Mary Camacho — A Different Kind of Vessel

What if there were a privacy-first federated AI model for women’s wellness? And what if we, collectively in femtech, were the ones who owned it?

The AI everyone is reaching for now is insufficient when the question is a woman’s body. Ask any of the big assistants about your cycle, your hormones, the way your body has been changing in midlife, and you will run aground on the same set of disappointments: generalities dressed as advice, recommendations calibrated to a body that is not yours, the polite agnosticism of a system that has not, in fact, been trained on what it is being asked. The infrastructure that would do this better has not been built. It could be. And the form it ought to take is not a company.

The wellness layer

Companies are good at making products. They produce specific benefits for specific customers and sell them at margin, and that form works, and most of what we use every day is the proof. There is another category of value, though. The kind that has to live across many companies to mean anything at all. It asks for a different vessel.

Consider what is missing.

The clinical record knows you when you are sick. It knows your diagnoses, your prescriptions, the labs you ran the last time something was alarming enough to send you in. It does not know your sleep through perimenopause. It does not know the years of cycle data you have already logged. It does not know that the week before your period has been quietly demolishing your concentration since you were thirty-four, or that the migraines have been worse this year, or what your own theory of why is.

Public health research has been catching up, slowly, on what it left out for a century. The drug trials that excluded women of reproductive age. The cardiac science calibrated to male physiology and generalised as universal. The long quiet about pain and exercise and nutrition and sleep in bodies that are not the bodies most of the world’s population lives in. The literature on this gap is now large. The correction is happening where it can. None of it is sufficient on its own, because the data women most need in order to understand themselves day to day does not live in clinical archives at all. It lives in the wellness layer.

It lives in the period app on the phone, the ring on the finger, the diary entry typed at 2 a.m. about a hot flash. It lives in millions of small private archives, kept by women who are tracking themselves because no one else thought to. This is where the data is. This is the layer the next generation of AI will need to be trained on if it is going to represent women’s bodies as they actually are: in transition, in rhythm, in the texture of what women themselves notice and choose to record.

Some of that data is being trained on now. The big AI labs license what they can, scrape what they can’t, and partner where the partnership produces a defensible corpus. The largest wellness operators have begun to train on their own data. A step forward. Also a sliver. Every operator’s dataset is shaped by who they sold to. Rings do not work for women with arthritic fingers. Wearables sell at prices many women cannot pay and in form factors many do not want. Fertility apps describe women tracking fertility. Period trackers describe women who menstruate. Each company’s dataset describes its users. None of them is the population. The combination begins to approach it.

So what if we built that combination together?

The shape of what could be built

A cooperative of femtech and women’s wellness operators. Member-built, member-governed, member-owned.

Here is what that means in practice. Each member operator runs training locally, on their own users’ data, inside their own infrastructure. The data never leaves the member’s environment. What does leave is the mathematical update, the encrypted summary of what the local model learned in a given round. Only that. A central process aggregates those updates into a shared global model. The members get access to the shared model on preferential terms. Non-members license it commercially. The protocols and tooling are open-sourced. The trained models, and the weights inside them, are the cooperative’s collective intellectual property.

This is not theoretical. It is the architecture proved out by MELLODDY, the European federated-learning consortium of ten of the largest pharmaceutical companies on earth, all direct competitors, all holding proprietary chemical data they could not share. They shared a model anyway, by sharing the gradients and never the underlying data. Audited. Production-grade. Across borders, across the most paranoid IP regime in industry. It has been done.

The cooperative would be domiciled in Europe. That is deliberate, and it has consequences. The European regulatory ground supports cooperative governance, supports privacy by design, and, at this moment, shelters intimate health data from political pressures that elsewhere are moving in the wrong direction. The cooperative would begin in wellness rather than the clinical scope. That, too, is deliberate. Wellness data sits outside the heaviest regulatory regimes that govern clinical AI: outside US Protected Health Information rules under HIPAA, outside the European Health Data Space’s clinical perimeter. The cooperative can launch under the EU AI Act’s wellness obligations, which are real and which it must meet, without fighting clinical conformity assessments designed for software that diagnoses disease. Clinical comes later. Clinical comes with the right partners, on the right credentialing, by deliberate progression. Wellness is the door the cooperative can walk through now.

Building this once, together, is a different proposition than every operator building its own. Right now, every serious wellness company is paying for its own GPUs, its own engineering hours, its own electricity, in parallel, to produce proprietary models that each describe a slice of women. The cost is being duplicated. The result is being fragmented. The cooperative form is what makes one good model possible instead of many inadequate ones.

The form has a long history.

The Associated Press has held news distribution as a cooperative of member newspapers for nearly two centuries. Visa, before it went public, ran for decades as a member-owned association of issuing banks. Land O’Lakes is dairy farmers. Ocean Spray is cranberry growers. Mondragón is cooperatives at federation scale. In the digital era, OHDSI runs federated health-data research across more than eighty countries. What every one of these structures shares is that the people doing the work also own the work, and the value flows back to them on terms they wrote themselves.

The structure has been built before, in other industries, for other reasons. What would be new here is the application. A women’s wellness operator cooperative, built privacy-first by architecture, oriented to representing this population correctly in the AI infrastructure of the coming decades, on terms its members choose.

In early 2026, Essity, Clue, Hertility, Daye, and Mooncup formed the Women’s Health Visibility Alliance to challenge censorship of women’s health content on social media. That coalition showed something useful. Operators in this sector will coalesce when the reason to coalesce is coordinated and clear. The reason for a wellness AI cooperative is larger. It is about who gets to be visible in the AI products of the coming decade, and on whose terms.

How I came to be writing this

Around 2019, I began saying out loud that the domain in which I most wanted to see distributed systems do real work was health. The intuition was specific. The articulation was rough. I have been working out the difference ever since.

At Holochain, the work sat at the infrastructure layer: distributed systems, peer-to-peer architecture, the slow craft of making it possible for information to travel without aggregating power that should not be aggregated. Healthcare came up often in those conversations, because the values fit was so clean. Years of building sharpened the frame.

That sentence is the destination of seven years of thinking, not the starting point. I am not in clinical health tech and never have been. The questions I wanted to work on lived closer to daily life than to the doctor’s office. That is how I came to the consumer wellness layer, and to building Cirdia, a privacy-architected intelligence engine for women’s wellness, where the apps run pattern intelligence on each user’s own data without aggregating that data into a central archive. The architecture is privacy-first because years working under the hood made it plain that privacy by promise does not last a funding round. Privacy by architecture might.

What I am doing

There is no coalition to announce yet. No brand. What is in front of us is a set of conversations, in earnest, with people who might find this proposition compelling enough to help it emerge.

The work is real. Convening founding members. Designing governance that holds across borders and across decades. Writing privacy and consent standards a member would actually be proud to sign. Standing up the technical foundation, in pieces, with partners who have built federated systems before. Securing anchor funding from grants and philanthropic capital willing to underwrite mission-aligned infrastructure that markets, on their own, cannot build.

None of this is fast. None of it is easy. The cooperatives that have lasted all went through years of patient, unglamorous work before their value was visible from outside. The people I am gathering are the people who would do that kind of work.

Operators in women’s wellness whose companies could be founding members. Technical leaders in federated learning, privacy-preserving computation, and cooperative infrastructure who have built this kind of system before. Philanthropic capital and program officers whose mandate is anchoring public-interest infrastructure that markets cannot build on their own. Convening authority in adjacent communities, trade alliances, women’s health policy, AI governance: the kind that touches this work and would lend its weight.

If you are one of those people, I would value being in conversation with you. The brief I am working from is a document I share with people who want to see it. Not as a finished plan. As the shape of what I have so far, open to the people who would help shape it into something better.

You can reach me on LinkedIn, or send me a message here.

The infrastructure that will represent women’s wellness in the AI of the next decade will be built one way or another. The question is by whom, and on what terms.

What I am doing is opening the conversation. What happens next depends on who joins it.

privacy-architecture ai femtech cooperative