I still remember the smell of the trading floor. A weird mix of stale coffee, sweat, and desperation. The roar was constant. A hundred people all yelling at once, trying to make themselves heard over the symphony of ringing phones. It was pure, unadulterated chaos. And for a while, I loved it.
I was a Wall Street trader. My days were a blur of numbers, charts, and gut feelings. We were cowboys, riding the waves of the market, making and losing fortunes in the blink of an eye. It was a rush, a high unlike any other. But deep down, I knew it wasn 's sustainable. The human element, the very thing that made it so exciting, was also its biggest weakness. We were emotional, irrational, and prone to making mistakes. I saw so many good traders blow up, not because they were dumb, but because they let their fear or greed get the best of them.
Then, something happened that changed everything. I stumbled upon a paper about using machine learning to predict stock prices. It was a revelation. Here was a way to take the emotion out of the equation, to make decisions based on data and logic, not gut feelings. I was hooked.
I started spending my nights and weekends learning to code, devouring everything I could find about AI and algorithmic trading. I honestly had no idea what I was doing at first. My first attempts at building a trading bot were a disaster. I lost more money than I care to admit. But I was determined. I knew this was the future.
The AI Revolution in Finance
Fast forward to today, and my life couldn't be more different. The trading floor is a distant memory. Now, my office is my laptop. I manage a fleet of AI trading bots that execute trades with a speed and precision that no human could ever match. It's a different kind of intensity, a quiet, focused intensity. Instead of shouting orders, I'm writing code, analyzing data, and constantly refining my algorithms.
Some people think that AI is going to take all the jobs in finance. And you know what? They're probably right. But that's not a bad thing. It's an opportunity. An opportunity to evolve, to learn new skills, and to build a better, more efficient financial system. The old way of doing things is dying. And that's okay. It's time for something new.
Of course, AI in finance is not without its risks. We've all seen the headlines about flash crashes and rogue algorithms. That's why risk management is so critical. My bots are designed with a whole host of safety features, from kill switches to position limits. I'm not just a developer, I'm a risk manager. And that's a lesson I learned the hard way on the trading floor.
For those interested in the nitty-gritty of how these systems work, I wrote a more technical piece on the architecture of a modern trading bot. It gets into the weeds of the tech stack and the data pipelines.
The Future is Now
People ask me all the time if I miss the old days. The adrenaline, the camaraderie, the big bonuses. And sure, there are times when I get a little nostalgic. But I wouldn't go back. Not for a second. What I'm doing now is so much more interesting, so much more challenging, and ultimately, so much more rewarding.
I'm not just a trader anymore. I'm a builder. I'm creating something new, something that has the potential to change the world of finance forever. And that's a feeling that no bonus can buy.
If you're a young person thinking about a career in finance, my advice is simple: learn to code. The future of finance is not on Wall Street, it's in Silicon Valley. It's in the hands of the engineers, the data scientists, and the AI experts who are building the next generation of financial technology. For more on this, check out my post on why every finance professional should learn to code.
So, what's the takeaway here? It's that the world is changing, and you can either change with it or get left behind. I chose to change. And it was the best decision I ever made.
Frequently Asked Questions
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.
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.