Many founders give up on AI drug discovery just before it starts to work.
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 drug discovery, the best practitioners often do less, not more.
The lesson: timing is everything in this game. It sounds simple. It's incredibly hard to execute.
The Reality Nobody Talks About
A lot of the advice on AI drug discovery 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 simplicity beats complexity every time. Once they make the switch, everything changes.
What I've Learned From 75 Companies
A few patterns in AI drug discovery come up again and again.
The founders who handle AI drug discovery well understand that the data tells a different story than your gut.
Real Talk: What Actually Matters
Here is what actually matters in AI drug discovery.
First, execution speed beats perfection. Every time. Companies rarely fail because they moved too fast on AI drug discovery. Many fail because they moved too slowly.
Second, measure everything. If you can't measure it, you can't improve it. Set up tracking from day one, even if it's basic.
Third, talk to your users. It sounds obvious, but many founders make decisions about AI drug discovery in a vacuum. Get out of the building. Talk to real people.
This connects to broader themes around AI mental health, AI diagnostics, clinical AI that I've been thinking about a lot lately.
Final Thoughts
There are no shortcuts in AI drug discovery, but there are smarter paths.
The strongest teams treat AI drug discovery 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 drug discovery, 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 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: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.