People always ask me how we built our recommendation engine. This is the real, behind-the-scenes story of the technical challenges, the team dynamics, and the leadership decisions that made it all possible.
The Counterintuitive Truth
In recommendation engines, the best practitioners often do less, not more.
The lesson: the data tells a different story than your gut. It sounds simple. It's incredibly hard to execute.
What I've Learned From 99 Companies
A few patterns in recommendation engines come up again and again.
The founders who handle recommendation engines well understand that you need to move fast and break things.
Why Most Approaches Fail
Many common approaches to recommendation engines are flawed at the root.
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 AI Angle
Any discussion of recommendation engines in 2026 has to include AI. As someone who's invested in Anthropic, OpenAI, Scale AI, and Hugging Face, I have a front-row seat to how AI is transforming this space.
The short version: AI makes good practitioners better and bad practitioners worse. It's an amplifier, not a replacement.
Some teams use AI to get far more out of recommendation engines. Others spend heavily on AI that solves the wrong problem. The difference comes down to understanding what you're actually trying to achieve.
This connects to broader themes around managing AI teams, AI change management, AI transformation leadership that I've been thinking about a lot lately.
Final Thoughts
There are no shortcuts in recommendation engines, but there are smarter paths.
The strongest teams treat recommendation engines 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 recommendation engines, 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
Q: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 234 startups including Anthropic, OpenAI, and Scale AI. I write about what I've lived.
Q: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.
Q: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.