Most founders think building a great AI model is enough. They’re wrong. Here's the uncomfortable truth about what it really takes to succeed in the brutal, regulated world of healthcare AI.
I’ve been in the Silicon Valley trenches for over a decade. I’ve built four companies, sold two, and now I spend most of my time investing in the next generation of founders. I’ve been fortunate to back some incredible companies like Anthropic, OpenAI, Scale AI, and Hugging Face. I’ve seen what it takes to win, and I’ve seen, more often, what it takes to lose.
And I’ve never seen a shift as massive as what’s happening with AI in healthcare. It’s not just another trend. It’s a fundamental rewiring of an entire industry. But here’s the thing everyone gets wrong: the tech is the easy part.
The Model is Not the Moat
When we started fundraising for our new AI mental health app, we had a killer model. It could predict depressive episodes with startling accuracy, just from analyzing text and voice patterns. We were proud of it. We thought investors would be throwing money at us.
We were wrong. The first few meetings were a disaster. I remember one VC, a sharp guy with a background in biotech, who cut me off mid-pitch. “I’ve seen a dozen of these models,” he said, not unkindly. “They’re all great. But how are you going to get this into the hands of patients? How are you going to get doctors to trust it? How are you going to get paid for it?”
That was a wake-up call. We had been so focused on the technology that we had completely neglected the three things that actually matter in healthcare: distribution, trust, and reimbursement.
It’s a lesson I learned the hard way with my last company, RemoteTeam. We were building HR tools for remote companies, and we were acquired by Gusto just 580 days after we incorporated. The entire process—from pitching investors to hiring the team to the final M&A negotiations—happened over Zoom. Not a single in-person meeting. We succeeded because we obsessed over the user experience, over the nitty-gritty of making our customers’ lives easier. The tech was just the enabler.
It’s the same in healthcare AI, but with the stakes infinitely higher.
The Real Grind: Distribution, Trust, and Reimbursement
So, we went back to the drawing board. We stopped talking about the model and started talking about the problem. We spent months talking to therapists, hospital administrators, and insurance companies. We learned that the real challenge wasn’t building a better AI, but integrating it into the messy, complex, and deeply human world of mental healthcare.
Here’s a breakdown of what we focused on to close our $25M round:
Distribution: Instead of trying to sell directly to consumers, we decided to partner with large hospital systems. This was a slower, more painful process, but it gave us immediate access to thousands of patients. We had to navigate byzantine procurement processes and endless security reviews. It was a slog. But it worked.
Trust: Doctors are rightfully skeptical of AI. They’ve been burned by over-hyped tech before. We knew we couldn’t just show them a bunch of data. We had to build relationships. We started a clinical advisory board with some of the top psychiatrists in the country. We ran a pilot study with a major university hospital and published the results in a peer-reviewed journal. We were transparent about our model’s limitations. We showed them we were serious about patient safety.
Reimbursement: This is the part that kills most healthcare startups. You can have the best product in the world, but if you can’t get paid for it, you’re dead. We hired a full-time reimbursement consultant before we even had a finished product. We spent a year working to get a dedicated CPT code for our technology. It was an excruciatingly slow and bureaucratic process. But it was the single most important thing we did.
My Advice to Founders in AI Healthcare
If you’re a founder in this space, I urge you to stop obsessing over your model. It’s important, but it’s not the thing that will make or break you.
Instead, obsess over your users. Obsess over the workflow. Obsess over the business model. Go spend a day in a hospital. Talk to a doctor. Talk to a patient. Understand their world. And then, and only then, start building.
The opportunity in AI healthcare is immense. But it’s not for the faint of heart. It’s a long, hard road. You will face rejection, regulation, and a whole lot of bureaucracy. But if you’re willing to do the hard work, if you’re willing to get your hands dirty, you can build something that truly matters.
I’m always happy to chat with founders who are tackling these hard problems. Feel free to reach out to me on X @sahin. Let’s build the future of healthcare, together.
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.
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.