Sahin Boydas
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An AI Startup's Pivot to Drug Discovery

After more than ten years in Silicon Valley, I’m sharing what I’ve learned from my startups and investments—the wins, the failures, and the unexpected strategies that actually helped us succeed.

2024-04-156 min read523 words

Many founders give up on drug discovery AI just before it starts to work.

After more than ten years in Silicon Valley, I’m sharing what I’ve learned from my startups and investments—the wins, the failures, and the unexpected strategies that actually helped us succeed.

The Counterintuitive Truth

In drug discovery AI, 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.

What I've Learned From 35 Companies

Across 234 angel investments, including Anthropic, a few patterns in drug discovery AI come up again and again.

The founders who handle drug discovery AI well understand that simplicity beats complexity every time.

Why Most Approaches Fail

Many common approaches to drug discovery AI 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 Numbers Don't Lie

Investing early in drug discovery 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 AI diagnostics, healthcare automation, biotech AI, drug discovery AI 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 drug discovery AI right on day one. You just need to get started and keep improving.

Founders who do well at drug discovery AI 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: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: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'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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