Five years ago, my co-founder and I were just two people in a cramped apartment with an idea. We had a few thousand dollars to our names and a shared belief that we could use AI to change a small piece of the financial world. Last month, we sold our company for $50 million.
This isn't another sanitized success story. This is the real, unfiltered version of what it takes to build something from nothing and sell it for a life-changing amount of money. I’m going to tell you how we did it, the mistakes we made, and what I’d do differently if I were starting again today.
The Spark
It all started with a simple observation. I was working in algorithmic trading, and I saw how inefficient the risk management process was. It was slow, manual, and prone to human error. Traders were losing money not because their models were bad, but because they couldn't react fast enough to black swan events. I knew there had to be a better way.
I started talking to my co-founder, a brilliant AI researcher, about the problem. We spent weeks brainstorming, whiteboarding, and drinking way too much coffee. We realized that we could use machine learning to build a system that could predict and mitigate risk in real-time. It was an ambitious idea, but we were convinced we could pull it off.
We quit our jobs and went all in. We had about six months of runway before we’d be completely broke. The pressure was on.
The Grind
The first year was a blur of coding, ramen noodles, and existential dread. We worked 16-hour days, seven days a week. Our apartment was our office, our kitchen was our conference room, and our social life was non-existent. We were completely obsessed.
We built the first version of our product in just a few months. It was ugly, buggy, and barely functional. But it worked. It could analyze market data, identify potential risks, and send alerts to traders. It was a proof of concept, but it was enough to get our first few users.
We onboarded a handful of small hedge funds for free. We told them, “Use our product, tell us what you think, and help us make it better.” Their feedback was brutal, but it was exactly what we needed. We iterated relentlessly, pushing out new updates every week. Slowly but surely, the product started to take shape.
The AI That Mattered
Everyone talks about AI these days, but most of it is just hype. For us, AI was the core of our product. We weren't just sprinkling some machine learning on top of an existing workflow. We were building a system that could think and reason like a human trader, but at a scale and speed that no human could ever match.
Our secret sauce was a combination of deep learning and reinforcement learning. We trained our models on massive datasets of historical market data, and we used reinforcement learning to teach them how to make optimal decisions in real-time. It was a huge technical challenge, but it was also our biggest competitive advantage.
We didn't just build a fraud detection system or a robo-advisor. We built a system that could understand the complex, dynamic nature of financial markets and make intelligent decisions under uncertainty. That's what made our company so valuable.
The Tipping Point
After about two years, we started to get some real traction. We had a solid product, a growing user base, and a clear vision for the future. We decided it was time to raise our first round of funding.
We pitched to dozens of VCs. Most of them didn't get it. They thought our market was too niche, our technology was too complex, and our team was too small. But a few of them saw the potential. We ended up raising a $5 million seed round from a top-tier Silicon Valley firm. It was a huge validation of our hard work.
With the new funding, we were able to hire a small team and accelerate our growth. We expanded our product to cover more asset classes, we signed up larger customers, and we started to build a real business.
The Acquisition
We weren't planning on selling the company. We were focused on building a long-term, sustainable business. But then we got a call from a large, publicly traded financial data company. They had been following our progress for a while, and they were interested in acquiring us.
At first, we were hesitant. We had poured our hearts and souls into this company, and we weren't ready to let it go. But the more we talked to them, the more it made sense. They had a massive distribution channel, a global customer base, and a deep understanding of our market. They could help us achieve our vision on a scale that we could never reach on our own.
We spent the next few months in intense negotiations. It was a rollercoaster of emotions. There were times when we thought the deal was going to fall apart. But in the end, we reached an agreement that we were all happy with. We sold the company for $50 million in cash and stock.
What I Learned
Building and selling a company is a wild ride. It's the hardest thing I've ever done, but it's also the most rewarding. Here are a few of the biggest lessons I learned along the way:
- Solve a real problem. Don't just build a product because you think it's cool. Find a real pain point and build a solution that people are willing to pay for.
- Focus on the user. Your users are your most valuable asset. Listen to their feedback, understand their needs, and build a product that they love.
- Build a great team. You can't do it alone. Surround yourself with smart, passionate people who believe in your vision.
- Be persistent. There will be times when you want to give up. Don't. Keep pushing forward, even when it feels like you're not making any progress.
The Next Chapter
Selling my company was a bittersweet moment. It was the end of one chapter, but it was also the beginning of a new one. I’m incredibly proud of what my team and I accomplished, and I’m excited to see what the future holds.
I’m not sure what my next move will be, but I know one thing for sure: I’m not done building. The world is full of problems to solve, and I’m just getting started.
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 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.
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.