Feb 18, 2026
How ideas travel
When Manatt's healthcare partner says "there are no successful AI companies in healthcare, just consulting companies," that idea doesn't stay in the room. It travels. Karlos at Seae hears it. It becomes a diligence filter. It shapes which companies they take meetings with. It shapes which companies get funded. It shapes the market.
When Define finds us on a "market map," someone produced that map. Someone decided the categories. Someone put DaisyAI in a box next to Cohere and Otter and Latitude. That categorization propagates — every investor who sees that map now thinks of us in that frame. The map shapes how people think about us before we ever get to speak.
When we tell an investor that our client has 95% ADT coverage and two AI engineers who still chose to work with us — that information travels too. It goes back to the fund. It goes into partner meetings. It might go to portfolio companies. It shapes how people think about what payers actually want.
These aren't just data points. They're signals in a network. And the network has properties:
Ideas that confirm existing frameworks travel faster. "SaaS apocalypse" is already in the air. When we say it, investors nod — they've heard it elsewhere. Reinforcement. The idea gets stronger.
Ideas that challenge frameworks travel slower but stick harder. "The adversarial dynamic is the real problem, not either side" — nobody is saying this yet. It's harder to land. But when it does land, it reorganizes how someone thinks about the entire space.
Categorization is powerful and hard to undo. Once you're on a market map as "concurrent review AI," that's how people will think of you until you give them a stronger frame. The clearing house vision is that stronger frame — but it has to propagate faster than the default categorization.
Information flows through portfolios. Define → Cohere. Refract → Charter Health. Seae → Otter, Felicity. Every investor conversation is potentially a broadcast to their portfolio. This isn't paranoia — it's just how networks work. It means every conversation is both a fundraising opportunity and a market signal.
The people who shape frameworks shape markets. Manatt's partner said one sentence about consulting companies and it's literally guiding diligence at a major healthcare fund. If DaisyAI can articulate the clearing house thesis compellingly enough, it becomes a framework that other people use to evaluate the market. You stop being categorized and start being the one who sets the categories.
You can't pay people enough to care
Dan Wilson said this. From the buyer's side. He's lived through vendors who show up with bodies and don't care, and he can tell the difference.
This is also the answer to the scalability question. You scale by finding people who care, not by hiring people who bill. That's slower. That's harder. And that's exactly why it's defensible.
The center holds
The biggest value destruction in healthcare isn't on the payer side or the provider side. It's at the interface. If you optimize for payer financial solvency, you fight the other side and everyone loses.
The move isn't to fight the current toward one side. It's to hold steady in the middle — a stable foundational point for both sides to latch onto. Work with payers, yes. But never think payer-first. Think patient-first. Think system-first. Look for solutions where both sides benefit. No win-lose.
The data is shared. It just exists. Nobody owns it. Everyone builds their own connectors onto it. We provision access responsibly, but we're all trying to make the right decision for the patient. We should be aligned on that point.
Get the patient treated well. Deal with the money later. That's the attitude. But also make sure people are financially solvent. Both things.
The translation is the product
What actually compounds through FDE work? Not code. Not knowledge exactly. Not relationships. It's the encoding — taking something implicit and making it operational.
There's real work to translate implicit knowledge into tacit knowledge, into explicit knowledge, and to encode it into systems. LLMs can operate off of any articulable system. If it's in the relationships, the timing, the subverbal things, the emotions — AI won't do well. That's what requires a human to go in, understand what's not being said, understand the elephant in the room, and address it.
Organizations are intuition encoded into practice. It becomes more efficient, more scalable, more teachable. That's what we're building.