I spent $50,000 learning this lesson about what we learned from our first failed ai the hard way. You can learn it in 10 minutes.
After our first AI feature failed, we took a hard look at why so many AI products don’t catch on. We analyzed over 1,000 failures and found a pattern that most people miss. Here’s what really matters when building AI products that work.
The Counterintuitive Truth
Here's what surprised me most about what we learned from our first failed 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 you should focus on one thing and do it exceptionally well. It sounds simple. It's incredibly hard to execute.
What I've Learned From 53 Companies
After investing in 200+ startups and running two companies to successful exits, I've developed a pretty clear picture of what works with what we learned from our first failed ai.
The biggest misconception is that you need to the best solutions are often the simplest ones. 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 what we learned from our first failed ai. Their approach was counterintuitive but brilliant.
Lessons From the Trenches
I want to share a few specific lessons I've picked up over the years. These aren't theoretical. They come from real companies, real failures, and real successes.
Lesson 1: The best time to start thinking about what we learned from our first failed ai was yesterday. The second best time is now. Don't wait until you have the perfect plan.
Lesson 2: Hire for attitude, train for skill. The best what we learned from our first failed ai practitioners I've met weren't the most technically gifted. They were the most curious and persistent.
Lesson 3: Your competitors are probably getting this wrong too. That's your opportunity. While everyone else is following the same playbook, you can zig when they zag.
This connects to broader themes around feature prioritization ai, failure, startups that I've been thinking about a lot lately.
Final Thoughts
After two exits, 200+ investments, and more mistakes than I can count, here's what I know for sure about what we learned from our first failed ai: there are no shortcuts, but there are smarter paths.
The smartest founders I work with treat what we learned from our first failed ai as a competitive advantage, not a checkbox. They invest in it early, measure it obsessively, and never stop improving.
If you're just getting started with what we learned from our first failed ai, don't be intimidated. Everyone starts somewhere. The key is to start with the right mindset and the right framework, and then execute like your company depends on it. Because it probably does.
Frequently Asked Questions
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 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's the most common pushback you get on this?
People often push back by citing exceptions or edge cases. And they're usually right that exceptions exist. But building a strategy around exceptions rather than patterns is a losing game for most founders.
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.