I've had this conversation about the counterintuitive truth about scaling an ai product team with at least 50 founders. Here's the distilled version.
I used to struggle with ai product management, thinking I had it all figured out. It led to burnout and a failed product. But after years of painful lessons, I discovered a counterintuitive approach to AI product development that changed everything. It wasn't about the tech, but about this one simple shift in perspective.
What I've Learned From 29 Companies
After investing in 200+ startups and running two companies to successful exits, I've developed a pretty clear picture of what works with the counterintuitive truth about scaling an ai product team.
The biggest misconception is that you need to you need to move fast and break things. That's backwards. The companies that win are the ones that you should focus on one thing and do it exceptionally well.
I remember sitting with the Anthropic team early on and discussing how they thought about the counterintuitive truth about scaling an ai product team. Their approach was counterintuitive but brilliant.
The Reality Nobody Talks About
Most people approach the counterintuitive truth about scaling an ai product team with assumptions that made sense five years ago. The world has moved on. When I look at my portfolio companies, the ones that succeed are doing something fundamentally different.
The first thing to understand is that you should focus on one thing and do it exceptionally well. I've seen this play out across dozens of companies. The pattern is unmistakable.
At RemoteTeam, we learned this the hard way. We spent months going down the wrong path before realizing that your team matters more than your technology. Once we made the switch, everything changed.
The Counterintuitive Truth
Here's what surprised me most about the counterintuitive truth about scaling an ai product team: 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 you need to move fast and break things. It sounds simple. It's incredibly hard to execute.
The Numbers Don't Lie
I've tracked the performance of companies in my portfolio that take the counterintuitive truth about scaling an ai product team seriously versus those that don't. The difference is stark.
Companies that invest early in the counterintuitive truth about scaling an ai product team see, on average, 2-3x better outcomes within 18 months. That's not a small edge. That's 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 ai product management, team scaling, leadership that I've been thinking about a lot lately.
What's Next
The world of the counterintuitive truth about scaling an ai product team is moving 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.
And if you're a founder building in this space, remember that the best time to get the counterintuitive truth about scaling an ai product team 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
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.
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.
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.
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.