While increased CapEx signals strength for cloud providers like Microsoft and Google (who sell that capacity to others), the market treats Meta's spending as a pure cost center. Every dollar Meta spends on AI only sees a return if it improves its own products, lacking the direct revenue potential of a cloud platform.

While the market seeks revenue from novel AI products, the first significant financial impact has come from using AI to enhance existing digital advertising engines. This has driven unexpected growth for companies like Meta and Google, proving AI's immediate value beyond generative applications.

Meta's stock soared because it demonstrated how AI investments are already improving ad revenue. In contrast, Microsoft hasn't yet proven that its AI integrations are driving significant new revenue from core products like Office. The market is rewarding immediate, measurable AI impact over long-term platform plays.

Previously, marketers told Meta who to target. With the new AI algorithm, marketers provide diverse creative, and the AI uses that creative to find the right audience. Targeting control has shifted from human to machine, fundamentally changing how ads are built and optimized.

Unlike competitors who would struggle to introduce ads into AI chat, Meta's user base is already accustomed to ads in their feeds. This gives Meta a unique advantage to monetize a proactive consumer AI agent that can surface sponsored suggestions for shopping or travel without creating user friction.

Meta's core moat is its ability to solve the classic advertiser's dilemma: knowing which half of their ad spend works. By providing granular data on impressions, conversions, and ROI, it created what Pat Dorsey called the perfect advertising platform.

According to SaaStr founder Jason Lemkin, the ultimate metric for judging whether an incumbent company has successfully integrated AI is not feature releases but growth re-acceleration. If revenue growth isn't picking up speed, the AI initiatives are merely performative. He points to Meta as a prime example of a company whose AI efforts are validated by this metric.

Seemingly small, quarterly AI improvements to Meta's ad platform (e.g., a 5% conversion bump) have a compounding effect. Performance marketers reinvest these gains back into the platform, creating a flywheel that reaccelerates revenue growth, explaining the stock's recent surge despite a mature business.

Meta's ad recommendations excel because Apple's privacy changes created a do-or-die situation. This necessity forced them to pioneer GPU-based AI for ad targeting, a move competitors without the same pressure failed to make, despite having similar data and talent.