''' I'm going to say what every VC in Silicon Valley is thinking but is too polite to say to your face: your new Chief AI Officer is probably a huge waste of money.
There. I said it.
It feels like every board meeting I attend, someone proudly announces they’ve hired a CAIO. They flash a slide with a smiling headshot and an impressive resume—usually a PhD from a top university, a stint at a FAANG research lab. Everyone nods approvingly. They’re "taking AI seriously." But I see it for what it often is: a six or seven-figure security blanket. A way to signal to the market that you're on the cutting edge, without doing the hard work of actually being on the cutting edge.
The Symptom of a Deeper Sickness
Hiring a CAIO is rarely the solution. It’s a symptom of a much deeper problem: the existing leadership, from the CEO down, doesn't have a clue about AI. And instead of learning, they delegate. They outsource the single most transformative technology of our lifetime to a new hire who has no real power.
Think about it. You don't have a "Chief Internet Officer" or a "Chief Mobile Officer." Why? Because a decade ago, any CEO who didn't understand how the internet or mobile was going to fundamentally reshape their business was already a dead man walking. It became table stakes. The CEO had to be the Chief Internet Officer. The CTO had to be the Chief Mobile Officer. It became part of their DNA.
AI is no different. It's bigger. It's more fundamental. If your CEO can't articulate a clear vision for how AI will reinvent your product, your customer experience, and your business model, you are in deep, deep trouble. Hiring a CAIO is like putting a Band-Aid on a bullet wound.
I saw this firsthand with a portfolio company a couple of years back. A solid B2B SaaS business, growing steadily. The board got spooked by all the AI hype and pressured the founder to hire a CAIO. They found a brilliant guy, a leading researcher in natural language processing. He spent his first nine months developing a grand, sweeping "AI strategy." It was a beautiful 100-page deck. The problem? The CEO didn't understand it. The CTO, who was already swamped running the core engineering team, saw it as a threat. The product team couldn't figure out how to translate the theoretical strategy into actual features.
After a year and over a million dollars in salary and team costs, they had nothing to show for it but the deck. The CAIO, frustrated and politically isolated, left for another "prestigious" CAIO role at a different company. The startup lost a year of momentum and a pile of cash. The core problem was never addressed.
The Founder's Burden: You Are the Chief AI Officer
When I invested in companies like Anthropic, Scale AI, and Hugging Face, I wasn’t looking for a CAIO on their org chart. I was looking for founders who were obsessed. Founders who could talk to me for hours about their data pipelines, their model training infrastructure, their annotation strategy. The AI vision wasn’t delegated; it was the very soul of the company, emanating directly from the founding team.
At my own companies, we never had a dedicated "strategy" person for new tech. When we were building RemoteTeam, the idea of using machine learning to predict team churn or optimize remote workflows wasn't a separate initiative. It was the initiative. I was in the weeds with the engineers. We were sketching out models on whiteboards, debating data sources, and pushing code. We didn't need a translator because the people running the company were fluent in the technology that was shaping it.
This is the only model that works. The leader must be the visionary. You cannot outsource the future of your own company.
When a founder pitches me now, I don't want to hear about their CAIO. I grill the founder. Tell me about your GPU budget. Tell me about your data moat. What proprietary dataset are you building that no one else can replicate? How are you using AI not just as a feature, but as a fundamental architecture for your entire business? If they defer to someone else in the room, it's a huge red flag. It tells me they aren't the one with the obsession, the vision. And in the AI age, the obsessed founder is the only one who wins.
What to Do Instead
So, if hiring a CAIO is a mistake, what’s the right move? It’s harder, but it’s also cheaper and far more effective.
Educate Yourself. Relentlessly. As a leader, you need to be the most informed person in the room about how AI can impact your business. This doesn't mean you need a PhD in machine learning. It means you need to put in the work. Go to the conferences. Read the papers—not just the summaries. Spend a day a week with your engineering team. Get a real, hands-on feel for the tools. If you aren't spending at least 20% of your time learning about AI, you are failing as a leader.
Empower Your Builders. Instead of a siloed CAIO, hire talented AI-focused Principal Engineers and Team Leads. Embed them directly within your product and engineering teams. Give them the autonomy to build and ship. The person with the title "Principal AI Engineer" who reports to the VP of Engineering and ships three revenue-generating features is infinitely more valuable than the CAIO who produces three strategy decks.
Focus on Problems, Not Titles. Don't start with a grand strategy. Start with a customer problem. Find one specific, painful problem that you can solve with AI. Is it customer support response times? Is it lead qualification? Is it churn prediction? Pick one. Assign a small, empowered team to it. Give them a tight deadline and a clear success metric tied to revenue or a core KPI. Win that first battle. Generate real value. Then, and only then, expand to the next problem. This is how you build momentum and an actual, practical AI capability.
Stop chasing the vanity title. Stop looking for a savior to delegate your most important job to. The truth is, the person who needs to be your Chief AI Officer is you. It’s the hardest job in the company. It’s also the only one that matters. ''')) Emitter.py:42] The current working language is English. All responses will be in this language. If you want to switch languages, please start a new session and use a different language in your first message. It looks like you
Frequently Asked Questions
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.
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.