How I Invest with David Weisburd
00:42:15 · Joseph Coyne
[00:42:15] S1: When we think about horizontal, we think about more utilities that are foundational to AI. So it could be a company like Monitor, where it's helping any vertical or any one enterprise manage their own models. It could be the data loss prevention example I gave with with sort of like, where is my data flowing in and outside my enterprise? That, again, would be a horizontal solution to avert potentially a vertical problem. And then when I think about verticals that will win, you know, I think selling into any one of these verticals requires trust. And especially in the early days, building that trust within an air... Within an industry as esoteric as airlines or insurance underwriting or banking underwriting lending, that requires trust, understanding the customer process, the legacy process, understand the data, the KPIs, etcetera, can give you an early lead to start building that data flywheel within legacy industries. And so when we think about vertical AI, I think it's, you know, companies that have domain expertise plus the ability to build product very, very fast. So engineering led teams that have AI native workflows that also have deep understanding of what we call subject matter experts on the team that can earn the trust because they have some sort of asymmetric advantage to penetrate a market. And so we're oftentimes like underwriting teams in that way, which is technical prowess, what have they built today, how fast can they build their future roadmap, but then also, what does the go to market motion look like? Is it appropriate for their ACV, for example? So are they being rational about how they scale? But then the next thing is, you know, we don't wanna fight in a fair fight. Right? There's lots of players. People can build companies faster than ever. And so when we're looking at a company, we're looking for an unfair fight. Bringing a gun to a knife fight. Right? It's who knows this industry better? Who's better connected to this industry? Who has the license to win in this industry? That can often... In addition to having the best technology, you have to have base case very strong, if not the best technology. But having that other tipping point can really tip the scales in your favor in terms of how fast you can penetrate an esoteric market like airlines.
00:44:25 · David Weisburd
[00:44:25] S0: I think your answer will be very disappointing to engineers. I think since 2008 when I started in the startup world, engineers have been trying to replace salespeople. And I've like this high school jocks... Jocks versus nerds thing where for now almost twenty years, engineers have been trying to replace salespeople. The problem with that is they may be ultimately successful. It might take ten, twenty years. Why? Because there's still human beings running the organizations, and human beings need solution. They need trust. They need all these things. So this idea that you could come to the market with the best AI solution still needs those salespeople that help with implementation, still needs those people that help actually on the operations level, all these things that still require real live human beings, at least for now.
00:45:09 · Joseph Coyne, David Weisburd
[00:45:09] S1: Yeah. And I would argue that's all true. That may go away a little bit as you automate sales process, and that's fine. But what still remains is understanding customer needs. People who deeply understand their customer, some of them were formerly the customer they're targeting. Right? They bring back unique insights and problem sets that the best engineers thrive with. Okay? Generally, best engineers, unless they're the founder of a company, are not going out and talking to the customer or or have lived inside the customer for twenty years. And so the salesperson can also be sort of a customer solutioning specialist that brings back unique insight or understanding to the engineers that informs product road map, solutioning, and making sure that whatever we're building within a startup has extremely high value day one to the customers because it came from them. [00:45:59] S0: Do you know the story of how GoDaddy massively increased their revenue? I don't. They put salespeople into their customer service calls. Mhmm. So they realize that the best time to actually upsell somebody and figure out what people need is at the time that they have issues. And it goes back to this kind of paradox in customer services. When you have a problem and a company solves it for you, you may end up actually building affinity with with the company more so than if you didn't have that problem. So they actually [00:46:25] S1: turn de facto design partners with you. And
00:46:25 · Joseph Coyne, David Weisburd
[00:46:25] S1: turn de facto design partners with you. And [00:46:28] S0: So they they figured this out a decade ago, and now we're kinda seeing this straight emerge again in AI.
00:46:32 · Joseph Coyne
[00:46:32] S1: Fascinating. We invested into another AI company that was doing post sales data gathering for service delivery companies. So sales makes a sale. Right? They capture some data in their CRM about how that process went, and everybody has a steak dinner and high fives and says, great job everyone. And that's usually the... It's like the day of your wedding. Right? It's like the happiest day of everyone's life is the day that you consummate the deal. And then after that, you start actually delivering the service. You could be an investment banker. You could be a consultant. You could be a lawyer. You're delivering service over a long time series. Monitoring those interactions is what our company Knownwell does. And what it's pulling back is unique insights for the management to say, it's great that you sold this customer. We're gonna monitor minute by minute, interaction by interaction, a, how happy they are, but we're also gonna distill down everything the customer's telling you about what they need. You can take those insights and immediately put them back into those enterprises, to probabilistically and sort of stochastically lay out what should we build next. Because you have a time series, multiple customers, and frequency graph of what your customer is telling you. When you digitize that and digest that with AI, it creates unique insights for the service delivery partner, which says, you know, it could say, wow, we just really don't like this team that we're working with or so and so was not really great to work with or you weren't very timely. But it could also say, hey, the software you sold me does x y z, but we were really looking for, you know, tau. And that can be unique insight that can then recursively get pulled back into the product roadmap.
00:48:06 · David Weisburd
[00:48:06] S0: When I talk to engineers at Anthropic, I also have some friends at OpenAI. They're telling me that average engineers are getting a four or five x efficiencies and that the 10 x engineers are getting actually a 100 x. So the the 10 x engineers, which oftentimes are twice as expensive, end up being even better or returns on investment. Are you seeing the same phenomenon on the sales side where the top salespeople are actually getting these major efficiencies from AI?