Everyone says data is the moat in AI, but they're wrong. I'll explain why data is just the fuel, and the real, defensible moat is building a product with data network effects, where every new user makes the system smarter for everyone.
The Counterintuitive Truth
In AI competitive moats, the best practitioners often do less, not more.
The lesson: most founders overthink this and underspend on execution. It sounds simple. It's incredibly hard to execute.
What I've Learned From 33 Companies
A few patterns in AI competitive moats come up again and again.
The founders who handle AI competitive moats well understand that the data tells a different story than your gut.
The Framework That Actually Works
Here is a simple framework for evaluating AI competitive moats. It's not complicated, but it requires discipline.
Step 1: timing is everything in this game
This is where most people go wrong. They skip this step entirely and jump straight to execution. Don't do that.
Step 2: simplicity beats complexity every time
Once you have the foundation right, this becomes much easier. I've watched founders struggle with this for months when the answer was staring them in the face.
Step 3: Iterate relentlessly
Nothing works perfectly the first time. The companies that get AI competitive moats right treat it as an ongoing process, not a one-time project.
What I Tell Founders
Before deciding anything about AI competitive moats, start with three questions:
What's your timeline? Because the right approach for a company with 6 months of runway is very different from one with 3 years.
What have you already tried? Most founders have tried something. Understanding what didn't work is often more valuable than knowing what might.
Who on your team owns this? If the answer is "everyone" or "no one," that's your first problem to solve.
These questions seem simple but they reveal a lot about where a company actually stands.
This connects to broader themes around AI exit strategies, AI due diligence, AI talent wars, AI competitive moats that I've been thinking about a lot lately.
Wrapping Up
I've shared a lot here, and I know it can feel overwhelming. But you don't need to get AI competitive moats right on day one. You just need to get started and keep improving.
Founders who do well at AI competitive moats tend to share one trait: they're relentlessly practical. They don't chase perfection. They chase progress.
That's the mindset I'd encourage you to adopt. Start where you are. Use what you have. Do what you can. And keep pushing forward.
As always, I'm rooting for you.
Frequently Asked Questions
Q:How has this view evolved over time?
My thinking on most topics has changed significantly over the years. Early in my career, I held many conventional views that experience proved wrong. I try to update my beliefs when the evidence changes.
Q:What experience informs this perspective?
This perspective comes from over a decade of building companies in Silicon Valley, two successful exits (RemoteTeam to Gusto, MovieLaLa to Gfycat), and investing in 234 startups including Anthropic, OpenAI, and Scale AI. I write about what I've lived.
Q:How can I apply this thinking to my own situation?
Start by identifying the core principle behind the opinion, not the specific example. Then ask yourself: does this principle apply to my context? If yes, test it in a small, low-risk way before going all in.
Q:Do all experts agree with this view?
No, and that's fine. The best ideas in business are often contrarian. I share my perspective based on my experience and data, but I encourage you to seek out opposing viewpoints and form your own conclusions.