BCG Model Assigns Companies $1.8 Trillion of 2030 AI Spending Need

A Boston Consulting Group model assigns companies about $1.8 trillion of the $2.55 trillion in annual spending needed for the artificial intelligence ecosystem in 2030, putting corporate budgets at the center of its funding scenario.

BCG’s Oct. 29 analysis gives the total as about $2.6 trillion, global but excluding China. The figure is a modeled annual run rate for end-market spending, not a prediction of actual spending or revenue. The analysis traces costs through data centers and model companies to the customers whose spending would support the ecosystem.

The model assigns $366 billion of the annual requirement to consumers, $204 billion to hyperscalers’ internal use and $200 billion to emerging technologies such as physical AI. The enterprise figure is rounded to one decimal place, while the overall model total is $2.55 trillion.

In an illustrative scenario, companies shift $857 billion from existing technology budgets and add $937 billion to IT budgets. BCG estimates that AI’s share of enterprise technology spending would rise from 5% at the time of the analysis to 25% in 2030. The scenario describes how the required spending might be funded; it does not establish that companies have committed to those amounts.

BCG’s infrastructure calculations assume AI chips have an average five-year lifespan. The analysis estimates that a gigawatt of compute capacity coming online in 2026 would need to generate $15 billion to cover its costs. It also warns that if demand falls short of the level needed, price competition could leave AI model providers and data center operators with losses.

A separate Gartner estimate puts more than $3.5 trillion in AI infrastructure spending at risk of impairment by the end of 2030 if providers maintain their current pace of capital spending. That estimate measures potential impairment over time, while BCG’s figure is an annual end-market spending requirement for 2030. TokenPost has examined a separate revenue hurdle for mature AI data centers, a different measure of the build-out’s economics.