AI isn't replacing expertise; it's collapsing the cost of performing it. The premium that sustained thought leaders, venture capitalists, and software engineers was never the output: it was the scarcity of credible production. When GenAI makes every output look competent, the surviving moat is judgment under novel conditions, and that's the one capability none of these three industries have figured out how to credential, price, or scale.
GenAI collapses the cost of performing expertise, creating a faux-expert pipeline that erodes the thought leadership category. Author rebrands fractional/embedded advisory as "thought doership" but misses that AI compresses the doer premium too. The durable moat isn't building speed: it's judgment under novel conditions.
The real VC disruption isn't AI replacing analysts: it's AI eliminating the customer. When a $300M-revenue company can reach unicorn status with 100 people and zero venture funding, the disruption is demand-side: startups don't need the capital. The "Moneyball for VC" thesis is flattering but structurally wrong; VC has a data poverty problem, not a data utilization problem.
Thompson's 70-developer feature buries the most important number in AI coding: Google sees 10% engineering velocity improvement while greenfield startups claim 20-100x. The gap isn't measurement error; it's the structural difference between writing new code and safely modifying systems that billions depend on. Pichai's metric (hours recovered, not lines produced) is more honest than any startup founder's. The demo is always greenfield; production is always brownfield.
