Bank of America Research: AI Capital Expenditure This Year Exceeds Expectations by 50%, Revenue Only by 20%
New Bank of America research shows that AI capital expenditure this year is 50% higher than forecast, while revenue is only 20% higher, and this mismatch runs through its subsequent analysis. The weighted average cost of capital for hyperscalers has risen from 4% in 2023 to 10%, and the risk-free rate is more than 4% above its pandemic low. The research still projects that capital expenditure by the top five hyperscalers plus neoclouds will be $956 billion in 2026, $1.32 trillion in 2027, and $1.5 trillion in 2028, with growth rates of 38% and 14%, respectively; its demand estimate holds that 212 GW of new data center capacity will be needed from 2026 to 2030, equivalent to about $9.5 trillion, which is $2.1 trillion higher than the current capital expenditure forecast of $7.4 trillion.
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Bank of America research presents the growth gap between AI capital expenditure and revenue, along with specific figures showing hyperscalers' cost of capital has doubled, which can serve as a reference for judging the pace of computing power investment.
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Bank of America’s new research finds AI capital spending running 50% above forecasts this year while revenue runs only 20% above, and that mismatch drives the rest of its analysis.
Hyperscalers’ weighted average cost of capital has risen from 4% in 2023 to 10%, as risk-free rates sit more than 4% above pandemic lows and debt costs, equity costs and betas have all climbed.
The BoA research still projects capex for the top 5 hyperscalers plus neoclouds at $956B in 2026, $1.32T in 2027 and $1.5T in 2028, which works out to growth of 38% and then 14%.
Its demand math puts 212GW of new data center capacity between 2026 and 2030 at about $9.5T, $2.1T above the current $7.4T capex forecast.