We updated our analysis of Meta's AI infrastructure spending after yesterday's Q4 call. Some specifics from the earnings call on where the money is going:

Ad models: They doubled the GPU cluster training their main ads ranking model. The architecture now reportedly "scales with similar efficiency as LLMs." They also consolidated multiple models into fewer, more capable ones (12% increase in ads quality) and moved their ads retrieval engine across NVIDIA, AMD, and custom MTIA chips, nearly tripling compute efficiency.

Internal tools: Management cited a 30% increase in output per engineer since early 2025, mostly from agentic coding tools. Power users saw 80% gains.

Ad products: Video generation tools hit a $10B combined revenue run rate. Incremental attribution drove a 24% lift in conversions.

On the financial side: Q4 ad revenue was $58.1B (+24% YoY). FY25 FCF was $43.5B, a 22% margin, down from 32% in FY24. They paused buybacks and signaled they may take on net debt. Management committed to FY26 operating income above FY25 in absolute terms against $162-169B in guided expenses.

For context, Meta's 2025 capex alone roughly equals the total lifetime investment in Reality Labs over 10+ years.

The article covers the full AI model stack (GEM, Lattice, Andromeda), capital intensity breakdown, and peer comparison.