I spent $50,000 learning this lesson about how we built and grew our recommendation engine the hard way. You can learn it in 10 minutes.
People often ask me what went into building our recommendation engine. I want to share the real story—the challenges we faced, how the team worked through them, and the decisions that shaped our journey.
The Counterintuitive Truth
Here's what surprised me most about how we built and grew our recommendation engine: 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.
The Reality Nobody Talks About
Most people approach how we built and grew our recommendation engine 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 the data tells a different story than your gut. 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 your team matters more than your technology. Once we made the switch, everything changed.
The Numbers Don't Lie
I've tracked the performance of companies in my portfolio that take how we built and grew our recommendation engine seriously versus those that don't. The difference is stark.
Companies that invest early in how we built and grew our recommendation engine 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 transformation leadership, AI change management, AI leadership skills, leading through uncertainty 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 how we built and grew our recommendation engine: there are no shortcuts, but there are smarter paths.
The smartest founders I work with treat how we built and grew our recommendation engine 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 how we built and grew our recommendation engine, 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
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.
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.