How I Use AI to Pressure-Test Every Investment, Every Assumption and Every Important Decision I Make

“What happened next permanently changed the way I think, work, and make decisions.” 

From crayon drawings to complex models, and what I learned about leverage, discernment, and the right way to think with machines. 

The Day AI Drew Me a Stick Figure House

I had amazing grandparents growing up, almost like a second set of parents. They were kind, steady, and deeply proud of me. One afternoon when I was about seven or eight years old, I sat at their kitchen table with a big yellow legal pad and a box of crayons and drew a house.

It was the kind of drawing you’d expect from a kid that age: a square house with big, obvious windows and a centered door. A stick figure family stood outside. Each one had five stick fingers extending from tiny circular hands, because my brain was just beginning to process smaller details. A tree stood nearby with visible roots reaching into the ground, and a bright yellow sun beamed down from the corner of the page. It was a perfect snapshot of childhood understanding.

My grandparents loved it. They taped it to their pantry door, and there it stayed for years. Even as I got older and started to cringe a little at how primitive it looked, they never took it down.

That picture lingered in my mind, probably more than I expected.

Especially the first time I ever used AI.

I’d heard people in real estate were starting to experiment with it, so I gave it a try. I asked it to help write a property listing. What it produced was laughable. It was written at what felt like a fifth-grade reading level, filled with bland clichés and generic phrases. And the moment I read it, I flashed back to that crayon drawing from my grandparents’ house. Because if you took that AI-generated description and sketched it out, it would’ve looked like the same little crayon house, basic, clumsy, and nowhere near acceptable for professional use.

The AI had described a house, about as well as I had drawn one at age seven.

It was completely unusable. And honestly, a little embarrassing.

But what happened over the next nine months completely reframed the way I approach AI.

Nine Months Later: From Crayons to Complex Modeling

About nine months later, after some reluctance, and quite a bit of gentle prodding from my property manager, who was using AI to dramatically streamline his business, I decided to give it another shot.

But this time, it was a completely different experience.

I wasn’t using it to write real estate listings. I was testing something much more advanced: investment analysis. Not “what stock should I buy” or “is this a good rental?” I mean real structural modeling, sensitivity analysis on proposed portfolio adjustments I was seriously considering.

At one point, after feeding in the core assumptions behind my portfolio structure, the AI prompted me:

"Would you like to run a sensitivity analysis on this?"

It caught me off guard. I didn’t realize it had that kind of capacity. Out of curiosity, I said yes.

What happened next permanently changed the way I think, work, and make decisions.

Within a few hours, we had run through over 20 iterations of complex what-if scenarios. Adjusting weightings, testing risk exposure, layering in yield shifts, stress testing against macro conditions, all of it done with astonishing speed and clarity.

Now, to be clear, I already knew how to do this type of work. I was trained to build complex models in Excel back in my early professional career. But building one good model, one accurate, dynamic iteration, would usually take a full week of focused effort.

And here I was, running months worth of sophisticated modeling in a single afternoon.

That’s when the lightbulb really went off.

The same technology that had once spit out a crayon-level property listing was now helping me pressure-test major portfolio shifts in real time. And it wasn’t doing it in the background, it was working alongside me as a personal thinking partner. A decision-making amplifier. A quiet architect of systems.

AI isn’t here to replace people.
It’s here to elevate what we’re capable of.

The Crash That Taught Me Control

Of course, I got a little overexcited.

Within hours of that breakthrough session, I started bumping into the limits of the technology, hard.

One minute, it was running beautifully structured asset allocation models and generating high-level insights. The next, I’d ask it to calculate a basic mortgage payment… and it would botch the math completely.

We’re not talking nuance here. I mean elementary errors on core financial functions.

Then came the bigger issue: hallucinations.

If you’ve used AI for any length of time, you know what I mean. It’s not that it just gets things wrong, it gets them wrong with total confidence. It invents numbers, misstates facts, or makes up citations. And if you're not careful, it can pull you into a kind of delusional feedback loop, where it builds false complexity on top of false assumptions, all while sounding calm, articulate, and completely certain.

That’s when it clicked for me:
AI isn’t a decision-maker. It’s a modeling engine.
It’s not a shortcut. It’s an amplifier.

And like any amplifier, it will multiply the clarity or the confusion of whatever signal you feed into it.

That realization didn’t make me abandon it. On the contrary, it made me respect it. Because once I understood its true power, and its clear limitations - I started to use it very differently.

I stopped treating it like a magic eight-ball (the toy that gives random yes/no answers).

And I started using it like a co-architect.

The First Use: A Thinking Partner, Not a Shortcut

Where I see a lot of people go wrong, then and now - is by trying to use AI as a shortcut.

A shortcut for output.
A shortcut for decision-making.
More dangerously, a shortcut for thinking.

And AI is nowhere near that. It wasn’t back then, and it certainly isn’t now.

But I wasn’t discouraged. Because think about what had already happened: in just nine months, it had evolved from generating clumsy, childlike real estate listings… to running dynamic financial simulations that would have taken me weeks to build manually.

That kind of exponential leap tells you where this is going.

So no, I didn’t abandon the tool. I just changed the way I use it.

Here’s how I use AI today:
I never use it to replace thinking.
I never use it to outsource creativity.
I use it for something deeper, and far more powerful.

I use AI as a thinking partner. A high-stakes sparring partner whose job is to pressure-test every assumption I bring to the table.

Let me explain.

Imagine you’re a lawyer in a courtroom. On the other side of the aisle is opposing counsel, sharp, well-prepared, relentless. You stand up and present your best case. Their job? Tear it apart. Find the weaknesses. Uncover the blind spots. Exploit every contradiction.

That’s how I use AI.

When I have an idea, especially one tied to investment structure, long-term strategy, or risk management, I’ll play both sides. One moment, I present the argument. The next, I switch roles and instruct AI to rip it to shreds. We walk through every assumption, every variable, every what-if scenario. We alternate roles again and again until the idea can stand on its own, without blind spots or bias.

I’ve reached the point in my life where my number one concern isn’t being “right”- it’s making optimal decisions.

In Built for Freedom™, I talk about how deeply cognitive bias shaped some of my worst investment decisions, and how hard it is to catch yourself in the moment. That’s why this process is so important. Because I don’t want AI to think for me.

I want it to challenge me.

This is exactly why I included AI-powered action steps throughout Built for Freedom™ - so you can experience firsthand how this kind of structured thinking can amplify your own decision-making.

The Second Use: AI as an Iteration Engine

The second way I use AI is as what I call an iteration machine.

I’ve been an entrepreneur nearly my entire adult life. That means I’m constantly generating ideas, new investments, new ventures, new systems. And while some ideas are throwaways, others are genuinely worth exploring.

But historically, that exploration process took time. A lot of it.

To really pressure-test an idea, run numbers, build scenarios, explore pros and cons, evaluate potential returns and risks, it could easily take six to eight months of scattered work and research. Especially if I was doing it alone.

Now? I can run that same process in a matter of hours.

Sometimes less.

That means instead of stress-testing one business concept every few quarters, I can stress-test dozens.

Think about that from a creative standpoint. From a risk-reduction standpoint. From a capital-efficiency standpoint.

If you can explore dozens of investment structures, strategic pivots, or entrepreneurial concepts, each with multiple iterations, detailed assumptions, and simulated stress scenarios, without spending a dollar… are you going to be a better investor?

A better founder?

Absolutely.

Because behind every good idea, I ask two core questions:
If I can build it once… can it scale?
And if it scales… can it earn for decades?

Because you’re not just throwing darts and hoping for a bullseye.
You’re building a system that filters out failure before it costs you anything.

In a world where speed is worshipped, the real edge comes from discernment, and AI gives me the power to test, explore, refine, and discard ideas rapidly, without attachment.

Sometimes the best decision is the one you don’t make.

The Third Use: Systemizing My Thinking Into a Living Framework

Throughout Built for Freedom™, I repeat one word over and over:

System.

Not because it sounds impressive.
But because in the real world, especially when money, risk, and freedom are involved, systems are what separate reactive guessing from calm, consistent execution.

That’s the third way I use AI: to systemize my thinking into a living, personalized framework.

In my case, that means mapping out the full structure of my investment system:

• Why each investment exists
• How it’s structured
• The role it plays inside my Freedom Portfolio™
• And how it interacts with the other layers of income, risk, and optionality

I’ve built this structure inside AI, so that when a new opportunity shows up, whether it’s a new asset class or a deal I haven’t seen before, I can immediately pressure-test it against my actual system.

Does it strengthen the durability of my portfolio?
Does it reduce overall risk?
Does it increase yield while preserving my Return on Headache™?
Does it fit the purpose-driven structure I’ve already built?

Because I’ve trained the model on my own filters, my definitions of quality, my personal tolerances, my specific Freedom Portfolio™ architecture, it’s not just giving generic advice. It’s helping me apply my system to every decision.

And that’s the real breakthrough.

I’m not using AI to explore random ideas.

I’m using it to protect and strengthen a system that already aligns with my life.

Final Thoughts

The truth is, I don’t use AI like most people.

I’m not here for shortcuts.
I’m here for optimal outcomes.

I don’t want AI to replace my thinking.
I want it to refine it.

I use it as a pressure tester.
As an iteration engine.
As a system-building partner.

And over time, that’s helped me make better decisions, avoid expensive mistakes, and build a Freedom Portfolio™ that fits my exact life, not someone else’s spreadsheet.

If you’re trying to build something real, something durable, something aligned, AI can help.

But only if you use it the right way.

Ready to pressure-test your own portfolio like this?
In Built for Freedom™, I walk you through the same decision filters, AI prompts, and portfolio models I use to build durable wealth, without hype, guesswork, or drawdown fear.
👉 Get the book here or explore more at BuiltForFreedomBook.com