For the first time in two decades, institutional investors are telling tech giants to pump the brakes on spending. Bank of America's latest Global Fund Manager Survey reveals mounting concerns about an AI bubble, with fund managers warning that companies are overinvesting at historic levels. The survey marks a pivotal shift in sentiment as the AI boom faces its first major skepticism from Wall Street's biggest players.
Wall Street's biggest money managers just delivered a reality check to Silicon Valley's AI spending spree. Bank of America's Global Fund Manager Survey dropped a bombshell this morning – for the first time in 20 years, institutional investors believe companies are overinvesting in technology infrastructure.
The timing couldn't be more significant. As Microsoft, Google, Amazon, and Meta continue pouring billions into AI data centers and computing power, the very investors who fund these ambitions are starting to question the math. According to the Bank of America survey, fund managers are explicitly warning hyperscalers to "slow down" their capital expenditure programs.
This marks a dramatic reversal from just six months ago, when the same institutional investors were praising tech companies for their aggressive AI investments. The shift reflects growing anxiety about whether current AI spending levels can generate proportional returns. Fund managers who previously cheered every new GPU purchase and data center announcement are now questioning the fundamental economics of the AI infrastructure buildout.
The numbers behind their concern are staggering. Microsoft alone committed to spending over $50 billion on AI infrastructure this fiscal year, while Google's parent Alphabet increased its capex by 62% year-over-year to fund AI initiatives. Amazon Web Services continues expanding its AI computing capacity, and Meta has allocated unprecedented resources to AI research and infrastructure development.
But here's what's got fund managers spooked – the revenue streams to justify these investments haven't materialized at the same pace. While companies tout AI capabilities and future potential, the monetization remains largely theoretical for many applications. Enterprise customers are still experimenting with AI tools rather than committing to large-scale deployments that would justify the infrastructure investments.