I’ve seen more pitches for AI in biotech than I’ve had hot dinners. And ninety-nine percent of them make the same mistake. They come in, bright-eyed and bushy-tailed, talking about genomics as the be-all and end-all of personalized medicine. They’ve got their fancy models, their PhDs from Stanford, and a deck that could blind you with its gloss. They’re convinced they’re going to change the world.
They’re wrong.
Look, I get it. The idea of decoding our DNA to predict and prevent disease is a powerful one. It’s a story we’ve been telling ourselves for decades. And don’t get me wrong, genomics is a crucial piece of the puzzle. But it’s just that—a piece. The future of personalized medicine, the real revolution, isn’t just in our genes. It’s in our data. All of it. And the AI that can make sense of it.
The Data You’re Ignoring
When I was building RemoteTeam, we were obsessed with data. We tracked everything. Not just the obvious stuff like payroll and time off, but the subtle signals—the communication patterns, the collaboration bottlenecks, the little things that told us if a team was happy and productive, or about to implode. We weren’t just managing a remote workforce; we were trying to predict its behavior. We were building a predictive health model for companies.
It’s the same in healthcare. Your genome is a static blueprint. It’s important, for sure. But what about the dynamic, real-time data of your life? Your heart rate from your Apple Watch. Your sleep patterns. The food you eat. The pollution in your city. Your social interactions. Your medical records, buried in a dozen different formats across a dozen different clinics. That’s the data that tells the real story of your health. And that’s the data that most founders in this space are completely ignoring.
They’re so focused on the elegant science of genomics that they’re missing the messy, complicated, but ultimately much more powerful reality of real-world health data. They’re building a beautiful engine but forgetting the fuel.
More Than a Model
I’ve been lucky enough to be an early investor in some of the foundational AI companies of our time—OpenAI, Anthropic, Scale AI, Hugging Face. I’ve had a front-row seat to the AI revolution. And the biggest lesson I’ve learned is this: the model is not the product.
This is the uncomfortable truth that most AI founders, especially in healthcare, don’t want to hear. They spend years building a perfect, state-of-the-art model, and then they’re shocked when nobody wants to buy it. They don’t understand that in the brutal, regulated world of healthcare, a great model is just the entry ticket. It’s not the win.
The real challenge is integration. It’s about building a system that can ingest and clean and harmonize all that messy, real-world data. It’s about navigating the labyrinth of hospital IT systems, each one a special kind of hell. It’s about dealing with the FDA, with HIPAA, with a regulatory burden that would make a grown man weep. It’s about building a user interface that a tired, overworked doctor can actually use without wanting to throw the computer out the window.
This is the hard part. This is the unglamorous, grinding work that actually makes a difference. And this is where most biotech AI startups fail. They’re so in love with their beautiful model that they forget about the ugly reality of the world it has to live in.
The AI-Driven Future
So what does the future look like? It’s not a single, magical genomic test. It’s a continuous, AI-driven predictive health system. It’s a system that’s constantly learning from your data, all of it, and giving you and your doctor actionable insights to keep you healthy.
Imagine a world where your phone buzzes, not with a notification from Instagram, but with a warning from your personal health AI. “Your inflammation markers are trending up. You’ve been eating a lot of processed food and not sleeping well. Let’s get you back on track. Here’s a simple meal plan for the next three days and a reminder to go for a walk.”
Imagine a world where a new drug isn’t developed through a decade of blind trial and error, but is designed in silico, tailored to the specific biology of a specific patient population, identified by an AI that has analyzed millions of patient records. This is the power of AI in drug discovery, and it’s already happening.
This isn’t science fiction. This is the future that’s being built right now, by the founders who are brave enough to tackle the hard problems. The ones who aren’t just building models, but are building systems. The ones who understand that the future of personalized medicine isn’t just in our genes, but in the intelligent application of data to every aspect of our lives.
It’s a harder path, for sure. It’s a path filled with frustration and setbacks. But it’s the only path that leads to a future where we can truly predict and prevent disease, and not just react to it. And for a founder, for an investor, for anyone who wants to make a real difference in the world, there’s no more exciting place to be.
I’m not interested in the hundredth company trying to find a new biomarker in the genome. I’m interested in the company that’s going to pull all the data together and build the predictive engine for human health. That’s the company that’s going to change the world. That’s the company I’m going to invest in.
The Data Janitor's Secret Weapon
Let’s be honest, no one gets into AI to become a data janitor. But in healthcare, that’s where the real war is won. I remember when we were building MovieLaLa, which was acquired by Gfycat. We were trying to predict what movies people would want to watch. We had data from all over the place – social media, reviews, trailers, you name it. It was a mess. A beautiful, glorious mess. And the team that won wasn't the one with the fanciest algorithm. It was the team that could clean and structure that data the fastest. The team that built the best data pipeline.
It's a thousand times harder in healthcare. You have EHRs in formats that look like they were designed in the 1980s. You have imaging data in proprietary formats. You have handwritten doctor's notes that are basically illegible scribbles. And you have to bring it all together, in a way that's secure and compliant with HIPAA. It's a nightmare. But it's also the biggest opportunity.
The companies that are going to win are the ones that are building the tools to solve this data problem. The ones that are creating the modern data infrastructure for healthcare. They're not the ones on the main stage at the big AI conferences. They're the ones in the trenches, writing the unglamorous code that makes everything else possible. They're the data janitors. And they're going to be the new kings of healthcare.
The Last Mile: Why Your Brilliant AI Is Useless Without a Great UI
Even if you solve the data problem, you're still only halfway there. You can have the most brilliant AI in the world, but if it's trapped in a terrible user interface, it's useless. Doctors are some of the most time-poor and stressed-out professionals on the planet. They don't have time to learn a complicated new system. They need something that's intuitive, that fits into their existing workflow, and that gives them the information they need, when they need it, without a lot of noise.
I've seen so many demos of amazing AI diagnostic tools that are hidden behind a user interface that looks like it was designed by an engineer for an engineer. It's a classic mistake. And it's a fatal one. In healthcare, the user experience isn't a nice-to-have. It's a must-have. It's the last mile. And it's where so many companies stumble and fall.
This is where my experience with consumer-facing products has been so valuable. At RemoteTeam, we knew that if our product wasn't easy to use, no one would use it. It didn't matter how powerful our backend was. The same is true in healthcare. You have to be obsessed with the user. You have to spend time with doctors, with nurses, with patients. You have to understand their pain points, their workflows, their frustrations. And you have to design a product that solves their problems, not a product that just showcases your cool technology.
Beyond the Pill: A New Era of Proactive Health
The ultimate vision here is a world where we're not just treating sickness, but we're proactively managing health. It's a world where the healthcare system is not a place you go when you're sick, but a partner that helps you stay healthy. And AI is the engine that's going to power this transformation.
Think about it. Right now, our healthcare system is almost entirely reactive. We wait until something is wrong, and then we try to fix it. It's like trying to steer a ship by looking at the wake. It's inefficient, it's expensive, and it leads to a lot of unnecessary suffering.
But what if we could see the iceberg before we hit it? What if we could use AI to analyze all the subtle signals from our bodies and our environment, and to predict when we're at risk of getting sick? What if we could get a gentle nudge from our personal health AI, telling us to get more sleep, or to eat more vegetables, or to see a doctor for a check-up, before we even feel any symptoms?
This is the promise of AI-driven predictive health. It's a new paradigm for healthcare. It's a shift from a sick-care system to a true health-care system. And it's the biggest opportunity of our lifetime. It's the reason I'm so excited about this space. It's the reason I'm investing my time and my money in the companies that are building this future. It's not going to be easy. But it's going to be worth it.
Frequently Asked Questions
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.
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 200+ startups including Anthropic, OpenAI, and Scale AI. I write about what I've lived.
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.