8 AI Strategies Billionaire Operators Are Using Right Now

All right, so I hosted a dinner with 9 and 10-figure operators, investors. It was an AI dinner, so I'm going to share some of the practical takeaways so you can take it and then you can grow your revenues faster. One of the takeaways is around AI compensation, and this is a topic that I actually brought up and I've been bringing up for the last couple weeks or so because I'm just like, okay, if I'm gaining all these powers and I'm helping these other people gain powers at my company, then it's like, well, how do we think about compensation here? Because if one person can do 5 or 10 times or 15 times or 20 times the work of someone else, How do we think about compensation? Right. So I'll give you a couple ideas here that were brought up. One is that you might think about compensating people like athletes. What that means is you think about how much money LeBron James is paid at age—he's in his 40s right now, right—for a superstar like that because he brings a lot more to the team. He brings a lot of experience. He can recruit people. He brings great leadership as well, and that's why he gets paid the big bucks, right? Separately from that, you might have some form of profit sharing. You might have some form of equity. You might figure out how you want to carve that out. Some people compensate based on. Story units, right? So imagine when you're storyboarding a product, you know, you might have different storyboards and then you have story units for the different pieces of that storyboard, right? And you compensate based on that. You know, some people are doing really well with that. Then you have people who are leading AI at these— some of these big companies and they bring it up to their HR team and their CEOs are thinking about it too. But the HR is just like, well, we've been doing this for a very long time and we have a structure here. So that's that. So it's more of a, hey, we've always done it this way. This is how we're going to continue to do it. And I think that's a nice way to fail if you don't innovate quickly enough on your compensation policy, then your best people are likely to find something else. Right. And so I think it's something that you have to keep top of mind. It's something that I've been keeping top of mind. So that's one piece. The other piece obviously is around kind of the token optimization piece. So you think about how much people are spending on Sable or SOL right now with ChatGPT. The problem with that is these frontier models, they cost a lot of money and they have a lot of guardrails on them. And now people are talking about using KIMI because it's open source and it's extremely powerful. People are thinking about, oh, how do we think about buying or renting local infrastructure where it's more cost effective and 80% of your tokens are running on something that is cheaper, right? And then maybe you're paying some for subscription and then maybe some you're paying for frontier models, but you don't want to go all frontier right now. So that's a key consideration too. And I would say another piece is around AI fluency and AI adoption piece, because fact of the matter is these companies are buying a lot of tooling their employees. So you might have Copilot, you might have a bunch of different things that you're paying for, but the reality is adoption's Our adoption rates are really low. And not only that, even if they're adopting it, right, if people are adopting these tools, that's one thing to say that they're using it. But also, what do they actually have in production right now? And more importantly, what do they have in production that's actually driving a return on investment right now? So return on investment, meaning save time, save money, whatever it is exactly. But if that's not being measured, like, okay, what are we doing here? Right. And I think another piece that we're talking about is how do you screen for someone that's AI native, that's AI fluent? So I'll just tell you how we do it, how we think about it right now. So how we're thinking about this right now is we ask people to show their work, whatever harness that they're using right now. They could be working in Cursor, could be working in Claude Code, could be working in Codex, but we want to see them work inside it. We want to see the projects that they have because once they open up their harness, you can see what their usage levels are. So are they maxing out on their usage? That's one signal that I look at. The other signal that I look at is, okay, so how are you maxing out on your usage, right? How's that looking right now? What do you have in production right now? Not just like some stuff that you've built because everyone can build anything right now. You can also ask them to open up their GitHub. I know it's not perfect, but you can see how their GitHub is. So that goes a long way. And then also you want to ask them questions like, hey, you know, I'll notice sometimes people aren't using the latest model, or I'll ask them, hey, how come your usage of the latest model isn't there? And they'll be like, oh, you know, well, it just came out, you know, because it just came out, I'm just used to using like old model, which that reasoning doesn't make sense, which means like, if you're not going to use the latest intelligence, like, your reasoning is flawed, right? And so to me, that's a problem. My whole thing is how you do anything is how you do everything. And so if your usage is low, and nothing you've built is in production right now, and you're not using the latest models because it's, it's too new right now or whatever it is exactly, whatever reasoning that you have, then that's how it's going to be working with you. And I don't know if I want to work with you if, if that's how you think. Those are a couple things I look at when it comes to looking for people who are AI native. And then the other piece is that Beat Claude challenge that we have. So that, that Beat Claude challenge, that's a Beat AI challenge that we forked from Anthropic. And so we just test them to see what their AI fluency is. And the whole idea here is that you have to beat Claude. And if you go to singlegrain.com/apply right now, we're actually hiring. So if you're interested in working with me, you can check that out because that's a, that's That's one of the first things that you're going to have to do. One other thing I'll share is that one person at the table, what they're doing, he actually stacked his entire recruiting team, his entire HR team. What's going on with that now is they're trying to send individual personalized landing pages to people. Okay, so these personalized landing pages, it's basically like, hey, we'd love to work with you. Here are the projects you're going to work on. Here's who you're going to work with. And if you're interested, we have a few questions for you. Right? So that prevents having to do the human screen for someone that you source. Because if you source someone, usually your best people are going to come from sourcing, but they're not going to usually want to jump through hoops. Because that's, that's how good they are, right? But if you show that you went the extra mile and you did some research on them, and then you show them kind of— you paint a story for them, you paint a vision, then they're likely to become a lot more, a lot more interested. Okay. By the way, if you're enjoying this operators video right now, you got to check out singlebrain.com. So Single Brain, that's where we make these managed revenue agents that live inside of your Slack, your Microsoft Teams to help you grow your revenues faster. So just go to Single Brain with a B, singlebrain.com. I will see you on the other side. And so the other thing I'll share, I asked the question, I said, hey, where are you guys actually seeing margin expansion in your businesses right now? Right? Meaning your gross margins are going up. Most people couldn't answer. Most people didn't have a really good answer in terms of— this is really a return on investment question when it comes to AI. But one person said, hey, like, we are seeing margin expansion when it comes to customer service. And that makes a lot of sense because customer service, you have a lot of documentation, you're answering oftentimes the same questions over and over. So it's a lot more verifiable than other areas. Coding obviously is very verifiable. And then I did ask another question. I said, you know, if you were to rebuild any team from scratch, which team would you rebuild? Everyone said IT. It all started with IT first, right? And so then you think to yourself, it's like, okay, well, what information technology? Like, why would you replace that team? And the reason for that is because usually IT, like IT teams that I've worked with in the past, they kind of seem to be stuck in their own ways and they're not really the type to innovate, right? And, you know, IT is supposed to bring stability to the things that you're doing, right? When it comes to your, your, your tech stack, they're not usually going to be like the product people that are supposed to innovate. So IT is one, recruiting would be another one, just because, you know, these teams are typically they don't, they don't innovate much, right? And so you have to look for people who innovate, you have to look for that AI-pilled talent. And so all these things are kind of coming together. It's like, okay, well, these teams aren't innovating quickly enough. Okay, we need more AI-pilled talent. Okay, well, how do you compensate this AI-pilled talent? It's all kind of moving over to that, right? We're talking about security, we're talking about permissioning, we're talking about governance, we're talking about token optimization, we're talking about all these things that people aren't really talking about right now. And I don't understand how there aren't more videos on this in general, which is why I'm even making this video right now, is why I'm doing this morning walk as I'm on vacation, right? Give you a little scenic walk. And probably a meta takeaway from all this is that But it's really important to understand that even if you feel that you're ahead in AI right now, I feel like I'm behind every day. And people think, oh, you know, I should be speaking on this stuff. I'm always like, you know, everyone in that group, I'll tell you this, we're all in this chat group with executives and we all feel like we're behind every single day. Even though we feel like we're ahead, we feel like we're behind. Okay. So that's a really important thing to just stay humble and also to keep in mind that you should be putting together peer groups. Okay. So if you're not in a peer group, put together a peer group of like-minded people. I don't care what stage you're at. You could be starting out in your career, you can be mid-senior level, you can be executive level, you can be a founder. But I, you know, this group that I'm in right now, you know, we're sharing things all the time, okay? And I think that that part is, that part is very important. You're sharing things all the time, we're getting on Zoom meetings, and, you know, when you hang out with like-minded people, you end up getting better a lot faster. And I think that's very valuable. Anyway, Let me know what you think I might have missed here. But, you know, these are learnings from a 9, 10-figure AI operators dinner that I hosted. Would highly recommend that you become the connector, you host these things as well. You know, you, if you curate a good group of people, you order dinner for everyone, right? You pick a nice restaurant, you order dinner for everyone, right? You take care of the ordering, you take care of the curation, you take care of the questions there. What ends up happening is that Everyone sees you as the connector. You're the node, right? And that's always worked out really well for me. That's always paid itself back in spades. So hope you enjoyed this video and we'll see you in the next one.