I spent $50,000 learning this lesson about what 10,000 hours of user research taught me the hard way. You can learn it in 10 minutes.
Building an AI startup is anything but glamorous. I want to take you behind the curtain and share the unfiltered reality of our journey. From the heated debates over our roadmap to the bug that almost derailed our launch, this is the real story of what it takes to build and ship an AI product.
The Counterintuitive Truth
Here's what surprised me most about what 10,000 hours of user research taught me: 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 the data tells a different story than your gut. It sounds simple. It's incredibly hard to execute.
Why Most Approaches Fail
Let me be direct: about 70% of the approaches I see to what 10,000 hours of user research taught me 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.
Real Talk: What Actually Matters
I'm going to cut through the noise and tell you what actually matters when it comes to what 10,000 hours of user research taught me.
First, execution speed beats perfection. Every time. I've never seen a company fail because they moved too fast on what 10,000 hours of user research taught me. I've seen plenty fail because they moved too slow.
Second, measure everything. If you can't measure it, you can't improve it. Set up tracking from day one, even if it's basic.
Third, talk to your users. This sounds obvious but you'd be amazed how many founders build their what 10,000 hours of user research taught me strategy in a vacuum. Get out of the building. Talk to real people.
This connects to broader themes around user research ai, customer feedback, ai ethics that I've been thinking about a lot lately.
What's Next
The world of what 10,000 hours of user research taught me 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 what 10,000 hours of user research taught me 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.
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.