AI Sells Labor, Not Software — Legendary Investor Elad Gil
In a Nutshell
Elad Gil argues AI's biggest shift is enabling companies to sell labor and work product rather than software licenses, unlocking markets like law firms that previously resisted traditional SaaS. The current environment favors consensus over contrarian bets—scaling foundation models is the highest-probability path because AI has opened many markets and created unprecedented openness to experimentation. Market quality matters more than founder quality; strong teams fail in closed markets, while "why now" signals like regulatory changes or new technology capabilities determine which opportunities are viable.
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Investors often begin with a set of beliefs about what is interesting or viable. After investing or starting a company, the original thesis can prove much harder than expected, leading to failure. Years later another company succeeds in the same space. The key question is what changed: technology may have improved, regulations may have shifted, markets may have moved, or other external factors may have altered the landscape.
An example is Harvey AI in legal services. Selling to law firms has traditionally been viewed as a poor business. Harvey achieved strong enterprise adoption by shifting from selling tools or software seats to selling work product and units of labor. Generative AI enables companies to sell human labor equivalents—work hours or cognition—rather than traditional SaaS licenses. This fundamentally different product opened markets that were previously closed.
Two competing schools of thought exist on what drives big company creation. The Y Combinator view holds that insufficient founders are the bottleneck; ten times more founders would produce ten times more large companies. The alternative view emphasizes that only a limited number of markets are open to innovation at any moment. Without a market shift or openness to change, building large companies is difficult regardless of founder quality.
AI has opened numerous previously closed markets through new capabilities and because every CEO is actively seeking an AI strategy. This creates unprecedented openness to experimentation. AI companies that fail to achieve rapid growth likely have fundamental product or execution issues, because the current environment allows faster scaling than historically possible. OpenAI and Anthropic reached tens of billions in valuation at unprecedented speed.
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