Sequoia Capital's Roloff Botha calculates that with ~$250 billion invested into venture capital annually, the industry needs to generate nearly $1 trillion in returns for investors. This translates to a staggering $1.5 trillion in total company exit value every year, a figure that is difficult to imagine materializing consistently.
The memo details how investors rationalize enormous funding rounds for pre-product startups. By focusing on a colossal potential outcome (e.g., a $1 trillion valuation) and assuming even a minuscule probability (e.g., 0.1%), the calculated expected value can justify the investment, compelling participation despite the overwhelming odds of failure.
Venture-backed private companies represent a massive, $5 trillion market cap, exceeding half the value of the 'Magnificent Seven' public tech stocks. This scale signifies that private markets are now a mature, institutional asset class, not a small corner of finance.
Benchmark Partner Ev Randall argues that large, multi-billion dollar VC funds struggle to generate the high-multiple returns (e.g., 5x net) that LPs seek from venture capital. He claims the sheer size of these funds "defies the laws of physics," positioning smaller, more constrained funds like Benchmark as better able to deliver traditional venture-like returns.
A counterargument to bearish VC math posits that the majority of the $250B annual deployment is late-stage private equity, not true early-stage venture. The actual venture segment (~$25B/year) only needs ~$150B in exits, a goal achievable with just one 'centicorn' (like OpenAI) and a handful of decacorn outcomes annually.
The standard VC heuristic—that each investment must potentially return the entire fund—is strained by hyper-valuations. For a company raising at ~$200M, a typical fund needs a 60x return, meaning a $12 billion exit is the minimum for the investment to be a success, not a grand slam.
Aggregate venture capital investment figures are misleading. The market is becoming bimodal: a handful of elite AI companies absorb a disproportionate share of capital, while the vast majority of other startups, including 900+ unicorns, face a tougher fundraising and exit environment.
Botha argues venture capital isn't a scalable asset class. Despite massive capital inflows (~$250B/year), the number of significant ($1B+) exits hasn't increased from ~20 per year. The math for industry-wide returns doesn't work, making it a "return-free risk" for many LPs.
The venture capital return model has shifted so dramatically that even some multi-billion-dollar exits are insufficient. This forces VCs to screen for 'immortal' founders capable of building $10B+ companies from inception, making traditionally solid businesses run by 'mortal founders' increasingly uninvestable by top funds.
The majority of venture capital funds fail to return capital, with a 60% loss-making base rate. This highlights that VC is a power-law-driven asset class. The key to success is not picking consistently good funds, but ensuring access to the tiny fraction of funds that generate extraordinary, outlier returns.
AI startups' explosive growth ($1M to $100M ARR in 2 years) will make venture's power law even more extreme. LPs may need a new evaluation model, underwriting VCs across "bundles of three funds" where they expect two modest performers (e.g., 1.5x) and one massive outlier (10x) to drive overall returns.