WEBVTT

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The AI gold rush is definitely on. Feels like

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everyone's racing to stake their claim, doesn't

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it? It really does. But what if the path, you

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know, the one most people are on, leads not to

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gold, but, well, nowhere fast? What if you're

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carefully building this fragile Jenga tower,

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you know, the kind that's just going to collapse

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instead of a solid skyscraper? Oh, we've seen

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that exact thing. It's what our sources are calling

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the AI agent trap. It's this low margin, really

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high stress cycle that just... burns people out.

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But here's the good news, right? Our sources

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show there's a clear way out. There's actually

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a blueprint for something, well, much more solid

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and rewarding. Welcome back to the Deep Dive.

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Today, we're taking a close look at some really

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fascinating research. It really challenges the

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common way people think about building AI solutions.

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It's titled, The AI Agent Business Model is a

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Trap. Here's what works. Yeah. And it highlights

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this really critical difference. Are you just

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building these quick one -off AI agents like

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individual automated tasks? Or are you aiming

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higher, building something more integrated, more

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fundamental, what they're calling an AI operating

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system? So our mission today is to really explore

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this shift. We'll look at why that common approach,

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the agent approach, often fails. We'll dig into

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what these operating systems actually are, how

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you might build one both strategically and technically,

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and probably the most important part for many

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listeners, how they can actually unlock sustainable

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recurring revenue for your business. Right. It's

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about getting off that exhausting freelancer's

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hamster wheel and stepping onto a path, a blueprint,

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that could lead to maybe $10Ks plus a month consistently.

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That's a total game changer. Buy it. OK, let's

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dive in. OK, so let's really unpack this AI agent

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trap that our sources describe. They paint a

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pretty clear picture. And honestly, it's a bit

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sobering looking at what's happening out there.

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Yeah, exactly. They call it the freelancer's

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hamster wheel. And it fits, right? You're just

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constantly running. You pour weeks, maybe months

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into building these complex AI agents for a client.

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You land a one time gig, maybe get a few thousand

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bucks. Client seems happy for a second and then

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poof. Gone. And you're right back to scrambling

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for the next project. It's exhausting. It is.

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And that cycle is just brutal. Our sources point

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to four specific fatal flaws that make this whole

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model feel like it's built on quicksand. Just

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fundamentally shaky for long term success. OK,

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so first up, it's becoming a commodity. Basic

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AI agents. you know, those sort of standard automated

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tasks using AI. Everyone and their brother is

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building them now, which naturally just drives

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the prices down, down, down. It's that classic

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race to the bottom. Nobody wins that. Exactly,

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which leads right into the second big problem.

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It's nearly always a one -time transaction. You

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do the work, you deliver, you get paid once,

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then you're back at zero, hunting again. Without

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that predictable income, that recurring revenue,

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it's just incredibly hard to build something

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truly sustainable, isn't it? No doubt. Then there's

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number three. What the sources call complexity

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addiction. I mean, I'll admit, I've kind of fallen

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into this trap myself sometimes. You build these

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workflows that look super impressive, all these

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moving parts, but really, they function like

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some crazy Rube Goldberg machine. Spaghetti workflows,

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they call them. Overly complicated, often inefficient.

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But the real insight is, usually, a simpler,

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more direct automation that often delivers more

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consistent value. And it's way more robust. Less

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likely to break. avoids that overengineering

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trap. That's so true. And the biggest flaw, number

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four, maybe the most fundamental, it solves the

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wrong problem. It focuses on selling a tool,

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you know, that specific AI agent instead of delivering

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a core lasting business solution. Right. So if

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we cut through all the hype around these individual

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agents, what's the real root issue with just

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selling those kinds of one -off things? It really

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boils down to being a low -value one -time sale.

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It just doesn't deliver that deep, long -term

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business impact that really changes things for

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a client. Okay, so if that's the trap, what's

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the escape hatch? The sources bring in this idea

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of an AI operating system, BEAT. Sounds kind

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of big, doesn't it? Almost futuristic, like something

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out of Star Trek, maybe? It does sound a bit

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grand, but it's actually very practical when

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you break it down. Think of it more like an AI

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automation power cube. That's the term they use.

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It's not just one single agent doing one task.

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It's a combination, four integrated parts all

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working together smoothly. And the crucial part

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is it's designed to solve a massive recurring

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business problem, not just automate a little

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task here or there. Power cube. Okay, I like

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that analogy. Makes you picture something powerful,

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integrated, self -contained. So what are these

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four components then? What makes up this system?

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Okay, component A is the brain. These are your

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sophisticated AI models, the parts that actually

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analyze information, transform data, do the heavy

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lifting of processing. That's the intelligence

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core. Got it. Makes sense. And then the nervous

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system, component B, what's its job? Ah, that's

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the automation layer. They mention tools like

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N8n, which, for listeners who don't know, is

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a really flexible, low -code platform for connecting

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apps and automating workflows. So this layer

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acts like the central coordinator. It connects

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everything, passes data between all the different

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systems, and basically orchestrates the entire

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flow to make sure everything talks to each other.

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Okay. And the memory, component C, that's for

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storing data, right? Like using Supabase or something

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similar, keeping track of user insights, that

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kind of thing. Exactly. Supabase provides that

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robust, stable database. You need persistent

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storage, a place to remember user interactions,

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data points, progress. It's absolutely key. And

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finally, component D is the body. This is the

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user -facing part, the application layer. A friendly

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front -end interface may be built with a tool

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like Bolt .new, which lets you build web apps

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quickly. Wow. Okay, picture that. The brain,

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the nervous system, the memory, the body, all

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working together totally seamlessly. It really

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is like stacking intelligent Lego blocks, isn't

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it? Data and logic combined to build something

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a business just can't do without. Whoa. I mean,

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imagine scaling that kind of integrated system,

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handling like a billion queries a day. That's

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just a whole different universe of impact compared

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to a single agent. That's precisely the goal.

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Right. Creating something so deeply embedded

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in a business's core operations that they literally

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cannot live without it. It becomes fundamental

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to how they succeed. That's where you find that

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sustainable, predictable, recurring revenue stream.

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So just to be crystal clear then, how is this

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PowerCube idea fundamentally different from just

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selling a standalone AI agent? It's an integrated

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multi -part system solving a core ongoing business

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need, not just a single isolated task automation.

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OK, this next part, for me, this work, it's really

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practical where the rubber hits the road. Our

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sources argue really strongly that success isn't

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just about what you build. It's about who you

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build it for. They emphasize this strategic framework

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that starts with really zeroing in on your ideal

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target customer. Right. This is step one. Choose

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your target. And they have these goldmine criteria.

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The advice is pretty blunt. Be a whale hunter.

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Stop chasing the small fish who just don't have

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the budget. And they list five key criteria for

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finding that perfect customer. First, and maybe

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most obviously, they have to have money. It sounds

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basic, but it's often just as hard to sell a

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$10 ,000 solution as a $1 ,000 one, so you might

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as well aim high. What they're really saying

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is you're not just looking for any client. You

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need businesses with a clear, urgent problem,

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yes, but also the resources and crucially the

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mindset to invest in a proper long -term fix,

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not just a cheap patch. Yeah, stop selling to

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broke businesses. It's literally a quote from

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the material. And it makes perfect sense. Second

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criterion. They have to value their time immensely,

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more than money, often. These are typically successful

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business owners, but they're drowning, overwhelmed,

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more cash than hours, basically. Third, they

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need to be easy to find. You should pick a niche

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where your potential customers gather, like specific

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online communities, industry groups, maybe certain

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types of events. Don't try to hunt in scattered.

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hard to reach markets. Fourth, focus on businesses

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with high margins. Think agencies, consulting

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firms, maybe high end local services like specialized

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contractors or medical practices. They understand

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the value of something that boosts their existing

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profitability and they actually have the budget

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to pay for that value. And the fifth one, they

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call it the ultimate cheat code. Target a growing

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market. Think about it. If you own just 10 %

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of a market that doubles every year, your business

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grows almost automatically. You're riding a wave.

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Okay, but finding them isn't the whole story,

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right? The sources then move to step two, validate

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customer resonance. And they stress this is absolutely

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vital. So many people skip this. They really

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do. You see builders hiding away for months,

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coding in secret, then they launch and did crickets.

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Nobody actually wants it. So our sources suggest

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this really clever technique. The magic wand

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method. Yeah, this is great. You find maybe 10

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people who fit your ideal target profile, right?

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From step one. Then you offer to work with them

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for free just for a short time. The goal is to

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get real testimonials, gather honest feedback,

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and then you ask the magic question. Look, if

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I could wave a magic wand and solve just one

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of the 20 problems you're dealing with, the single

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one that keeps you tossing and turning at night,

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which problem would that be? And their answer,

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that's your golden nugget. It forces them to

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cut through the noise and pinpoint the single

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most valuable, most urgent pain point they have.

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This lets you rigorously check if there's real

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demand before you commit to building the supply.

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It's a huge de -risking step. So why is validating

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that demand up front just so incredibly critical

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before you even write a line of code? It stops

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you from wasting months, maybe years, building

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something nobody actually wants to buy. Plain

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and simple. Okay. To make this whole AI operating

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system idea less abstract, the sources give us

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a really solid case study, the LAPS dashboard.

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It's designed to tackle a super common, really

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painful problem for service -based businesses,

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converting leads effectively. Exactly. Most service

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businesses suffer from this leaky sales funnel.

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They spend good money, sometimes a lot of money,

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generating leads, but then potential customers

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just drop off at every stage from initial contact

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to actually becoming a paying client. That's

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where this LAPS framework fits in. Right. LAPS

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leads, appointments, presentations, sales. That's

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the standard flow. But the money ball insight

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here, as they call it, that's the really interesting

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part. They argue that the place with the most

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leverage, almost always, is improving the conversion

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rate specifically from a lead to an appointment.

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Think about a typical funnel. Maybe you get 100

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leads and that eventually results in, say, three

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sales down the line. Right. But if you use an

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AI optimized system focused just on boosting

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that lead to appointment rate, you might double

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it. Suddenly, those same hundred leads, they

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turn into six sales. You haven't spent a single

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extra dollar on getting more leads. Just by improving

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that one specific conversion point, you've potentially

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doubled the business's revenue. That is huge.

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It really is significant. And the proposed solution

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is pretty clever. Replace those boring static

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contact us forms or maybe those downloadable

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PDF lead magnets that nobody reads. Replace them

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with an interactive personalized assessment dashboard.

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Ah, okay. So instead of just passively collecting

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info, this thing actively engages the user. It

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gives them immediate value like a personalized

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action plan based on their answers. It pushes

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them towards taking the next step with a clear

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call to action properly to book that appointment.

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And crucially, it scales infinitely. Build it

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once, serve potentially. thousands of customers.

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Precisely. It turns that passive, easily ignored

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interaction into something dynamic, a problem

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-solving experience that feels tailored just

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for them. So what is it about the LAPS dashboard

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that makes it so effective at fixing that leaky

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funnel problem? It delivers immediate personalized

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value, which engages users deeply and drives

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them directly towards booking appointments. All

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right, now for the how -to. Our sources don't

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just talk theory. They actually lay out the technical

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bones, the architecture. They call it the assembly

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manual for your first Power Cube. I love it.

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And it's like they're assembling the Avengers

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of tech tools, each playing a specific part.

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Bolt .new is Iron Man handles the front end,

00:12:16.399 --> 00:12:18.580
letting you build web apps just by describing

00:12:18.580 --> 00:12:22.039
them in natural language. Super cool. NAN, that

00:12:22.039 --> 00:12:24.720
workflow automation tool, is Nick Fury, the master

00:12:24.720 --> 00:12:26.879
orchestrator, pulling the strings behind the

00:12:26.879 --> 00:12:28.440
scenes, managing all the back end automation,

00:12:28.600 --> 00:12:31.600
making sure all the systems communicate. Scoop

00:12:31.600 --> 00:12:33.940
.js is Captain America, you know, the strong,

00:12:33.940 --> 00:12:36.220
stable, reliable foundation. That's your database

00:12:36.220 --> 00:12:38.659
holding all the persistent data securely. And

00:12:38.659 --> 00:12:41.200
OpenAI, well, that's got to be Hulk, right? The

00:12:41.200 --> 00:12:44.580
raw brute force intelligence providing that incredible

00:12:44.580 --> 00:12:47.419
power for analysis, personalization, making sense

00:12:47.419 --> 00:12:49.299
of the inputs. That's a pretty awesome lineup.

00:12:49.500 --> 00:12:51.799
And the workflow they map out sounds really clean,

00:12:51.879 --> 00:12:54.519
actually. User signs up on the Bolt front end.

00:12:54.580 --> 00:12:57.799
That triggers a webhook over to N8n. N8n sends

00:12:57.799 --> 00:13:01.399
the info to OpenAI for the analysis part. OpenAI

00:13:01.399 --> 00:13:04.139
sends results back. N8n stores them in Supabase.

00:13:04.299 --> 00:13:06.860
Bolt pulls from Supabase to display the results

00:13:06.860 --> 00:13:09.639
to the user. And then N8n can trigger automated

00:13:09.639 --> 00:13:11.940
email follow -ups based on those results. It's

00:13:11.940 --> 00:13:14.460
a really neat, cohesive loop. It is cohesive.

00:13:14.720 --> 00:13:16.779
And the material breaks down the actual build

00:13:16.779 --> 00:13:19.679
process into clear phases, almost like assembling

00:13:19.679 --> 00:13:23.240
IKEA furniture, but way more powerful, step by

00:13:23.240 --> 00:13:25.860
step. Okay, phase one is the NERBA system. That's

00:13:25.860 --> 00:13:39.409
setting up the N8n. Then phase two, the body.

00:13:40.519 --> 00:13:42.960
Building the actual web application using Bolt.

00:13:43.080 --> 00:13:44.960
And this is where that super prompt idea comes

00:13:44.960 --> 00:13:46.759
in again, which sounds like a massive time saver.

00:13:46.820 --> 00:13:49.639
Instead of building UI elements one by one, you

00:13:49.639 --> 00:13:51.980
apparently give Bolt one big comprehensive prompt

00:13:51.980 --> 00:13:54.440
describing the whole app, its function, layout,

00:13:54.600 --> 00:13:57.679
goals, and the AI just builds a functional structure

00:13:57.679 --> 00:13:59.639
for you in minutes. That sounds pretty amazing.

00:13:59.820 --> 00:14:02.559
Yeah, total game changer for speed. Okay, phase

00:14:02.559 --> 00:14:06.799
three, the memory. Getting Supabase ready. Configuring

00:14:06.799 --> 00:14:08.659
the database, setting up the tables to store

00:14:08.659 --> 00:14:11.139
user assessments, handling user login and authentication,

00:14:11.419 --> 00:14:14.179
the data backbone, and phase four, the brains.

00:14:14.600 --> 00:14:17.799
This is defining the AI processing logic itself.

00:14:18.080 --> 00:14:20.379
And here's a crucial point I make. This isn't

00:14:20.379 --> 00:14:23.200
about building one giant, super complex AI agent

00:14:23.200 --> 00:14:25.059
that tries to do everything. Instead, it's about

00:14:25.059 --> 00:14:27.639
using multiple, simpler, specialized AI models.

00:14:27.860 --> 00:14:29.879
Maybe one focuses just on business analysis,

00:14:30.019 --> 00:14:31.860
another on financial implications, a third on

00:14:31.860 --> 00:14:33.860
strategic recommendations. Each has a narrow,

00:14:33.919 --> 00:14:36.899
specific job. Ah, okay. So it's like maybe 90

00:14:36.899 --> 00:14:39.759
% standardized structure and content, but then

00:14:39.759 --> 00:14:42.980
that crucial 10 % is personalized by these focused

00:14:42.980 --> 00:14:45.799
AI models. And that makes so much sense because

00:14:45.799 --> 00:14:48.460
honestly, I still wrestle with prompt drift myself

00:14:48.460 --> 00:14:50.279
sometimes, you know, where you give an AI the

00:14:50.279 --> 00:14:52.620
same prompt later and the results get kind of

00:14:52.620 --> 00:14:55.539
weird or less accurate. So breaking it down into

00:14:55.539 --> 00:14:58.539
these smaller specialized AI jobs sounds way

00:14:58.539 --> 00:15:00.919
more reliable, more predictable output. Exactly.

00:15:00.919 --> 00:15:03.139
It really maximizes the impact while ensuring

00:15:03.139 --> 00:15:06.190
scalable. consistent delivery, it makes the whole

00:15:06.190 --> 00:15:08.389
system far more robust and predictable over the

00:15:08.389 --> 00:15:11.710
long haul. Okay, so how exactly does using multiple

00:15:11.710 --> 00:15:15.350
focused AI models improve reliability compared

00:15:15.350 --> 00:15:18.710
to just one big complex AI agent? Each model

00:15:18.710 --> 00:15:21.529
has a simpler specific task, which reduces overall

00:15:21.529 --> 00:15:23.649
complexity and makes its output much more predictable

00:15:23.649 --> 00:15:26.309
and consistent. Okay, so once you've built that

00:15:26.309 --> 00:15:28.990
functional standard model, your first AI operating

00:15:28.990 --> 00:15:31.669
system, the sources then suggest ways to upgrade

00:15:31.669 --> 00:15:33.580
it. Think of it like adding premium features

00:15:33.580 --> 00:15:35.679
to a car, making it a high -performance machine

00:15:35.679 --> 00:15:37.740
that justifies a higher price point and delivers

00:15:37.740 --> 00:15:40.350
even more value. Right, like adding dynamic and

00:15:40.350 --> 00:15:43.490
interactive elements. Imagine the dashboard updating

00:15:43.490 --> 00:15:46.210
scores in real time as a user answers questions.

00:15:46.509 --> 00:15:49.289
Or maybe embedding personalized video messages

00:15:49.289 --> 00:15:51.769
directly into the dashboard, tailored based on

00:15:51.769 --> 00:15:53.710
their specific inputs. That's pretty slick, right?

00:15:53.789 --> 00:15:55.789
Really elevates the whole experience. Definitely.

00:15:55.990 --> 00:15:58.029
Then they talk about user experience optimization.

00:15:58.529 --> 00:16:01.529
Things like progressive disclosure, revealing

00:16:01.529 --> 00:16:04.009
information bit by bit, keeping the user engaged

00:16:04.009 --> 00:16:06.909
and curious, rather than overwhelming them up

00:16:06.909 --> 00:16:10.259
front. Maybe adding gamification elements, progress

00:16:10.259 --> 00:16:12.779
bars, achievement badges to encourage people

00:16:12.779 --> 00:16:15.080
to complete the assessment, and of course, mobile

00:16:15.080 --> 00:16:17.539
optimization. That's just table stakes these

00:16:17.539 --> 00:16:20.379
days. Absolutely non -negotiable. And finally,

00:16:20.460 --> 00:16:22.360
they suggest building in smart conversion mechanics,

00:16:22.720 --> 00:16:25.980
using subtle scarcity or urgency cues like countdown

00:16:25.980 --> 00:16:28.659
timers for special offers, or embedding powerful

00:16:28.659 --> 00:16:31.100
social proof like short video testimonials or

00:16:31.100 --> 00:16:33.320
success stories from similar clients right there

00:16:33.320 --> 00:16:35.519
within the dashboard experience itself. Little

00:16:35.519 --> 00:16:38.379
nudges to drive action. And all this leads to

00:16:38.379 --> 00:16:42.320
the big question, why this prints money? The

00:16:42.320 --> 00:16:44.860
analogy they use is powerful. It's not like being

00:16:44.860 --> 00:16:47.120
a house flipper, doing a quick project, selling

00:16:47.120 --> 00:16:49.360
it, moving on. It's like being a commercial real

00:16:49.360 --> 00:16:51.960
estate developer. You build a valuable asset

00:16:51.960 --> 00:16:55.340
once your AI operating system, and then you collect

00:16:55.340 --> 00:16:58.909
recurring rent from multiple. tenants, your clients.

00:16:59.129 --> 00:17:01.610
That's it, exactly. You deliver way higher value

00:17:01.610 --> 00:17:03.669
to each client. You get predictable recurring

00:17:03.669 --> 00:17:05.950
revenue, which is the holy grail for any business.

00:17:06.150 --> 00:17:09.029
And crucially, it's scalable delivery. You can

00:17:09.029 --> 00:17:11.029
onboard more clients without linearly increasing

00:17:11.029 --> 00:17:13.809
your own workload or costs. You position yourself

00:17:13.809 --> 00:17:16.890
as the premium, indispensable solution in your

00:17:16.890 --> 00:17:19.049
niche. And what about keeping those clients long

00:17:19.049 --> 00:17:21.670
-term? Retention. They introduced this idea of

00:17:21.670 --> 00:17:24.390
the sticky building. Your AI operations has become

00:17:24.390 --> 00:17:26.750
so deeply woven into the client's daily workflow,

00:17:26.970 --> 00:17:29.089
their processes, their data. It's like their

00:17:29.089 --> 00:17:31.069
virtual office building. They simply can't imagine

00:17:31.069 --> 00:17:33.579
operating without it. Yeah, the moving costs,

00:17:33.660 --> 00:17:36.480
the pain and expense of trying to switch to something

00:17:36.480 --> 00:17:39.019
else become incredibly high. They're locked in,

00:17:39.140 --> 00:17:42.000
but in a good way. Plus, you keep adding value.

00:17:42.099 --> 00:17:44.500
You keep upgrading the building, adding new features,

00:17:44.740 --> 00:17:47.299
improving performance, maybe leveraging network

00:17:47.299 --> 00:17:49.680
effects as more clients use it. It gets better

00:17:49.680 --> 00:17:51.759
and better over time. It creates this really

00:17:51.759 --> 00:17:54.819
powerful lock -in, yes. But it's a lock -in based

00:17:54.819 --> 00:17:57.599
on genuine, ever -increasing value, not just

00:17:57.599 --> 00:17:59.900
contractual obligation. So, boiling it down,

00:18:00.059 --> 00:18:03.380
when we talk actual... What's the single biggest

00:18:03.380 --> 00:18:05.680
difference in revenue generation between the

00:18:05.680 --> 00:18:09.000
old AI agent model and this AI operating system

00:18:09.000 --> 00:18:11.599
model? AI operating systems are built to generate

00:18:11.599 --> 00:18:13.940
predictable, recurring revenue for many clients

00:18:13.940 --> 00:18:16.460
over long periods, unlike the one -off payments

00:18:16.460 --> 00:18:19.920
from isolated agent projects. Sponsor. So let's

00:18:19.920 --> 00:18:21.220
pull this all together. What does this really

00:18:21.220 --> 00:18:23.920
mean for you listening right now? The big idea,

00:18:24.039 --> 00:18:26.779
I think, is a really profound shift, a fundamental

00:18:26.779 --> 00:18:28.960
change in how we should approach building AI

00:18:28.960 --> 00:18:31.799
solutions for businesses. Absolutely. The core

00:18:31.799 --> 00:18:34.720
message is loud and clear. Stop selling those

00:18:34.720 --> 00:18:37.599
one -off AI agents. They trap you on that exhausting

00:18:37.599 --> 00:18:40.339
freelancer's hamster wheel. They're getting commoditized

00:18:40.339 --> 00:18:42.640
fast, they provide only temporary value, and

00:18:42.640 --> 00:18:44.519
ultimately they solve the wrong problem, both

00:18:44.519 --> 00:18:46.799
for the client's long -term success and for your

00:18:46.799 --> 00:18:50.779
own business's sustainability. Instead... The

00:18:50.779 --> 00:18:52.619
path forward, according to this research, is

00:18:52.619 --> 00:18:55.720
to build AI operating systems. These integrated

00:18:55.720 --> 00:18:58.400
power cubes combining AI models, automation,

00:18:58.920 --> 00:19:01.839
databases, user interfaces, all meticulously

00:19:01.839 --> 00:19:04.559
designed to solve one huge recurring business

00:19:04.559 --> 00:19:07.819
problem for a very specific high value type of

00:19:07.819 --> 00:19:10.000
client. Right. And it requires that strategic

00:19:10.000 --> 00:19:12.279
thinking up front, targeting the right clients,

00:19:12.420 --> 00:19:14.700
rigorously validating the problem using techniques

00:19:14.700 --> 00:19:17.200
like Magic Wand, and then leveraging powerful,

00:19:17.420 --> 00:19:19.799
often no -code or low -code tools like Bolt,

00:19:19.940 --> 00:19:23.079
NAN, and Supabase to build efficiently. It's

00:19:23.079 --> 00:19:25.299
all geared towards creating massive, scalable

00:19:25.299 --> 00:19:27.640
value that really moves the needle for businesses.

00:19:28.019 --> 00:19:30.480
And that scalable value, as we've discussed,

00:19:30.759 --> 00:19:33.319
is what translates directly into predictable,

00:19:33.519 --> 00:19:36.180
recurring revenue for the builder. Often the

00:19:36.180 --> 00:19:38.900
target is $10 ,000 a month or more. And you build

00:19:38.900 --> 00:19:41.339
these sticky client relationships because your

00:19:41.339 --> 00:19:43.940
solution becomes truly indispensable to their

00:19:43.940 --> 00:19:46.700
daily operations. Yeah, but the sources are also

00:19:46.700 --> 00:19:48.940
really clear. This isn't some kind of get rich

00:19:48.940 --> 00:19:51.740
quick thing. It definitely isn't easy. It demands

00:19:51.740 --> 00:19:54.380
that you learn the tech, develop real business

00:19:54.380 --> 00:19:56.740
acumen, become obsessed with understanding your

00:19:56.740 --> 00:19:59.599
customers deepest pains and just stick with it.

00:19:59.839 --> 00:20:02.660
Persistence is key. True. But for those who are

00:20:02.660 --> 00:20:05.660
willing to, you know, pay that. price of admission,

00:20:05.940 --> 00:20:08.740
as they put it, the potential rewards are really

00:20:08.740 --> 00:20:10.779
substantial. It's not just about the recurring

00:20:10.779 --> 00:20:13.420
revenue or the scalable growth. It's about building

00:20:13.420 --> 00:20:15.839
deep, integrated partnerships with clients where

00:20:15.839 --> 00:20:18.339
you're delivering lasting, transformative impact.

00:20:18.680 --> 00:20:21.279
And the sources suggest, maybe optimistically,

00:20:21.359 --> 00:20:23.640
but maybe not, that your first $10 ,000 a month

00:20:23.640 --> 00:20:25.980
could be just one well -designed, well -executed

00:20:25.980 --> 00:20:28.460
system away. It really feels like a call to action.

00:20:28.680 --> 00:20:31.039
Time to stop just building little tools and start

00:20:31.039 --> 00:20:33.460
solving fundamental business problems at scale.

00:20:34.410 --> 00:20:37.130
operating system revolution, it seems, has begun.

00:20:37.349 --> 00:20:40.029
The only real question left is, will you be leading

00:20:40.029 --> 00:20:41.930
it or watching it happen from the sidelines?

00:20:42.089 --> 00:20:43.910
Thanks for joining us for this deep dive. Really

00:20:43.910 --> 00:20:46.369
fascinating stuff. Until next time, keep exploring.

00:20:46.650 --> 00:20:47.869
Out to your row, music.
