META Raises Capex Guidance to $145B While Cutting 10% of Its Workforce

Meta's earnings signal and subsequent announcements reveal a company in radical restructure mode. Mark Zuckerberg's strategic shift toward AI represents not merely a product pivot but a wholesale reallocation of human and capital resources. By cutting 8,000 jobs while canceling 6,000 open roles, the company is shedding roughly 10% of its headcount; simultaneously, it is redeploying another 7,000 employees into AI-focused teams. The message is unambiguous: human-centric product development and platform maintenance are being subordinated to AI infrastructure and training.

Capex guidanceCompany-issued forecasts of future financial performance. of $145 billion underscores the commitment's scale. This figure rivals the annual GDPGross Domestic Product — total US economic output. Released quarterly in three estimates: Advance (1 month after quarter), Preliminary, Final. of small nations and positions Meta as a co-driver of the broader AI capex arms race alongside Amazon, Google, and Microsoft. The company is not simply buying chips; it is building frontier model training capacity and inference clusters to power its own products and, potentially, to compete in the third-party model marketplace. The move signals confidence that the AI spending cycle will not encounter severe demand destruction.

From a market perspective, Meta stock is being repriced. With Q1 earnings beats and forward guidanceCompany-issued forecasts of future financial performance. reinforcing AI momentumThe empirical fact that winners keep winning over the medium term., institutional buyers are reassessing the multiple despite a 2024-era valuation reset. The company trades with what some analysts call "a fairly large margin of safety," meaning that earnings growth of only modest magnitude would justify current or higher valuations. The Meta narrative is no longer about advertising density; it is about becoming an AI infrastructure powerhouse.

The counterargument centers on execution risk and unknown payoff timing. AI infrastructure capex is front-loaded; returns on that investment depend on successful model deployment, competitive advantage in inference, and pricing power in third-party licensing. If the model landscape becomes commoditized or if rivals achieve similar scale at lower cost, Meta's capex advantage may not translate to earnings. Additionally, the layoffs may signal an inability to scale platform advertising efficiently, a concern that predates the AI pivot.