Interview with Nicole Junkermann · 7 min read
September is when the year restarts. Budgets get revisited, plans that were vague in July acquire dates, and investors return to a pipeline they left in the summer.
Nicole Junkermann thinks it is a good moment to look again at femtech, a category that has spent a decade being described as promising without ever quite being treated as mainstream. Artificial intelligence has moved faster in almost every adjacent part of technology, and the reasons for the lag here are commercial rather than technical.
An entrepreneur and investor who has backed health technology for years, she argues the constraint was never the software. It was the market structure around it.
“The category was defined by what it was not”
Why has femtech remained a niche label rather than simply being health technology?
Because it was named as a departure from the default, and categories defined that way tend to stay small.
Health technology was the general term. Femtech was carved out as the specialist case, which is revealing, since it describes products serving roughly half the population. You do not usually give a majority its own subcategory.
The label did useful work early on. It gave a set of companies a shared identity, made them easier to find, and created a conversation that had not existed. Nicole Junkermann’s view is that its usefulness has largely expired, and that the strongest companies in it now describe themselves in terms of the problem they solve rather than the demographic they serve.
Does the label actively hurt?
It narrows who considers the opportunity, which has a commercial cost.
If a business is filed under a specialist heading, generalist investors treat it as outside their remit and pass it to somebody else, and frequently there is no somebody else. The company is then competing for a smaller pool of capital than its actual market size would justify.
That is a structural disadvantage that has nothing to do with the quality of the business, and it compounds. Less capital means slower growth, slower growth means less evidence of a category working, and less evidence means the next company faces the same reception.
“The evidence base is thinner, and that is a genuine technical constraint”
On where the technology genuinely does hit a limit, Nicole Junkermann is precise about what causes it.
Is there a technical dimension as well?
There is one, and it is worth understanding because it is not obvious.
These systems learn from accumulated records. Where a field has decades of well-structured data behind it, the tools arrive and work almost immediately. Where the underlying record is thinner or less consistent, the same tools arrive and underperform, and the reason is upstream of anything an engineer can fix.
Parts of health research have historically been better documented than others, and that unevenness is now inherited by anything trained on the record. It is not a flaw in the models. It is a description of what was available to learn from.
How does that change what a company should build?
It changes the sequence, and Nicole Junkermann thinks this is where the interesting businesses are.
In a mature field you build analysis on top of good data. In a field where the record is patchy, the first commercially valuable thing you can do is generate a better record as a by-product of being useful to somebody. The product earns its place immediately, and the data asset accumulates while it does.
That is a genuinely strong position, because the resulting dataset is proprietary, it is difficult for a competitor to replicate, and it improves as the business grows. It is one of the few durable advantages available to a smaller company operating anywhere near larger ones.
“Trust is the asset, and it is easier to keep than to rebuild”
What do the best companies in this category do differently?
They treat user confidence as a commercial asset rather than as an administrative matter.
These products handle personal information, and the people using them are increasingly thoughtful about where it goes. Being genuinely clear about what is collected, what it is used for, and who else sees it is not a box to tick. It determines whether people use the product properly.
Why does that matter to the business rather than only to the user?
Because a user who is uneasy will use a product partially, and partial use produces poor data.
Nicole Junkermann makes the point that incomplete records are worse than absent ones, because they look usable and are not. A company that has earned enough confidence for people to engage fully ends up with a materially better asset than a competitor who collected more aggressively and got less.
So the commercial argument and the ethical one point the same way here, which is not always true and is worth taking advantage of when it happens.
“The funding gap is an inefficiency, not a complaint”
The category attracts a small share of health technology investment. Why?
Partly because familiarity drives conviction more than anyone likes to admit.
Investors move fastest on problems they recognise. Where a market is outside the direct experience of the people allocating capital, it needs to be explained rather than immediately understood, and explanation is always slower than recognition. That is an observation about how conviction forms rather than an accusation about anyone’s intentions.
Is it changing?
Slowly, and for the least romantic reason available, which is that some of these businesses are now working.
Nicole Junkermann’s view is that a gap sustained by unfamiliarity rather than by economics closes once returns become visible, because capital is not sentimental about who it was previously ignoring. A handful of companies demonstrating durable growth will do more for the category than a decade of argument about fairness.
“What a durable business looks like here”
Asked what she actually looks for, Nicole Junkermann applies the same tests she would anywhere else.
What separates the companies that last?
The same things that separate them in any category, applied honestly.
Whether something accumulates. A dataset a competitor cannot obtain, a workflow the customer would have to redesign around leaving, a relationship that is genuinely difficult to replace. If nothing accumulates, the company is only as good as its current advantage, and its current advantage is rented.
Whether the economics are understood. These businesses carry a real cost every time somebody uses them, unlike traditional software, and a company that has not separated that out is not yet managing it.
And whether the founders describe the problem in operational terms rather than in technological ones. Nicole Junkermann notes that the strongest teams in the category tend not to lead with the technology at all. They lead with a process that is broken and an account of why they are the people to fix it.
Anything that makes you cautious?
Companies whose entire proposition is a capability that a larger platform could add as a feature. That is not specific to this category, but it is common in it, because the barrier to launching something is lower than the barrier to keeping it.
Is there a risk the category simply gets absorbed and loses focus?
It is a fair worry and I think the opposite risk is larger.
A specialist label protects a small amount of attention and caps a much larger amount. Companies that outgrow it do not lose their focus, they gain access to capital and customers that were previously filed elsewhere. The focus lives in the product and in who the team is, not in which conference track they are put on.
The businesses I would expect to lead the next few years are the ones already being described as health technology companies with a particular strength, rather than as examples of a category. That shift is mostly a matter of language, and language moves valuations more than people think.
“What I would want to see this autumn”
If you were setting priorities for the coming year?
Three things.
Businesses that build a proprietary data asset while being useful from day one, rather than promising to become useful once they have enough data.
Founders who can explain the durability question without becoming defensive, since that is usually a proxy for whether they have thought about it at all.
And a quieter shift away from the specialist label towards describing these companies by what they do. The category framing helped establish that the market existed. Nicole Junkermann suspects the next stage of growth depends on leaving it behind, and on these businesses being assessed the way any other health technology company would be.
A final thought?
That the hard part here has never been the technology.
The models are the same ones being applied everywhere else. What has been missing is attention, capital and good data, and all three of those are the result of decisions people are making right now. That is a more optimistic position than it sounds, because decisions can change a great deal faster than technology can.
About Nicole Junkermann
Nicole Junkermann is an entrepreneur and investor. Her work spans health technology and the question of how new tools reach the people they are meant to serve. She records the AI Overview, a set of short pieces taking one idea at a time, at nicolejunkermann.ai. Nothing here is investment advice.
