Here's How AI Could Pay for Itself

AI is going to have to earn its keep by inspiring $4 trillion of as yet unknown applications and services, with annual spending on AI infrastructure reaching $1.5 trillion in the next five years. That's according to Bain & Company's Technology Report , which notes that the size and cost of AI data centers is doubling every 12 to 16 months. Last year, Meta's 600MW Prometheus cost about $24 billion to build, and data centers currently in the works at 1-2GW will cost between $40 billion and $80 billion to build — climbing to $200 billion for a 9GW data center in 2030, the analyst firm predicted. That suggests annual spending on AI infrastructure will top $1.5 trillion by 2031, Bain predicted, including new data centers and compute, as well as upgrades to existing GPUs, memory, and networking equipment. Doubling costs That's double what's expected to be spent this year, with Bain predicting $780 billion in capital expenditure across the five key hyperscalers, though that does include investments beyond AI. IDC predicts AI infrastructure spending of $497 billion this year, while Gartner puts it at more than $1 trillion, including a wider range of equipment. What they do agree on is significant growth: Bain has the figure doubling to $1.5 trillion by 2031, IDC predicts $1.2 trillion by 2030, and Garter sees total AI spending doubling in just two years. That raises a challenge: can AI earn enough to cover these costs? Bain argues yes – but with some caveats. "The infrastructure is being built ahead of the demand curve, and funding it sustainably will require adding approximately 1% to the annual global GDP growth rate," Bain's analysts say. "The question is whether the applications arrive in time to pay for it." Paying for AI First, the analysts at Bain argue that CapEx for cloud providers is usually about a quarter of industry revenue, meaning AI will need to earn $6 trillion in revenue by 2031 to maintain that margin. By 2031, consumer AI products could contribute between $200 billion to $400 billion by 2031 via subscriptions and advertising. "Enterprise adoption could contribute another $1 trillion to $1.4 trillion in gains to providers alone as AI delivers meaningful productivity gains to enterprises across software development, sales, marketing, customer service, and IT operations," Bain's analyst predict. That leaves about $4.2 trillion of revenue missing. Bain figures this will come from four new categories, starting with search and advertising at $100 billion to $200 billion, with ads integrated into chatbots as traditional online search is disrupted. Missing $4 trillion That leaves $4 trillion. Bain says $400 billion could come from autonomous operations of cars, drones, and robotaxis, as well as logistics, while physical AI could add $900 billion via simulations, digital twins, and the deployment of robotics – including humanoid ones. That leaves another $2.7 trillion to be made up in new product development, anything from drug discovery to materials sciences, Bain suggests. The point isn't that AI is expensive, but that companies need to do more with it than merely shave a few percentage points from their bottom lines. "The economics required to generate ROI from AI infrastructure are demanding trillions in new revenue, not just cost savings," Bain said. "The industry needs a wave of application innovation comparable with what mobile and cloud unlocked, not just productivity gains on existing workflows." Cannibalising tech While the tech industry is seeking revenue for AI, that same technology is eating away at margins in other departments. Bain noted that software revenue growth has "moderated significantly" from 20% annually in 2022 to half that last year. On the flip side, Bain predicted that agentic AI could prove to be a $100 billion opportunity for SaaS providers. Beyond costs and revenue, Bain also warned in its technology report that the $5 trillion data center rollout still faces constraints from power, chips, skilled labor, and permits. "Individual projects are finding workarounds, but they can’t be replicated at scale, and some are controversial. Colossus (from xAI, now part of SpaceXAI) was built in under a year by repurposing a factory and leasing dozens of gas turbines without federal clean air permits, drawing a lawsuit," Bain said. The analysts added: "Meeting the global ambition instead will require system-level fixes outside the usual free-market playbook. A grand challenge on this scale demands grand solutions, and executives planning on business as usual will get blindsided."