Morgan Stanley projects $2.9 trillion in global data center capex from 2025-2028 against only $30 billion in generative AI revenue in 2025, creating a 97:1 investment-to-revenue ratio that risks massive stranded assets if AI monetization stalls

Morgan Stanley forecasts $2.9 trillion in cumulative global data center capital expenditure between 2025 and 2028, while total generative AI market revenue in 2025 is expected to reach only $30 billion, creating an investment-to-revenue ratio of approximately 97:1 that echoes the infrastructure overbuild of the late-1990s telecom bubble, and McKinsey research indicates many enterprise AI projects struggle to move beyond pilot phases to production deployment at scale. Why it matters: Hyperscalers and private equity firms are committing to 15-20 year facility lifespans based on demand projections that assume exponential AI adoption, so if enterprise AI deployment stalls at the pilot stage or efficiency breakthroughs (like DeepSeek's training cost reductions) reduce compute demand per unit of AI output, so hundreds of billions in data center assets become underutilized or stranded, so investors and lenders face write-downs that ripple through REIT markets and infrastructure debt portfolios, so the resulting financial correction could choke off funding for legitimate AI infrastructure needs just as the technology matures enough for widespread productive use. The structural root cause is that data center investment decisions are being driven by a competitive 'arms race' mentality among hyperscalers (Microsoft, Google, Amazon, Meta) where falling behind on GPU capacity is perceived as an existential risk, combined with abundant cheap capital from infrastructure funds seeking long-duration assets, creating a classic principal-agent problem where the decision-makers committing capital are rewarded for growth and penalized for caution, regardless of whether the underlying AI demand materializes at the projected scale.