← The Koerner Office - Business Ideas and Deep Dives with Chris Koerner
The Koerner Office - Business Ideas and Deep Dives with Chris Koerner
The Easiest Way to Profit From AI Right Now⏐Ep. #278
00:48:41
Loading summary
Transcript155 lines
- [00:00]
A
So you may have been wondering, what happened to Nick? What happened to Holdco Bros. What happened to your best friend Chris? Well, I'll tell you what happened. He took the last four months off podcasting of making content. He went underground and he has been deep in the AI world, making agents, playing with openclaw and Claude, and we just reconnected for the first time in months so he could tell me everything he's learned and how we can make money off of everything he's learned. So if you're interested in AI agents, openclaw, Claude, all the latest and greatest in AI explained in a simple use user friendly way and how you can monetize it, this is the episode for you. Welcome back, Nick. I want you to prove this tweet wrong. That's the purpose of this episode. Okay.
- [00:39]
B
Really? I'm in.
- [00:40]
A
Yeah. All right. I'm going to read this out loud to you. You ready?
- [00:43]
B
100.
- [00:43]
A
Dude, I have 10 agents running while I sleep. No one is prepared for AGI in two years. So what are you building, bro? All my smartest friends are vibe coding until 3am every night. It's all about agency. Intelligence is a commodity, man. So. So what are you building? Do you even study exponentials? Have you seen the latest METR chart? You're going to be stuck in permanent underclass, bro. So what are you building? Did you even set up openclaw? I'm maxing out my token budget every day, man. So what are you building? I promise you, I'm 10x more productive. Bro, you just don't understand. Please, bro, just. I know you use this stuff every day too, but you must not be prompting it right. Please, bro. 1.3 million views, dude.
- [01:20]
B
Prove me wrong to my profile. Swipe right. Swipe right. Right, That's.
- [01:25]
A
That's my profile. That's mine.
- [01:28]
B
All right, scroll down there. There. Click on that one. Okay, dude, so I can keep the lights on and I just like read all night? It's insane, bro. So what are you even building though? Bro, you don't understand. My smartest friends are running copper wire through the walls and could ring a bell on the other side. Yeah, but what are you even building? Didn't you see they strung lights? Like actual lights on a Christmas tree at the Edison office. They just. Freaking decorations, bro. But what are you building? Dude, they're projecting pictures onto a wall and people are paying money to watch that. Like this technology runs everything. Everything. That was my response to this freaking dude's tweet.
- [02:03]
A
I didn't know you responded to this. I'm completely surprised I never saw this tweet. This is amazing. Okay.
- [02:09]
B
Yeah, because it's like. It's like, okay, yeah, 99% of the things that people are messing around with don't have a use case, and they won't have a use case. But how are people supposed to build the skill?
- [02:18]
A
Muhammad edit out. Nick, reading that tweet because it makes me look bad. Sorry. Keep going.
- [02:24]
B
No, dude, but you. You know what I mean? Like, the original tweet has a point. What are you building? What have you built? Right? Because who's that guy? Alex Finn, I think, who's like, every single day coming onto Twitter, he's like, bro, this freaking changed my life. My life. Like, he. What does he really built? I don't even know what he's actually built. But the flip side is this technology is changing so quickly. People are learning it so quickly. By the time you actually start seeing results, it's game over, bro. I think it's a really silly thing to say. Like, what have you even built? First of all, we don't know, like, how quick the turnaround time is going to be on this technology. It's new, and it looks like it's going to be faster than anything we've ever seen in our lifetime. But, like, legitimately, we're talking about the last few months that people. That people have been working on stuff. It's so soon in the cycle to be pulling out the. What are you even building? What are you even done with that? We don't know. We don't know what's going to look like yet. And the reason I pulled up the analogy of electricity and Thomas Edison is because there was a big lag. Like, it was a novelty for a long period of time. Like, what are you going to do with electricity? Oh, oh, you can light your house.
- [03:22]
A
Are you.
- [03:23]
B
You're going to read books after hours, Are you? You know what I mean? Like, people just didn't get it until all of a sudden people got used to the new technology, and creative people were like, huh? I think I could use this for, you know, whatever it is. And pretty soon we've got our modern technology. So I just think it's a pretty dumb way to approach it. What do you think?
- [03:41]
A
I'm convinced you sold me. I'm that easily influenced.
- [03:44]
B
Yes, yes. I'm an influencer.
- [03:48]
A
You're an influencer of influencers. Can I show you something real quick before we pivot? I just want to add an analogy to your analogy of electricity. So last Night I went to Chipotle with some kids at church that are starting a business. They bought this printer. It's not a 3D printer, it's not a normal printer. It's a printer that can print anything on anything. Okay, so like, if you want to like engrave your tumbler, you know, and it has a rounded surface or make a T shirt or whatever, or make like a 30 foot long banner, you can print it. And so they wanted business ideas from me. And where we finished the conversation was like, dude, you need to put this in your backpack, take it to school, plug it in, and print stuff on demand. Like, you need to use this as your vehicle for rapid testing and prototyping because there's an endless amount of things you can print. You can print anything on anything. Like, you could print the wrestling team's weight classes on their shirts.
- [04:38]
B
Cool.
- [04:39]
A
You could print like, they're on the track and field team. A lot of the track and field runners, they customize their spikes right on their shoes. You could print something on that, like their PR on the spike. Like, we could sit here for 10 hours and just scratch the surface of all the ways you could make money with this thing. But don't try to go down those rabbit holes. Just use this as your rapid testing machine and just learn, where's the demand? How much will people pay? What colors do they like? Do they want tumblers? Do they want their phone cases, engraved stickers, whatever. That's what you should do with this and leave your options open and then just chase the energy. And what we're talking about here is like use Claude, use AI, use LLMs, use agents as your rapid testing vehicles. Like, use it to have fun and to learn, but to don't, don't get married to anything just yet. Just be ready to pounce. And when something comes up, you, you're going to have all the skills necessary to execute on that thing. Dude.
- [05:29]
B
Okay, first of all, I'm going to say something. I know it's going to be edited out, but I was just saying to the same kids, don't go chasing waterfalls. You stick to the rivers and the lakes like you used to. I know it's going to be edited out. I just had to say it.
- [05:38]
A
Please edit out, legitimately plead it out.
- [05:40]
B
But second, I'm going to say something right now that I think is going to shock you. I think it's going to shock your audience. To most people who are sitting there that want to be entrepreneurs, that want to figure out, how do I do something with AI, I'm going to say something. Go get a job. 100%. Go get a job. And I'm going to prove to you right now why. I think it's. I think it's the best thing that you can do if you're early in your career, especially to work for a company. So first thing I'm going to show you. This is nuts. Chris is like, I'm going to hold it in. I'm going. I'm going to give him some rope. I'm going to give him some latitude here.
- [06:10]
A
I just love that you're speaking in, like, Stephen Bartlett clips. You're like, I'm going to say something that's shocking to everyone in the sound of my voice.
- [06:18]
B
Clip it, clip it, send it. If you're on Twitter, you've seen this.
- [06:23]
A
This is a graph or explain to our audio listeners.
- [06:26]
B
Yep. So this is a chart or a graph with 2,500 dots. Each dot represents 3.2 million people. So, effectively, what we're doing is we're showing the entire population of the world. 8.1 is in there. Yeah. 8.1 billion people. Most of the dots are gray. And then you've got kind of like maybe a tenth of the dots that are green, and then you've got some yellow dots and some red dots. So the gray dots are people in the world who have never interacted with AI. They've never heard of AI, and that is like, let's just say 80% of the world. Then you've got this green band, which is 10% of the world, who have used a free chatbot. So they've gone and sat down with ChatGPT, or they've used Gemini or whatever, and they made me ask it a question like, how do I cook a pot roast if I ain't got no pot and I ain't got no roast? No. And then there's like, the yellow part, which are now 1, 2, 3, 4, 5, 6, 7. So 7 times 3.2. We're talking about 24 million people who pay $20 a month for AI. And then there's one little tiny dot. What dot here? Of people who actually use coding scaffolding. So what we're talking about here is Codex from OpenAI. We're talking about clock code. We're talking about replit. Lovable cursor. Anyways, so when you look at this, you're like, holy crap. There's a lot of people who have no idea what's going on. And if you just lived in the Twitter bubble, which is Basically the yellow people. You would think it's ubiquitous at this point, but it's not. We're very early in the use cases, and as we're early in the use cases, we're also early in the way that the technology is advancing. So I'm going to show this chart real quick. This is Gemini. They just released a model. And if you look at this, nobody's going to know what ARC AGI is. But effectively what it is, it's measuring reasoning and knowledge. And you're like, holy crap, 84.6%. Oh, and there's Claude Opus 4. 6, which just released. That's at 68.8%. Whoa. It's way higher than that. But there's not really any context for what this means. Right? You don't really know. But when I look here, this was released in December. This was measuring the models that were out at that point how many tasks they could do in human time in basically one output. And so in December we had.
- [08:27]
A
This is like an AI version of Horsepower. It's like a way to measure it.
- [08:30]
B
Sure, yeah. So GPT5 in December, in like one iteration, I ask it a question to. To code something and then it code something. It could do about two hours of human work in sort of one output of a prompt with 50% accuracy. Now, forget that for a second. But that's just the measurement that we're using. Claude Opus 4. Five in December, this is December, could do about five hours. So like, dude, that's pretty good. And at that point, it was doubling every four months. So you would expect December, April. Okay. By April, we should be able to have these models doing 10 human hours in one output. Well, this is what actually happened Because Cloud Opus 4.6 just released. This is where that 4.5 was on that graph. And now if you look way up here. Whoa. 15 hours in one output. So if I ask it something now, like, hey, code this, blah, blah, blah, its output would do the same amount of work of 15 human hours in one output. So it didn't double.
- [09:27]
A
So from five to 15 in a month or two.
- [09:30]
B
A month and a half. Yeah. So it tripled in a month and a half when it had been doubling every four months.
- [09:35]
A
What's the accuracy difference?
- [09:37]
B
It's the same. It's 50. So 50 is the bar, is the threshold, basically. Okay, so it's not necessarily saying like, oh, great, it's going to replace everything, but that's just the best metric that we have to measure how well it's actually accomplishing human Tasks in a specific period of time. This, when I saw this for the first time, was mind blowing. Because I'm like, okay, this isn't just a gradual. We're getting into the exponential territory. Who knows what the, what the coming models are looking at? So in December, you know, this, I had a house fire. That's why I'm sitting in this house right now. I haven't podcasted for a little while, and I did a lot of thinking, like, all right, if I believe that AI is the future, there's no better use of my time than to invest in and leverage something in AI. But what is that? And I looked at starting other businesses, I looked at investing in businesses, and I wanted to have the biggest impact possible. And a friend of mine who works for a large publicly traded company reached out to me and he was like, hey, would you ever think about coming back? And for whatever reason, it was just like, yeah, I want to see if all this stuff that I've been working on actually works at a company like you need.
- [10:37]
A
It's like you became a Formula one racer and you have no car to drive.
- [10:40]
B
That's exactly right. It's like, I live in a retirement community and I need a golf cart, but someone gave me a Ferrari. And I'm like, I'm sorry, guys, I. I gotta go. I gotta, I gotta go drive this thing. I can't have this thing cooped up in my garage all day long. And so I was like, like I didn't want to, you know this. We talked about it. It's like, I don't, I don't wanna, like, get a job, but there was no other opportunity where I felt like I could have a bigger impact and see if these things actually work right. So I'm here to report back to you, Chris. Corporate America's worse than any of us assumed. Nobody knows anything about anything legitimately. I'm here to report back. The state of corporate America is worse than you're assuming. It's worse than the echo chamber that Twitter is paints it to be. They know nothing. They know nothing. I went to an off site with the executive leadership team. I had a conversation with them. All of a sudden, a like 15 minute conversation ballooned into a five hour conversation because they were just asking me questions. How do you do that? You can do that right now. And by the end of that conversation, the CEO said to me, this is the first time I actually realized it's happening. Like, I have an insane amount of urgency because I finally get it. I thought it was Talk. I finally get it. And the stuff that I'm showing them is not anything mind blowing to people who are listening to this right now. Right. But to people who like, have real jobs that actually have to like, do something on a daily basis, like they're doing accounting or they're doing caregiving. They don't have time to go play around with this stuff. They don't know what it actually looks like. And so when you come and show them, like, oh, that's interesting. We just had a conversation. I recorded it. Let me upload the transcript. It's going to spit out a PDF of what we talked about and there'll be a presentation form. It's like I just came from the future and showed them the Jetsons. And like, what I've done now is I've manipulated time. You're still living in a cave 300
- [12:21]
A
years ago, 10x that nobody manipulated time.
- [12:25]
B
But seriously, because for so long that it's been viewed as a toy, that now they're like, oh crap, it's kind of here. Like the promise of it is kind of here. And so if you can go and work at a company do for a year, who cares, and just get an idea of what are their pain points, what are they struggling with? These are billion, multi billion dollar corporations that have real problems. You get in there really quickly, you're going to see, holy crap. They don't, they don't even know how to like Google something, let alone use AI in my opinion. There's this huge gap to bring the current state to the future by individuals who are humble enough to say, yeah, I'd rather start something, but I think if I went and actually worked at a company, I'll learn their pain points and then I can do whatever the freak I want, whenever I want because people will think that I'm a magician. I've been talking a lot.
- [13:12]
A
Cool. But how do we make money on
- [13:14]
B
this, Nick, we're going to get there. We're going to get there. Chill out.
- [13:17]
A
Okay, okay, okay, okay. You are a magician in the context of what they know to be true, right?
- [13:22]
B
100%.
- [13:23]
A
I could go learn an easy magic trick on YouTube and show it to a 3 year old and they'd think I'm amazing. A 30 year old would not. So in that kid's eyes, I'm a magician. You are a magician in their eyes, therefore you are a magician.
- [13:36]
B
One of the members of the team was like, when I started showing them all the stuff that I was using, they were like, I'm Glad you showed. I'm just going to say something. I think I speak for everybody. I was intimidated by you. Like, I thought that you were a genius's genius. She's like, but I'm not saying that you're not. But now I get it. Like, I get how you were able to do so much in such a short period of time, like capture information, synthesize it, create things. And I'm not working, you know, 80 hours a week. This is pretty simple stuff. Like, I'm synthesizing data, so I have a couple use cases, and I, like, legitimately think this is the template that could make somebody. You're not gonna make a million dollars tomorrow, but this will set you up, I think, for the rest of your life just to be a professional, bringing people into the future. Why are you smirking?
- [14:17]
A
I'm drooling. I'm on the edge of my seat right now. You're still talking in Stephen Bartlett clips. And I'm just loving all of this.
- [14:24]
B
Oh, Chris, can I show you, to answer that question, what have you built? Can I show you some of the stuff I've built? All right, here we go. I guess it's go time. I'll show you.
- [14:34]
A
This.
- [14:34]
B
This was. I just wanted to see competitors in this space. And so I literally went. It's all publicly available.
- [14:40]
A
What space are we talking about?
- [14:41]
B
Thank you. A home health and hospice senior living. And I get curious. So I'm like, I wonder what everybody else is doing. Oh, there's publicly traded companies, they publicly report things. I want to go and see what they're doing. And so I just went and pulled their 10Ks and 10Qs, which are quarterly or yearly earnings calls, and I scraped all of their transcripts because I wanted to see, like. So honestly, I went to Gary and I said, gary, tell me how to do this. And Gary was like, oh, hello, this is my little British gentleman. So this is Gary. Gary's my claw.
- [15:09]
A
Explain to us what we're looking at.
- [15:10]
B
Yep. So Gary is my cloud bot that I set up. I set it up a couple of weeks ago in the beginning of February. And I'll. I'll explain in more detail what the cloud bot is because I have a whole thing on it. But effectively what it is, is it's like the first window into mass adopted usage of agents. So you bring. You bring Gary on and. Well, not. It's not Gary, it's open claw, but I call him Gary. I brought Gary on and I gave him access to my emails and my texts and my Google accounts and online and everything that I have. And now I just go through him and I ask him questions. And so the first thing we just started iterating was like, how could I get these? I was like, oh, let me go look. Goes and searches online.
- [15:47]
A
This is locally hosted, right?
- [15:49]
B
Yeah. I don't host it on the web at this point. That scares me. So this is locally hosted on my Mac. I will probably turn another one of my Macs just into this so that it's running all of the time. But right now it's locally hosted on the Mac that I use.
- [16:01]
A
And that means your, your token cost is basically zero.
- [16:05]
B
No. Well, let's talk about this in a minute. Okay. There's a couple different ways that you can do Gary, or not Gary, but Clapbot, you can run a model locally on your computer and there are models that have been created that are open source for free. Okay. But if you want to use a model like Gemini or opus.
- [16:21]
A
Okay, okay.
- [16:21]
B
ChatGPT. I've got to pay for that inference, of course. Yeah, yeah. So every time I use it, I'm paying for that token usage.
- [16:27]
A
Sorry. Could you build like a cloud bot with deep Seek and have it essentially be free because it's open source and then you're essentially, you essentially have all the security features you need because it's, it's locally hosted on your computer. Like, how complicated is it for the average listener or watcher right now? Someone ages 20 to 50, that's Internet native, Internet literate, how complicated is something like this for them to set up just as you've done it?
- [16:52]
B
It's complicated, but it's not impossible. Like I would say. The hard part is it does take a lot of time because it's not just downloading the software and putting it on your computer and running it. And I'll show this later. Like you, you've got to have a plan for how you're going to use it. That's what people miss with AI. Dude, I remember us talking about this a year and a half ago where we were like, if you can prepare and just think, what would I use an employee for? Now you're going to use AI for that. That's how you should use AI.
- [17:18]
A
Yep.
- [17:18]
B
And, and people still understand that concept. So if you understand, like, oh, I know exactly what I would use them for. Here's the training material, here's how I'm going to oversee them, then it becomes a lot easier. But if you don't, then this is like this messy, iterative process. So, so Downloading it and putting it on your computer, fairly simple, but like getting the mileage out of it. That's where I feel like people are falling short because they're buying a Ferrari when all they needed was a golf cart. And so, like, of course they're like, so what do I do now? Well, like, dude, you, you live in like south southern Florida's retirement communities. You can't go faster than 15 miles an hour. So buying the Ferrari was kind of a waste. The cool thing though is like, so this is a, a model that came out, I don't know, a couple of weeks ago. I mean, the model itself hasn't come out, but it's. The update came out a couple of weeks ago. And you can see how it's benchmarked against OpenAI, Gemini and Claude.
- [18:06]
A
Right.
- [18:06]
B
It's about as good. And the thing about this model is it's an open model. So Claude, Gemini, all those other, those are closed models, the proprietary models. They sell you the ability to use those models. This model, this Mini Max, is open. Anybody could go and download it and they could adjust the weights on it and they could kind of make it their own little LLM. So this you could download and run locally on your computer. And if that was the case, then you're not using any inference from Anthropic or OpenAI or Gemini.
- [18:34]
A
What inference means.
- [18:35]
B
So, so if I've got something that I want to do on my computer, if I want AI to go and do something on my behalf, that costs money, and it costs money in the form of compute from one of these companies. Because I'm using them, I'm effectively renting their model to do something, search the web, scrape databases, send an email campaign, whatever it is. But I'm, I have to pay something order to do that. For models like Gemini, you're paying like $5 per million tokens. For a model like OpenAI's newest model, you're probably paying $10 per million tokens. For Claude's Opus 4.6, you're paying $25 per million tokens.
- [19:13]
A
But it's so good.
- [19:14]
B
But it's so good. It's so good. I mean, that's why, I mean, that's why it's a problem. You get it anyways. So yes, theoretically you could download this, run this locally, and then you're not paying for any of that inference and you're running cloudbot on a, on a local server.
- [19:30]
A
But it's not going to be as good. It depends on your use case.
- [19:33]
B
You might not 100%, 100%. It may. It may not be as good, depending on what you need it for. And back to the analogy. Like, you think you need a Ferrari, you probably just need a golf cart. Like, you just. Just be real with yourself.
- [19:44]
A
Sinking calendar events or. Yeah, dude, responding to emails. You're.
- [19:49]
B
You're not lighting the world on fire.
- [19:51]
A
You're.
- [19:51]
B
You're sending a cold email campaign. Okay? Like, let's. Let's be real with it. So I went and I said, I want to know what the other publicly traded companies are doing. And so I asked Gary, I said, gary, how would I find this information? It was like, this information is available on the Internet. And it went. And it found an API. So I went to Ninja API or API Ninjas, which I didn't know was a website. And I was like, oh, buy this. $20 a month. You get access to all these other APIs. I'm like, cool. So I go there, I get all of the 10Ks, and then I'm like, well, I want to display it. And so it starts telling me how to display it. And I end up building this site where I can see when the next public reporting from these companies are, how they've done over the last quarter, where the revenue is, how they look. I can. I can go all the way and see just the themes. You know, how are things looking this quarter? I can see what questions analysts have been asking. And so from this, like, I showed some of the people at my company like, hey, this is pretty cool. And they're like, actually, did you know how long it takes to prepare for quarterly earnings calls? I was like, nah. And they started telling me about their quarterly earnings call. So it gave me this idea. I'm like, I wonder if I could help people prepare for a quarter learning cost. Because what you have to do is you have to take all the data.
- [20:54]
A
Oh, how much could you charge for something like that?
- [20:56]
B
My broski, you're about to see. So these people, they have to report. It's an SEC filing requirement, right? Because they're publicly traded, they've got to do it on a quarterly basis. And they got all the stuff that they got to report. Financials, whatever their time is worth.
- [21:08]
A
Their time is worth how much an hour.
- [21:10]
B
So they get together and they spend, like, a couple days a quarter every quarter. The whole executive team like, talking about, should we use great or should we use amazing? Should we use the word wonderful or should we use the word exceptional? You know what? I'm like, they're just. They're debating These words back and forth and like, so my mind just starts going. I'm like, dude, all I have to do is look at your past transcripts. Easy peasy. I create a voice for you. I know exactly what the template's going to be. You dump the data in, I'll give you a first draft. I'll save you a day. And so I went, and sure enough, I created this earnings pipeline. Stop scrambling before earnings start running a pipeline. And it's literally intake gives you strategy. It's a workshop script, refinement and export. And this walks you through exactly what you need to do if you're a publicly traded company to get the output that you want.
- [21:54]
A
Sorry, why do you not own earningspipeline.com it's available. You need to take that.
- [21:58]
B
You do it. You're better at just pump anybody. I'll venmo you. I honestly didn't know. I don't do anything unless Gary tells me no. My wife's like, did your best friend Gary tell you that? I'm like, yeah, he's a really good guy. So, like, this is the landing page for it. But you could go scrape all of the publicly reported 10Ks, 10Qs, build profiles for each one of the executives, build a template for what exactly they talk about and when they talk about it. And then an ingestion pipeline, which like I've done to be like, just dump all of your data in here and I'll populate this for you. And then you have a first draft. Cause that's all this is. You populate it. Then you go through like this strategy session of AI making some recommendations, then you workshop it, then you actually generate the first script, then you refine it and then you export it. And like, again, the thing that people miss here is they think AI just does everything. It doesn't. It's a good help. But like, you still have to workshop things. You still have to massage the messaging. And so this is an actual representation of me working an earnings call. Yeah, I spent 12 hours working. Like, this is to the point I could sell it. Chris. It's kind of like for me, you know me, I don't finish stuff. Like, this is finished. This is this. I, like, I could take this to market. It's pretty. It's pretty bonkers. And so when I show this to people and they're like, holy crap. As an executive, like, you just got the 4 highest paid people on the entire company 10 hours a year back in their time.
- [23:18]
A
That's.
- [23:18]
B
That's high leverage. That's high value. Right? So anyways, this. This was a fun one. Took me like 12 hours. But these are toys. What I really want to show is what I think is the most amazing thing. And that's Gary. So openclaw is really cool. It's got some protocols built into it. It's pretty plug and play in the way that it remembers things people talk about. It's got infinite in a memory. It doesn't have infinite memory. It, like, it takes time to learn things, but as it learns you, it adds things to different files so that every time it loads, it remembers, oh, Chris doesn't like it when I spit out this output. Or Chris doesn't like it when I do X, Y and Z. I was already in the process of, like, creating something that was my second brain, and then openclaw came out and I just adapted it to it. But we get bombarded with stuff, especially if you're an executive. And this is just like, whatever, tons of messages per week. There's literally no way for you to process all of this information. The only way for you, though, to get out of AI, what the promise is, is if you have clean data that it can extract, analyze, and then spit out actionable information to you.
- [24:23]
A
If the data garbage out.
- [24:24]
B
Exactly. If the data's not clean, you don't get anything good. So a lot of these companies have data, but they're not. They're not cleaning it. So I'm like, I'm looking at this.
- [24:31]
A
Give me an example of clean data in, clean data out, or vice versa.
- [24:34]
B
Okay, so here's dirty data, dirty data. Boom, boom. Your company, you have tens of thousands of contracts, okay? But they're all saved in different folders. And all of your DME contracts are saved in like, what's this? Dme Durable Medical equipment contracts are saved in. Some of them are saved in a DME contract folder. Some of them are saved in a Durable medical equipment folder. Some of them are saved in a hospital equipment folder. Some of them are saved in a skilled nursing facility equipment folder. The data is just in a bunch of different places. There's no rhyme or reason to it. Second layer that could be the problem is you've got data laying around that is in PDF, it's in Word, it's in HTML, it's in jpeg. And so AI looks at that and they're like, I don't know how to extract all of those at the same time. Or the naming conventions are off. Maybe you're like, oh, I'm going to save all of The ABC CO information in this folder, I'm going to call it ABC co but you accidentally name it ABD one time or you accidentally name it B C, D one time, right? Like just common mistakes. So how does AI find all that stuff? It can't if the data is not clean. Now clean data would be clear taxonomy, which a taxonomy would just be a hierarchical way for you to identify information and everybody stays with kind of the same saving file naming structure, right? So for me what I did is I was like, I want to create Gary. I want to create my own AI bot that like knows me. So I went and I exported all of my ChatGPT data, all of my cloud data, all of my texts, because I can connect to my imessage through an mc. Never deleted any text, Never, never through an MCP server.
- [26:14]
A
This is going to haunt me, isn't it? You're about to cancel.
- [26:16]
B
Dude, my boy, right now, right now I just pull up the most disgusting creep.
- [26:24]
A
No, there's nothing anyways, nothing in there.
- [26:26]
B
So it took all of that data and I was just like, I want you to take that and standardize it for me. And so it did. I mean it took some time and some prompting, but it did, it standardized it for me. And then I was like, all right, we gotta structure it. And so we started structuring it and putting it in a way where it's easily retrievable so that I can go and search and then I can create automations on top of it. And then eventually you can create agents. But if the data is dirty, you can't get good outputs. So that would be like the first thing I would say is just helping companies get clean data is a multi million dollar business. Just going in there and being like, I'll help you organize this and helping them clean it up.
- [27:01]
A
How can that be automated? Or is that just like the handheld messy process that it is, the cleanup?
- [27:07]
B
Yeah, it can't be automated until you've built the system for it. You can get 80% of it done, but that last 20% takes a long time. So for example, if you've got a company that's got tens of thousands of files, I could probably run AI on it and categorize everything to 80% confidence. But there's still, let's say, 20,000 files in there that you don't know where they go. How do you then figure out how to clean that stuff up? And that that's where somebody who has a lot of experience can come in and be like, oh, that's simple, just do X, Y and Z. But even beyond that, putting it in a format where it can query and use the data is something in and of itself. So you know this Gemini. Well, let me back up. These models, these LLMs have what's called a context window. So think of it like this. If I wanted to buy a business, I'd go talk to Chris, right? Chris knows everything about businesses. He's like done every business, seen every business under the sun. But in order to get him up to speed, I've got to spend a couple of hours with him to tell him like, hey, look, this is how much the business costs. This is the market, this is the industry, this is the demand, this is how much money I have. And then by the end of that hour or so, Chris can give me an actual response. He can say like, oh, you should do X, Y and Z. So Chris, he's the model, he's Claude, he's Gemini, he's been trained on all this data and he's just knowledgeable. That hour of me spending time to get him up to speed, that's the context window. That context window is pretty finite. And for a long time it only went up to like 200,000 tokens. It finally just hit a million tokens. But even with a million tokens, if we're talking about tens of thousands of documents, we're talking about billions of tokens. It can, it literally cannot, it's not physically possible for it to query all of that data and return things. And so you've got to organize this data in a way where you parse it more efficient, you chunk it and you clean it and you tag it and you embed it, whatever, and then you set up these systems. So I've been doing some of this stuff and I was like, I want to build this for myself. So I exported all my chat, GPT conversations, all my Claude, all my emails, everything. And I implemented OpenClaw. So OpenClaw has access to everything. This is just like a. These are the seven files in openclaw. So openclaw has a soul. Because you want, if you want to give it a personality, the user MD is just like what you want OpenCloth to know about you. If you want any agents run, this is where you would put instructions for agents. The memory, this is long term memory. These are things that you want OpenCloth to sort of always remember. I work at XYZ company, I have XYZ skillset tools. You can go and research what agents and tools are the heartbeat. This is just like Jobs that come every hour, two hours that continue to run, and then if you want to give it an identity. So those are like the seven basic files that it builds over time. And like, the beauty of openclaw is the more you use it, the more openclaw builds these automatically. So what I built was like, I had already had this, I added this, I had a people framework. So I asked it and I, like, I had a whole set of prompts to do this where it was like, how do I manage relationships? How do I manage failure? Like, it knows if I showed you some of this stuff right now, you'd be like, that's pretty spot on.
- [29:58]
A
The response was just two words. You don't. You don't.
- [30:01]
B
Yeah, you don't. So when I'm talking to it now, like, literally Gary will be like, nick, it kind of feels like you're spiraling here. We're like, nick, it kind of feels like you need to get back to this person. Oh, Nick, you probably see, I know you said you should take that on, but that's not a good idea because you take on too much stuff. It's incredibly helpful to have from, from the get go. It also build like a taxonomy for me. It also created a bunch of projects. So I, like, I was going into this job and I was like, I just want to be organized and make sure that I'm not letting stuff fall through the cracks. And so it just created all the projects, all the people, it took all the conversations that I had and it synthesized them into these documents. And so when I started, I had openclaw that was already working and then I layered Gary on top of it. And now literally I can say, hey, what do I have? Outstanding to so and so. What did I say I was going to do? It will remind me because I've got jobs set up for it to come and remind me and say, like, hey, remember at the end of the day you're supposed to get XYZ thing to so and so. It will preemptively give me a spreadsheet based on what I said I was going to do. I was like, hey, does this look good? Like, just give me a first draft of this stuff. But it's because I went through and spent the time so that it had the context to understand my people framework commitments, the decision framework, my personal context, the taxonomy, the extraction methodology, all that stuff. And so you can see like, this is all the crap that obviously it spit out anyways. And the way that it's built is like literally queryable I can query just about anything that I want to know. So if I'm like, hey, what did Chris and I talk about the last time? If I'm getting ready for a meeting, if I'm getting ready to give a presentation, like, I can't tell you how many times in the last few weeks people have asked me to do something and I'll, like, come prepared to a meeting with a presentation, and people are like, huh? Wait, you did what now? Oh, my.
- [31:43]
A
Yeah, right.
- [31:45]
B
Because, like, the old paradigm is this took you five hours. The new paradigm is it took me like five minutes. All the setup took me a long time, but now I'm just able, I'm able to access it. So anyways, I'm going to stop talking because I feel like I'm just on a heater.
- [31:58]
A
Okay, so with everything that you built for yourself, how much of your context window did you use up?
- [32:03]
B
It depends on how it's being used and when I'm utilizing it. So if I'm asking it specific questions about people, it will go then and look at the people file and pull it. So it's not loading in the context every single time, but it is loading into the context when I'm asking about specific.
- [32:21]
A
Is that what the RAG is? So, no, explain what rag is.
- [32:27]
B
All right, so remember how we were talking about how you've got all this data? So if I've got tens of millions of tokens of data, but I can only ever ingest a hundred thousand tokens, how do you make things queryable? Well, there's this RAG approach which is retrieval, augmented generation. And so what you do is you tag all of that data with metadata. So, for example, think of it like a library. If there's a book that's written on
- [32:52]
A
ancient Rome, like a Dewey Decimal System.
- [32:54]
B
A Dewey Decimal System, yeah. It's going to be like it's in row 8, column B, categorized with the rest of these things, right? So if you search a word, it's going to pull up where that might be located and then allow you to access that stuff. The Gary and openclaw is a little bit different.
- [33:11]
A
It's.
- [33:11]
B
It's on this thing called qmd, which is quick markdown. It's not a vector data. This is like, way too technical. The gist is it allows for semantic searches and the results are much more accurate. So everything that, that I would have are semantic searches because they're meetings, they're being transcribed. Right. If I had a big database with numbers, like, you know, maybe, maybe I'm Using more of that computer.
- [33:35]
A
You're using it like chatgpt, not like a coder would use it to search it.
- [33:39]
B
Exactly, exactly.
- [33:40]
A
A code database.
- [33:42]
B
The reason though, that it's important is because now it unlocks all of that data that I've had sitting there, all of that context. I don't get to the middle of a conversation with Gary and all of a sudden he's like, you can't use me anymore. I've run out of memory because it's constantly updating itself. I don't get stuck in the middle of a conversation with them. Like, the memory is persistent. It's very helpful. It's. It's fantastic. So if I were to say the lowest hanging fruit though, that I've seen, it's so dumb because I can show all of this stuff and it was like agents and skills and oh, MD files, whatever. I'll tell you right now, here's the 80, 20. If you want to unlock the most value, record your meetings, period. Record your meetings, transcribe them, have a vehicle or a way for you to actually get a summary and a synopsis of that, and then build in yourself some type of an accountability mechanism for you to then say, hey, this was a due out that you committed to. I will make you millions of dollars. I've seen it now because right now the traditional way within companies is like, what are they doing? They're writing something down or they're like, they're trying to remember it, or maybe they use Copilot, which sucks. There is no way for them to capture what was done in that meeting, save it to some type of a archival system that you can then access and query later and then follow up with individuals. Like, that's, that's always been the hardest part, right? Follow up, follow up and follow through. I said I was going to do one thing and I didn't do it. Why didn't I do it? Well, maybe you forgot, maybe something slipped through the cracks. But if you just record meetings, document what was said and then put it in a place where you can go and get back to later, or build something that reminds you you're ahead of 95% of people because they're not using it for that right now. People get so tripped up on, like, I'm going to build this agent or I'm going to build this skill. No, literally record a meeting, summarize it, put in a transcript, put it somewhere that you're going to check in. And then all of a sudden you've got this superpower. Because you've got this massive database that you can go back to.
- [35:33]
A
How can people make money learning how to do this and then doing it for individuals or for companies? Is that a viable opportunity right now? Like a picture? If I'm an executive watching this video right now, I'm like, trying to find your contact info, right? Cause I'm, like, seeing this and I'm overwhelmed. And it's like. And this isn't a sales pitch. Like, Nick has nothing to sell us, but it's like, I feel like my wife.
- [35:55]
B
Wish I did.
- [35:57]
A
People could learn how to do what you've done and. And charge for it.
- [36:01]
B
So the first thing I would say is, you and I are so freaking lucky. Like, we're so lucky that over the last two years we've just been able to, like, dabble, you know, like, how does that work? Oh, that's interesting. And we just start learning about it. So just devoting the time to this, you're ahead of 95% of people because they don't. They don't have the time and they don't want to make the time. And by the time they get home from work from doing all the things that they're supposed to be doing, they don't have the time to ingest this information and then figure out a way that makes it applicable. So the first thing I would say is just learn. Just learn, bro. The second thing that I would say is anytime that you've been within an organization where they're like, I wish there was a better way to do this, that's an opportunity. If someone's using a spreadsheet, that's an opportunity. If you're on a meeting, that could have been an email, that's not right.
- [36:45]
A
But, like, how do we make money here, Nick?
- [36:47]
B
Chris, I promise you, we are going to get there. Okay, give me a minute to finish this up.
- [36:51]
A
Yes, sir.
- [36:53]
B
If you're on a meeting, that could have been an email, that's an opportunity. If somebody let something slip through the cracks, that's an opportunity. I think that now the cost of building custom code. I didn't even show you all the other stuff. I have, like, little survey software or tracking things. The cost of custom code is so low that you can build customized tools that save people 80% of their time. And it doesn't have to be, like, on the mass corporate scale. It can be on the small scale. So anyways, first one would be learn. The second thing is, I think there's huge demand for corporations just to be in the know. If you get educated and you just cold call, literally. You could set up a cloud bot to be like, here are all the publicly traded companies because I can go and scrape all of that data. Cold email. Go and find the executive information, because all that information is also public. Cold email every single one of those executives and say, hi, I'm Nick. I've been deep in the AI in the last year and a half. I know what's coming around the corner and 95% of your competitors don't. I'd love to have five minutes with you so that I can update you on what's coming down the pike. You're probably going to get rejected by most people and you can probably perfect that sales pitch much. But like, you, you would do amazing with this cold outreach. But if you can get on their calendar and just have like, what I had with that executive team, like, within a couple of minutes, they're going to be like, oh, I get it, I get it. I want this guy every single week just giving me an update. They've asked me to like, hey, would you just, would you do a course for the next 12 weeks, one hour a week for the executive team? They want to know. They just, they don't know how to use it. They're kept from the truth because they know not where to find it. Chris. Oh, I can't wait for, like, the few people to be like, oh, brother Elder. But just putting yourself in a position where you can relay yourself as a subject matter expert. And again, this is like 2010 social media, where it's like, oh, you have a Facebook account. Will you run social for us? That's. That's what it's like in AI right now. Oh, you kind of use. Claude, can you run AI for us once a week? You could come in and, and pay consulting services. Like, do you remember when you went and met with that unnamed billionaire and he was just asking you questions, like, just extracting information from you? I think just doing that session alone, you could charge a couple thousand dollars just to give these executives a taste of kind of what's coming around the corner because they don't know. They don't have the time to do it. And that's kind of where I was like, of course you don't know. Of course all of your day is spent managing people. The second you meet somebody like me, you're like, okay, I get it. Holy crap, train is coming. I'm about to get hit. So I think an executive boot camp, I think weekly roundtables, I think a fractional AI officer. It is 100% in the offering. This is more of a newsletter, but some type of a briefing service. You don't even have to be an expert in Vibe coding, just like, hey, I'm going to keep you up to date. I do think custom Vibe coded tools are massive. You and I have talked about that for an AI agency for a very long period of time. Probably the biggest unlock, though, is if you can figure out how to get proprietary data sets within an organization accessible to that organization. Massive unlock. Because right now they have no way to do it. They're like, well, I can get Power BI and I can do a SQL database like that. That's hard. Somebody has to have a skill set to do that. If you can get a AI UI on top of that data so that people can just search, hey, show me where we have the largest efficiency in labor costs in the company right now. If that would return an answer that blows people's minds. And you can do that right now. It's not like you have to build a, you know, the SQL language in place. You can, you can AI UI on top of this data. So if you can unlock the data, it becomes really valuable. I was talking to somebody about this. It's almost like fracking. Remember how fracking was this new way to extract oil out of the ground? So you're no longer just going deep, but you're going like spreading out to me? That's what AI is. It's like you, you're fracking. You're leveraging that data that was unaccessible before and doing it in a way that is much cheaper and much more accessible than it's ever been. So I think that piece in just coming into an organization, you could just do it with one. Like, are you a healthcare expert? Like, I am. Cool. Hey, I will show you how to get every single one of your quality reports for every one of your locations in the next six weeks. I wouldn't say like in a weekend, you know, give a reasonable time period, but then you have an opportunity to actually learn and implement it. Does that make sense?
- [41:16]
A
Yes.
- [41:16]
B
I can't tell if you're quiet because it sucks or if you're quiet cause like you're thinking about or.
- [41:20]
A
No, I already told you, it's a banger. Nick, what do you want? I have a headache. This is good. This is really good. I'm just thinking all these things. I'm like, what will the audience think? What should I do right after this? Like, how quickly can I implement this on My computer. I'm just thinking. My mind is just going nuts.
- [41:36]
B
Like, for you. Cold. Like, I was talking to somebody about this because I was at this executive off site and one of the kids, I was. I had mentioned you, and they're like, is that guy on TikTok? I was like, yeah. I was like, is that the Kerner office? Anyways, I think for somebody like you, if you have OpenClaw, you could be sending cold email campaigns 24 7. Because there's this window of time right now where people aren't sick of too much AI. They're getting there. But, like, pretty soon, everybody's gonna catch up. Everybody's gonna be doing the same cold email outreach, and all of a sudden, that channel is gonna be flooded and you no longer have arbitrage in that channel. Right now you have arbitrage in those channels. If you set up a cloud bot, you're very clear with who your customer is and you know the distribution channels, and then hit it. There's arbitrage. You are going to find people in the next six months, once everybody kind of figures it out, those channels are flooded and there's going to be a new opportunity. I don't know what that is, but, like, right now, there is leverage if you know something. So in my mind, it's like, what is your secret sauce? And now with Clawbot, I can unlock it, because I could hire, like, two or three people to be my minions. Go all in on it. Go learn. Just go play. You will figure something out, and it will be incredibly valuable.
- [42:44]
A
When you're talking to Gary. What model do you normally use?
- [42:47]
B
Opus 4. 6.
- [42:49]
A
So good.
- [42:50]
B
It's really good.
- [42:51]
A
Is it time to leave ChatGPT behind, dude?
- [42:54]
B
Yeah. So, like, I use ChatGPT for what I would call, like, the Honda Accord things.
- [43:01]
A
You're running the grocery store.
- [43:02]
B
It's amazing at what it does, and it's reliable. And I know what I'm going to get every single time. And so if I have large data sets that I need to extract stuff from, I'm going with CHAT GPT. But frontier models like Claude, it's pretty Incredible. Gemini's new 3.1 model. It's pretty incredible. Like, it's weird to say because it doesn't feel like it was that long ago that there's, like, weirdness with some of these models. But Claude feels like I have an expert in every topic known to man at my fingertips all the time. It costs a lot, but I. Yeah, I. I use Claude all the time. Here's my Stack though. I'll tell you my stack. So I use Claude to Plan 4.6. And when I'm building software now, I'm like, okay, this is my idea. Help me write sort of my prd, my product, product requirement document. There we go. So it writes my PRD of like what I'm hoping to accomplish, but then I will send it out to like Gemini and Codex to say I tell it, go do an adversarial audit, have them tell us what we're missing. And I go through like four rounds of that. And then after those four rounds, I've got something that's pretty good and I start now the planning phase. All right, let's plan something. Go do another adversarial audit on the plan implementation. Not just like the build spec, but the plan implementation. And it goes, you know, it goes through all those steps. So I'll use other models as a way to sort of glean other insights that I might have missed. But once I have all of the data and I just need good analysis opus, I mean I just. That opus is the one that's like incredible when it comes to analysis.
- [44:32]
A
So what I'm about to show you is like super, super low tech compared to what you're doing. But I, I posted this interview with this woman as a snail mail subscription business and the interview was doing really well. So I thought, how could I monetize this video even further? How can I like create a super hyper detailed business plan and then just make a stripe link that goes to a Google Doc of the business plan and I can sell it for five to ten bucks. And so I'm going to show you how bad this prompt was. This is my prompt. I pasted the YouTube transcript of my own video. This is a transcript of an interview. I want to use this data plus other data from online to make a very comprehensive business plan for someone that wants to start a snail mail subscription business similar to hers. Find other people doing a similar business and their reported number of subscribers, revenue or profit. Include charts or graphs as appropriate. Make it very thorough and tactical. How can people get stamps, stationary, custom envelopes printed, et cetera? Marketing. Such a good prompt, very comprehensive. Take your time. I'll be selling your output for $5 in the pinned comment. For people that are serious about starting a business in this space, why did you want to know what I'm doing with it? I don't know. It just felt right. It just felt right, context right. And so here's what it gave me. This was one prompt I did not edit the prompt at all. So it's got sharks.
- [45:53]
B
So good. Oh, my gosh.
- [45:56]
A
It's 30 pages and it's 6,500 words. It took like 10 minutes. Okay. Okay, cool, Right? Pretty good budget.
- [46:03]
B
Yeah. Yeah.
- [46:03]
A
Let me show you what's really cool. Here's. I went into Stripe and I said, it's loading. It's loading. I went to the description of the YouTube video, clicked edit, and I said, hey, if you want to learn more, you know, I made a 6,500 word business plan. And then I went to the pinned comment, copy and pasted the exact same message in three clicks. I made a Stripe payment link. See all those $9 transactions every 10, 15 minutes?
- [46:28]
B
Shut the freak up.
- [46:29]
A
Okay, next.
- [46:30]
B
Are you kidding me?
- [46:31]
A
Yeah, you see, Yesterday it was $5 transactions.
- [46:34]
B
You raised it.
- [46:35]
A
Okay. And then I thought, huh, let's a b test it. So I made this simple.
- [46:39]
B
You crazy, man?
- [46:41]
A
I made a simple google sheet. 0.24% of people that viewed the video paid $5. 0.23% of people who watched the video paid $9. Same conversion rate for almost twice the price.
- [46:53]
B
Oh, my gosh.
- [46:54]
A
So what that did was my revenue per thousand views went from 1355 to 3528. It almost tripled with one prompt in Opus 4.6. Almost tripled.
- [47:08]
B
Nick, I'm going to uplevel this. I'm going to zoom out. So we circle back, drill down on this.
- [47:14]
A
Yeah, yeah.
- [47:14]
B
Like if you had an open claw, you could have. You could now say, hey, I know I want you. I want you to have an automation where every time I have a video that gets above this many views, you go and create a business plan. Create the Stripe link. Create, like it can create that. Yeah, right. It's like I want to know the analysis and then every day message me because I want to see what the update is, which blows my mind. And I know that sounds so simple because people are like, oh, that's so easy, dude. I could just freaking create the sheet. I could just freaking do X, Y and Z. Yeah, you could. You add up all of those over. Gosh, dude, I sound like Ed Milet. You add that up over a day, I'm gonna kick your butt. You add that up over a week. I'm way ahead of you, bro. You're just mad. I'm living in the future and you're living in the cave trying to do 300 years ago. No, but seriously, like, it's all of those little time saving things that allow you then to go focus on the highest leverage use of your time, which is creating content, being creative. It's not the administrative stuff. That's the unlock of using something like openclaw.
- [48:12]
A
That's the same thought I had today was I could use openclaw for this.
- [48:15]
B
You would beat the bell of the ball with openclaw, my friend.
- [48:21]
A
Dude, I'm at max capacity in my brain right now. You gotta call it.
- [48:24]
B
I love.
- [48:24]
A
All right.
- [48:25]
B
Love you too.
- [48:27]
A
Where can people find you, Nick?
- [48:29]
B
Twitter co founders Nick, I have a YouTube channel called Nickonomics. I'm firing it back up. I'm. I'm back in the game. This was nice. Thank you.
- [48:37]
A
That's the most resigned thing I've ever heard you say. Twitter. You knew that, like, it was the lowest, worst value of your call to action. You're like, oh, Twitter. Because what else is there nowadays?
- [48:47]
B
You can find me at Nick Consulting. $5,000 an hour. I probably should, actually.
- [48:54]
A
I mean, seriously. All right, what'd you think? Please share it with a friend and we'll see you next time on the Kerner Office.