A founder asked me last week about behind the scenes: how we built and scaled. My answer surprised them, and it might surprise you too.
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
Here's what surprised me most about behind the scenes: how we built and scaled: 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 customer feedback is the only metric that matters. It sounds simple. It's incredibly hard to execute.
The Framework That Actually Works
I'm going to share the exact framework I use when evaluating behind the scenes: how we built and scaled. It's not complicated, but it requires discipline.
Step 1: most founders overthink this and underspend on execution This is where most people go wrong. They skip this step entirely and jump straight to execution. Don't do that.
Step 2: timing is everything in this game 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 behind the scenes: how we built and scaled are the ones that treat it as an ongoing process, not a one-time project.
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 behind the scenes: how we built and scaled.
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 need to move fast and break things.
I remember sitting with the Anthropic team early on and discussing how they thought about behind the scenes: how we built and scaled. Their approach was counterintuitive but brilliant.
Lessons From the Trenches
I want to share a few specific lessons I've picked up over the years. These aren't theoretical. They come from real companies, real failures, and real successes.
Lesson 1: The best time to start thinking about behind the scenes: how we built and scaled was yesterday. The second best time is now. Don't wait until you have the perfect plan.
Lesson 2: Hire for attitude, train for skill. The best behind the scenes: how we built and scaled practitioners I've met weren't the most technically gifted. They were the most curious and persistent.
Lesson 3: Your competitors are probably getting this wrong too. That's your opportunity. While everyone else is following the same playbook, you can zig when they zag.
This connects to broader themes around AI leadership skills, chief AI officer, AI transformation leadership that I've been thinking about a lot lately.
Final Thoughts
After two exits, 200+ investments, and more mistakes than I can count, here's what I know for sure about behind the scenes: how we built and scaled: there are no shortcuts, but there are smarter paths.
The smartest founders I work with treat behind the scenes: how we built and scaled 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 behind the scenes: how we built and scaled, 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
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.
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.
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.
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.