Goldman Sachs says terminal value now accounts for about 75% of the S&P 500’s equity value, near a 25-year high, and estimates a 1 percentage point drop in long-term growth assumptions would cut S&P 500 enterprise value by roughly 15%. The note highlights growing AI-disruption risk, with the S&P 500 software and services index already down about 17% this year. Goldman expects uncertainty around terminal values to persist for several quarters as investors reassess long-term growth assumptions.
The market is starting to reprice software less as a recurring-revenue compounder and more as a long-dated cash-flow claim whose discount rate is suddenly less forgiving. That matters because the damage is not linear: once investors question terminal assumptions, the multiple compression feeds back into product strategy, M&A currency, and employee retention, creating a self-reinforcing de-rating cycle over the next several quarters. The biggest vulnerability is not the obvious incumbents with exposed seat-based pricing, but the “quality-growth” franchises with elevated EV/sales and weak near-term free cash flow, where a small reduction in long-horizon growth can erase years of expected outperformance. Second-order winners are likely to be the infrastructure and picks-and-shovels layers that monetize AI adoption without being asked to defend legacy workflows. Compute, networking, data-center power, and certain semis can still grow even if application-layer software slows, because customers may redeploy budgets from SaaS subscriptions into model inference and orchestration. That creates a relative-value setup: the market may punish software as a category while underappreciating the durability of the AI toolchain and the firms that sit between cloud spend and application spend. The key catalyst is not a single earnings season but a series of management commentary inflections over the next 2-4 quarters: if CFOs stop defending long-term growth and start emphasizing efficiency, the de-rating can persist; if they show AI-driven productivity gains are expanding margins faster than competitive pressure is compressing them, the trade reverses. A sharp selloff also increases the odds of consolidation, as larger platforms with stronger balance sheets use weakness to buy distribution and data assets cheaply. Conversely, if AI adoption proves more augmentative than substitutive, the current move could be an overshoot and the highest-multiple names may rebound violently on even modest stabilization in guidance. The contrarian miss is that terminal-value anxiety may be too broad-brushed: many software businesses do not need heroic decade-out growth if they can sustain mid-teens free-cash-flow growth and buy back stock. The market may also be conflating “AI can automate tasks” with “AI can commoditize the entire stack,” when in practice customer data, workflow integration, and compliance create moat persistence. That suggests this is as much a duration shock as a secular collapse, which favors selective long/shorts over blanket de-risking.
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