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.
The Counterintuitive Truth
In AI in healthcare, the best practitioners often do less, not more.
The lesson: you need to move fast and break things. It sounds simple. It's incredibly hard to execute.
What I've Learned From 48 Companies
A few patterns in AI in healthcare come up again and again.
The founders who handle AI in healthcare well understand that timing is everything in this game.
The Reality Nobody Talks About
A lot of the advice on AI in healthcare rests on assumptions that made sense five years ago. The world has moved on.
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.
Many teams spend months on the wrong path before realizing that timing is everything in this game. Once they make the switch, everything changes.
The AI Angle
Any discussion of AI in healthcare in 2026 has to include AI. As someone who's invested in Anthropic, OpenAI, Scale AI, and Hugging Face, I have a front-row seat to how AI is transforming this space.
The short version: AI makes good practitioners better and bad practitioners worse. It's an amplifier, not a replacement.
Some teams use AI to get far more out of AI in healthcare. Others spend heavily on AI that solves the wrong problem. The difference comes down to understanding what you're actually trying to achieve.
This connects to broader themes around clinical AI, healthcare automation, biotech AI, AI radiology that I've been thinking about a lot lately.
Final Thoughts
There are no shortcuts in AI in healthcare, but there are smarter paths.
The strongest teams treat AI in healthcare 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 healthcare, 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: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: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 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.