Managing an AI team is hard. Managing one remotely is even harder. After 12 years of trial and error, here are my most surprising and effective lessons for leading a distributed team of AI experts.
The Reality Nobody Talks About
A lot of the advice on remote AI teams rests on assumptions that made sense five years ago. The world has moved on.
The first thing to understand is that the data tells a different story than your gut. I've seen this play out across dozens of companies. The pattern is unmistakable.
Many teams spend months on the wrong path before realizing that the best solutions are often the simplest ones. Once they make the switch, everything changes.
The Counterintuitive Truth
In remote AI teams, the best practitioners often do less, not more.
The lesson: simplicity beats complexity every time. It sounds simple. It's incredibly hard to execute.
The Framework That Actually Works
Here is a simple framework for evaluating remote AI teams. 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: 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 remote 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 remote 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 remote 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 change management, chief AI officer, leading through uncertainty 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 remote AI teams right on day one. You just need to get started and keep improving.
Founders who do well at remote AI teams 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:Can I implement all of these at once?
I'd strongly recommend against it. Pick the 2-3 items that resonate most with your current situation and focus there. Trying to do everything simultaneously is a recipe for doing nothing well.
Q:Are these recommendations still relevant in 2026?
Absolutely. While specific tools and tactics change, the underlying principles remain consistent. I update my thinking regularly based on what I'm seeing in the market and across my portfolio companies.
Q:How were these items selected?
Each item on this list comes from direct experience, either from building my own companies or from patterns I've observed across the 234 startups I've invested in. I prioritize practical, actionable items over theoretical concepts.