Sahin Boydas
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The $1.2 Billion Mistake: New Data Shows Why Most Hospitals Are Implementing AI Wrong

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.

2025-01-077 min read699 words

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

A few patterns in healthcare AI come up again and again.

The founders who handle healthcare AI well understand that timing is everything in this game.

The Framework That Actually Works

Here is a simple framework for evaluating healthcare AI. 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 that get healthcare AI right treat it as an ongoing process, not a one-time project.

The Reality Nobody Talks About

A lot of the advice on healthcare AI rests on assumptions that made sense five years ago. The world has moved on.

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.

Many teams spend months on the wrong path before realizing that your team matters more than your technology. Once they make the switch, everything changes.

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 healthcare AI 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 people at healthcare AI are rarely the most technically gifted. They are 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

Best practice in healthcare AI is changing 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.

If you're building in this space, the best time to get healthcare AI 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.

FAQ

Frequently Asked Questions

Q: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.

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.

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: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.

Written by

Sahin Boydas

Serial entrepreneur, #1 angel investor, author of Becoming Top 1%

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