With trillion-dollar IPOs likely, the old model where early VCs win by having later-stage VCs "mark up" their deals is obsolete. The new math dictates that significant ownership in a category winner is immensely valuable at any stage, fundamentally changing investment strategy for the entire industry.
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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.
For a proven, hyper-growth AI company, traditional business risks (market, operational, tech) are minimal. The sole risk for a late-stage investor is overpaying for several years of future growth that may decelerate faster than anticipated.
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.
The venture capital paradigm has inverted. Historically, private companies traded at an "illiquidity discount" to their public counterparts. Now, for elite companies, there is an "access premium" where investors pay more for private shares due to scarcity and hype. This makes staying private longer more attractive.
Contrary to common belief, the earliest AI startups often command higher relative valuations than established growth-stage AI companies, whose revenue multiples are becoming more rational and comparable to public market comps.
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.
This provides a simple but powerful framework for venture investing. For companies in markets with demonstrably huge TAMs (e.g., AI coding), valuation is secondary to backing the winner. For markets with a more uncertain or constrained TAM (e.g., vertical SaaS), traditional valuation discipline and entry price matter significantly.
Conventional venture capital wisdom of 'winner-take-all' may not apply to AI applications. The market is expanding so rapidly that it can sustain multiple, fast-growing, highly valuable companies, each capturing a significant niche. For VCs, this means huge returns don't necessarily require backing a monopoly.
True alpha in venture capital is found at the extremes. It's either in being a "market maker" at the earliest stages by shaping a raw idea, or by writing massive, late-stage checks where few can compete. The competitive, crowded middle-stages offer less opportunity for outsized returns.
