Invest Like the Best with Patrick O'Shaughnessy
00:24:12 · Christina Cacioppo, Patrick O'Shaughnessy
[00:24:12] S1: Total comp. It's probably like a factor of 10. Wow. I think it actually should be higher. My comp philosophy is you never pay the top people. Enough. Enough. You should always try and you will never hit it. And there's a bunch of people you pay much more than their contribution. [00:24:28] S0: Say a bit more about what those top people are doing that's so valuable, typically. [00:24:32] S1: High agency. Get shit done. That sounds so milk toast and that's worth millions of dollars. But like, yes, actually it is. The high context person who understands the company and the customers and the domain, making generally reasonable decisions and sometimes bad ones and then iterating on their bad ones and just moving is so valuable. [00:24:54] S0: Do you think it'll be 10x again in five more years? Or is that starting to get too crazy? Is the rate at which the most valuable people are getting more valuable, staying the same, accelerating? [00:25:04] S1: Accelerating. The counterforce is at what point will investors get more concerned about dilution and stock based comp, which they weren't throughout the twenty tens, late twenty tens. We had more of a like stock based comp conversation as an industry in like 2022, 2023. Now we're definitely not talking about it. That's the counter side. Without that push on like dilution, I think people just get paid more and more. That's probably probably should. [00:25:30] S0: How much do you care about your valuation and multiple and the engineering of those things?
00:25:30 · Patrick O'Shaughnessy, Christina Cacioppo
[00:25:30] S0: How much do you care about your valuation and multiple and the engineering of those things? [00:25:40] S1: I've historically not cared and sometimes even done things to like demonstrate my not caring. I care about ARR and ARR growth. I pay a ton of attention to those and those are the metrics. I think I've now been through enough evaluation environments. Late twenty tens, 2021, 2022, seen the like multiple roller coaster. Where I just don't put a bunch of stock in that or I used to not. I think the thing I care a bunch about that then will get me to caring about these multiples is the sentiment of people who work at Vanta. And that they feel like they're at a company that is growing and growing and then both of them in general growing and then growing relative to other... Their other opportunities in the market. There's a part of the market that is raising money every six months at five x the prior evaluation and that's a real talent competitor. [00:26:33] S0: And what about things like tenders? Have you done this historically? [00:26:36] S1: We have not done them historically, but the game on the field is you're doing a tender every time and you gotta do them. [00:26:44] S0: Does this feel unhealthy? [00:26:45] S1: Totally. My opinions about it don't matter. And I have all the feelings I want in whatever direction about tenders. They are normalized and people expect them and they expect it as part of their comp. And if you are not giving it to them, you got to give them a bunch of other stuff to make it up.
00:26:45 · Christina Cacioppo, Patrick O'Shaughnessy
[00:26:45] S1: Totally. My opinions about it don't matter. And I have all the feelings I want in whatever direction about tenders. They are normalized and people expect them and they expect it as part of their comp. And if you are not giving it to them, you got to give them a bunch of other stuff to make it up. [00:27:00] S0: How does all this affect your exit ambitions to go public or to be sold or realize some endgame for Vanta? Not at all? [00:27:09] S1: Not really, which is again a very counterprivileged statement. But the end game is how long can we keep this going for? And 99% of the big independent software companies in The United States are public. Yes. IPO in that sense. I don't have it as a bucket list item myself. It's more of like means to an end. Lots of people during companies didn't want the bucket list item. So founder privilege caveat here again. But Goal is big independent standalone company. [00:27:37] S0: On all this stuff, tools for building, ways of building, kind of all these kinds of things I'm asking about. What do you think is overhyped and underhyped?
00:27:37 · Patrick O'Shaughnessy, Christina Cacioppo
[00:27:37] S0: On all this stuff, tools for building, ways of building, kind of all these kinds of things I'm asking about. What do you think is overhyped and underhyped? [00:27:45] S1: Underhyped is knowing the company and the customers relative to knowing the skill set. It is still really hard to hire, train, onboard, make effective people who join a company. Company brains make this better and easier and like put information at fingertips. But there is still totally something to be said about someone who's been in a company, knows how to navigate the company, knows the customers, all of that. I'm at a few years right now where we value that skill set more than like the vertical skill set of you're good at sales, you're good at customer success. And would take a worse salesperson on sales skill who knows the Vanta pieces more. There's something in that. And I think especially as a lot of the labs and fast growing AI companies are like building out go to market teams. I think they're actually very much evaluating. You're a good salesperson. That's slightly off. And then I think as we think about building agents for all of this, the company context piece, the customer context piece is probably being relatively undervalued. To like, you are a good salesperson. You're the less valuable skills slightly today than it was four years ago. [00:28:52] S0: What about overhyped? [00:28:53] S1: Token based pricing. Low margin for the sake of having low margins. Custom training models for the marketing value.
00:28:53 · Christina Cacioppo, Patrick O'Shaughnessy
[00:28:53] S1: Token based pricing. Low margin for the sake of having low margins. Custom training models for the marketing value. [00:29:01] S0: We all just say we post trained a model on our stuff. Go Explain [00:29:05] S1: talk to the inference providers and sometimes give them, depending on the provider, give them true serum. They will say like, look, the reason to post train a model is cost. Some businesses have that need. But there is not a quality reason to do it. There's kind of a marketing reason to do it right now in this environment. And I think there's a lot of post training for marketing reasons, for investors and for customers, mostly for investors actually. [00:29:27] S0: If one of those people were pitching the other side of this, the value of post training, what is the pitch to you? Yeah. When these companies comes to you and it's like, we'll do X, Y, Z for you, what are they saying and what do they say the benefits are? [00:29:37] S1: You are putting stake in the ground of being a real AI company. Customers will notice. I think that's the primary one. I do think there's a secondary one in some use cases of like a form factor thing. And I think especially with large enterprises, you want to deploy this piece of software wall to wall that requires integrations and 17 people to sign off. Oof. That's the whole thing. Why don't you just give them a like model specific to them for this use case? I don't know. Semi reasonable.
00:29:37 · Christina Cacioppo, Patrick O'Shaughnessy
[00:29:37] S1: You are putting stake in the ground of being a real AI company. Customers will notice. I think that's the primary one. I do think there's a secondary one in some use cases of like a form factor thing. And I think especially with large enterprises, you want to deploy this piece of software wall to wall that requires integrations and 17 people to sign off. Oof. That's the whole thing. Why don't you just give them a like model specific to them for this use case? I don't know. Semi reasonable. [00:30:05] S0: So if I was one of these people and I came into Vanta, what does post training literally mean? What would it mean in your context? What are you doing? What are the steps that you would go through? [00:30:14] S1: So we would need a bunch of data from that specific customer and then go write a bunch of eval then change model weights based on that customer specific data. [00:30:24] S0: The process would be they're trying to choose something, they've got some data, you tune a model to achieve the thing. [00:30:30] S1: Exactly. [00:30:30] S0: Increase the eval and then Exactly. Sell them On an ongoing basis. And your point is that that's overrated because it doesn't actually improve their...