BCG: AI build-out needs $2.6 trillion a year by 2030

BCG’s analysis puts a number on how much of the AI build-out ultimately has to be paid for by corporate buyers rather than by the technology vendors themselves. The $2.6 trillion figure is global spending excluding China, and the infrastructure costs are concentrated mainly in the U.S., where most new capacity is being built. Within that total, enterprises are the largest source at $1.8 trillion, ahead of consumers at $366 billion, hyperscalers’ internal use at $204 billion, and emerging technologies such as physical AI at $200 billion.

BCG offers an illustrative path for how companies could reach that enterprise figure: reallocating $857 billion from existing technology budgets and adding $937 billion in new spending. That would put enterprise tech budgets roughly 13% above International Data Corporation’s IT spending forecast, with AI’s share of those budgets rising from 5% today to 25% in 2030. On this framing, the industry’s growth leans heavily on corporate budgets expanding rather than merely being reshuffled.

BCG argues the build-out is more resilient than past infrastructure booms, partly because hyperscalers have financed much of their investment through cash flow rather than debt, while cautioning that the ability to finance infrastructure doesn’t guarantee adequate returns. O’Niell points to two indicators worth watching: how infrastructure investment is financed, with a sustained shift toward debt signaling greater risk, and whether deployed compute generates sufficient revenue. As frontier labs enter public markets, their disclosures could reveal whether revenue is keeping pace with compute costs, which matters because weaker-than-expected demand could trigger price wars and losses for model providers and data-center operators.