Hot take: hiring a Chief AI Officer is the biggest mistake most companies are making right now. I'll explain why this role is often a symptom of a deeper problem and what you should be doing instead.
Why Most Approaches Fail
Many common approaches to chief AI officers are flawed at the root.
The root cause is usually one of three things:
Copying what big companies do without understanding why they do it. What works for Google doesn't work for a 10-person startup.
Over-engineering the solution when a simple approach would work better. I've seen teams spend six months building something that could have been done in two weeks.
Ignoring the human element. Technology is the easy part. Getting people to actually use it is where the real challenge lives.
The Reality Nobody Talks About
A lot of the advice on chief AI officers rests on assumptions that made sense five years ago. The world has moved on.
The first thing to understand is that most founders overthink this and underspend on execution. 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 market doesn't care about your roadmap. Once they make the switch, everything changes.
The Counterintuitive Truth
In chief AI officers, the best practitioners often do less, not more.
The lesson: simplicity beats complexity every time. It sounds simple. It's incredibly hard to execute.
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 chief AI officers 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 chief AI officers 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, managing AI teams, leading through uncertainty that I've been thinking about a lot lately.
Final Thoughts
There are no shortcuts in chief AI officers, but there are smarter paths.
The strongest teams treat chief AI officers 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 chief AI officers, 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
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 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 234 startups including Anthropic, OpenAI, and Scale AI. I write about what I've lived.
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.