I’m going to tell you a story I’m not proud of. It’s a story about ego, about chasing hype, and about how I, a so-called Silicon Valley veteran, almost drove my own company into the ground. My mistake? A disastrously bad AI strategy. My ego led us to the brink of collapse. This is the vulnerable story of how I failed as an AI leader, and the hard lessons I learned clawing my way back.
It was 2023. The AI hype train was at full throttle, and I was the conductor. Fresh off a successful exit and with a pocketful of angel investments in some of the biggest names in AI, I thought I was invincible. I was the guy who saw the future, right? So when it came to my own company, I was determined to go all-in on AI. Not just dip our toes in, but a full-on, deep-end dive.
My grand vision was to build a fully autonomous, AI-powered platform that would revolutionize our industry. I’m not going to mention the industry, because the specifics don’t matter. What matters is the sheer arrogance of my ambition. I wanted to build the AI to end all AIs. I sketched out a product roadmap that was more science fiction than business strategy. We were going to build a system that could think, learn, and adapt on its own. It was going to be my magnum opus.
I assembled a team of the brightest AI engineers I could find. We had PhDs from Stanford and MIT, people who had published papers in top AI conferences. We had a war chest of funding. We had the buzz. What could possibly go wrong?
Everything. Everything went wrong.
The Road to Hell is Paved with Good Intentions (and a Bad AI Strategy)
My first mistake was thinking that a team of brilliant engineers could magically conjure up a revolutionary product without a clear, focused problem to solve. I gave them a vague, aspirational goal and told them to “innovate.” I was so caught up in the technology that I forgot about the customer. I didn’t ask what problems they were facing. I just assumed that a sufficiently advanced AI would solve all their problems.
Our engineers, brilliant as they were, were lost. They spent months building complex, elegant models that had no practical application. We had a system that could generate beautiful, abstract poetry, but it couldn’t solve a single real-world problem for our customers. We were burning through cash at an alarming rate. Our burn rate was north of $500,000 a month, with zero revenue to show for it.
Team morale plummeted. The engineers were frustrated. They were used to solving hard, well-defined problems. Now they were just flailing around, trying to read my mind. The rest of the company, the non-AI people, felt alienated. They saw the AI team as a black box, a money pit that was sucking up all the resources. The sales team had nothing to sell. The marketing team had nothing to market.
I remember one particularly brutal all-hands meeting. Our head of sales, a guy who had been with me since the beginning, stood up and asked, “Sahin, what are we selling? I have a team of hungry salespeople and they have nothing to sell. What do I tell them?”
I didn’t have a good answer. I mumbled something about “long-term vision” and “disruptive technology.” The room was silent. I could feel the trust and respect that I had built over the years evaporating.
The Wake-Up Call
The breaking point came during a board meeting. Our lead investor, a seasoned veteran who had seen it all, looked me straight in the eye and said, “Sahin, this is a science project, not a business. You have six months to turn this around, or we’re pulling the plug.”
That was the slap in the face I needed. I walked out of that meeting humbled and terrified. I had to face the fact that I had failed. My ego, my ambition, my obsession with being the “AI visionary” had led my company to the brink of disaster.
That night, I couldn’t sleep. I stayed up all night, staring at our burn rate charts, our product roadmap, our customer feedback (or lack thereof). I realized that I had been so focused on the “what” – the technology – that I had completely ignored the “why” – the problem we were trying to solve.
The Turnaround: From AI Visionary to AI Pragmatist
The next day, I called an emergency all-hands meeting. I stood in front of my team and I did something that I had never done before: I admitted that I was wrong. I told them that our AI strategy was a failure, and that I was solely responsible.
I then laid out a new plan. A simple, pragmatic plan. We were going to shelve the grand, science-fiction vision. We were going to stop trying to build the AI to end all AIs. Instead, we were going to focus on solving one, specific, painful problem for our customers.
I had spent the night talking to our customer support team, reading through every single support ticket, every single customer complaint. And I found a recurring theme. Our customers were struggling with a tedious, manual process that was costing them hours of work every week. It was a small, unsexy problem. But it was a real problem.
I told the team, “We’re going to build a simple AI tool that automates this one process. That’s it. No more moonshots. We’re going to solve this one problem, and we’re going to solve it better than anyone else.”
Some of the AI engineers were skeptical. They had signed up to build the future, not a simple automation tool. A few of them quit. But the ones who stayed were the ones who were passionate about solving real problems. They were the ones who were willing to get their hands dirty.
We spent the next three months building that tool. It wasn’t glamorous. It wasn’t going to win any AI awards. But it worked. It solved a real problem for our customers. We released it to a small group of beta testers, and the feedback was overwhelmingly positive. For the first time in over a year, we had a product that people were willing to pay for.
Lessons from the Brink
That experience taught me some hard lessons about AI leadership. Here are the most important ones:
- Start with the problem, not the technology. Don’t get seduced by the hype. Fall in love with the problem, not the solution. Talk to your customers. Understand their pain points. Then, and only then, think about how AI can help.
- Think small, not big. Don’t try to boil the ocean. Start with a small, well-defined problem. Get a quick win. Build momentum. Then, you can start to tackle bigger, more ambitious problems.
- Bring your whole team on the journey. AI is not just for engineers. You need to involve everyone in the company, from sales and marketing to customer support. They are the ones who are closest to the customer. They are the ones who can tell you what problems are worth solving.
- Be a leader, not a visionary. Your job as a leader is not to have all the answers. Your job is to create an environment where your team can find the answers. Be humble. Be open to feedback. And be willing to admit when you’re wrong.
It’s been two years since that near-death experience. We’re a different company now. We’re not the sexiest AI company in the Valley. We’re not the one that gets all the headlines. But we’re a real business, solving real problems for real customers. And I’m a different leader. I’m no longer the AI visionary. I’m the AI pragmatist. And I’ve never been more proud of the work we’re doing.
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.
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.