Claude's New "Revenue Mode" is INSANE

The companies winning with AI right now are not using better tools. They are running a completely different playbook. Most businesses are still experimenting. The ones pulling ahead already have agents doing real work, real systems that do real tasks with credit cards and everything. I have 12 agents running inside my company right now. They handle sales, content, SEO, and recruiting without my team touching it. One of the agents, the finance agent, even saved me $500 grand the first time I used it. The gap between who gets this and who doesn't is opening fast. Here's the exact playbook we're using to win with AI this year. So first, let's talk about the roles that agents actually replaced. What happened when they actually took over? So here's a very simple example. This is an agent that helps us on sales across the board. So literally what you're looking at on my screen is me asking it to pull up information on a cold emails campaign. It will actually create all the cold email sequences. It'll come up with all the leads, it'll scrub the leads, it'll deduplicate the leads as well, and it will send it on a sequence and it constantly iterates over time. It constantly self-improves. So this agent we have that's in our stack, we have a stack called Single Brain, which is all of our revenue agents, right? And that's the company that we have that runs this stuff for our clients, right? So within Single Brain, you know, our team can work with these agents and ask these questions and then the agent can handle the work. We're talking end-to-end. So typically what would happen is sometimes you might need to go back and forth with it, but once you have this set up, you can have it set up as a repeating cron job or even like a repeated trigger. So if the numbers start to fall down, it can recursively self-improve itself, right? So the good news is, you know, once this is set up, you're going back and forth with your agent over here, it can improve on actually generating meetings for you. So we've actually generated recruiting meetings, we have generated sales calls from this, and these sales calls are legit sales calls. And we're doing this for customers right now. But this is us just working with the agent inside of Slack, and this is our team collaborating with the agents as well. So not only are you becoming more well-resourced with these agents, but your team is becoming more enhanced with these agents as well. Now, not only that, we have an agent known as Flash, which handles content repurposing. So it helps with sales. That's cool. But it also helps with content. So this piece of content over here, I talked about how to practically deploy Jack Dorsey's World Intelligence, which is exactly what we do at Sync single brain with these, these revenue agents. 348,000 views on it. Okay, and this little diagram over here, the single brain piece, all this concept, I'm just explaining how we do it at my company. This is generated using a workflow that we have inside of Gemini. But this entire piece over here, I might have, I might have helped edit it for 15 minutes or so, and you should have a human in a loop here, but this is a task that continually repeats. So what happens is it will look for trending topics on X, and then it'll think about all the interesting ways in which I work with my agents, and I'll come up with these topics. And it got 348K views here. Not only that, it got another 150,000 because Gary Chan retweeted one of my other posts from, from Y Combinator. Not only that, in generated— we're talking about a lead from a multi-billion dollar company, actually 2 multi-billion dollar companies— is interesting, right? Because you're not just talking about generating views, you're actually talking about generating pipeline, and the agent is helping you do that. Okay, so I'm not saying you should just let your agent YOLO and do whatever it wants all the time, but you should let it get you to the point where a human needs a review and then you're okay to publish it, right? That to me is end-to-end, and this is what's happening right now. And so, and then obviously you're going to continue to improve these over time as well. We have another one called Oracle. The SEO team, they've actually built their own little app called Tiger Claw, and Oracle is syncing in with Tiger Claw. And the whole idea here is that Oracle can work with the team inside of Slack, answer whatever questions, and then use it for their SEO workflows. There's trade-offs here. These agents work pretty well. I mean, probably 90-95% of the time they're working pretty well, but 5-10% of the time they're broken. You got to reset them, you got to rework them, you got to, you know, adjust the configuration. They're not always going to work, which is why you need someone managing this. So that's probably the downside around it, but I think the trade-offs are, are worth it. What surprised me is once I added these agents into Slack, it changed the game completely, and we'll get to that in a little bit. So let's talk about how we actually train the agents. So the most important thing is when you're setting up these agents, you want to set its own So give it a personality, talk about what you like, what you don't like. Memory.md. So for persistence, obviously you want it to remember things as well. And lessons.md too. You're going to want self-correction after mistakes, right? So you don't want it to just— you make a mistake and you keep making the same mistakes over and over. You want lessons.md to remember the mistakes and also help you not just call out the mistakes but cover them, right? I would say that the more time you spend giving it feedback, the better it's going to get. Obviously, I think with the first 30 days or so, you want to spend a lot of time training it, but you shouldn't think like you're, you're done after that. The training never stops. It's almost like employees that you have in your company. You never really stop training them. training them. You want them to continue to get better over time. One additional thing I would add is expert panels that grade output. Okay, so if I'm shipping content, I would put together an expert panel of copywriters or landing page experts or persuasion experts and say, hey, that's how you can get better output. And then you can tweak it. Obviously you shouldn't just take it at face value, but these are a couple of tricks that you're able to do here. And by the way, what I would actually say too is we have open sourced a lot of our AI marketing skills, and there's about 1,700 stars on GitHub just for this repo right now. It's completely free. You can go to singlegrain.com/skills. Again, singlegrain.com/skills. Skills, you can get access to that repo because a lot of stuff I'm talking about in terms of training your agents right now, you can just go there and then swipe what we have and just fork it, make it your own. That's totally okay. So let's also talk about the real margin play here. Okay, so previously, like, I have an ad agency called Single Grain, but we have Single Brain, which manages revenue agents. We customize these revenue agents for our clients. Okay, and so what's different here is that we have these 4 deployed marketers that would jump in, very smart marketers, and What will happen is they'll customize these agents for our clients, and from there, obviously, again, you need someone tweaking it. You need someone upgrading it. You need someone that's managing these agents. That's what we do over at Single Brain. But instead of having four humans that has, you know, maybe a task might take you three weeks or so, like a cold email campaign might take you two three weeks. You need copywriters. You need email sending experts. You need all these things. Maybe it's three people helping you. Okay, maybe it takes you three four weeks in the past. Now you have one human that can probably do it in a day or two, right? And that's being generous. Probably takes a day. You maybe need to have some reviews done, but then you're often sending the emails the next day, right? Just the Human timelines don't work anymore. It's really based on AI timelines. And so I think if you're able to set up these agents the right way, you're able to compress a lot of the things that used to take a long time to do. And that's just no longer a factor anymore, right? People saying things are too hard, that's going to take too long, I need more headcount. Those excuses are now invalid in this world and you should know that. And that's going to directly affect your margins in a good way. So then that leads up to the concept of the 24/7 company, meaning that your company will run 24/7 and doesn't sleep. You have agents that are infinitely patient. And what happens is you have these triggers that are firing at any given time. You have cron jobs that are firing at 2 AM, which is what we have, that will ingest all of my content from my podcast, my YouTube channels. We have cold outbound launching on Saturday, sending thousands of emails before Monday, right? You can do that. We have deal resurrection crons that will find the right time to reach out to people that maybe we lost a deal 60 days plus in the last couple of years or so. We'll try to revive those, especially if we pitch them, right? You have these agents constantly working in the dead of night and you're constantly building this, this fleet. And that to me is one of the most interesting things because you can just get a lot more done with a lot less. If you want to triple your pipeline, double your revenues without having to cut headcount, you have to check out the revenue agents from Single Brain. That's singlebrain.com. And within these agents, they'll chat with you, they'll do data pulls for you, they'll strategize for you, they'll even handle outbound for you, they'll handle the sequences, they'll handle everything end to end. singlebrain.com. And we'll see you over there. So we should also talk about intelligence versus judgment. So at my company, for whether it's Single Grain or Single Brain, we want people that have good judgment, because people that have good judgment, they're using AI right now, and they're becoming turbo brains, they're very, they're moving very quickly, and they're making the right moves. But if you have someone that has bad judgment and you're using AI, they become slot cannons. How do you decide what to automate and what to never let AI touch? Well, it depends on your business. Every single business is different. Like, what security levels are we talking about over here? If you give it access to this, for example, you let an agent have write access to your emails, it's going to send whatever it wants. And that's an attack factor that you don't want to have, you know, because if the agent gets compromised, that attacker decides to have your— maybe they use your email to send emails to all of your employees to ask for money. Maybe they can do that, right? Or maybe they could get access to all your bank accounts and things like that. So you want to be very careful there. There's a lot of information you have that you don't want to maybe to give it read or write access to. So that's a judgment call for you to make. You know, content production, for example, maybe I wanted to write stuff that I can review, but I don't want to just publish on autopilot. Okay, that's something that's very dangerous. Client relationships are judgment too. Like, you want to make sure that you keep that human. And so really, I think you should look at every department and go, you know, line by line, and then make it— make a call from there. And then maybe give a little permissions first, and then maybe you can give it more permissioning if you want in the future. All right, so let's talk about where this is all going. So I literally mapped this out yesterday with my agent. Infrastructure scaling. So I'm looking at how much infrastructure we have to grow by, especially on the Zegobrain side, if we're these customized revenue agents for people. And this is how many H100s I need. So this is from Nvidia. These are their GPUs, right? And so if we're in our full bull case scenario in the next couple of years, I might need 336 of these H100s, probably more if I'm being honest. Base case, maybe I need, I don't know, up to 40 or so. And then bear case, maybe I don't need that many because of Jevons paradox, which means, again, the more something gets— it becomes more widely available, the more useful it is, the more widely it gets used, like the internet or electricity, for example, or AI in this case. I just see it going parabolic. And so you're going to be creating a lot more of these agents. This is where AI is going. You've got to be mapping out, especially if you're a business, how your costs are going to go up over time. You're going to have a token budget for each of your employees because if they're going to be managing a fleet, like they're going to be consuming tokens, right? So for us, we were thinking about how are we— what percent of our token budget is going to be frontier models? And then what percent is going to be local models where we're running open source models? And those are obviously going to be a lot cheaper. And you want to think of this as maybe the frontier models, that's like your Ferrari where you're using it for coding and other more complex tasks. And then for the other 85% or so, you're using it for more basic stuff like getting the groceries, for example, right? Maybe you're just querying and asking random questions or doing research and things like that, or just like random data pulls. But that's what I would ultimately say. This is where this stuff is all going and you have to pay attention. All right. So if you liked this video and you want to see a more in-depth explanation of my AI agents, you can check out the video, the next video over here, and we'll see you over there.