No Priors: Artificial Intelligence | Technology | Startups
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?
00:09:39 · Elad Gil, Alexander Taubman
[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? [00:09:45] S1: Yeah. Our private equity team comes from you know, our M and A team comes from top private equity firms. Like, so Manny, one of our cofounders and M and A lead, came from GTCR. He actually worked on the most profitable deal in their history the month before he left to come to Long Lake. You might get embarrassed from me saying this, but I think he was named employee of the month at GTCR the month he he resigned. But, you know, GTCR, one of the top performing large cap bio funds. We have folks from Blackstone, TPG, HIG. And mostly, the reason they come to Long Lake is because, you know, even though these firms are extraordinary firms at what they do, they're not AI native. And so I think for those subsegment of those M and A professionals that really believe in our thesis, there's not many places that look like Long Lake today. And so we can provide a unique environment for them to deploy AI into all these companies. [00:10:37] S0: And I think this led to this really unique opportunity, which is taking private 110 old business, which is American Express global business travel. And again, I mean, I haven't seen anything of this magnitude happen in the AI world up until now, which is pretty impressive. I think it's a $6,300,000,000 potential take private. How did you all come up with the idea to do this? And I know there's only limited things you can say [00:11:03] S1: since this is
00:11:03 · Alexander Taubman, Elad Gil
[00:11:03] S1: since this is [00:11:03] S0: a public company and it's going through this transition, but whatever you can share in terms of the thinking behind it. [00:11:08] S1: Well, we've admired this company for as long as I I think I've been a customer. Many of us involved in our Some of our investors that have traveled a lot in their careers have used this company as a customer. And by [00:11:22] S0: the way, this company, I think, got started when the only thing you could book on it was railways and boats. Again, it's been around for a while. I think this predates air travel. That's