Three years ago, I sat across from a founder who was about to make the same mistake I made with why data moats are a real problem in. I told them the truth.
After over ten years in Silicon Valley, I've seen few changes as big as AI in healthcare. I'm opening up about what I've learned from my startups and investments—the mistakes, the successes, and the unexpected approaches that actually make a difference.
Why Most Approaches Fail
Let me be direct: about 70% of the approaches I see to why data moats are a real problem in are fundamentally flawed. Not slightly off. Fundamentally flawed.
The root cause is usually one of three things:
- Copying what big companies do without understanding why they do it. What works for Google doesn't work for a 10-person startup.
- Over-engineering the solution when a simple approach would work better. I've seen teams spend six months building something that could have been done in two weeks.
- Ignoring the human element. Technology is the easy part. Getting people to actually use it is where the real challenge lives.
The Framework That Actually Works
I'm going to share the exact framework I use when evaluating why data moats are a real problem in. 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: most founders overthink this and underspend on execution 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 why data moats are a real problem in are the ones that treat it as an ongoing process, not a one-time project.
The Reality Nobody Talks About
Most people approach why data moats are a real problem in with assumptions that made sense five years ago. The world has moved on. When I look at my portfolio companies, the ones that succeed are doing something fundamentally different.
The first thing to understand is that you need to move fast and break things. I've seen this play out across dozens of companies. The pattern is unmistakable.
At RemoteTeam, we learned this the hard way. We spent months going down the wrong path before realizing that the best solutions are often the simplest ones. Once we made the switch, everything changed.
The Numbers Don't Lie
I've tracked the performance of companies in my portfolio that take why data moats are a real problem in seriously versus those that don't. The difference is stark.
Companies that invest early in why data moats are a real problem in see, on average, 2-3x better outcomes within 18 months. That's not a small edge. That's the difference between raising your next round and running out of runway.
One of my portfolio companies went from struggling to profitable in under a year after they finally got serious about this. The founder told me later that they wished they'd started sooner.
This connects to broader themes around AI diagnostics, biotech AI, healthcare automation, AI radiology, AI mental health that I've been thinking about a lot lately.
What's Next
The world of why data moats are a real problem in is moving fast. What worked last year might not work next year. That's both the challenge and the opportunity.
My advice: stay curious, stay humble, and stay close to the people who are actually doing the work. Read less thought leadership and do more experiments. Talk to fewer consultants and more practitioners.
And if you're a founder building in this space, remember that the best time to get why data moats are a real problem in right is before you need to. Don't wait for a crisis to force your hand.
I'll keep sharing what I learn. This stuff matters too much to keep to myself.
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.
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.
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.