AI will NOT make you rich. Not the tools. Not the agents. Not the prompts. Not the automations.
None of it… at least not by itself.
The people who are scaling AI-powered companies past a million dollars aren’t doing it by chasing the latest model release or obsessing over which chatbot is smartest.
They’re doing it by understanding a simple truth that most people completely miss…
Problems make you money. Tools do not.
With that foundation in place, let’s break down exactly how to actually get rich with AI.
Part 1: Stop Selling the Tool
The first and most common mistake people make is treating AI itself as the product.
AI is a tool. Full stop.
It’s like a database, or an internet connection. It’s infrastructure.
And infrastructure, on its own, doesn’t make anyone wealthy.
Think about a carpenter. A carpenter who sells hammers makes maybe $10 or $15 on a transaction.
A carpenter who uses hammers (along with every other tool in their belt) to fix a massive roof leak?
That person charges $5,000 to $10,000 and the customer is grateful to pay it.
The shift isn’t about the tool. It’s about what the tool helps you solve.
You don’t need a single AI tool. You need a toolkit.
But more importantly… you need to know how to use every piece of in the toolkit.
And that brings us to the first tool worth mastering.
Part 2: The Hammer – Large Language Models (LLMs)
The hammer in your AI toolkit is the large language model: Claude, ChatGPT, Gemini, Grok and any others.
Powerful? Absolutely. But like a real hammer, it requires skill to use well.
An amateur with a hammer doesn’t just fail to hit the nail… They create new problems.
Extra holes, wasted materials, a bigger mess than before.
A professional, on the other hand? They barely seem to think. The nail goes in on the first swing. Clean and fast.
Most people use LLMs the same way amateurs use hammers: as a fancy search engine.
Type a question, copy the answer, paste it somewhere else, move on.
The output is mediocre and they wonder why.
The fix is having a prompting framework. A structured approach to how you talk to these tools.
One framework that works consistently is called MAPS:
- M – Mission: Start with the outcome, not the task. Don’t say “find me leads.” Say “I need 30 new customers per month to hit my revenue target.” Give it direction.
- A – Ask: What specifically do you need it to do? Be precise. “Help me with leads” is vague. “Give me 40 qualified leads including email and phone number” is an ask.
- P – Parameters: What context does it need? Your ideal customer profile, what’s worked before, constraints and preferences. The more you give, the sharper the output.
- S – Shape. What should the output look or feel like? CSV? Bullet points? Markdown? Conversational or formal? Long or short? Specify it.
Follow MAPS consistently and the quality of your outputs will improve dramatically.
It’s not that the LLM is any smarter than before. You just learned how to “swing the hammer” properly.
Part 3: The Screwdriver – AI Automation
Not every problem is a nail. Some problems require a different tool entirely.
The screwdriver in your AI toolkit is automation: tools like Claude Cowork, n8n, Zapier, Make, and similar platforms.
Where an LLM requires you to manually prompt it each time, automation lets you build a workflow once, and then let it run forever.
That’s a different kind of leverage.
The analogy holds: a screw, driven in once, stays put. You set it up and it doesn’t come apart. The same is true for a well-built automation. Set it and forget it.
Here’s an example: every Friday, a report lands in Slack that automatically analyzes every sales call across every company you’re involved in and summarizes how each one is performing (all without you lifting a finger).
That’s your “screwdriver” doing its job.
How do you know if something is worth automating? Apply the Rule of R:
- Repetitive: Do you do this task at least once a week? Daily? If so, it’s a candidate.
- Rule-based: Does it follow the same inputs and outputs every time? Consistent processes automate well. Judgment calls don’t.
- Return: Does automating it save more time than it takes to build? Don’t spend 60 hours building a system that saves you two minutes a week.
If the answer is yes to all three, build the automation.
Part 4: The Power Drill — Agentic AI
This is where things get genuinely exciting.
If a hammer needs you to swing it and a screwdriver needs you to turn it, a power drill just needs you to point it and pull the trigger.
You’re not doing the heavy lifting anymore. You’re setting the direction, and the machine handles the rest.
That’s agentic AI.
Agentic systems don’t just complete tasks. They take over entire workflows.
Start by giving them the outcome. “Build me a nutrition app. Model this financial scenario.”
And it handles everything in between, end to end.
LLMs complete a step. Automations run a process. Agents deliver a complete workflow.
The concept to understand here is human on the loop (not in the loop). Rather than a human pushing things forward at each stage, the agent runs the full loop autonomously.
The human shows up to inspect, guide, and redirect.
Here’s how to start working with agentic AI:
- Pick a full workflow: From initial idea to completed output. That’s your challenge.
- Use the MAPS framework: to brief the agent on what you need.
- Resist the urge to jump in: If your instinct is to review it, don’t. Have the agent review its own work first. That’s an advanced move most people never try.
- Guide toward outcomes, not methods: The agent knows 100 ways to accomplish what you want. Tell it where you need to land, not how to drive there.
One powerful technique: use separate agents to check the work of other agents.
A coding critique agent, for instance, can review every piece of code another agent writes, return a list of improvements, and send it back for revision.
All without human input.
Specialized agents, just like specialized humans, do better work in their area of focus.
Part 5: The Orchestrator – The Actual Source of Wealth
Here’s what ties everything together, and why owning all three tools still isn’t the answer.
Knowing when and where to use each tool is what generates real money.
Most people fail because they bounce between them without purpose.
They learn about every new platform, collect capabilities like trading cards, and never connect any of it to a real problem.
The orchestrator does something different.
They look at a problem, choose the right tool for it, and sell the solution.
Remember the carpenter. They don’t walk into your house and say, “I’ll fix your roof. And by the way, I’m using a really great power drill.”
You don’t care about the drill. You care about your roof not leaking anymore.
The same principle applies to AI services.
If you can deliver something faster, cheaper, and better using AI, you don’t have to tell the customer it’s AI. You just deliver the result.
The customer wants their problem solved. That’s what they’re paying for.
There’s a version of this that comes up constantly: someone offers an AI-powered service for $500 that their customer used to pay $5,000 for, and then struggles to sell it.
The answer isn’t to lower the price further. It’s to charge $5,000 and keep the $4,500 difference.
The customer is already used to paying that. The AI-enabled margin is yours.
But this is the most important mental shift of all: become a director, not a doer. Don’t try to use AI more. Use AI so that you do less, and the output keeps improving. That’s when the leverage becomes real.
And that’s when the money follows.
-DM