Sahin Boydas
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By 2030, Your Annual Physical Will Be Conducted by an AI. Here's What That Looks Like.

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.

2024-11-096 min read561 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.

The Counterintuitive Truth

In AI in healthcare, the best practitioners often do less, not more.

The lesson: customer feedback is the only metric that matters. It sounds simple. It's incredibly hard to execute.

Why Most Approaches Fail

Many common approaches to AI in healthcare are flawed at the root.

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 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 AI mental health, biotech AI, clinical AI, AI diagnostics 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 you don't need to get AI in healthcare right on day one. You just need to get started and keep improving.

Founders who do well at AI in healthcare tend to 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.

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'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: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.

Written by

Sahin Boydas

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

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