What Happens When Compute Becomes Cheap—and Where the Real Moats Move Next
There’s a popular idea floating around that the stock market does fine in inflationary environments because stocks are “real assets.” Money gets printed, and that money has to go somewhere—so you see it flow into stocks, real estate, gold, and anything that feels like it can hold value.
There’s truth in that… but right now, there’s a complication.
A huge part of recent market performance has been driven by a small number of companies—many tied directly to the AI boom. That matters because concentration creates fragility. If the market is being pulled upward by a handful of names, you don’t have a “broad market story.” You have a leadership story.
And leadership stories change.
So let’s talk about AI in a way that actually helps you invest: not hype, not fear—just a Rule #1 AI investment strategy built on moats, owner earnings, and valuation discipline.
The AI Buildout Is Real: Compute at Industrial Scale
AI isn’t just “an app trend.” It’s an infrastructure buildout.
Training and running advanced AI models requires massive compute, which requires:
Data centers
Specialized chips and hardware
Energy and cooling
Network infrastructure
A whole ecosystem of tools and services
This isn’t small. It’s capital intensive. And it’s accelerating.
That’s why you’re hearing about tens of billions in spending—and why investors treat AI as a once-in-a-generation platform shift.
So far, so good.
But Rule #1 thinking gets interesting when we ask the next question: Where will the durable profits actually land?
When Compute Becomes Cheap, AI Competitive Advantage Moves
Here’s a principle that shows up again and again in investing:
When an input becomes abundant and widely accessible, it becomes cheaper—and less differentiating.
If compute becomes widely available, it can start to feel commodity-like. And if everyone can access powerful AI, then “having AI” won’t be a moat.
That’s where the real investing question begins:
If AI capability spreads, who captures the profits?
That’s not a tech question. That’s a moat question—and it sits at the center of any smart AI investment strategy.
Rule #1 Moat Thinking: The Winners Aren’t Always the Builders
When a technology becomes broadly available, value often moves to the layer that controls one or more of the following:
Distribution:
Who owns the customer relationship?
Switching costs:
How hard is it to leave?
Workflow integration:
Is it embedded into daily operations?
Proprietary data advantage:
Do they have unique data that improves outcomes?
Ecosystem / network effects:
Does the product get better as more people use it?
Brand + trust:
Especially when AI is making decisions for you
This is where AI competitive advantage tends to live—not in the generic ability to “use AI,” but in the ability to turn AI into:
Sticky products
Indispensable workflows
Trusted platforms
High switching-cost ecosystems
Durable pricing power
Compute can be a great business layer. But if it becomes utility-like, it may be capital intensive, competitively pressured, and not always high-moat from a pricing-power standpoint.
So we keep our eyes on the same Rule #1 destination:
Durable owner earnings.
The Rule #1 Danger Zone: Confusing “Hot Theme” With “Wonderful Business”
AI is exciting. That’s exactly why it’s dangerous.
Hot themes do two things to investors:
They encourage story-driven investing (“this will change everything”)
They justify paying any price (“it’s the future!”)
Rule #1 asks you to step back and do something boring—but powerful:
Is it a wonderful business?
Does it have a moat you can explain?
Is management solid and owner-oriented?
Are the economics strong and durable?
Can you estimate intrinsic value with reasonable assumptions?
Are you buying with a margin of safety?
AI doesn’t replace that checklist. If anything, AI makes the checklist more important—because the stories get louder, faster, and more persuasive.
How to Evaluate AI-Exposed Companies Without Chasing Hype
Here’s the simplest translation into Rule #1 actions—your practical AI investment strategy:
1) Identify Where the Moat Really Is
If the product is easily copied, the moat is weak. If it’s embedded in workflows with switching costs and trust, the moat is stronger.
2) Watch for Commoditization Risk
If the company’s advantage is “we have access to X,” ask: Will everyone have access to X in a few years? If yes, the moat may shrink.
3) Look for Durable Pricing Power
In a commodity-like environment, pricing power is hard. The businesses that keep it are differentiated in a way customers truly care about.
4) Separate Revenue Growth From Owner Earnings
AI stories can inflate growth projections. Rule #1 investors care about owner earnings: what’s left after the business reinvests to stay competitive.
5) Don’t Ignore Concentration Risk
If the market is relying on a handful of names, volatility can spike when sentiment shifts. That doesn’t mean “get out.” It means: don’t overpay.
The Takeaway: AI May Be Everywhere—and That Changes the Game
AI is not a fad. But AI competitive advantage may not belong to whoever shouts the loudest, raises the most money, or builds the biggest data center.
If compute becomes more abundant, advantage tends to shift to:
Whoever owns distribution
Whoever controls the customer workflow
Whoever has unique data or trust
Whoever can maintain pricing power
That’s exactly the kind of thinking Rule #1 was built for: moats, owner earnings, and buying at the right price.
If you want to get better at spotting real moats—and learning how to estimate intrinsic value so you’re not buying on excitement—join the Rule #1 Investing Workshop. We’ll walk through the process step-by-step so you can evaluate opportunities (including AI-related ones) with clarity and confidence.
