00:00Let's just start with the basics, $3.5 billion for Fund 7, what the intention is, the strategy,
00:07the thesis in how you'll deploy out of this fund.
00:10Sure. Well, Ed and Caroline, thanks so much for having me on. Thesis continues to be as the same
00:15as it's been in Funds 3, 4, 5, 6, 7. This will just be a continuation of what we're already
00:23doing.
00:23We have a very specific framework, which we call the Lead Edge 8, which is certain financial
00:29criteria. Are you $10 million plus in revenue or are you growing 25% plus a year? We'll continue to
00:35do
00:35that across the life cycle of different types. Literally, we can lead a primary around, put money
00:43on the balance sheet of a company. We can buy out early investors or early employees. We can fund
00:48the continuation vehicle from an investor that needs to get out. We can buy an entire company in a buyout.
00:54It's really like the full life cycle. And then actually, we also invest in some companies when
00:58they're public as well. The rules-based approach is very interesting, whether you call it venture
01:04capital or private growth equity, because I'm curious how it works in practice. So if you're
01:11saying, OK, we only invest in companies that reach these criteria, do you find them or do they
01:17find you? It's a great question. So the way we've always sourced, and it's the way the three of us
01:24who run
01:24the firms, our backgrounds, our best for venture partners and insight. So we have a team of about
01:3018, 22 to 24-year-olds that pick up the phone and call companies all day long. Gone are kind
01:38of the
01:38days of cold calling company or actually leading voicemails. But our team of 18, 22 to 24-year-olds
01:46think of these people as a lot of former college athletes, people that were debaters,
01:51people that were entrepreneurs. We want people that are really persistent because the great
01:54company doesn't call you back right away. You sometimes call that CEO a half dozen times
01:58before they call you back. Right.
02:00But again, we'll speak to about 9,000 companies a year. And if I said you had to meet all
02:05eight
02:05criteria, it'd only be about 90 or 1%. But we say that if you meet at least five, it's about
02:12a 10%
02:13yield. So at a 9,000 companies, 900 meet five or more criteria, due diligence on 150 to do five
02:21to seven deals a year. Mitchell, I can't help but feel that those ex-college athletes and those 22
02:28to 24-year-olds are going to be very well-helped or maybe disrupted by Argenta KI. And I want
02:34to hear
02:34your play on the whole software investment thesis, because this is what caught my attention.
02:42Yeah. So what I would say is, again, as long as the person that works at the edge of 22
02:52to 24-year-old
02:53is on the other end of a phone talking to another human being, like a CEO or CFO of a
03:00company,
03:01human-to-human interaction is still really, really important. However, where AI can be really
03:08helpful now is, and where I did this, you know, the three of us who run the firm all started
03:15our
03:15careers doing this 15, 20-plus years ago. And, you know, today, an analyst or an associate,
03:22cold calling companies, can be a lot, can use AI to be a lot more educated when talking to an
03:29entrepreneur. You know, they can have done a bunch of research that would have taken a couple hours
03:34before, and they can do it in five minutes. You can chat to our Claude. You know, they can look
03:38up all
03:39the competitors. They can get the value prop. They can ask, like, what do you think the right
03:42questions to ask are? So actually, AI, it can help you find more, if you say, hey, I just spoke
03:48to ABC
03:49company, like Toast and the restaurant point of sale system, give me all the competitors so I can then
03:54call them as well. So it actually, like, makes the analyst associates even more powerful.
03:58And Mitchell, how many, therefore, of the calls do you think will still be going to software
04:02companies that have been beaten up that you think still look attractive? Because that has been the narrative
04:06that Agenic AI is coming to eat their lunch, and therefore, they're going to see significant pressure on their
04:11ARRs and their future growth.
04:13For us, it's always been about, look, software is a very broad word, right? There's lots of different types of
04:23software companies. Just like if you say, I'm going to Europe. Like, there's lots of places in Europe. You could
04:29be like, oh,
04:29in Europe, it rains. No, but some places, a lot of places in Europe, it doesn't rain. So for us,
04:34like, software,
04:35so we're looking for companies, and we always look for companies that have, like, very high gross dollar
04:40retention. Now, again, you might say, well, AI is going to change all that. Well, then you're looking for
04:44software companies that have a moat, that have, that own the data, that are systems of record, that tend to
04:51sell
04:51the large enterprises. You know, you could have most, you know, if you have people on here from
04:57most banks, most banks still don't even allow their employees to access, you know, access AI from
05:04their systems. These big enterprises, when they buy software, want security. You know, look, you've
05:10been able to buy open source software for decades. Yeah.
05:13companies like Elastic and Grafana and Databricks and Red Hat exist because they provide an enterprise
05:20level on top of what was already free software. And so I think you're going to continue. And again,
05:25we invest outside of software as well. We invest in consumer internet. We invest in marketplaces.
05:30We invest in financial exchanges. We invest in payments companies like TransferWise. But yeah,
05:35we will continue to definitely invest in software.
Comments