I’m going to tell you something that might get me in trouble. Forget everything you think you know about leadership. The AI era demands a new playbook, and I'm here to share the counterintuitive strategies that actually work.
I’ve been in the trenches of AI-driven companies for years. I’ve seen it all. The hype, the failures, the breakthroughs. And I’ve learned that leading AI teams is nothing like leading traditional software teams. It’s a different beast entirely.
I remember when I was building MovieLaLa, my second startup. We were trying to use machine learning to predict which movies would be hits. This was back in 2012, when “AI” wasn’t the buzzword it is today. We had a team of brilliant engineers, but we were flying blind. I honestly had no idea what I was doing. We were making it up as we went along. And you know what? It was the best thing that could have happened to us.
Because it forced us to throw out the old rules and write our own.
The Black Box Problem
Back then, we were wrestling with what I call the “black box” problem. We would feed our models all this data—casting, genre, budget, even the color palette of the movie poster—and it would spit out a prediction. Sometimes it was right, sometimes it was wrong. But we had no idea why. The model was a black box.
I remember one time our model predicted that a low-budget indie film would be a blockbuster. We all laughed. It made no sense. But then, a few months later, the movie came out and it was a surprise hit. We were stunned. The model had seen something we hadn’t. But what? We spent weeks trying to reverse-engineer the model’s logic. We never fully figured it out. It was a humbling experience. It taught me that in the world of AI, you have to be comfortable with a certain level of mystery.
This is a huge mindset shift for leaders who are used to having all the data and all the answers. You have to learn to trust the machine, even when you don’t understand it. And that’s a scary proposition.
The Myth of the All-Knowing Leader
Here’s the thing: in the world of AI, nobody has all the answers. The technology is moving too fast. What was state-of-the-art six months ago is already obsolete. So if you’re a leader who thinks you need to be the smartest person in the room, you’re going to fail.
Your job is not to have all the answers. Your job is to create an environment where your team can find the answers. That means fostering a culture of experimentation, where failure is not just tolerated, but encouraged. It means giving your team the freedom to explore, to take risks, and to make mistakes.
I’ll give you an example. When we were building RemoteTeam, which was later acquired by Gusto, we had a team of engineers working on a new AI-powered feature for automating contractor payments. They spent weeks on it, and it just wasn’t working. The results were all over the place. It was a mess. A traditional manager would have pulled the plug. But I told them to keep going. I said, “I don’t care if you fail. I care if you learn.”
And they did. They eventually figured out the problem, and the feature ended up being a huge success. But it never would have happened if I hadn’t given them the space to fail.
The
Data Isn't Everything
There’s a mantra in Silicon Valley that “data is the new oil.” And it’s true, to a certain extent. You can’t build AI without data. But data isn’t everything. In fact, I’d argue that in the AI era, “taste” is more important than data.
What do I mean by taste? I mean having a deep understanding of your customers and your market. I mean having a point of view. I mean having the courage to make a decision, even when the data is inconclusive.
I’ve seen so many companies get paralyzed by data. They have so much of it that they don’t know what to do with it. They run endless A/B tests, they build complex models, but they never actually make a decision. They’re so afraid of being wrong that they do nothing at all.
In the AI era, you can’t afford to be afraid. You have to be willing to take a leap of faith. You have to be willing to trust your gut. Because sometimes, the most valuable insights can’t be found in the data. They can only be found in your own intuition.
The Power of Uncertainty
Leading in the AI era is about embracing uncertainty. It’s about being comfortable with not knowing. It’s about trusting your team to handle the unknown.
I know that’s scary. As a leader, you’re supposed to have a plan. You’re supposed to have a roadmap. But in the world of AI, the roadmap is constantly changing. The best you can do is have a compass.
Your compass is your vision. It’s the North Star that guides your team through the fog of uncertainty. As long as you have a clear vision, you can give your team the autonomy to figure out how to get there.
And that’s where the magic happens. When you give your team the freedom to chart their own course, they will surprise you. They will come up with ideas you never would have thought of. They will find solutions to problems you didn’t even know you had.
The Communication Gap
One of the biggest challenges of leading AI teams is bridging the communication gap between the technical and non-technical folks. Your engineers are speaking in a language of algorithms and data sets. Your marketing team is speaking in a language of customer personas and value propositions. And you’re in the middle, trying to translate.
It’s not easy. I’ve seen it go wrong so many times. The engineers build something that’s technically brilliant, but nobody knows how to sell it. Or the marketing team makes a promise that the technology can’t deliver. It’s a recipe for disaster.
So how do you fix it? You have to create a shared language. You have to get your engineers and your marketing team in the same room, talking to each other. You have to force them to explain their work in simple terms that anyone can understand. It’s painful at first. There will be a lot of blank stares and confused looks. But eventually, they’ll get it. And when they do, it’s a beautiful thing.
The Ethical Tightrope
Let’s be honest, the ethical implications of AI are terrifying. We’re building machines that can make decisions that have real-world consequences. And we’re doing it with very little oversight or regulation.
As a leader in this space, you have a responsibility to think about these things. You can’t just bury your head in the sand and pretend they don’t exist. You have to be the one who asks the tough questions. Is this fair? Is this just? Is this the right thing to do?
I don’t have all the answers. Nobody does. But I do know that we need to have these conversations. We need to be transparent about what we’re building and why we’re building it. And we need to be willing to change course if we realize we’re heading in the wrong direction.
I’ve had to make some tough calls in my career. I’ve had to shut down projects that I knew would be profitable, but that I also knew would have a negative impact on the world. It’s not easy. But it’s the right thing to do.
The Human Element
At the end of the day, leading AI teams is not about the technology. It’s about the people. It’s about building a team of passionate, curious, and resilient individuals who are not afraid to challenge the status quo.
It’s about creating a culture of psychological safety, where everyone feels comfortable speaking up, sharing their ideas, and admitting when they don’t know something. It’s about being a human being, not a manager.
I’ve been an angel investor in over 200 companies, including some of the biggest names in AI, like Anthropic, OpenAI, Scale AI, and Hugging Face. And I can tell you that the most successful AI companies are the ones that put people first.
So, if you’re a leader in the AI era, my advice to you is this: stop trying to be the smartest person in the room. Start trying to be the most human person in the room. Your team will thank you for it. And your company will be better for it.
What do you think? Am I crazy? Let me know in the comments. And if you’re interested in learning more about my journey as an entrepreneur and investor, you can check out my book, “Becoming Top 1%”. You can also read more about my thoughts on leadership in my post on how to be a great CEO.
The Diversity Imperative
If you want to build AI that is fair, unbiased, and works for everyone, you need a team that reflects the diversity of the world. It's as simple as that. If your team is a bunch of guys who all look the same, think the same, and have the same background, you're going to build AI that has the same blind spots.
I've seen this happen time and time again. A team of all-male engineers builds a facial recognition system that doesn't work for women. A team of all-white engineers builds a language model that spews racist and sexist garbage. It's not because they're bad people. It's because they have a limited perspective. They can't see the world through anyone else's eyes.
That's why I've always made it a priority to build diverse teams. At MovieLaLa, we had people from all over the world. We had men and women, people of color, people with disabilities. And it made our product better. It made our company better. And it made me a better leader.
Building a diverse team is not just about checking a box. It's about creating a culture where everyone feels welcome, respected, and valued. It's about making sure that everyone has a voice, and that those voices are heard.
The Future is Human-in-the-Loop
There's a lot of talk these days about AI taking over the world. And I get it. It's a scary thought. But I don't think it's going to happen. At least, not in the way that people think.
The future of AI is not about replacing humans. It's about augmenting them. It's about building tools that help us be better, smarter, and more creative. It's about creating a partnership between humans and machines, where each one does what it does best.
I'm a big believer in the human-in-the-loop” model. That’s where you have a human and a machine working together to solve a problem. The machine does the heavy lifting, the data processing, the pattern recognition, and the human provides the context, the judgment, and the common sense.
I think we’re going to see a lot more of this in the future. We’re going to see AI-powered tools that help doctors diagnose diseases, that help lawyers prepare for cases, that help teachers personalize learning for their students. The possibilities are endless.
But it’s not going to happen overnight. It’s going to take a lot of hard work. It’s going to take a lot of trial and error. And it’s going to take a new generation of leaders who are not afraid to think differently.
The Real Secret to Leading AI Teams
So, what’s the real secret to leading AI teams? It’s not about being a technical genius. It’s not about having a crystal ball. It’s not even about having all the answers.
It’s about being a human being. It’s about being curious, humble, and empathetic. It’s about creating a space where people can do their best work. And it’s about having the courage to lead with your heart, not just your head.
I know that sounds cheesy. But I believe it’s true. The most successful leaders I know are the ones who are not afraid to be vulnerable, who are not afraid to admit when they’re wrong, and who are not afraid to put their people first.
So, if you’re a leader in the AI era, my advice to you is this: stop trying to be the smartest person in the room. Start trying to be the most human person in the room. Your team will thank you for it. And your company will be better for it.
What do you think? Am I crazy? Let me know in the comments. And if you’re interested in learning more about my journey as an entrepreneur and investor, you can check out my book, “Becoming Top 1%”. You can also read more about my thoughts on leadership in my post on how to be a great CEO.
Frequently Asked Questions
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.
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.
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.
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.