We just closed a $25 million Series A for our new AI mental health app. And I have to be honest, it was one of the most brutal fundraising processes I’ve ever been through. That’s saying something, considering I’ve seen two of my companies get acquired and have personally invested in over 200 startups, including some of the biggest names in AI like Anthropic and OpenAI.
Everyone is throwing money at AI right now, especially in healthcare. You’d think it would be easy. It’s not. Most founders I talk to are dangerously naive about what it takes to build and fund a company in this space. They think a clever model and a slick pitch deck are enough. They’re dead wrong.
Here’s the uncomfortable truth about what it really takes to succeed in the brutal, regulated world of healthcare AI.
The Idea Is The Easy Part
The idea for our company came from a deeply personal place. I saw friends and family struggle to find good mental healthcare. Therapists are overbooked, costs are skyrocketing, and the quality of care is inconsistent. It’s a system at its breaking point. I knew AI could help, not by replacing therapists, but by augmenting them, making them more effective and accessible.
We developed a platform that uses AI to analyze therapy sessions (with full consent, of course) and provide actionable insights to both the therapist and the patient. It can identify patterns in speech, detect cognitive distortions, and track progress over time. It’s a tool to superpower therapists, not render them obsolete.
That was the vision. It got us in the door with investors. But it wasn’t what got us the money.
Welcome to the FDA, a.k.a. "The Great Filter"
The moment you say “healthcare AI,” you’re playing in a different league. You’re not just building an app; you’re building a medical device. And that means you have to deal with the FDA. For most tech founders, this is a foreign and terrifying concept.
I remember one of my early angel investments, a brilliant team out of Stanford working on an AI-powered radiology tool. They had a model that could detect certain types of cancer with 99% accuracy in a lab setting. They thought they were going to change the world overnight. They burned through their seed money in 18 months just trying to figure out the paperwork for their 510(k) clearance. The company died before it ever saw a single real patient.
That lesson was seared into my brain. For our mental health app, we hired a team of regulatory experts before we wrote a single line of production code. We spent the first six months and nearly a million dollars of our seed round just on mapping out our regulatory strategy. We treated the FDA approval process not as a hurdle, but as a core feature of our product.
Investors saw this and it immediately set us apart. We weren’t just tech guys with a cool algorithm; we were a serious company that understood the battlefield. We could show them a clear, albeit long and expensive, path to market. That’s what they invest in.
Your Model Is a Commodity. Your Data Is The Moat.
Another hard truth: your AI model is not that special. With the rise of open-source models and platforms like Hugging Face (one of my portfolio companies), the barrier to entry for building sophisticated AI has collapsed. If you could build it, a dozen other teams can too.
What they can’t replicate is your proprietary data. In healthcare, data is everything. But getting it is a nightmare. You have to navigate HIPAA, build trust with hospitals and clinics, and ensure patient privacy is absolutely sacred. It’s a slow, painful, relationship-driven process.
During our fundraise, one of the sharpest VCs I met, a partner at a top-tier firm, spent almost the entire meeting grilling me on our data acquisition strategy. He didn’t ask a single question about our model architecture. He asked about our partnerships with therapy clinics. He asked about our data anonymization process. He asked about the consent flow for patients.
He knew that the company that wins is the company that has the best data. We were able to show him our signed partnership agreements with a network of 50 therapists who were part of our beta program. We demonstrated our end-to-end encrypted data pipeline. We had a 20-page document detailing our HIPAA compliance and data governance policies. That was our moat. The AI model was just the machine that ran on top of it.
The $25 Million Grind
Even with a solid regulatory strategy and a data moat, the fundraising process was a grind. We pitched over 100 investors. We got 90 rejections.
The “no’s” were brutal. Some didn’t believe in the market. Some thought it was too crowded. Some were scared off by the regulatory risk, even with our detailed plan. One investor told me straight up, “I love the idea, but I don’t invest in things that could get people sued.”
But the “yes’s” came from the investors who understood the deep, systemic problems in healthcare. They were the ones who had seen the failures of purely tech-focused approaches. They appreciated our obsession with the unsexy, grinding work of compliance, data acquisition, and building trust with clinicians.
In the final meeting with our lead investor, the conversation that sealed the deal wasn’t about the tech. It was about a story I told them. I spoke about a therapist in our beta program who was on the verge of burnout. She was managing 30 patients and felt like she was failing them. Our tool helped her spot a critical pattern in a suicidal patient that she had missed. She told us it not only helped her save that patient but also restored her faith in her own ability to be a great therapist.
That’s what we’re building. Not just an AI company, but a company that understands the human element of healthcare. We’re not just shipping code; we’re trying to solve a real, painful problem for millions of people.
Raising $25 million was incredibly difficult. But it was also a validation of our approach. The hard-won lessons from my past startups and investments all pointed to this: in healthcare AI, you win by focusing on the boring stuff. The regulations. The data. The trust. The real-world workflow of the professionals you’re trying to help.
Most founders want to build a great AI model. That’s the fun part. But it’s not enough. If you want to succeed in this world, you have to be willing to do the hard, unglamorous work that others won’t. That’s the only way to build something that lasts.
Frequently Asked Questions
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.
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 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.