Equinix’s board has approved a dividend hike of 5% to be announced next quarter, reinforcing its commitment to reaching an Adjusted Funds From Operations (AFFO) per share target of over $50 by fiscal 2029. This increase marks the seventh consecutive annual raise and is backed by robust global capacity expansion: the company added 12,400 IBX cabinets in 2025, a 9% year-over-year rise, and saw monthly recurring revenue (MMR) per cabinet climb 7% to $2,350. Equinix’s AFFO per share reached $32.10 in fiscal 2025, up from $29.75 the prior year, driven by accelerating AI workloads. The dividend boost is designed to offset execution volatility from large-scale xScale campus deployments and to reassure income-seeking investors of the REIT’s resilient cash flow generation.

Equinix ended 2025 with net debt to EBITDA of 3.4x, providing room for ongoing power and land acquisitions to support planned data center yield expansion. During the year, the company closed 3 new land parcels totalling 210 acres in Northern Virginia, Silicon Valley and Frankfurt, projected to add 25,000 cabinets by 2028. Capital expenditure guidance of $3.8 billion for 2026 prioritizes high-growth xScale campuses and AI-optimized colocation, with expected project-level returns above 15%. Liquidity remains healthy at $4.2 billion in unrestricted cash and available revolving credit, positioning Equinix to capitalize on opportunistic M&A and long-term renewable power agreements to lock in average energy costs below $0.07 per kWh.

In a strategic collaboration with Resolute CS, Equinix has integrated the Resolute NEXUS™ platform into its Equinix Fabric® service, enabling enterprises to automate design, pricing and ordering of last-mile access across more than 270 data centers in 77 markets. The carrier-neutral ecosystem now features over 3,200 network providers in 180 countries, delivering transparent routing options and streamlined procurement through the Equinix Customer Portal. This enhancement is expected to accelerate edge-to-cloud deployments for next-generation AI workloads, reducing time-to-connect by up to 40% versus manual sourcing processes and lowering last-mile costs by an estimated 12% on average.