No Priors: Artificial Intelligence | Technology | Startups
00:03:44 · Alexander Taubman
[00:03:44] S1: That's right. We've seen very, very high retention of our team members across all of our acquisitions. And actually, this is the vision. This is the vision Long Lake. We want to basically be the best place to work in every industry that we operate so we can get give the best people the best tools with the best customers, and that flywheel becomes self perpetuating. Because if you now leave Long Lake or you leave one of our partner companies to go to a competitor, you have to start doing all this mundane work again. You 25% of your day, 30% of your day, you have to go do that again. And the thought of it's like giving up email or something. You know, you're not going to It's just So actually, we've started to become a real talent magnet in these industries and our companies. And by the way, we can pay people the most. Because they're the most productive, they're actually making more money, and we're delighted about that. So we can pay you the most, give you the best tools, and we're growing the fastest. Know, that's part of our vision is it's really making things better for team members and customers. And by the way, the other important thing is you give your team members superpowers with AI, the customers are much, much happier. So, we're seeing customer retention going up. We're seeing response times much faster, errors going down in sort of things like board reporting, budgeting, email, you know, and that's driving up customer retention.
00:05:00 · Elad Gil, Alexander Taubman
[00:05:00] S0: That's so cool. Why do this via acquisition versus just offering software to people? In other words, the traditional Silicon Valley playbook would be you find the HOA industry, you realize that there's a need in terms of software, you build software for the industry, then you sell it as a vendor, and in this case sell it as a sort of AI product or tool. Yeah. Why not do that? Or why did you decide to go down the acquisition path? [00:05:22] S1: We think that you can drive better win win business outcomes with deeper alignment. And so by actually owning the companies and owning those customer relationships directly, we can drive better better results. Software companies are wonderful. We partner with many of them. But when you're just selling software and you don't actually then care what happens with the business outcomes, you just don't see the same business [00:05:45] S0: And your engineers are just viewing your employees as their customers in some sense then. Is that correct?
00:05:45 · Elad Gil, Alexander Taubman
[00:05:45] S0: And your engineers are just viewing your employees as their customers in some sense then. Is that correct? [00:05:50] S1: That's right. Our team views our employees and our team members in the field as the customer, and that feedback loop internally that's the other point is we have a much tighter feedback loop. So, you know, the old skunkworks thing of you want the engineers in the factory to be co located so you can have more innovation, that's what we have at Long Lake. So our team members and our engineers are together in the field all the time. I think there's of our engineering team, they're probably in 20 different states right now sitting with team members across our architecture business, across our HOA business, across our HR services, or, you know, specialty tax business. And so they're sort of there's a deep amount of change management that's involved. So this is a lot of, you know, sitting with the team members, understanding their pain points. And so there's a real solutions orientation of how do we take the pain point, and then we build a tool within Nexus to solve it. And that feedback loop is really important, so you get to better outcomes this way.
00:06:41 · Elad Gil
[00:06:41] S0: No, it's pretty amazing because I think one of the biggest issues for actually adoption of AI is change management, changing processes, changing organizational design. And I guess if you own the actual company, then you can make those changes. If you're just selling software, you can't really impact it very much. I think in general, in order to do this very well, and I've talked to dozens of people trying to do different forms of AI roll ups and things like that, is you really need three competencies it seems like. It seems like you need some folks who are great at the private equity style motion of purchasing things. You need somebody who's great at engineering and building out the AI stack, and then you need really good change management. How were you able to pull those three disciplines together? Because very rarer. And again, I've seen very, very few companies in this area who've done this. Was it a magic initial founding team? Was it just how you hired? I'm sort of curious how you've Because you've also gotten exceptional engineers, which most folks aren't able to get in this industry.
00:07:39 · Alexander Taubman, Elad Gil
[00:07:39] S1: Yeah. Well, thank you for saying that. Yeah. So because we were purpose built from day one to kind of be this cross functional company with technology and DNA, change management, and M and A, we were able to attract, you know, the right type of people from network. And so I think 100% of our first 20 people were through network. We knew them really well. And people from, you know, places like Palantir, Ramp, Robinhood, you know, some of the top glean, some of the top sort of modern AI and data companies. And so, you know, Erasmus, our co founder and CTO, he and I were connected through one of our early investors and board members who've We've all known each other for kind of fifteen plus years. We all started our careers together. [00:08:22] S0: It's really rare, by the way, I think, for many business people to have those deep technical networks. What I've observed is that often these are separate worlds, and technologists are very bad at hiring business people early on at least in their careers. And vice versa, business people tend to be awful at hiring engineers. So you end up with these mismatches on the early teams. So it's pretty amazing you were able to pull people out of some of these great companies.
00:08:42 · Alexander Taubman, Elad Gil
[00:08:42] S1: Yeah. Well, thank you. I mean, I think it's a really exciting project. I think that's the idea of bringing AI into the real world. There's a lot I mean, what the labs are doing is extraordinary, obviously, and they're enabling all of this and all the, you know, hundreds of billions of investment that's going in. Models are getting part of our thesis was the models are gonna get better every day. There's gonna be a tremendous amount in the trillions of investment. But who's gonna take all that and actually make it work in the real economy? There felt like a huge, huge gap. And so a lot of the folks that came together for our founding team actually were founders before in technology. Many of them had their own startups on the engineering team. They were either the co founder, the CEO, CTO of an applied AI company. But what people realized is it's actually, for the reasons we talked about earlier, it's actually really hard to sell software into these services. It's much better to It's like if you can't beat them, join them. And so what we we're building sort of a team of founders. Mhmm. And we want Long Lake to be a place where entrepreneurial applied AI engineers can come and do some of their best work. [00:09:39] S0: And then you also have pulled, I think, some people out of great private equity firms. Mhmm. What are some of the places that those folks have come from?