I’m going to say something that might get me kicked out of a few Silicon Valley dinner parties, but it needs to be said. Hiring a “Chief AI Officer” is probably the single biggest waste of money for most companies right now.
There, I said it. It feels like every time I open my inbox, another company is proudly announcing their new CAIO. They’ve poached someone from a FAANG company, given them a fancy title, and issued a press release about how they’re now “AI-first.” It’s a great PR move. It makes the board feel good. But nine times out of ten, it’s a smokescreen.
I’ve seen this movie before, and it doesn’t end well. A year from now, that expensive new hire will be gone, and the company will be no further along in its AI journey. Why? Because the very existence of a CAIO role often signals a deep, fundamental misunderstanding of what it takes to actually transform a business with artificial intelligence.
The Fallacy of the AI Savior
Look, I get the appeal. The pressure from investors and the board to “have an AI strategy” is immense. Every headline is about some new model that’s going to change the world, and nobody wants to be the dinosaur left behind. In that environment, hiring a designated AI guru feels like a quick, decisive action. It’s a signal to the market that you’re taking this seriously. You’ve checked the box.
But what does this person actually do day-to-day? More often than not, they become an internal consultant, isolated from the core business. They build a small, centralized team of PhDs who exist in an academic bubble. They spend their time creating beautiful PowerPoint decks about “the art of the possible” and holding workshops that are high on inspiration but low on practical application. They don’t have P&L responsibility. They don’t have the authority to force a product team to change its roadmap. They are a figurehead.
I saw this happen at a mid-sized e-commerce company I was advising a couple of years back. They were terrified of Amazon and felt they needed to “do AI.” So they hired a brilliant data scientist from a well-known research lab. This guy was the real deal—published papers, deep expertise in reinforcement learning, the works. He spent nine months and a significant budget building an incredibly sophisticated recommendation engine. On paper, it was a work of art. The only problem? It was completely impractical to implement. It required a real-time data infrastructure the company didn’t have and would take the engineering team a year to build. The business units, who were measured on quarterly sales, just shrugged and went back to their old, simple recommendation system that actually worked. The CAIO left within 18 months, frustrated that nobody would “listen to him.” He wasn’t wrong, but he was solving the wrong problem. He was in an ivory tower, not in the trenches.
AI is a Team Sport, Not a Solo Act
The right way to think about AI is not as a vertical function, but as a horizontal capability that should be woven into the fabric of your entire organization. You don’t need a Chief AI Officer. You need an AI-powered Head of Product, an AI-savvy Head of Marketing, and an AI-literate Head of Sales.
The people who are closest to your customers and your business problems are the ones who should be empowered to use AI to solve them. Your product managers know what features will reduce churn. Your marketers know which customer segments are most valuable. Give them the tools and the mandate to use AI to do their jobs better.
When we were building RemoteTeam, we never even considered hiring a single AI leader. The idea would have seemed absurd. Our mission was to make remote work easier for companies. We used AI as a tool to achieve that mission. For example, we had a huge problem with expense reporting. It was manual, slow, and everyone hated it. So, our product team, led by a fantastic PM named Julia, decided to automate it. They worked with a couple of engineers to use a combination of OCR and a simple classification model to automatically read receipts, categorize expenses, and submit reports. It wasn’t a separate “AI project.” It was just a feature. But it saved our customers thousands of hours and became a key selling point. Julia didn’t have a PhD in machine learning, but she understood the customer’s pain, and she was empowered to solve it.
This is the model that works. It’s the model I see in my most successful portfolio companies. Look at companies like Scale AI or Hugging Face. Their understanding of AI is so fundamental to their DNA that the idea of a CAIO is laughable. AI isn’t a department; it’s the core of what they do. Everyone is an AI person.
How to Actually Lead an AI Transformation
So, if hiring a CAIO is the wrong move, what’s the right one? How do you lead a real transformation?
Get Your Data House in Order. This is the single most important—and most unglamorous—part of the process. AI is useless without clean, accessible, and relevant data. Most companies are a mess on this front. Before you even think about hiring a single data scientist, you need a clear strategy for your data infrastructure. This is a job for a strong Head of Engineering or a CTO, not a CAIO.
Start with the Problem, Not the Tech. The question should never be, “How can we use large language models?” The question should be, “What is the most painful, expensive, or inefficient process in our business?” Once you’ve identified the problem, then you can explore whether AI is the right solution. Sometimes it is, and sometimes a simpler solution is better.
Build a Culture of Experimentation. You have to create an environment where it’s safe to fail. Encourage your teams to run small, fast experiments. Not every AI idea will work. In fact, most won’t. The goal is to learn quickly and cheaply. I tell my founders to reward smart failures. If a team runs a well-designed experiment that proves an idea is bad, that’s a win! It saves the company from investing in the wrong thing.
Upskill Your Existing Talent. Instead of hiring a team of external “experts,” invest in educating your current employees. You don’t need every product manager to be able to write Python, but they should understand the basic concepts of AI, what it’s good at, and what it’s not. A little bit of knowledge can unlock a massive amount of creativity from the people who already know your business inside and out.
Stop Chasing Titles, Start Building
The obsession with the Chief AI Officer title is a distraction. It’s a shortcut that doesn’t lead anywhere useful. It mistakes the symbol of innovation for the substance of it.
The companies that will win in this new era are not the ones with the fanciest titles on their leadership page. They are the ones that treat AI as a fundamental tool, like electricity or the internet, and empower their entire organization to use it.
So, my advice is simple. Stop looking for a savior. Stop chasing the hot new title. Instead, turn to the leaders you already have. Give them the mandate, the resources, and the education to become AI leaders in their own right. Don’t build an AI ivory tower. Build an army of AI-enabled builders. That’s how you’ll win.
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.
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.