The gap between theory and practice in AI mental health is enormous.
After more than ten years in Silicon Valley, I’ve seen how AI in healthcare isn’t just hype. I want to share what I’ve learned from my startups and investments—the successes, the mistakes, and the unexpected lessons that actually matter.
What I've Learned From 49 Companies
A few patterns in AI mental health come up again and again.
The founders who handle AI mental health well understand that most founders overthink this and underspend on execution.
The Counterintuitive Truth
In AI mental health, the best practitioners often do less, not more.
The lesson: simplicity beats complexity every time. It sounds simple. It's incredibly hard to execute.
The Framework That Actually Works
Here is a simple framework for evaluating AI mental health. It's not complicated, but it requires discipline.
Step 1: the data tells a different story than your gut
This is where most people go wrong. They skip this step entirely and jump straight to execution. Don't do that.
Step 2: your team matters more than your technology
Once you have the foundation right, this becomes much easier. I've watched founders struggle with this for months when the answer was staring them in the face.
Step 3: Iterate relentlessly
Nothing works perfectly the first time. The companies that get AI mental health right treat it as an ongoing process, not a one-time project.
Real Talk: What Actually Matters
Here is what actually matters in AI mental health.
First, execution speed beats perfection. Every time. Companies rarely fail because they moved too fast on AI mental health. Many fail because they moved too slowly.
Second, measure everything. If you can't measure it, you can't improve it. Set up tracking from day one, even if it's basic.
Third, talk to your users. It sounds obvious, but many founders make decisions about AI mental health in a vacuum. Get out of the building. Talk to real people.
This connects to broader themes around clinical AI, AI diagnostics, medical AI, AI mental health, AI radiology that I've been thinking about a lot lately.
Final Thoughts
There are no shortcuts in AI mental health, but there are smarter paths.
The strongest teams treat AI mental health as a competitive advantage, not a checkbox. They invest in it early, measure it obsessively, and never stop improving.
If you're just getting started with AI mental health, don't be intimidated. Everyone starts somewhere. The key is to start with the right mindset and the right framework, and then execute like your company depends on it. Because it probably does.
Frequently Asked Questions
Q: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 234 startups including Anthropic, OpenAI, and Scale AI. I write about what I've lived.
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: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.