We spent six months analyzing the data from over 500 AI teams. The results were not what we expected. Here's the single most important factor that predicts whether an AI team will succeed or fail.
What I've Learned From 61 Companies
Across 234 angel investments, including Anthropic, a few patterns in AI teams come up again and again.
The founders who handle AI teams well understand that most founders overthink this and underspend on execution.
The Counterintuitive Truth
In AI teams, 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.
The Framework That Actually Works
Here is a simple framework for evaluating AI teams. It's not complicated, but it requires discipline.
Step 1: the market doesn't care about your roadmap
This is where most people go wrong. They skip this step entirely and jump straight to execution. Don't do that.
Step 2: most founders overthink this and underspend on execution
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 AI teams right treat it as an ongoing process, not a one-time project.
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 AI teams 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 people at AI teams are rarely the most technically gifted. They are 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, managing AI teams that I've been thinking about a lot lately.
The Bottom Line
None of this is rocket science. But it does require intentionality, consistency, and a willingness to learn from mistakes.
If you take one thing from this article, let it be this: start now, start small, and iterate. The founders who win at AI teams aren't the ones with the best strategy on paper. They're the ones who execute, learn, and adapt faster than everyone else.
Companies that take AI teams seriously tend to pull ahead of the ones that don't.
If you're working on something interesting in this space, I'd love to hear about it. Drop me a line.
Frequently Asked Questions
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: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.
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.