Mel van Londen

YC has quietly built a parallel tech ecosystem. Not just a network - an entire operating system for startups. Think about it: A YC founder can build their entire stack using only YC companies. Infrastructure, analytics, payment processing, marketing tools - it's all there. At friend-and-family prices. When you launch a product to the YC network, you get something priceless: A group of technical founders who actually understand what you're building. They become your first users, your debuggers, your evangelists. This is why PostHog is now used by 65% of new YC companies. This is why Brex dominated fintech... and why each YC batch makes the next one stronger. A true compounding advantage.

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One of the strongest pitches we’ve seen in 2024. Excited to announce our investment in Level Zero Health, a health tech startup redefining hormone tracking. For decades, hormone monitoring has relied on invasive blood draws that capture just a single moment in time. Level Zero Health is changing that—bringing real-time, continuous hormone monitoring to healthcare, from IVF to testosterone therapy and menopause care. This is deep tech at its best—creating an entirely new category, much like continuous glucose monitors transformed diabetes management. 🧬 Why this matters: ✔️ A breakthrough technology turning scientific research into engineering. ✔️ A category-defining innovation in personalized healthcare. ✔️ A massive market opportunity—hormone tracking is fundamental to human health. Huge congratulations to Ula Rustamova, Irene Jia and the entire Level Zero Health team. We’re excited to back this vision from the very beginning. Onwards and upwards! 🚀 Link to Techcrunch in the comments. #HealthTech #Biotech #HormoneHealth #HealthcareInnovation #DeepTech #Startups #VentureCapital

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One of the best parts of HLTH USA was seeing all the doctors, nurses and patient advocates who've become founders. Over the past decade we've seen the barriers to entry in software development diminish. From cloud-based platforms to no-code tools, and now the latest incarnations of vibe coding and AI agents. This flips the traditional model on its head. Instead of boardrooms who measure software in dollars and engineers who see solutions in zeros and ones, we're getting products that start with the clarity, frustrations and lived experiences of the end user. The cardiologist building the EHR she wishes she'd had. The nurse creating documentation tools that actually fit her workflow. The former patient designing the symptom tracker they needed during treatment. They understand the problems intimately because they've lived them. And now they can actually build the solutions. I'm incredibly optimistic about a rising tide of better UX in healthcare, led by the people who know the problems best.

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Ok, time for a rant. 90% of "AI Startups" aren't even AI. I've seen founders that use LLMs or have a chatbot somewhere on their site and call it an "AI startup". I've seen people who vibe code or use Cursor/Lovable and then say they have an "AI startup". I've seen people continue to build ChatGPT/Claude/Llama wrappers and call it an "AI startup". If you haven't build a custom model or used meaningful proprietary data then you aren't an AI startup, you're AI Enabled. AND THAT'S FINE. You don't have to be an AI company to solve a problem. So many companies and VCs are going to have poor performance because they have little differentiation and have claimed to build/invest in "AI Companies" and we will soon learn they are not really "AI".

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Just spoke with 2 founders who were about to give up on their startup. My reaction: "CUT THE BULLSH*T!" They were selling HIPAA compliant scheduling forms to dentists. One of said: "I approached dentists, but it's a no-go." So I asked, "What exactly do you mean by approached?" "We send out 100 emails." "OK. How many got delivered?" "50 delivered. 1 person booked a demo." "You talked to ONE f*cking person? And based on that, you're saying the experiment failed? That's not good enough. That's straight up bullsh*t. You have to do more." You see, most startups fail simply because founders give up too f*cking early. I actually saw potential in their company. If you send out a stupid email to a dentist, and they got on a phone call with you, there's more to dig there. Design Real Experiments Before you start: - Define EXACTLY what success looks like (10% response rate? 5 paying customers?) - Determine your minimum viable sample size - Set concrete metrics to measure - Commit to completing the full experiment How These Founders Should Have Done It 👇 Their single demo request actually showed PROMISE. A 2% conversion from delivered emails to demos isn't terrible for cold outreach! But they needed to: - Talk to 30+ dentists before drawing conclusions - Test multiple messaging angles (GDPR compliance, time savings, patient experience) - Try different channels (direct calls, dental conferences, partnerships with equipment suppliers) - Get rejected for the RIGHT reasons (price, features, timing – not just poor outreach) Real-World Example A SaaS founder I know targeted law firms: - First 50 calls: 0 meetings but he learned what matters, doesn't matter - Next 50: 2 meetings, 0 sales - Refined pitch, next 100: 12 meetings, 3 sales - Today: $1M ARR Had he quit after those first 100 contacts, his company wouldn't exist. The Bottom Line Your startup deserves a real f*cking chance. Be methodical, be persistent, collect meaningful data. Most "failed" startups didn't actually fail – their founders just gave up too soon.

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Product-market fit genuinely feels different...and it'll show up in the data Demo to close rates are the easiest thing to look at for product-market fit right now. Vertical AI startups in our portfolio are seeing 50%+ demo to close rates, even with brand new account executives selling the product. It's happening in every market from AI takeoffs to AI CSR/Voice software to AI procurement solutions. When you see a demo to close rate over 50% chances are you have a jaw dropping customer experience and you should put your foot on the gas. If you're seeing something dramatically lower in mid-market or SMB ACV categories, it's worth revisiting your product and/or your pitch.

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Recently, I was giving a talk to a group of health tech founders, and someone asked how I make product decisions. I said intuition. That clearly caught people off guard. Most expect a structured model—something that crunches data, compares past experiments, and spits out the perfect answer. If that were possible, every company would have the same model, and product decisions would be easy. But the reality is much messier than any framework can capture. One of my biggest career regrets is that I didn’t trust my intuition sooner. Almost every time I’ve hesitated, waiting for more data instead of acting on what I already knew, I’ve regretted it. And nearly every time I ignored my gut because I couldn’t back it up with numbers, I later realized my intuition was right. The first decade of your career should be about fine-tuning your internal model—your personal LLM on intuition. Read the right papers. Follow the right people. Experiment rigorously. Build a foundation of real experience. And, most importantly, use great products. My wife is an architect, and I once asked her how she sharpens her design sense. She said, “By spending time in beautiful cities.” Tech is no different. You have to surround yourself with the best products, study the startups that break through against all odds, reverse-engineer what works, and keep refining your mental model until you develop real taste—your own intuition. Once your intuition starts outperforming the people around you, you have to start trusting it. Yes, data matters. But data should refine your worldview, not replace it. We’ve all seen data twisted to fit the wrong conclusion. But a fine-tuned intuition? That rarely fails. Over the past two years, I’ve leaned harder into my product intuition than ever before—and I’ve performed better. In startups, great intuition = product taste. And great product taste is knowing what good looks like before the rest of the world catches on. Very few people have it.

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