The bar for reaching Series A has gotten 3x higher

3 trends in startups/tech/venture

📈 The bar for reaching Series A has gotten 3x higher

• According to Silicon Valley bank, the median Series A startup now has $2.8M in ARR (annual recurring revenue), up from <$1M 5 years ago. What makes a series A “top-quartile?” $6.9M in ARR. These benchmarks are growing faster than inflation!

• The takeaway? Raising a successful Series A requires strategically picking the right pre-seed and seed investors who can help the team scale their revenue from day 1 to exceed industry benchmarks.

🤖 AI is turning sleep data into a diagnostic powerhouse

• Stanford’s new SleepFM foundation model can predict over 130 health conditions from a single night’s recording. By analyzing 600,000 hours of multimodal data (brain waves, heart rate, and muscle signals) from 65,000 participants, the model uses "mismatched" body signals (e.g., a racing heart during deep sleep) as early warning signs for disease.

• The SleepFM model can now reportedly predict Parkinson's with 89% accuracy, dementia at 85%, heart attacks at 81%, and general overall risk of death at 84%.

• This is one of many recently launched models (including OpenAI for Healthcare, Claude for Healthcare and Google’s Health AI) that seeks to capture the growing business opportunities related to sleep, health and longevity.

💰 Becoming a unicorn no longer guarantees a big payday

• According to Silicon Valley Bank, of the 726 U.S. unicorns in existence:

  • Only 182 have $300M+ revenue, and from that, only 37 meet the "Rule of 40" (Revenue Growth % + Profit Margin % ≥ 40%) required for a healthy IPO.

  • 28% (200+) unicorns are seeing declining revenue and 52% (375 unicorns) are growing at <20% annually.

  • Overall, only ~5% of unicorns are actually IPO-ready, and even less want to go public (e.g. Stripe wants to stay private).

• As more venture capital funds are willing to sell their stakes in unicorns via venture secondaries for liquidity, the key questions for later stage investors to consider include 1) how to choose which unicorns are a top 5% deal (e.g. revenue and growth metrics beyond just name recognition), 2) how to determine which unicorns have a pending liquidity event (e.g. IPO or M&A), and 3) at what price to invest (e.g. Brex was acquired by Capital One for $5bn, an unexpectedly steep discount from its previous valuation of $12.3bn).

2 theses on what’s next

🤖 The best AI models will be context aware

• While AI excels at processing data, it struggles with tacit knowledge (the 90% of organizational wisdom that is never written down).

• The next generation of winning AI models won't just be fast and proactive; they will be better at understanding the broader context. A “good” AI agent can proactively draft personalized emails for outreach that sound exactly like you. A “great” AI agent will know that your colleague should really be the one to send the email because he/she knows the recipient better.

🤔 Relationships are the scarcest resource

• As AI drives the marginal cost of technical execution toward zero, human connection will increasingly become the scarcest resource.

• In a market where everyone seems to have the "perfect" automated solution, the only part competitors won’t be able to copy is an organization’s people (and what they bring in terms of connections, shared history, and reliability).

1 tool I love

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