I’ve seen a lot of hype in Silicon Valley. I’ve seen bubbles inflate and burst. I’ve seen “the next big thing” come and go. So when the AI hype machine started churning out stories about revolutionizing healthcare, I was skeptical. Deeply skeptical.
I’ve been in the trenches for over a decade, building and selling my own companies. RemoteTeam was acquired by Gusto, and MovieLaLa was acquired by Gfycat. I’ve also been fortunate enough to be an angel investor in over 200 companies, including some of the biggest names in AI like Anthropic, OpenAI, Scale AI, and Hugging Face. I’ve seen firsthand what it takes to build a successful company, and I’ve also seen how easily things can go wrong.
So when I started hearing about AI in drug discovery, my first thought was, “Here we go again.” Another wave of buzzwords and empty promises. But I have to admit, I was wrong. Completely wrong.
The Brutal Reality of Drug Discovery
Before we get into the AI part, let’s talk about why drug discovery is so ripe for disruption. The traditional process is, to put it mildly, a nightmare. It’s a long, expensive, and incredibly risky journey.
Think about it like this: imagine you’re looking for a single, specific grain of sand on a massive beach. That’s what it’s like trying to find a new drug candidate. Scientists have to screen millions of compounds, hoping to find one that has the desired effect on a specific disease target. It’s a process that can take over a decade and cost billions of dollars. And even then, the vast majority of drugs that enter clinical trials fail.
It’s a system that’s fundamentally broken. We’re throwing massive amounts of time, money, and human effort at a problem with a ridiculously low success rate. There has to be a better way.
The AI Promise: A Glimmer of Hope
This is where AI comes in. The promise of AI in drug discovery is that it can help us search for that grain of sand more intelligently. Instead of randomly scooping up handfuls of sand, we can use AI to analyze the entire beach and tell us exactly where to look.
AI can do this in a few ways. For example, generative models can design entirely new molecules from scratch that are specifically tailored to a particular disease target. This is a huge leap forward from the traditional method of screening existing compounds. It’s like being able to design the perfect key for a lock, rather than trying millions of random keys.
AI can also help us understand the complex biology of diseases in a way that was never before possible. By analyzing massive datasets of genomic, proteomic, and clinical data, AI can identify new drug targets and predict how a drug will behave in the human body. This can help us de-risk the drug discovery process and increase the chances of success in clinical trials.
My Journey from Skeptic to Believer
As I said, I was skeptical at first. I’ve seen too many AI companies that are all sizzle and no steak. They have a fancy model and a slick pitch deck, but they don’t have a real understanding of the problem they’re trying to solve. And in healthcare, that’s a recipe for disaster.
So I did my homework. I spent months talking to experts in the field, from scientists and clinicians to founders and investors. I read every research paper I could get my hands on. I wanted to understand the technology, the market, and the regulatory landscape. I wanted to separate the hype from the reality.
And what I found surprised me. While there are certainly a lot of overhyped AI companies out there, there are also a handful of companies that are doing truly groundbreaking work. These are the companies that are not just building cool technology, but are also deeply embedded in the world of drug discovery. They have teams of world-class scientists and engineers who are working together to solve real-world problems.
The 10x Platform That Changed My Mind
One company in particular stood out to me. I won’t name them here, because this isn’t about promoting a single company. It’s about the broader trend that they represent. But I will tell you what made them so special.
First, they had a platform that was truly end-to-end. They weren’t just focused on one small piece of the puzzle, like molecule generation or target identification. They had a fully integrated platform that covered the entire drug discovery process, from initial hypothesis to preclinical development. This is a huge advantage, because it allows them to create a virtuous cycle of data and learning. Each new discovery feeds back into the platform, making it smarter and more effective over time.
Second, they had a team that was a perfect blend of a-list talent. They had PhDs in machine learning and computational biology, but they also had seasoned drug hunters who had spent their entire careers in the pharmaceutical industry. This combination of technical expertise and domain knowledge is incredibly rare, and it’s what allows them to bridge the gap between the world of AI and the world of drug discovery.
Third, they had a business model that was aligned with the interests of their partners. They weren’t just selling software. They were forming deep partnerships with pharmaceutical companies and academic institutions to co-develop new drugs. This means they have skin in the game. They’re not just a vendor; they’re a true partner.
A Real-World Example: From Years to Months
Let me give you a concrete example of what this looks like in practice. One of the company’s partners was working on a particularly nasty type of cancer. They had been trying for years to find a drug that could target a specific protein that was driving the growth of the tumors. They had screened millions of compounds, but nothing had worked.
Using the AI platform, they were able to design a new molecule from scratch that was specifically tailored to the target protein. The entire process, from initial design to synthesis and testing, took just a few months. And the results were astounding. The new molecule was 100 times more potent than any of the compounds they had previously tested. It’s now in preclinical development, and the early results are incredibly promising.
This is the power of AI in drug discovery. It’s not about incremental improvements. It’s about a 10x or even 100x leap forward. It’s about doing things that were previously thought to be impossible.
The Uncomfortable Truth About AI in Healthcare
Now, I don’t want to paint a completely rosy picture. The reality is that building a successful AI company in healthcare is incredibly hard. It’s not enough to have a great model. You also need to have a deep understanding of the science, the regulatory landscape, and the clinical workflow. You need to be able to navigate the complex web of stakeholders, from doctors and patients to regulators and payers.
And here’s the uncomfortable truth: most AI companies in healthcare will fail. They’ll fail because they don’t have the right team, the right technology, or the right business model. They’ll fail because they underestimate the challenges of working in a highly regulated industry. They’ll fail because they’re more focused on hype than on creating real value.
But for the few companies that get it right, the opportunity is massive. They have the potential to not only create enormous economic value, but also to have a profound impact on human health. They have the potential to cure diseases that were previously thought to be incurable. They have the potential to save millions of lives.
The Future is Now
I started this journey as a skeptic, but I’m now a true believer. I believe that AI is going to fundamentally transform the world of drug discovery. It’s not a question of if, but when. And the when is now.
We’re at the very beginning of a new era in medicine, an era where we can design drugs with a level of precision and speed that was previously unimaginable. It’s an incredibly exciting time to be an investor, an entrepreneur, and a human being. The future of medicine is being written today, and I’m thrilled to have a front-row seat.
Frequently Asked Questions
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.
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.
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.
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.