Y Combinator just released their Fall 2025 Requests for Startups – essentially their public wishlist of areas they want to fund. These requests offer a useful signal about where smart money sees emerging opportunities, even if they don’t tell the full story.

As I read through their latest batch, I kept thinking about how these broader tech trends might translate into women’s health innovation. I’ve always believed that real disruption happens at intersections – where established sectors meet emerging technologies, where demographic shifts create new market realities, and where regulatory changes unlock previously impossible business models.

So what do YC’s six key areas signal for women’s health innovators and founders? Let’s break it down.

1. Retraining Workers for the AI Economy → Healthcare’s Skills Gap Problem

YC identifies a massive skills gap in physical infrastructure jobs needed for AI buildout. There’s a parallel problem in healthcare: We have a shortage of workers trained specifically in women’s health specialties.

The numbers are stark. OB/GYNs represent less than 5% of all physicians, yet they’re responsible for half the population’s reproductive health. Meanwhile, conditions like endometriosis take an average of 7-12 years to diagnose, partly because general practitioners aren’t trained to recognize the symptoms. And when it comes to fertility care, many women end up waiting months just to see a reproductive endocrinologist, when their OB/GYN could potentially handle basic fertility assessments and treatments.

But here’s where it gets interesting: Nurse practitioners are increasingly filling primary care gaps, yet most lack specialized women’s health training. Despite potentially being able to help, many OB/GYNs aren’t involved in fertility care beyond basic monitoring. There’s a real opportunity to build AI-powered training platforms that upskill healthcare workers in these areas. Imagine personalized curricula that get nurse practitioners competent in hormonal health or menopause management, or training modules that help OB/GYNs expand into fertility care.

Healthcare systems would likely pay for training that reduces costly delays and misdiagnoses. Unlike traditional vocational training, you could theoretically scale one effective program or AI teacher infinitely – solving the quality bottleneck that has historically limited these programs.

2. Video Generation as a Primitive → Health Education That Gets Personal

When YC talks about video generation becoming a basic building block of software, I immediately think about how broken women’s health education is. They mention the possibility of “an AI-native successor to TikTok, where every video is made for exactly one viewer” – which sounds promising for health education but also raises some red flags about echo chambers.

Women’s health education is deeply personal. A 22-year-old understanding her menstrual cycle has completely different needs than a 45-year-old navigating perimenopause. Someone with PCOS needs different information than someone with endometriosis. Yet most health education operates like it’s 1995.

AI video generation could change this fundamentally. Instead of generic explainer videos, imagine personalized content that adapts to individual conditions, life stages, and learning styles. An app that generates custom videos explaining your specific hormone panel results. Content that shows you exactly what to expect during your particular fertility treatment protocol.

The challenge is ensuring personalization doesn’t become isolation. Health misinformation already spreads rapidly through algorithmic feeds. The last thing women’s health needs is hyper-personalized content that reinforces dangerous misconceptions or creates echo chambers around unproven treatments. The technology to personalize health education finally exists – but we’ll need to be thoughtful about how we use it.

3. The First 10-person, $100B Company → Why Healthcare Isn’t SaaS

YC believes AI tools now allow tiny teams to build massive companies with minimal funding. This thesis works well for pure software plays, but healthcare – especially women’s health – operates under fundamentally different constraints.

You can’t “move fast and break things” when pregnancy outcomes are involved. You can’t A/B test breast cancer treatments. You can’t pivot away from patient safety. The regulatory burden isn’t just red tape – it exists because the stakes are often life and death.

More fundamentally, women’s health problems are often systemic issues that require systemic solutions. The maternal mortality crisis isn’t a software problem – it’s a healthcare access problem, a racial bias problem, a medical education problem. Endometriosis taking 7-12 years to diagnose isn’t something you solve with a better app – it requires changing how doctors are trained and how medical research is funded.

The “10-person unicorn” model might work for productivity software, but applying it uncritically to women’s health risks oversimplifying complex, deeply rooted problems. The most impactful women’s health companies will likely need to engage with the messy realities of healthcare delivery, not engineer around them.

4. Infrastructure for Multi-Agent Systems → The Care Coordination Problem

YC is looking for tools that make managing fleets of AI agents as routine as deploying web services. This actually maps well onto one of women’s health’s biggest challenges: Care coordination.

Take fertility treatment as an example. You’re juggling reproductive endocrinologists, embryologists, mental health counselors, insurance coordinators, pharmacy management, cycle tracking, lab result interpretation, and lifestyle modifications. Patients essentially become their own project managers, coordinating between completely disconnected systems and providers.

What if you could build infrastructure that orchestrates these complex journeys using multi-agent AI systems? One agent handling insurance pre-authorizations while another interprets lab results, a third coordinates scheduling, and a fourth provides emotional support – all working together seamlessly.

The technical challenge is real: These systems need to handle sensitive health data, integrate with legacy healthcare IT, and maintain reliability when people’s reproductive futures are on the line. But if you can solve the coordination problem, you’re not just improving patient experience – you’re fundamentally changing how complex healthcare gets delivered.

5. AI Native Enterprise Software → Starting Over With Women in Mind

Here’s where YC’s thesis gets really interesting for women’s health. They want to fund companies rebuilding enterprise software around AI, disrupting incumbents who can’t adapt quickly enough.

In women’s health, the incumbents aren’t just slow to adapt – they’re at times actively problematic. Most healthcare infrastructure was designed by men, for men, decades ago. Electronic health records that don’t track menstrual cycles. Clinical trial databases that exclude pregnant women by default. Diagnostic tools trained primarily on male bodies.

The opportunity here is massive: Build AI-native infrastructure that redesigns women’s healthcare from first principles. EHR systems that understand hormonal health patterns. Insurance platforms that recognize the economic value of preventive reproductive care. Diagnostic tools trained on diverse female bodies across different life stages.

Unlike other sectors where incumbents might eventually catch up, women’s health systems aren’t just technically outdated – they’re conceptually flawed. That creates a much wider competitive moat for companies willing to rebuild from scratch.

6. Using LLMs Instead of Government Consulting → The Limits of AI in Regulatory Space

YC sees opportunities to replace expensive government consulting with AI-powered tools. Take this analysis with a grain of salt – regulatory strategy isn’t my area of expertise, but it’s worth thinking through why women’s health regulation might be particularly resistant to AI solutions.

Women’s health regulation isn’t just expensive because consultants are greedy – it’s expensive because the regulatory landscape is genuinely complex, constantly evolving, and deeply political. Consider what happened after Dobbs: Overnight, the legal landscape for reproductive health became a patchwork of state-by-state regulations in the U.S. that change almost monthly, while guidance on contraceptive access etc. shifts with political winds.

It will be difficult to train an LLM on this complexity because much of it isn’t written down anywhere – it lives in the relationships between regulators, the institutional knowledge of long-time practitioners, the political context that shapes how rules get interpreted and enforced.

The regulatory challenges in women’s health aren’t just technical problems waiting for better software solutions. They’re political and social problems that likely require political and social solutions. An AI tool might help you navigate FDA submissions more efficiently, but it will have its limitations helping you navigate a political environment where reproductive health is a lightning rod.

The Bigger Picture: From Intersection to Integration

What’s particularly interesting about applying YC’s framework to women’s health is how these opportunities aren’t just additive – they’re multiplicative. The multi-agent infrastructure that supports fertility treatment could also coordinate menopause care journeys. The AI-native EHR systems could integrate with personalized video education to explain test results. The healthcare worker training platforms could use the same video generation technology to create patient education content.

This points to something bigger: Women’s health isn’t just another vertical to apply general tech trends to. It’s a fundamental rethinking of how we approach human health when we center half the population that’s been systematically underserved.

The founders who will build the next generation of women’s health companies won’t just be applying AI to existing problems – they’re using AI to ask entirely new questions about what women’s healthcare could look like.

And based on YC’s signals, the market is ready to fund those answers.