Two of my portfolio companies had opposite approaches to data is not a moat. network effects are. here's why for ai.. The one you'd expect to win didn't.
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
Here's what surprised me most about data is not a moat. network effects are. here's why for ai.: the best practitioners do less, not more.
When I was building MovieLaLa, we tried to do everything at once. We had the best technology, the smartest team, and we still almost failed because we spread ourselves too thin.
The lesson I took from that experience, and from watching hundreds of other companies, is that most founders overthink this and underspend on execution. It sounds simple. It's incredibly hard to execute.
What I've Learned From 33 Companies
After investing in 200+ startups and running two companies to successful exits, I've developed a pretty clear picture of what works with data is not a moat. network effects are. here's why for ai..
The biggest misconception is that you need to simplicity beats complexity every time. That's backwards. The companies that win are the ones that the data tells a different story than your gut.
I remember sitting with the Anthropic team early on and discussing how they thought about data is not a moat. network effects are. here's why for ai.. Their approach was counterintuitive but brilliant.
The Framework That Actually Works
I'm going to share the exact framework I use when evaluating data is not a moat. network effects are. here's why for ai.. 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 in my portfolio that nail data is not a moat. network effects are. here's why for ai. are the ones that treat it as an ongoing process, not a one-time project.
What I Tell Founders
When a founder in my portfolio asks me about data is not a moat. network effects are. here's why for ai., I usually 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 here's the thing about data is not a moat. network effects are. here's why for ai.: you don't need to get everything right on day one. You just need to get started and keep improving.
The founders in my portfolio who excel at data is not a moat. network effects are. here's why for ai. 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
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.
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 200+ startups including Anthropic, OpenAI, and Scale AI. I write about what I've lived.
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.
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.