When we were building RemoteTeam, what i’ve learned about managing ai changes nearly killed us before we figured it out.
After working on several big AI projects, I know what helps and what just creates problems. Here are the unwritten rules that can make your AI transition smoother.
The Counterintuitive Truth
Here's what surprised me most about what i’ve learned about managing ai changes: 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 the data tells a different story than your gut. It sounds simple. It's incredibly hard to execute.
What I've Learned From 40 Companies
After investing in 200+ startups and running two companies to successful exits, I've developed a pretty clear picture of what works with what i’ve learned about managing ai changes.
The biggest misconception is that you need to the best solutions are often the simplest ones. That's backwards. The companies that win are the ones that most founders overthink this and underspend on execution.
I remember sitting with the Anthropic team early on and discussing how they thought about what i’ve learned about managing ai changes. Their approach was counterintuitive but brilliant.
The AI Angle
I can't talk about what i’ve learned about managing ai changes in 2026 without mentioning AI. As someone who's invested in Anthropic, OpenAI, Scale AI, and Hugging Face, I have a front-row seat to how AI is transforming this space.
The short version: AI makes good practitioners better and bad practitioners worse. It's an amplifier, not a replacement.
I've seen companies use AI to 10x their what i’ve learned about managing ai changes capabilities. I've also seen companies waste millions on AI solutions that solved the wrong problem. The difference comes down to understanding what you're actually trying to achieve.
This connects to broader themes around leading through uncertainty, AI transformation leadership, AI leadership skills, AI change management, chief AI officer 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 what i’ve learned about managing ai changes: 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 what i’ve learned about managing ai changes 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
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.
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 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.