How I Invest with David Weisburd
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buy in, CEO level or CIO level buy in of these sorts of products or AI in in general, which now I think is almost a bit outdated.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.
00:23:54 · Chaz Englander, David Weisburd
So I really think we do an incredible job at working closely during this free discovery phase, whether that's GPs, whoever it might be, identify areas that they should immediately be thinking about applying artificial intelligence. That's what we've become very good at. Now, does that actually look like in terms of use cases? Well, for now, it's more of those inefficient areas of the business that you can have these productivity gains. As I said, I think in future that will become more how you're able to drive insight that wasn't possible pre AI, but for today, it's productivity. [00:24:25] S0: What are we talking about budget wise? What size of firm do you need to be to to hire a firm like Model ML? Or maybe how much do these projects cost? We as a business [00:24:37] S1: don't do many kind of like five, 10, 20 seater type deals. We're really looking at, you know, in the hundreds of seats at the minimum, if not the thousands, right? That's changing and quickly as we're scaling the team is enabling us to work on those smaller deals. And really that's just a question of focus. Again, we wanna make sure we over deliver when we're working with our customers, at the moment it's in the hundreds of seats, I think that's gonna come down to the five and tens within the next few months. Pricing wise, depending on whether you're buying data through us, etcetera, you should really be budgeting for true AI tools, you know, anything from a $100 a month to maybe $300 a month, you know, per seat. But again, it just depends on scale.
00:24:37 · Chaz Englander, David Weisburd
[00:24:37] S1: don't do many kind of like five, 10, 20 seater type deals. We're really looking at, you know, in the hundreds of seats at the minimum, if not the thousands, right? That's changing and quickly as we're scaling the team is enabling us to work on those smaller deals. And really that's just a question of focus. Again, we wanna make sure we over deliver when we're working with our customers, at the moment it's in the hundreds of seats, I think that's gonna come down to the five and tens within the next few months. Pricing wise, depending on whether you're buying data through us, etcetera, you should really be budgeting for true AI tools, you know, anything from a $100 a month to maybe $300 a month, you know, per seat. But again, it just depends on scale. [00:25:17] S0: Chaz, I wanted to do a live discovery. So we have a media side of our business. We have an asset management side of the business. So what what questions would you typically ask a client? And maybe we could role play a bit. [00:25:27] S1: I always ask, this is a question that I think is important is I ask people about their own AI journey as as a as an individual. Right? You know, how... What are they using AI for inside and outside of work? Right? And the reason I ask that is I like to understand their appetite towards AI in general.
00:25:27 · Chaz Englander, David Weisburd
[00:25:27] S1: I always ask, this is a question that I think is important is I ask people about their own AI journey as as a as an individual. Right? You know, how... What are they using AI for inside and outside of work? Right? And the reason I ask that is I like to understand their appetite towards AI in general. [00:25:42] S0: I do kind of look like AI as a person. Sometimes they disappoint... He or she disappoints me and sometimes it comes through. I found... So we have on the media side of our business, we have, a lot of our podcasting, the production, the editing. But we also have YouTube and thumbnail and packaging and all this. And I have found very specific cases where AI is very good, in certain specific cases where it's extremely bad. And even worse than it being extremely bad, it's extremely confidence in those cases. And whenever I ask it how confident it is, it gives me like full confidence and then it's completely off. So that's been kind of my downside of working with AI where it not only gives me poor work work output, but also tells me with very high confidence. [00:26:21] S1: If I would say to you areas that frustrate you, that you feel are inefficient but you don't necessarily know how and where to apply artificial intelligence? Like, what are the things that springs to mind?