I keep hearing it. At dinners, in boardrooms, on podcasts. “Sahin, we’re in an all-out war for AI talent.” Founders tell me they’re losing candidates to FAANG companies offering eye-watering salaries. VCs tell me the number one risk for their portfolio companies is the inability to hire machine learning engineers.
I’m going to say something that might be unpopular in Silicon Valley: The AI talent war is a myth.
It’s a convenient excuse. It’s a narrative that lets founders and executives off the hook. It’s a story we tell ourselves to avoid facing the much harder truth.
The problem isn’t a shortage of talent. It’s a shortage of leadership.
I’ve seen it dozens of times. I’ve invested in over 200 companies, including some of the biggest names in AI like Anthropic, OpenAI, and Scale AI. I’ve built and sold two companies of my own. And I can tell you that the companies that win aren’t the ones with the deepest pockets. They’re the ones with the best leaders.
The Allure of the Exit
Let’s talk about my first company, MovieLaLa. We were a team of movie lovers who built a social platform for discovering new films. We were scrappy. We didn’t have the resources of a Google or a Facebook. But we had a clear vision, a passionate team, and a culture that celebrated creativity and experimentation. We eventually got acquired by Gfycat, and it was a life-changing experience.
My next company, RemoteTeam, was born out of my own frustrations with managing a distributed team. We built a platform to help companies manage their remote employees, and we were acquired by Gusto.
In both cases, the exit wasn’t the primary goal. It was a byproduct of building something that people wanted, with a team that was excited to come to work every day. And that’s the part that so many founders get wrong, especially in the AI space.
They’re so focused on the exit, on the valuation, on the next funding round, that they forget what really matters: building a great company. And a great company starts with great leadership.
What AI Talent Really Wants
I had a founder come to me a few months ago. He was trying to hire a top-tier AI researcher, and he was getting into a bidding war with a major tech giant. He was convinced he was going to lose the candidate.
I asked him, “Have you told her why she should join you? Not just what she’ll be working on, but why it matters? What’s the mission? What’s the vision? What’s the story?”
He looked at me blankly. He’d been so focused on the “what” that he’d completely forgotten the “why.”
Top AI talent doesn’t just want a big paycheck. They want to work on interesting problems. They want to have an impact. They want to be part of something bigger than themselves. They want to be led, not just managed.
They want to work for someone who has a clear vision, who can articulate that vision in a way that inspires and motivates them, and who has the courage to see that vision through.
The Leadership Deficit
The so-called “talent war” is a symptom of a much deeper problem: a leadership deficit in the tech industry. We have a lot of smart people, a lot of ambitious people, a lot of people who are good at raising money. But we don’t have enough leaders.
We don’t have enough people who are willing to do the hard work of building a great company. The work of creating a culture of trust and respect. The work of mentoring and developing their people. The work of making tough decisions and taking responsibility for the outcomes.
It’s easier to blame a “talent war” than it is to look in the mirror and ask yourself if you’re the leader your company needs you to be.
So, What’s Your AI Startup Actually Worth?
This brings me to the question of valuation. I get asked this all the time. “Sahin, what’s the current exit multiple for an AI company?”
And my answer is always the same: It depends.
It depends on your sub-sector. It depends on your business model. It depends on your growth stage. But more than anything, it depends on the quality of your leadership.
A company with a great leader who can attract and retain top talent, who can articulate a clear vision, and who can execute on that vision is worth infinitely more than a company with a mediocre leader who’s constantly churning through employees and struggling to find their way.
I’ve seen it happen. A company with a brilliant idea and a team of rockstar engineers that goes nowhere because of poor leadership. And I’ve seen a company with a less-than-brilliant idea and a team of good-but-not-great engineers that becomes a massive success because of a great leader.
Stop Complaining, Start Leading
So, the next time you’re tempted to complain about the “AI talent war,” I want you to do something different. I want you to look in the mirror and ask yourself, “Am I the leader my company needs me to be?”
Am I creating a culture where people can do their best work? Am I communicating a clear and compelling vision? Am I investing in my people and helping them grow? Am I leading from the front?
If the answer to any of those questions is no, then you have work to do. And it’s the most important work you’ll ever do as a founder.
Stop blaming the “talent war.” Start leading.
Frequently Asked Questions
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.
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.
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.