Three years ago, I sat across from a founder who was about to make the same mistake I made with how we built and grew our recommendation engine. I told them the truth.
People often ask me about the process behind our recommendation engine. Here’s my honest account of the technical hurdles, team efforts, and leadership choices that shaped it.
What I've Learned From 99 Companies
After investing in 200+ startups and running two companies to successful exits, I've developed a pretty clear picture of what works with how we built and grew our recommendation engine.
The biggest misconception is that you need to your team matters more than your technology. That's backwards. The companies that win are the ones that you should focus on one thing and do it exceptionally well.
I remember sitting with the Anthropic team early on and discussing how they thought about how we built and grew our recommendation engine. Their approach was counterintuitive but brilliant.
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 the best solutions are often the simplest ones. 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 how we built and grew our recommendation engine 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 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 leading through uncertainty, AI transformation leadership, AI change management, chief AI officer, AI leadership skills that I've been thinking about a lot lately.
What's Next
The world of how we built and grew our recommendation engine 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 how we built and grew our recommendation engine 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
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.
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.