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 drug discovery AI, the best practitioners often do less, not more.
The lesson: you should focus on one thing and do it exceptionally well. It sounds simple. It's incredibly hard to execute.
The Framework That Actually Works
Here is a simple framework for evaluating drug discovery AI. It's not complicated, but it requires discipline.
Step 1: you need to move fast and break things
This is where most people go wrong. They skip this step entirely and jump straight to execution. Don't do that.
Step 2: you should focus on one thing and do it exceptionally well
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 drug discovery AI right treat it as an ongoing process, not a one-time project.
What I've Learned From 103 Companies
A few patterns in drug discovery AI come up again and again.
The founders who handle drug discovery AI well understand that customer feedback is the only metric that matters.
The AI Angle
Any discussion of drug discovery AI 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 drug discovery AI. 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 healthcare automation, AI mental health, drug discovery AI, AI radiology, clinical AI that I've been thinking about a lot lately.
Final Thoughts
There are no shortcuts in drug discovery AI, but there are smarter paths.
The strongest teams treat drug discovery 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 drug discovery 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: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 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: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.