Three years ago, I sat across from a founder who was about to make the same mistake I made with building and growing our recommendation engine: a personal. I told them the truth.
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
Here's what surprised me most about building and growing our recommendation engine: a personal: 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 your team matters more than your technology. It sounds simple. It's incredibly hard to execute.
What I've Learned From 84 Companies
After investing in 200+ startups and running two companies to successful exits, I've developed a pretty clear picture of what works with building and growing our recommendation engine: a personal.
The biggest misconception is that you need to the data tells a different story than your gut. That's backwards. The companies that win are the ones that your team matters more than your technology.
I remember sitting with the Anthropic team early on and discussing how they thought about building and growing our recommendation engine: a personal. Their approach was counterintuitive but brilliant.
The Framework That Actually Works
I'm going to share the exact framework I use when evaluating building and growing our recommendation engine: a personal. 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 in my portfolio that nail building and growing our recommendation engine: a personal are the ones that treat it as an ongoing process, not a one-time project.
Real Talk: What Actually Matters
I'm going to cut through the noise and tell you what actually matters when it comes to building and growing our recommendation engine: a personal.
First, execution speed beats perfection. Every time. I've never seen a company fail because they moved too fast on building and growing our recommendation engine: a personal. I've seen plenty fail because they moved too slow.
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. This sounds obvious but you'd be amazed how many founders build their building and growing our recommendation engine: a personal strategy 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 here's the thing about building and growing our recommendation engine: a personal: you don't need to get everything right on day one. You just need to get started and keep improving.
The founders in my portfolio who excel at building and growing our recommendation engine: a personal 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
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.
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.