People often ask me about the journey of creating our recommendation engine. Here’s the honest story about the technical problems we faced, how our team worked through them, and the decisions I had to make as a leader.
The Counterintuitive Truth
In recommendation engines, the best practitioners often do less, not more.
The lesson: your team matters more than your technology. It sounds simple. It's incredibly hard to execute.
What I've Learned From 84 Companies
Across 234 angel investments, including Anthropic, a few patterns in recommendation engines come up again and again.
The founders who handle recommendation engines well understand that your team matters more than your technology.
The Framework That Actually Works
Here is a simple framework for evaluating recommendation engines. It's not complicated, but it requires discipline.
Step 1: timing is everything in this game
This is where most people go wrong. They skip this step entirely and jump straight to execution. Don't do that.
Step 2: you should focus on one thing and do it exceptionally well
Once you have the foundation right, this becomes much easier. I've watched founders struggle with this for months when the answer was staring them in the face.
Step 3: Iterate relentlessly
Nothing works perfectly the first time. The companies that get recommendation engines right treat it as an ongoing process, not a one-time project.
Real Talk: What Actually Matters
Here is what actually matters in recommendation engines.
First, execution speed beats perfection. Every time. Companies rarely fail because they moved too fast on recommendation engines. Many fail because they moved too slowly.
Second, measure everything. If you can't measure it, you can't improve it. Set up tracking from day one, even if it's basic.
Third, talk to your users. It sounds obvious, but many founders make decisions about recommendation engines in a vacuum. Get out of the building. Talk to real people.
This connects to broader themes around AI transformation leadership, chief AI officer, managing AI teams that I've been thinking about a lot lately.
Wrapping Up
I've shared a lot here, and I know it can feel overwhelming. But you don't need to get recommendation engines right on day one. You just need to get started and keep improving.
Founders who do well at recommendation engines tend to share one trait: they're relentlessly practical. They don't chase perfection. They chase progress.
That's the mindset I'd encourage you to adopt. Start where you are. Use what you have. Do what you can. And keep pushing forward.
As always, I'm rooting for you.
Frequently Asked Questions
Q: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.
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.