Many founders repeat the same mistakes with recommendation engines.
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
A few patterns in recommendation engines come up again and again.
The founders who handle recommendation engines well understand that you should focus on one thing and do it exceptionally well.
The Counterintuitive Truth
In recommendation engines, the best practitioners often do less, not more.
The lesson: the best solutions are often the simplest ones. It sounds simple. It's incredibly hard to execute.
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 Numbers Don't Lie
The gap between companies that take recommendation engines seriously and those that don't is usually clear.
Investing early in recommendation engines can be 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
Best practice in recommendation engines is changing 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.
If you're building in this space, the best time to get recommendation engines 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
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: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.
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.