Despite OpenAI's massive success, its capital-intensive nature means early seed investors see returns around 25x. While good, this isn't the massive fund-returner many assume, highlighting the risk of capital-consumptive businesses for seed funds, even when they become unicorns.
Sam Lessin predicts massive losses for seed VCs backing companies branded as "AI businesses." These ventures are too capital-intensive and commoditizable to generate traditional venture returns, even if they become massive. AI should be a tool, not the business model itself.
The seemingly rushed and massive $100 billion funding goal is confusing the market. However, it aligns with Sam Altman's long-stated vision of creating the "most capital-intensive business of all time." The fundraise is less about immediate need and more about acquiring a war chest for long-term, infrastructure-heavy projects.
While OpenAI's projected multi-billion dollar losses seem astronomical, they mirror the historical capital burns of companies like Uber, which spent heavily to secure market dominance. If the end goal is a long-term monopoly on the AI interface, such a massive investment can be justified as a necessary cost to secure a generational asset.
A multi-billion dollar exit's impact is relative to fund construction. For a concentrated Series A fund (30 companies), a $20B exit is a "Grand Slam." For a diversified seed fund (300 companies), the same exit is just a "Home Run" because it needs a 200x return, not a 30x, to be a true "fund returner."
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.
Unlike traditional capital-intensive industries, OpenAI's model is asset-light; it rents, rather than owns, its most expensive components like chips. This lack of collateral, combined with its cash-burning operations, makes traditional debt financing impossible. It is therefore forced to raise massive, dilutive equity rounds to fund its ambitious growth.
The AI boom's sustainability is questionable due to the disparity between capital spent on computing and actual AI-generated revenue. OpenAI's plan to spend $1.4 trillion while earning ~$20 billion annually highlights a model dependent on future payoffs, making it vulnerable to shifts in investor sentiment.
VC outcomes aren't a bell curve; a tiny fraction of investments deliver exponential returns covering all losses. This 'power law' dynamic means VCs must hunt for massive outliers, not just 'good' companies. Thiel only invests in startups with the potential to return his whole fund.
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.