because clearly it will. I don't think it takes a rocket scientist to figure out, you know, you go and chat GBT and do anything on any of these AI products. There's a huge efficiency game that's happened and happening. So there has to be a structural change. So the ones that are really thinking about things structurally, as an overall organisation, I think are the ones that are the best early adopters. If you want me to be specific, that really means, well, if you take your more junior members of the team, and you're saying, even if we're pessimistic, and we're seeing a 10, 20% overall possible efficiency gain, well, where is that 10, 20% going? It needs to be reallocated. And and the best firms and the best leaders are the ones that are thinking ahead, and they're saying, okay. Well, we're gonna take this pool of, say, 10% of the overall team, and they're gonna become our AI experts. A lot of time they're self identified, know, we all have friends or people that naturally in their roles are like super interested compared to their peers at AI, they're probably the ones that you want to use because you don't need to write code in this sort environment, right? So the ones that are actually saying, okay, we need to move these people and your job is to either assess AI tools or build AI workflows in a product like ModelML, they're the ones that I think are doing incredibly well. The last part of your question is where do you start? What areas of the business do you focus on? I think this can be on a case by case, and so that's where we've been very value add in this discovery phase.