The Exchanges

Q Ridgeline gain a decided advantage. That's why customers call it miraculous, game-changing, and an awakening. If that's not how you would describe your investment management tech, request a demo at ridgeline.ai. And now, back to the show. So I'd love to turn to how you've gone about implementing this super critical stage investment strategy, and maybe we would just walk through it. How do you go about sourcing these ideas?

A One thing about super critical stage companies is that they've raised capital before. And so that does kind of help constrain the universe. Of course, everybody today knows who has raised capital before. So that also means that you, you need to be very focused on like who you're going to spend your time with and who you're, you aren't. And so we think about it in a few different ways. We tend to be broadly thesis driven. We have like a few big themes that we like that Can manifest itself in many ways. We spent a lot of time right after we closed the fund looking at a lot of payments companies. Stripe is a hundred billion dollar company. It's a payments company that's focused on merchants. There's trillions of dollars that still go through paper checks and POs, and we were looking at a bunch of horizontal platforms and thinking about that. We really thought the payments world was going to be more verticalized for in these large industries, and so that led us to invest in Built technologies that does construction loan management software and also payments for, for construction, which is about a trillion dollars a year in the U S that led us to invest in Nate, which is one click checkout for any merchant on the internet. So like any commerce payment provider where the customer is actually the consumer, not the merchant. And so it's a really interesting one because it puts a bunch …

AI assessment note: “We tend to be broadly thesis driven. We have like a few big themes”

Q What are some examples of like those types of questions?

A We think a lot about fatal versus fixable as kind of a framework that we'll use. Is this something that is fatal to the company's outcome, or is it something that can be changed? Things that end up being fatal are deep customer issues. Often when you look at companies at an early stage, marketing is not efficient. CAC might be low, but it's because it's against a small number, and those are your, you know, most excited users, and can that scale? And often it's trying to dig in and figure out, is marketing inefficiency due to Do you mismatch a product market fit? Are you just getting there based off of discounts and promotions? Or is this because that infrastructure isn't built yet and you can hire that team and you can optimize that? So one example of that fixable fatal framework that we think about is back when I was at IVP, I led the series B of Glossier. And at the time, like it wasn't obvious. The revenue was all over the place and it was because they were selling out of products. They couldn't keep stuff on the shelves. They would get stuff and it would sell out. And then obviously, Revenue is zero for a product that you can't sell. And digging into the data, that became obvious, and it was like, oh, like, getting better at forecasting, getting better at inventory management, or also the company was capital constrained, like having more capital, being able to hold more inv…

AI assessment note: “We think a lot about fatal versus fixable as kind of a framework”

Q So when you guys decided to launch this firm, you'd known each other for a long time. What was the difference between that and becoming partners in a business?

A There was a lot of work between that dinner and telling our firms. And here's the thing is like, we respected each other, liked each other as people. We respected each other and liked each other as investors, right? Like one of the first things we did was open up our track records. Cause I had a close friend who was starting a firm with somebody who had misrepresented their track record. And so like, we wanted to get out there first thing, like totally open the books and the, here's our actual performance, right? Making sure that we're really aligned there. And then the bigger thing was around, can we be partners? Can we like better careers on one another? Because like, at the end of the day, we didn't have to do this. We had great jobs. So we had like seats that people would kill to have, like great firms that loved us and that we love. And like, we want to go do something crazy. And so if we're going to do something crazy, we got to believe in one another or better our careers on one another. Right. And so. We actually hired a coach, Khalid Halim, who we love. He did some marriage counseling with us, and it was a lot of work. It's also like the soul searching of codifying your own values, what's actually really important to you, and making sure that you're well aligned with the person that you're working with. And the point that you brought up earlier on tension between focus…

AI assessment note: “We actually hired a coach, Khalid Halim, who we love. He did some marriage counseling”

Q How do you go about solving that key compensation issue in this world where talent is getting more expensive and it's more competitive?

A That is something that everybody is feeling right now because talent is a market and so much capital in the system means that market is also a very hot market. And there's a lot of data that's around today, but frankly, like all of that data is backwards looking. It's not real time. And so that's something that we do a lot of work with our companies on. It's a really common thing that comes up. And frankly, it's often a combination of data, either Susan or a compensation consultant. And also a lot of it is Around like precedent setting. Everything you do here, you have to realize you're going to have to do again. And so a lot of it is about how do you set up a system that you feel is generous and fair and aggressive and lets you get what you want from a talent perspective, but at the same time, you're comfortable with and can sustain. If you do something for one person, it's going to be expected again and again and again. And so how do you actually try to systematize a lot of that to make it fair and equitable?

AI assessment note: “often a combination of data, either Susan or a compensation consultant”