AI Business Manifesto Live - August 12 2026

Premium is required to create. Okay. Hello, hello, hello. Welcome everyone. Rich Sheffrin here, just getting set up to go live. And welcome. Excited to have you here. There we go. Oh wow, there's like a globe behind me on Instagram. That's kind of interesting. Cool. All right. So As you know, we're here because I just recently released a manifesto, and it was all about how to, one, really use AI correctly today so that you can maximize the income that you're making. 2, how to capture your judgment, because I believe that that will be the competitive advantage going into the future. And yeah, howdy, Kevin. Oh, whoops, I forgot. Yeah, Oscar, this is a yo-yo. So it's funny that Oscar just said yo-yo. This is a vagus nerve simulator that I happen to have had on. So anyway, I was yesterday, we were talking about capturing judgment, right? And then how I believe that we're moving into a new time. I'm trying to get into the right place here. And I'm just— maybe I need to move this camera a little bit. Bear with me one sec. Is that this one? Yeah. All right, let's move this like that. I can stay. There we go. All right, cool. Now I can hopefully not— yeah, that's much better. Now I don't have to, um, now I don't have to be like all the way at the end of my desk talking to you. Um, There we go. Cool. All right. So what we're talking about today is judgment, right? Like we've been talking about, but we're looking at the other side of the equation. We're looking at, uh, your own judgment, right? So somebody asked me yesterday how you'd know when your own judgment is good, right? Uh, and so everybody is working on making their AI smarter. Almost nobody is working on the thing that decides what the AI gets points for. And that one is still entirely yours, and that's your own judgment. So how do you actually get better at your own judgment? Not just capture it like with the imprint, but improve it. And so before I get into that, right, like just to remind everyone that, uh, what I am doing these for is so that I can actually help you. So if you have a If you have a chance, if you've read the manifesto and you have any challenges as it relates to it, I would love you to show up here and have— let me help you fix that problem. And so I'm just posting this. This is where you'll find the manifesto, and I will show this on the screen. So that is The primary reason I'm doing these. And just so that you know, I've gotten amazingly great feedback about the manifesto. So if you haven't read it yet, you should really pick it up and skim it, see if it's interesting to you. I believe that it will be. So with that said, right, let's kind of get into it. So why has judgment for most people Stopped improving. Right. And here's why. The intuition, which is part of judgment, needs 2 conditions. And if either one of these conditions is missing, um, experience just causes confidence. It doesn't increase your judgment or your accuracy. So the 2 things that you need, One, a regular environment where there are real repeating patterns that are there that you could learn from. So a real repeating pattern that you can learn from. And then number 2 is feedback that actually comes back, and it comes back fast enough, clean enough, and you get enough reps to actually absorb it. That's why some people who have like our 30-year veterans are genuinely great and have like really like 30 years worth of experience and judgment all compounded. And then others are no better than a beginner with just a better story. And they went through the same effort, but they didn't go through the same loop. So you've been making decisions for however many years you've been making decisions. Are you that many years better? For some of you, yes. For most of you, probably not, because you didn't get the feedback that you needed in a timely enough manner for it to actually make a difference. So I think like a day ago, two days ago, three days ago, sometime relatively recently, I was I when I had gotten back from Agora. I had told you about Joe Schriefer and how he took Agora Financial from $30 million to $400 million, that he spent over 20 years at Agora reading a sales letter every single day, and then looking at the numbers that came back from that sales letter over 20 years. That's the regular environment with fast feedback and an insanely enormous rep count, which is why he has the single best judgment of what will— what promos will work and what won't. I mean, this is like a textbook condition set, and that's why Joseph Schrieffer's judgment is literally worth hundreds of millions of dollars to Agora, and why his imprint is so desirable to be captured. Does that make sense? If you're watching and you're hearing my voice, can you let me know if that makes sense? There's quite a few of you on, but I feel like a lot of times I'm talking to empty space without feedback. Thanks, Mahatma. Appreciate you. Thank you, Sean. How do you actually capture a fleeting thing like judgment? Sean, you— that's what the manifesto is all about. And after you read the manifesto, I actually have a few free Git— well, GitHubs that you can download and actually will start capturing your judgment and updating your judgment. It is not a clone. A clone is totally different. Nowhere near as powerful. It is— a clone does not capture your judgment. It just captures information. And the part that makes it, um, That makes it really interesting, at least in Joe Schrieffer's case, right, is that Joe cannot call whether a promo is either going to be okay or blockbuster. He can identify the ones that are not going to make it, like ones that will not work. But once it passes Joe's filter, it'll work. Whether it'll work great or just work, he can't determine just by looking at it. Because the environment sets the ceiling and he doesn't have enough of a difference to be able to effectively identify. So for anyone that wants to learn more about what I just said about those 2 conditions, it comes from Gary Klein and Daniel Kahneman. Gary Klein is one of the top experts in intuition. He's written a lot of great books. I've read them all. And Daniel Kahneman is the Nobel Prize winner in behavioral economics. So they co-wrote Conditions for Intuitive Expertise: A Failure to Disagree. And it was in American Psychologist in 2009, 2 researchers who spent their careers disagreeing about whether intuition could be trusted Right? Because Kahneman is System 1, System 2. Gary Klein is all intuition, which is System 1, which is the one that's the impulse. They sat down and published the conditions that they both agree on. The paper is the thing worth loading into your AI tonight. I'm going to put this in the— I'm going to put this in the chat. There we go. Sorry about that, Dante, if you've been waiting. I'm going to put this on the screen as well. Sorry if it cuts out my head. All right. Kahneman and Klein, Conditions for Intuitive Expertise: A Failure to Disagree, American Psychologist, 2009. If you load it into your AI, your AI will understand what needs to happen in order for your own judgment to improve. Does that make sense? So this is where you can find out more information. So I will continue. All right. So when I talked to AI about this and like asked AI to recommend, okay, like how can we improve our judgment based on that article? It said, write down 10 decisions you actually make consistently in your business, right? So 10 decisions. And if you can't think of 10, think of 5, but the decisions that you make consistently In your business, and next to each one, write one of two things: How long until I find out I was wrong? And would I ever notice? And then you want to sort them into three piles in the next two weeks. The first pile is where you can get sharp feedback fast. The third pile is where you get no feedback, and the middle pile somewhere in between. You're going to be surprised at how much of your business, how much of the decisions that you make end up in the third pile. And the third pile is a pile that you learn nothing. The third pile is exactly what your AI can change. Not by deciding for you, but by being the only thing in your business that remembers what you expected and what came back to you, regardless of whether you notice it or not. That makes sense. So it can actually augment your own judgment. By having keying you more into the loop, because judgment only compounds when a loop closes. You run a campaign, you paid for the data, right? With real money, real work, real effort, real time—3 months. If nobody goes back and actually compares the results that you expected. It's the equivalent of spending all that money for the data and then throwing it out. Do that 10 times and you have 10 years of experience and 1 year of learning. Do it the right way, you have more than 10 years of learning. So a lot of people have heard of Deming, right? The guru of systems and quality control. Deming's loop is plan, do, study, and act. Everybody does the plan and do. Almost nobody does the study. And the reason isn't laziness. You know, an SEO play takes six months to finally find out if it worked or not. And in those 6 months, a big client can cancel, an important employee can quit, and you just forget to monitor until at some point, right? There's a guy I follow, his name is Cedric Chin. He writes a blog called, or like blog articles, whatever you want to call it, called Common Cog. And his entire archive sits in my vault. And I had my system look back at his own operating career and described what he saw as a graveyard of incomplete execution loops, which means that he almost lost the lesson from his own biggest hit because it went viral and the attention made him forget he had written down a hypothesis to start. He only caught it by rereading his own documents. Right? So what that means is, is that before you launch anything, before you launch a campaign, before you hire a new employee, before you change a price, why not take a second, write a sentence? If you really feel like up to it, write a paragraph. What you expect to happen, why you expect that to happen, and the date that you'll know. One sentence, one paragraph, whatever. Then on that date, read that paragraph before you look at the numbers. The order, right? Coming up with that before looking is important. If you look at your results first, your brain will quietly rewrite like what you expected, and then you'll learn nothing while you feel like you actually learned something. And so one of the reasons why I am such a big fan of AI capturing your judgment It's the one thing that it can do better than any human can ever do because it will remember and it will come back and it will check. I expect this to do X by the 15th. It files it, and on the 15th, it puts it in front of you. This is what you expected. This is what happened. The imprint captured what you decided, right? This, if you do it, captures what you expected. The gap between those two is the only thing that has taught anybody anything. Okay, so if you know you write this list, you have that pile. Decisions where feedback never comes back. Which market, which product, which partner, which key hire, right? You may be able to— you get maybe 5 or 10 of each of those in an entire career. So direct experience mathematically cannot make you good at them from your own experience. So guess what? What do you do if your own experience isn't enough? Anyone have an idea? You borrow experience. You borrow it. And there's a reason that works. Experts in messy domains don't reason from principles. They reason by comparison. Against a library of cases that they've already seen. It's one of the reasons why, like, if you go to like Harvard Business School, most of the work that you're doing are case studies because you learn through experience over and over and over again. A library of cases that you've seen. So you import cases. That's why business schools teach by case study. That's why the Marines run sand table scenarios instead of only live fire. But the obvious way to do it doesn't work. And this correction is the sharpest thing that you might hear today. You don't read for lessons. You read for instantiations. Not, here's what this teaches us. Instead, oh, so that's what it looks like when it works. And that's one more way that it can show up. Okay, so let me tell you a story that my AI told me to share with you about Morris Chang. In 1958, second job out of college, Texas Instruments hands this man a production line running at 0% yield. 4 months later, he's at 25 to 30%, twice as good as IBM's own production line. He goes on to invent learning curve pricing. Sell the chip below cost, take the whole market, run the line flat so your yields improve faster than anyone else's, then cut the price again, even when nobody asked you to. His words sowed despair in the minds of my opponents. It made Texas Instruments the market leader of the era. It's still used in semiconductors today. It's funny, actually. I studied this in college. It's called the experience curve, that you can predict how much your cost is going to drop by volume, and you can then project that kind of volume if you take over the market by lowering the price so cheap that price no longer is the blocker. And so Back to the story. They moved him to consumer products. Same man, same play. Digital watches at $19.95. A year later, $9.95. Commodore came in at $7.95. Texas Instruments shut the watch division down in 1981 and laid off roughly 3,000 people. Chang, who invented the play, finished the Stanford PhD in 2 and a half years and ran a division to market leadership, was put out to pasture when he was 52. Read that for lessons and you get use learning curve pricing, which is how you lose 3,000 jobs. Read it for instantiation and you get something you can actually use. That's what that play looks like when the conditions hold, and that's what the identical play looks like when they don't. And now you know which conditions to look for. The second is judgment. So you want to aim for 10 to 20 cases, not 200. And after each one, think through, can you come up with a sentence? This is an example of X, and this is one more way that it can show up. Never write, therefore I should, because the moment you write, therefore I should, you've turned a case into a rule. And a rule is not a judgment. A rule will be wrong in a different situation that you'll follow anyway. I have over 379 Founder transcripts. Founder Notes is a podcast I listen to, and the full 575 posts of Common Cog Archive sitting in my vault. Biographies There's lots of biographies in them. Biographies work. Business histories work. So does the oldest source there is. Ask somebody older than you to tell you the story of what happened to them. These are some of the ways that you begin to actually improve your judgment. Now, I also shared some other stuff that I've learned over the years. Yesterday, like I told you about mapping cause and effect. So here's like the concept: two people look at the identical dashboard; one sees nothing, and one sees everything, including problems. But they're looking at the same dashboard. The difference is not the data; it's the causal model in their head. Upgrade that model, the causal model in their head, and every future decision gets read through a better lens and comes up. With a better judgment. I brought this up yesterday because what I was saying was, is that when I taught Theory of Constraints and I had people map out the cause and effect of their business, they just got better at business in general, which was kind of a surprise to me because in Theory of Constraints, you start with the problems that you have in your business. Like every project we undertake, you know, takes twice as long and the conversion rates are half as much. It's difficult to get our affiliates to mail. It's, difficult, whatever. You start with all the problems and then you ask why and you keep asking why. And what you find is, is that as you go deeper and deeper, there are generally less causes causing more effects. So there's a convergence of cause. And by doing that, just the act of doing the root cause analysis, forcing yourself to understand why this happens and why that thing that causes that to happen, why did that happen? And why did that happen? Understanding those linkages will make you better at business and makes it so that when you look at a dashboard, you see more than that what's there. You're seeing the causes and the interrelationships. It's the re— I'm an accountant by training and I'm damn good at it. You know, it happens to be one of the skills I'm, I'm better at. And so because I'm good with accounting, like really, I can look at a spreadsheet and usually notice the number that isn't right. Looking at the whole spreadsheet just because of my understanding of the numbers. So my judgment on that is really solid. And I've done— I've taught theory of constraints. So I'm very well aware of the cause and effects relationships inside a business. So that's another way. Another way, and this was another thing that AI recommended to me last night when we were talking about this. Here's what AI told me. Judgment improves by running into disconfirmation. Your AI is simultaneously the cheapest disconfirmation machine ever built and the most agreeable thing you've ever talked to. And which one you get is set entirely on how you ask. Right. So it says, uh, your own line from episode 4 lands harder here than it did there. Thanks, AI. Rick Rubin said this: AI has every point of view, therefore it does not have a point of view. And your observation on top of it— that's to me— the moment you assert a strong opinion, it folds, right? Whenever you assert a strong opinion to AI, it folds. Oh yeah, that makes sense. That's not it agreeing with you. That's it having nothing to disagree from. Never ask your AI, what do you think of this? You already know what it thinks. It thinks what you think. So two fixes to that: make it argue the other side with you before you say what you believe. Have it argue the other side, and then when you do put your position in, put it in as somebody else's. Someone I respect told me this. If you really want to get it ripped apart, my friend gave me this. I think he's completely wrong. You can't outsource your judgment, especially to AI, but you can have AI capture your judgment. And by capturing your judgment, um, you are setting yourself up To really, one, have your AI be able to do more and more for you right now. But even more important than that, let me kind of just walk you through what's going on in the most clear language that I can. AI is speeding up execution. The more things that get done, right, the more decisions need to be made. The more decisions that need to be made, the more judgment is needed to make those decisions. Output is increasing. Everybody can see that with their AI. More decisions about that output need to be made. That's obvious. Where does the judgment come from to make all those decisions? And guess what? It doesn't exist. And therefore, those people that have their judgment captured will have their AI be able to work longer than others. And those people who don't have it will be a slave to just like what comes out of the box. So if I'm writing a follow-up report, 20, 19 years ago, I wrote a report called The Attention Age Doctrine 1 and 2 because attention was becoming the scarcest commodity online. And when I wrote it back in 2007, it wasn't obvious like it is today. And the next, if I were to write a report about the next coming constraint, it would be the judgment age. Because it's not only the fact that, like, getting your judgment in there so that it can op— it can sit in your seat and make calls like you did— it's that that is needed, period, across the board. There is going to be a lot more judgment needed, and we do not have the judgers. You can map it out. It's going to be the single biggest constraint In the next decade, and I'm absolutely positive of it. All right, half an hour. Like I say every day, I really want to build here. I want to build these things. I don't want to just talk about them, but I want to build them for you. I want you to read the manifesto. I want you to understand everything that I'm sharing with you. People pay us a lot of money. Trust me, Agora is paying. mid-six figures for like our help with AI. I am offering it to you for free. I have a half an hour to kill every day I'm here. Read the manifesto. You will be a better entrepreneur because of it. You will be much smarter about AI. I would imagine that if you read it, you will think it's great and you'll wanna share it. That's why there's no opt-in or anything else. And then show up here and let me help you actually get your shit working. Let me get your AI. dialed. That's why I'm here. So I will be back tomorrow. Uh, please read the manifesto. If you read the manifesto and you liked it, you got value from it, please share it. That is my primary reason for doing this and writing the manifesto. I want to get the word out there, and you can help me dramatically by just posting it, downloading it, sharing it. I do not care. They don't have to come to my site to get it, whatever. And I'll see everyone tomorrow. To higher profits beyond. Rich Schefren, over and out.