Your AI Product Has No Moats. Except One.

You just saw the news. Jensen Huang’s first tweet ever. 63 million views. 76 companies sign a letter. Then Kimi K3 open-sources a 2.8 trillion-parameter model that beats Claude Opus on benchmarks. And you thought: Great. Now every product I build can be copied in a weekend.

I get it. A product manager friend of mine spent six months learning prompt engineering, adding “familiar with mainstream LLMs” to his resume. K3 drops, and his first thought wasn’t excitement—it was that line just became worthless.

But here’s the twist: model homogeneity isn’t your death sentence. It’s your liberation. Because when every AI product can call the same model, the only thing that separates you is what you consider ‘good enough.’ And that’s something no model can steal.

Let me show you why.

The CUDA Illusion

Jensen isn’t afraid of Chinese open-source models. He’s afraid of developers moving off CUDA. His real moat isn’t the chip—it’s the ecosystem. Every AI engineer writes code in CUDA. Switching costs are brutal.

Your situation is identical. You think your moat is the model you choose, the prompt you engineered, or the feature you shipped first. Wrong. All of those are ephemeral. The model changes in two months, prompts get built into the next release, and features are copy-pasted by competitors using the same API.

What’s actually permanent? The set of rules you’ve built to decide what counts as a correct answer in your specific business context.

The Fake vs. Real Differentiation

Let’s be brutal. These are not moats:

• Picking the right model (everyone has access to the same API)
• Prompt engineering (next model version will do it natively)
• Feature count (AI features are trivial to replicate)
• First-mover (your technical debt is someone else’s fresh start)
• AI knowledge (you’ll never outrun the algorithm engineers)

These are actual moats:

• Domain-specific evaluation sets
• Switching costs built into workflows
• Human-in-the-loop division of labor
• Proprietary scenario data

Notice something? Four of them require a company, users, and time. But one of them—evaluation sets—you can start building tonight, alone, with zero budget. That’s your entry point to being irreplaceable.

What ‘Knowing What’s Right’ Actually Looks Like

The best product managers I’ve met don’t talk about model architecture. They talk about tolerance. “90% accuracy is enough for this use case because the remaining 10% is caught by a human review that costs $0.02 per failure.” That sentence is a moat. It took someone six months of working with real users to write it.

Here’s a concrete example. You’re building an AI screener for product manager resumes. You think the hard part is the model. It’s not. The hard part is defining, in writing, what counts as a pass. Questions like:

• Does a side project count as “AI project experience”?
• Do career switchers get a pass or a fail?
• Is “familiar with LLM APIs” without any project a plus or zero?

Most people can’t answer these consistently. They’ll mark the same resume differently on Monday and Friday. That’s why building an evaluation set is the hardest and most valuable thing you can do. Your evaluation set is the only asset that gets more valuable every time a model updates.

How to Build Your Evaluation Set (Tonight)

Step 1: Cut the scenario until it’s testable. Not “AI resume screening.” Given a JD and a resume, output ‘interview’ or ‘reject’ with one sentence reason.

Step 2: Define what “correct” means. Write the rules. You’ll find you don’t actually know. That’s the point.

Step 3: Create 50–100 examples. 40% obvious passes, 40% obvious rejects, 20% borderline. The borderline cases are where the value hides.

Step 4: Label them yourself. Then wait a week and label again. If your answers differ, your rules aren’t clear. Go back to step 2.

Step 5: Run the same set through GPT-5, Claude, K3, and a small open-source model. You’ll get a table that shows exactly where each model fails. That table is your product.

Then use it in interviews. Don’t say “I built an evaluation set.” Say: “I found that the hardest cases are whether a personal project counts as experience. Four models disagreed on this. I turned those 20 edge cases into rules, and the rest I let the model handle.” That’s a product manager who thinks like a founder.

The True Cost of Model Switching

Models change every two months. Teams without evaluation sets spend weeks guessing, A/B testing, and rolling back. Teams with evaluation sets run a batch, see results in two days, and decide. In an industry where models iterate faster than you can hire, two days vs. two weeks is the difference between leading and following.

And your evaluation set grows. Every real-world failure gets added. After a year, it’s a living history of every mistake your product has made. No one can copy that. Not because it’s secret, but because it’s experience encoded.

Your CUDA Is Your Evaluation Set

Jensen Huang protects CUDA because it’s the ecosystem where developers live. Your evaluation set is your CUDA. It’s the place where your business knowledge becomes executable. The model can be swapped, the API can change, the benchmarks can inflate—but the question “what does ‘good’ mean for our users?” only gets answered by someone who has been in the room.

That’s you. And now, you have a way to prove it.

The best time to build your evaluation set was yesterday. The second best time is tonight.