I've been wrong about personalized medicine isn't genomics, it's ai-driven predictive health more times than I'd like to admit. But the last mistake taught me something I can't unlearn.
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 29 Companies
After investing in 200+ startups and running two companies to successful exits, I've developed a pretty clear picture of what works with personalized medicine isn't genomics, it's ai-driven predictive health.
The biggest misconception is that you need to your team matters more than your technology. That's backwards. The companies that win are the ones that most founders overthink this and underspend on execution.
I remember sitting with the Anthropic team early on and discussing how they thought about personalized medicine isn't genomics, it's ai-driven predictive health. Their approach was counterintuitive but brilliant.
The Counterintuitive Truth
Here's what surprised me most about personalized medicine isn't genomics, it's ai-driven predictive health: 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 most founders overthink this and underspend on execution. It sounds simple. It's incredibly hard to execute.
What I Tell Founders
When a founder in my portfolio asks me about personalized medicine isn't genomics, it's ai-driven predictive health, I usually start with three questions:
- What's your timeline? Because the right approach for a company with 6 months of runway is very different from one with 3 years.
- What have you already tried? Most founders have tried something. Understanding what didn't work is often more valuable than knowing what might.
- Who on your team owns this? If the answer is "everyone" or "no one," that's your first problem to solve.
These questions seem simple but they reveal a lot about where a company actually stands.
This connects to broader themes around medical AI, drug discovery AI, AI radiology, clinical AI, AI mental health that I've been thinking about a lot lately.
Final Thoughts
After two exits, 200+ investments, and more mistakes than I can count, here's what I know for sure about personalized medicine isn't genomics, it's ai-driven predictive health: there are no shortcuts, but there are smarter paths.
The smartest founders I work with treat personalized medicine isn't genomics, it's ai-driven predictive health 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 personalized medicine isn't genomics, it's ai-driven predictive health, 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
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.