Meta navigates AI investment cycle as Street weighs costs against returns

June 11, 2026

Meta navigates AI investment cycle as Street weighs costs against returns
Meta navigates AI investment cycle as Street weighs costs against returns Proactive uses images sourced from Shutterstock

Meta Platforms Inc (NASDAQ:META, XETRA:FB2A, SIX:FB) is at a crossroads, pouring capital into artificial intelligence infrastructure while investors wait for signs that the spending will pay off, according to Bank of America analysts.

The report comes after Meta’s third-quarter 2025 earnings call, when the company guided to a material step-up in both 2026 operating expenses and capital expenditures. Since then, the stock has dropped roughly 24%, compared to a 5% gain for the NASDAQ, as investors grapple with a more capital-intensive business model, uncertain ROI on AI-related spend, and the anticipated drag from depreciation and amortization on margins.

Bank of America analysts say Meta’s elevated compute demand is structural, tied to frontier model development, ad model improvements, and new business opportunities. Within the core advertising business, AI is already delivering: content recommendation and ad targeting have improved, driving higher usage and ad spend. Integration of Muse Spark is expected to extend those gains. But analysts note the near-term return profile on frontier AI investments remains less clear.

Potential monetization pathways have begun to take shape in recent weeks, including subscriptions, enterprise offerings, and business agents. Analysts say the market is still questioning Meta’s ability to generate meaningful revenue from these products, but expect greater clarity over the next two earnings cycles as AI initiatives move from concept to release.

The bull case rests on faster-than-expected AI capability gains, including frontier-level model development over the next nine months, scaled adoption of new AI products, continued advertising outperformance, and the emergence of new revenue streams.

Bears, meanwhile, point to rising infrastructure spending, limited large language model innovation, weak early traction for new AI products, and intensifying competition from established AI platforms.