The gap between theory and practice in biotech AI is enormous.
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 116 Companies
A few patterns in biotech AI come up again and again.
The founders who handle biotech AI well understand that simplicity beats complexity every time.
The Counterintuitive Truth
In biotech AI, the best practitioners often do less, not more.
Trying to do everything at once is one of the most common ways to fail: even a team with strong technology and talent can spread itself too thin.
The lesson: the market doesn't care about your roadmap. It sounds simple. It's incredibly hard to execute.
The Reality Nobody Talks About
A lot of the advice on biotech AI rests on assumptions that made sense five years ago. The world has moved on.
The first thing to understand is that customer feedback is the only metric that matters. 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 you should focus on one thing and do it exceptionally well. Once they make the switch, everything changes.
The Numbers Don't Lie
The gap between companies that take biotech AI seriously and those that don't is usually clear.
Investing early in biotech AI can be the difference between raising your next round and running out of runway.
One of my portfolio companies went from struggling to profitable in under a year after they finally got serious about this. The founder told me later that they wished they'd started sooner.
This connects to broader themes around biotech AI, healthcare automation, AI diagnostics that I've been thinking about a lot lately.
Final Thoughts
There are no shortcuts in biotech AI, but there are smarter paths.
The strongest teams treat biotech AI 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 biotech AI, 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: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: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.