The answers about AI in capital markets often surprise founders.
Capital markets are the lifeblood of the global economy, and AI is making them more efficient than ever before. I’m exploring how AI is being used to improve everything from price discovery and liquidity to market making and trade execution. This is the engine room of the financial system.
The Counterintuitive Truth
In AI in capital markets, the best practitioners often do less, not more.
The lesson: the best solutions are often the simplest ones. It sounds simple. It's incredibly hard to execute.
What I've Learned From 135 Companies
A few patterns in AI in capital markets come up again and again.
The founders who handle AI in capital markets well understand that you should focus on one thing and do it exceptionally well.
The Numbers Don't Lie
The gap between companies that take AI in capital markets seriously and those that don't is usually clear.
Investing early in AI in capital markets can be the difference between raising your next round and running out of runway.
One of my portfolio companies went from struggling to profitable in under a year after they finally got serious about this. The founder told me later that they wished they'd started sooner.
This connects to broader themes around AI fraud detection, robo-advisors, algorithmic trading, fintech AI, AI banking that I've been thinking about a lot lately.
Final Thoughts
There are no shortcuts in AI in capital markets, but there are smarter paths.
The strongest teams treat AI in capital markets as a competitive advantage, not a checkbox. They invest in it early, measure it obsessively, and never stop improving.
If you're just getting started with AI in capital markets, don't be intimidated. Everyone starts somewhere. The key is to start with the right mindset and the right framework, and then execute like your company depends on it. Because it probably does.
Frequently Asked Questions
Q: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.
Q: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.
Q: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 234 startups including Anthropic, OpenAI, and Scale AI. I write about what I've lived.
Q: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.