I spent $50,000 learning this lesson about the black swan event that ai risk models can't predict. the hard way. You can learn it in 10 minutes.
Everyone in finance is obsessed with using AI to predict the future. But what about the events that nobody can predict? I’m exploring the limitations of AI risk models and why they’re still vulnerable to the black swan events that can wipe out entire funds in a matter of hours.
The Counterintuitive Truth
Here's what surprised me most about the black swan event that ai risk models can't predict.: the best practitioners do less, not more.
When I was building MovieLaLa, we tried to do everything at once. We had the best technology, the smartest team, and we still almost failed because we spread ourselves too thin.
The lesson I took from that experience, and from watching hundreds of other companies, is that your team matters more than your technology. It sounds simple. It's incredibly hard to execute.
The Reality Nobody Talks About
Most people approach the black swan event that ai risk models can't predict. with assumptions that made sense five years ago. The world has moved on. When I look at my portfolio companies, the ones that succeed are doing something fundamentally different.
The first thing to understand is that most founders overthink this and underspend on execution. I've seen this play out across dozens of companies. The pattern is unmistakable.
At RemoteTeam, we learned this the hard way. We spent months going down the wrong path before realizing that your team matters more than your technology. Once we made the switch, everything changed.
Why Most Approaches Fail
Let me be direct: about 70% of the approaches I see to the black swan event that ai risk models can't predict. are fundamentally flawed. Not slightly off. Fundamentally flawed.
The root cause is usually one of three things:
- Copying what big companies do without understanding why they do it. What works for Google doesn't work for a 10-person startup.
- Over-engineering the solution when a simple approach would work better. I've seen teams spend six months building something that could have been done in two weeks.
- Ignoring the human element. Technology is the easy part. Getting people to actually use it is where the real challenge lives.
The Numbers Don't Lie
I've tracked the performance of companies in my portfolio that take the black swan event that ai risk models can't predict. seriously versus those that don't. The difference is stark.
Companies that invest early in the black swan event that ai risk models can't predict. see, on average, 2-3x better outcomes within 18 months. That's not a small edge. That's 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 algorithmic trading, fintech AI, AI trading, robo-advisors, AI risk management that I've been thinking about a lot lately.
Wrapping Up
I've shared a lot here, and I know it can feel overwhelming. But here's the thing about the black swan event that ai risk models can't predict.: you don't need to get everything right on day one. You just need to get started and keep improving.
The founders in my portfolio who excel at the black swan event that ai risk models can't predict. share one trait: they're relentlessly practical. They don't chase perfection. They chase progress.
That's the mindset I'd encourage you to adopt. Start where you are. Use what you have. Do what you can. And keep pushing forward.
As always, I'm rooting for you.
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 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.
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.