5 Ways You Can Get Paid Many Times for the Same Work
Patrick McFadden • July 17, 2013
Last night I attended a local Chamber of Commerce event in my area. During this event I had the opportunity to have a great discussion with someone about passive income. While talking with this person I quickly discovered that they wanted to move from active income (when you do the work; you get paid—only once) to passive income (when customers pay you over and over again for the same work—work you only did once).
Now from my experience when people think about marketing the last thing they think about is expansion, new markets, new income streams and new products. But this is part of marketing too. Plus complacency is a killer and it’s a killer because things change all around us.
Often the best way to overcome complacency, ensure company growth, and be responsive to market changes and influences is to develop new products, services, and streams of income.
So, discussing this was exciting for me because I’m working on a secret project called “Project Imagine and Implement.” (I plan to write about this in another post.)
As the conversation continued I started giving this person ways to increase their time and income through turning their active income into passive income. They soon said, “like the way you think”, and this inspired me to write this post to help you figure out how to transform your active income into passive income.
Here are 5 ways you can get paid many times for the same work :
- Turn your expertise into a product. Yes, you are an expert. Everyone reading this post, right now is an expert in something. Everybody has a passion, a hobby and a cause. And you’re justifiably proud of your point of view. Don’t make the mistake I made and just assume every one knows what you know. They don’t.
- Turn your blog posts, radio interviews or podcasts into an ebook, guide, workbook or book. Over and over again, we see that people will pay for the convenience. And the convenience you can provide will be having your content all in one place. Plus, you can incorporate insights (comments and stories) you get from readers and take the content to the next level. Incidentally this is the purpose I started blogging. My blog is my “virtual chalk board.”
- Turn your coaching or consulting into products. Often I find myself providing the same advice over and over again and when you find yourself providing the same advice over and over again, it’s a candidate for turning it into a product or series of products. I currently have some products like this on the drawing board.
- Turn a speech into a product. My mentor Zig Ziglar implemented this passive income technique in his product “Secrets of Closing the Sale.” See sample here. You can record it on video or even audio then turn that into a CD, mp3 file or DVD you sell. You can add value by including a study guide, worksheets or a transcript .
- Turn a live event into an online training experience. The first step in this process is to record the event. After that’s done then you can turn it into modules with workbooks, transcripts, or other promotional material and deliver it via a membership site.
I’m always thinking about how you or your company can turn active income opportunities into passive ones, so you get paid many times for the same work. If you need help with this i would be delighted to talk with you.
Question: What opportunities do you notice in your business to turn active income into passive ones?
Everyone is scaling outputs. Almost no one is scaling judgment.
Ask anyone in tech where AI is headed, and they’ll tell you: “The next leap is reasoning.” “AI needs judgment.” “We need assistants that think, not just answer.” They’re right. But while everyone’s talking about it, almost no one is actually shipping it. So we did. We built Thinking OS™ —a system that doesn’t just help AI answer questions… It helps AI think like a strategist. It helps AI decide like an operator. It helps teams and platforms scale judgment, n ot just generate output. The Theory Isn’t New. The Implementation Is. The idea of layering strategic thinking and judgment into AI isn’t new in theory. The problem is, no one’s been able to implement it effectively at scale. Let’s look at the current landscape. 1. Big Tech Has the Muscle—But Not the Mind OpenAI / ChatGPT ✅ Strength: Best-in-class language generation ❌ Limitation: No built-in judgment or reasoning. You must provide the structure. Otherwise, it follows instructions, not strategy. Google DeepMind / Gemini ✅ Known for advanced decision-making (e.g., AlphaGo) ❌ But only in structured environments like games—not messy, real-world business scenarios. Anthropic (Claude), Meta (LLaMA), Microsoft Copilot ✅ Great at answering questions and following commands ❌ But they’re assistants, not advisors. They won’t reprioritize. They won’t challenge your assumptions. They don’t ask: “Is this the right move?” These tools are powerful—but they don’t think for outcomes the way a strategist or operator would. 2. Who’s Actually Building the Thinking Layer™? This is where it gets interesting—and thin. Startups and Indie Builders Some small teams are quietly: Creating custom GPTs that mimic how experts reason Layering in business context, priorities, and tradeoffs Embedding decision logic so AI can guide, not just execute But these efforts are: Highly manual Difficult to scale Fragmented and experimental Enterprise Experiments A few companies (Salesforce, HubSpot, and others) are exploring more “judgment-aware” AI copilots. These systems can: Flag inconsistencies Recommend next actions Occasionally surface priorities based on internal logic But most of it is still: In early R&D Custom-coded Unproven beyond narrow use cases That’s Why Thinking OS™ Is Different Instead of waiting for a lab to crack it, we built a modular thinking system that installs like infrastructure. Thinking OS™: Captures how real experts reason Embeds judgment into layers AI can use Deploys into tools like ChatGPT or enterprise systems Helps teams think together, consistently, at scale It’s not another assistant. It’s the missing layer that turns outputs into outcomes. So… Is This a New Innovation? Yes—in practice. Everyone says AI needs judgment. But judgment isn’t an idea. It’s a system. It requires: Persistent memory Contextual awareness Tradeoff evaluation Value-based decisions Strategy that evolves with goals Thinking OS™ delivers that. And unlike the R&D experiments in Big Tech, it’s built for: Operators Consultants Platform founders Growth-stage teams that need to scale decision quality, not just content creation If Someone Told You They’ve Built a Thinking + Judgment Layer™… They’ve built something only a handful of people in the world are even attempting. Because this isn’t just AI that speaks fluently. It’s AI that reasons, reflects , and chooses. And in a world that’s drowning in tools, judgment becomes the differentiator. That’s the OS We Built Thinking OS™ is not a prompt pack. It’s not a dashboard. It’s not a glorified chatbot. It’s a decision architecture you can license, embed, or deploy— To help your team, your platform, or your clients think better at scale. We’ve moved past content. We’re building cognition. Let’s talk.
In every era of innovation, there’s a silent bottleneck—something obvious in hindsight, but elusive until the moment it clicks. In today’s AI-driven world, that bottleneck is clear: AI has speed. It has scale. But it doesn’t have judgment . It doesn’t really think . What’s Actually Missing From AI? When experts talk about the “thinking and judgment layer” as the next leap for AI, they’re calling out a hard truth: Modern AI systems are powerful pattern machines. But they’re missing the human layer—the one that reasons, weighs tradeoffs, and makes strategic decisions in context. Let’s break that down: 1. The Thinking Layer = Reasoning with Purpose This layer doesn’t just process inputs— it structures logic. It’s the ability to: Ask the right questions before acting Break down complexity into solvable parts Adjust direction mid-course when reality changes Think beyond “what was asked” to uncover “what really matters” Today’s AI responds. But it rarely reflects. Unless told exactly what to do, it won’t work through problems the way a strategist or operator would. 2. The Judgment Layer = Decision-Making in the Gray Judgment is the ability to: Prioritize what matters most Choose between imperfect options Make decisions when there’s no clear answer Apply values, experience, and vision—not just data It’s why a founder might not pursue a lucrative deal. Why a marketer might ignore the click-through rate. Why a strategist knows when the timing isn’t right. AI doesn’t do this well. Not yet. Because judgment requires more than data—it requires discernment . Why This Is the Bottleneck Holding Back AI AI can write. It can summarize. It can automate. But it still can’t: Diagnose the real problem behind the question Evaluate tradeoffs like a founder or operator would Recommend a path based on context, constraints, and conviction AI today is still reactive. It follows instructions. But it doesn’t lead. It doesn’t guide. It doesn’t own the outcome. And for those building serious systems—whether you’re running a company, launching a platform, or leading a team—this is the wall you eventually hit. That’s Why We Built Thinking OS™ We stopped waiting for AI to learn judgment on its own. Instead, we created a system that embeds it—by design. Thinking OS™ is an installable decision layer that captures how top founders, strategists, and operators think… …and makes that thinking repeatable , scalable , and usable inside teams, tools, and platforms. It’s not a framework. It’s not a chatbot. It’s not another playbook. It’s the layer that knows how to: Think through complex decisions Apply judgment when rules don’t help Guide others —human or AI—toward strategic outcomes This Is the Missing Infrastructure Thinking OS™ isn’t just about better answers. It’s about better thinking—made operational. And that’s what’s been missing in AI, consulting, leadership development, and platform design. If you’re trying to scale expertise, install judgment, or move from tactical to strategic… You don’t need a faster AI. You need a thinking layer that knows what to do—and why. We built it. Let’s talk.
