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The AI industry must generate $6 trillion in annual revenue by 2031 to keep funding the infrastructure needed to meet anticipated demand, and this will require imaginative new uses beyond simply enhancing employee productivity.
That $6 trillion figure comes from the 2026 annual Global Technology Report from management consultants Bain & Company, which put its thinking cap on to figure out how the AI sector can possibly meet this challenge.
It says the massive demand for AI compute infrastructure has revived the hardware industry. Some of the fastest growing segments include high-bandwidth memory (HBM) used in GPUs, advanced packaging and custom silicon, and application-specific integrated circuits (ASICs) all scaling rapidly.
As The Register reported last year, AI spending is actually keeping the US economy out of recession, with datacenter infrastructure and model development providing the only significant growth areas.
Bain points to the “arms race” among hyperscalers for AI capacity, claiming that capital expenditure by Microsoft, Google, Amazon, Meta, and Oracle could hit $780 billion for the whole of 2026, nearly five times the level seen just three years earlier.
By 2031, it estimates that annual spending on AI infrastructure could reach $1.5 trillion, a figure that is not far off the $1.6 trillion forecast separately by analyst firm Omdia. The $6 trillion comes from the Bain's assumption that capital expenditure will amount to about 25 percent of industry revenue, an ambitious but reasonable percentage based on trends among cloud providers, it says.
Bain expects that existing applications of AI will grow, but estimates these will add up to a total between $1.2 trillion and $1.8 trillion in revenue. This includes consumer AI, via subscriptions and advertisements, plus enterprise AI, through software development, sales, marketing, customer service, and IT operations.
That leaves a remaining $4.2 trillion of additional revenue for the industry to find from somewhere.
Four possible categories emerge from Bain’s research as possibilities to fill this gap. It says that AI models are replacing search engines and integrating ads to generate new revenue, which means the developers could unlock $100 billion to $200 billion of extra cash.
Secondly, AI to make autonomous cars, trucks, and drones, as well as other industrial automation, could create new products and services worth about $400 billion, Basin estimates.
A similar category is physical AI, in which category it includes simulations, digital twins, and robotics, with a calculated worth of as much as $900 billion.
Those three add up to $1.5 trillion, leaving Bain with a $2.7 trillion shortfall. What could make up the rest of the AI industry revenue? It puts forward “new products and uses that don’t exist today,” which is the kind of answer that your granny could have come up with.
In this category it stuffs AI-driven drug discovery, mental health support that might address billions of dollars in unmet demand, materials science breakthroughs in areas such as battery technology and semiconductors, and accelerating scientific research in fields from neuroscience to fusion energy.
“The debate today is fixated on employee productivity. The economics of AI infrastructure demand trillions in new revenue beyond productivity gains. What the industry needs is a wave of innovation that will dwarf what mobile and cloud unlocked,” said David Crawford, chairman of Bain’s global Technology practice.
It is only a year since a previous report of Bain’s forecast that the industry would need to hit $2 trillion in revenue by 2030 to keep feeding the infrastructure beast. That figure has now trebled, which is perhaps an indication of just how much the investment going into AI has ballooned over the past 12 months.
There are reasons to think the AI industry is not going to hit $6 trillion in annual revenue by 2031. The Register has published numerous reports indicating that AI rollouts are just not paying off, such as this one, this one or this one.
At the same time, doubts are being cast over whether all those AI infrastructure projects will be realized. A report from investment bank Jefferies found only half the US datacenter capacity scheduled for 2026 is actually under construction and work is yet to begin on as much as 80 percent of the 2028 pipeline.
Another report from the same company suggested that chip manufacturing constraints will limit how many planned AI server farms can be brought online over the next several years. ®
