During the MovieLaLa days, we learned something about the $1.2 billion mistake: new data shows why that I still apply to every investment I make.
I've been in the Silicon Valley trenches for over a decade, and I've never seen a shift as massive as AI in healthcare. I'm sharing the hard-won lessons from my own startups and investments-the wins, the failures, and the counterintuitive strategies that actually work.
What I've Learned From 96 Companies
After investing in 200+ startups and running two companies to successful exits, I've developed a pretty clear picture of what works with the $1.2 billion mistake: new data shows why.
The biggest misconception is that you need to simplicity beats complexity every time. That's backwards. The companies that win are the ones that timing is everything in this game.
I remember sitting with the Anthropic team early on and discussing how they thought about the $1.2 billion mistake: new data shows why. Their approach was counterintuitive but brilliant.
The Framework That Actually Works
I'm going to share the exact framework I use when evaluating the $1.2 billion mistake: new data shows why. It's not complicated, but it requires discipline.
Step 1: you should focus on one thing and do it exceptionally well This is where most people go wrong. They skip this step entirely and jump straight to execution. Don't do that.
Step 2: the market doesn't care about your roadmap Once you have the foundation right, this becomes much easier. I've watched founders struggle with this for months when the answer was staring them in the face.
Step 3: Iterate relentlessly Nothing works perfectly the first time. The companies in my portfolio that nail the $1.2 billion mistake: new data shows why are the ones that treat it as an ongoing process, not a one-time project.
The Reality Nobody Talks About
Most people approach the $1.2 billion mistake: new data shows why 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 the data tells a different story than your gut. 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.
Lessons From the Trenches
I want to share a few specific lessons I've picked up over the years. These aren't theoretical. They come from real companies, real failures, and real successes.
Lesson 1: The best time to start thinking about the $1.2 billion mistake: new data shows why was yesterday. The second best time is now. Don't wait until you have the perfect plan.
Lesson 2: Hire for attitude, train for skill. The best the $1.2 billion mistake: new data shows why practitioners I've met weren't the most technically gifted. They were the most curious and persistent.
Lesson 3: Your competitors are probably getting this wrong too. That's your opportunity. While everyone else is following the same playbook, you can zig when they zag.
This connects to broader themes around biotech AI, healthcare automation, AI mental health, medical AI, drug discovery AI that I've been thinking about a lot lately.
What's Next
The world of the $1.2 billion mistake: new data shows why is moving fast. What worked last year might not work next year. That's both the challenge and the opportunity.
My advice: stay curious, stay humble, and stay close to the people who are actually doing the work. Read less thought leadership and do more experiments. Talk to fewer consultants and more practitioners.
And if you're a founder building in this space, remember that the best time to get the $1.2 billion mistake: new data shows why right is before you need to. Don't wait for a crisis to force your hand.
I'll keep sharing what I learn. This stuff matters too much to keep to myself.
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.
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 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.
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.