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By 鈥�

Everywhere you look, venture headlines imply that seed rounds have meaningfully changed shape.

raised $1 billion for a company that didn’t exist a week earlier. launched with $6.2 billion out the gate. hit $475 million two months after founding.

It’s easy to read those headlines and conclude the venture model has been rewritten, that AI is a once-in-a-generation opportunity requiring once-in-a-generation capital.

We disagree. And so does the data.

The biotech parallel

At , we鈥檝e built deep domain expertise in biotech, the sector with the longest history of mega first rounds in venture.

Biotech mega-seeds are common because the science requires it, you can’t run a Phase 1 trial on $3 million, but the return profile is often humbling. Large first rounds in biotech have produced a handful of strong outcomes for first-check investors 鈥� and a very long tail of modest ones. Our experience with this trend in biotech motivated us to compile a dataset and pressure-test our intuition more broadly.

We pulled every publicly available $100 million-plus first round we could find over the last 15 years (roughly 200 deals) and found that only 20% had recorded exits. Of those, only a few delivered what we’d call a venture-like return: 10x MOIC or better for the first-round investor. In other words, approximately 1% of companies that publicly raised $100 million or more in their first financing round generated returns that justify the asset class. Capital intensity, as it turns out, actually worked against venture outcomes.

That distribution will improve with a few well-placed AI outcomes this year. and alone will essentially double the number of outlier returns in this data set when they exit. But even there, the return math is nuanced for first round investors. According to reports, first-round investors are looking at 30-40x returns at OpenAI鈥檚 projected IPO valuations.

That’s a fantastic outcome, but it’s also a fraction of what early institutional investors made on the generational outcomes of prior eras.

and each turned roughly $12.5 million of their checks into around $4 billion, driving reported returns somewhere north of 300x. reportedly turned a roughly $500,000 investment in into $2.5 billion 鈥� nearly 5,000x.

These are exponentially larger outcomes. Why? The difference wasn’t a byproduct of company quality but of entry price. Those historical investors got in at a price that left room for the upside to actually compound.

The mega round is real, but not replacing the market

The number of $50 million-plus seed rounds has exploded since 2018. But traditionally sized first rounds are also growing. The headline-grabbing rounds are a small fraction of what’s actually getting funded, and an even smaller fraction of what will return venture-scale capital.

Moreover, the companies people now hold up as AI winners started small, only further reinforcing this point.

‘s first round was less than $10 million. ‘ was $2 million. ‘s was $11 million. ‘s was $25 million. Even at the frontier-model layer, ‘s first round was $5 million. Today, every one of those companies is valued north of $5 billion and generating hundreds of millions in revenue.

Cursor at less than $10 million is the more representative data point. Project Prometheus at $6.2 billion is the exception.

Capital intensity is not a moat

Raising a massive first round doesn’t inherently make a company more likely to generate venture size returns for its investors. Sometimes it’s a necessary cost of doing business, but the venture math is unforgiving.

High entry prices leave less room for the upside to accrue, regardless of the underlying opportunity. The playbook that has worked across every prior technology wave is to buy meaningful ownership in capital-efficient companies at prices that leave room for the upside.

That playbook doesn’t make for dramatic headlines in 2025. But it’s what the historical data, from Google to Uber to Cursor, consistently vindicates.

A few of today’s mega-seeded AI companies will absolutely deliver 10x-plus MOICs, just as a few winners have in every era. But the data鈥檚 been consistent for 15 years, and building a portfolio around the exceptions, rather than the pattern, is a bet with a long losing track record.

is a principal at , where she draws on a decade of infrastructure and technology investing experience as well as a systems engineering background to support exceptional entrepreneurs building the next generation of frontier technology companies. Prior, McDonald was an investor at , where she focused on growth-stage climate tech companies. She began her career in‘ power and utilities group and then at , where she developed deep expertise across energy, infrastructure and project finance.

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Related reading:

• The Largest Recent Seed Rounds Are All For AI Companies
• In Charts: Seed Deals Keep Getting Bigger As Odds Of Reaching Series A Fall Dramatically
• Data: The Seed Funding Boom Is Concentrating Capital In The San Francisco Bay Area
• Seed Funding Is Bigger Than Ever 鈥� And Harder To Get

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