$2 trillion in new revenue needed to fund AI’s scaling trend - Bain & Company’s 6th annual Global Technology Report

• Even with AI-related savings, investors are still $800 billion short in annual revenue required to profitably fund the data centers of 2030
• Agentic AI innovation is unprecedented, but most companies remain in experimentation mode before taking their "hands off the wheel"
• Quantum computing could unlock as much as $250 billion in market value across industries

SAN FRANCISCO - September 23, 2025 - Two trillion dollars in annual revenue is what'sneeded to fund computing power needed to meet anticipated AIdemand by 2030. However, even with AI-related savings, the world is still $800 billion short tokeeppace with demand,new research by Bain & Company finds.

Bain's sixth annual Global Technology Report released today shows, by 2030, global incremental AI computerequirements could reach 200 gigawatts, with the US accounting for half of the power. Even if companies in the US shifted all of their on-premise IT budgets to cloud and reinvested the savings from applying AI in sales, marketing, customer support, and R&D into capital spending on new data centers, the amount would still fall short of the revenue needed to fund the full investment,as AI's computedemand grows at more than twice the rate of Moore's Law, Bain notes.

"If the current scaling laws hold, AI willincreasingly strain supply chains globally,"said David Crawford, chairmanof Bain's Global Technology Practice. "By 2030, technology executives will befaced with the challenge of deploying about $500 billion incapital expenditures and finding about $2 trillionin new revenue to profitably meet demand. Meanwhile, because AI compute demandis outpacing semiconductor efficiency, the trends call for dramatic increases in power supply on grids that have not added capacity for decades. Add the arms race dynamic between nations and leading providers,and the potential for overbuild and underbuild has never been morechallengingto navigate.Working through the potential for innovation, infrastructure, supply shortages, and algorithmic gainsis critical to navigate the next few years."

Agentic AI develops at breakneck speed; couldoffer another round of gains

Whilecomputational demand increases, leading companieshave moved from piloting AI capabilities to profiting from AIas organizations scale the technologyacross core workflows delivering 10% to 25% earnings before interest, taxes, depreciation, and amortization (EBITDA)gainsover the last two years. Yetmost companies today remainstuck in AI experimentation modeand are satisfied with modestproductivity gains,the report concludes.

Tech-forward enterprisesare pursuingagentic AI capabilities which is making for an unprecedented rate of innovation, the report finds. Over the next three to five years, 5% to 10% of technology spending could be directed toward building foundational AI capabilities including agent platforms, communication protocols, real-time data accessand discoverability for agents. As much as half of overall technology spending by companies couldbe used on AI agents running across the enterprise, Bain estimates.

As AI evolves,the report shows leaders will pull further ahead oflaggards infour levels of maturity including(1) large language model (LLM)-powered information retrieval agents,(2) single-task agentic workflows, (3) cross-system agentic workflow orchestration, and (4) multi-agent constellations. Levels 2 and 3 are where capital, innovation, and deployment velocity are converging. With AI innovation moving at breakneck speed, leaders are compounding their advantage while followers are getting further behind.

Meanwhile, enterprise IT architectures are struggling to realize the vision of contextually-informedsecure agents freely collaboratingacross multiple applications and databases to automate the diverse tasks accomplishedby humans. Bain finds that a north star architectureis critical,butprofit motives and security demands will lead to uneven progress towardthe four levels of agentic maturityover years.

AI is disrupting SaaS, but whether it disrupts a SaaS provider is up to them

SaaS providers are facing disruption from the rise of generative and agentic AI, but this does not mean obsolescence. In many cases this can be totaladdressable market (TAM)-additive for SaaS providers. Companies should evaluate two independent characteristicsas they plan to benefitfrom AI in their industry: the potential for AI to(1)automate SaaS user tasks and (2) penetrate SaaS workflows. SaaS incumbents are well-positioned to lead, but thiswill require high-stakes strategic bets-such as selective open-sourcing or a shift in the monetization model-and will yield a unique, durable industry influence position. To stay ahead, providersmust own the data, lead on standards, and price for outcomes, not log-ons, in an AI-first world.

New norm for business leaders as sovereign AI creates a fragmented world

Tariffs, export controls, and the push by governments worldwidefor sovereign AI are accelerating the fragmentation of global technology supply chains, Bain finds. Cutting-edgedomains such asAI are no longer just catalysts for economic growth butare conduits for countries' political powerandnational security.As semiconductorsupply chains fragment, the US and China remain at the forefront of the decouplingmovement, with China accountingfor roughly 20% of globalchipmanufacturing capacity just this year, the report shows.

"Sovereign AI capabilities are increasingly seen as a strategic advantage on par with economic and military strength," said Anne Hoecker, head of Bain's Global Technology practice. "While sovereign AI is a global priority, individual countries' goals vary. Therefore, for most countries, achieving full-stack independence is not feasible, at least not today. Considering these differences, global AI standards are unlikely to converge.

"To succeed, multinational firms will need to localizenot just compliance, but also their technology architecture. Businesses need to make decisions with optionality, moving boldly where confidence is high and prioritizing flexibility where uncertainty rules."

Companies eye quantum computing advancements, rise of humanoid robots

Alongsiderapid acceleration of AI initiatives are two separate technological phenomena: the advancement of quantum computing, and the rise of humanoid robots.

Quantum computing has the potential to unlock as much as $250 billion in market value across industries such as pharmaceuticals, finance, logistics, and materials science, posing opportunities that willbe gradual-though not guaranteed, Bain estimates.While the potentialposed by quantum computing is immense, to reach full market potential, a fully capableandfault-tolerant computer will be neededat scale and is still years away, the report concludes.

In parallel, interest in humanoid robots has increased from viral videos to billion-dollar valuations. As humanoid robots become more prevalent, commercial success will hinge on ecosystem readiness, and companies that pilotthemearly will be best positioned to become the leaders during a new eraof growth. While robots draw both headlines and capital, most deployments remain early-stagewith heavy reliance on human supervision, Bain finds.

Technology private equity deals slow in second half of 2025, though temperament remainsupbeat

Gone are the days of easy technology private equity (PE) deals as deal momentum fueled by software salesslows.According to Bain's2025analysis of PE technology deals in North America, deal-making has not been immune to tariff-related uncertainties and geopolitical tensions despite an increase in tech deals in the first half of this year. While software spending continues to outpace overall GDP growth, software's penetration is topping out across major sectors,such as manufacturing and retail,posing new challenges for tech investors who will need to work harder to find new sources of top-tier growth.Despite deal process slowdowns, investors remainupbeat as Technology has outperformed most other sectors in terms of dealmaking, Bain's analysis shows.

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