As AI infrastructure investment accelerates, the world must confront the next challenge: whether the economic value created can justify the capital deployed. According to Bain & Company's 7th annual Global Technology Report, funding AI's insatiable compute demand would require $6 trillion in annual revenue by 2031 and much of the value lies in new innovation beyond employee productivity.
Existing applications of AI will continue to grow, but new categories of innovation are required to fund the next wave of growth. The report finds that enterprise and consumer AI applications alone will not be sufficient to sustain the massive capital deployed in AI compute and infrastructure.
Bain's research highlights that the current debate fixated on employee productivity is insufficient to fund AI's future. While consumer AI through subscriptions and advertisements and enterprise AI through software development, sales, marketing, customer service, and IT operations could total between $1.2 trillion and $1.8 trillion in revenue, an additional $4.2 trillion must come from new innovation.
According to Bain, four key categories will drive this remaining value. First, model providers are replacing search engines and integrating ads to generate new revenue. Second, autonomous everything particularly in automobiles, trucks, and drones, as well as other industrial automation, will create new products and services. Third, physical AI, including simulations, digital twins, and robotics, will unlock a wide range of new applications in R&D and manufacturing. And last, new products and uses that do not exist today will enable new markets from abundant intelligence, including drug discovery, mental health, and energy generation.
"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. AI infrastructure is being built well ahead of the demand curve and funding it sustainably will require adding approximately 1% to the annual global GDP growth rate," said David Crawford, chairman of Bain's global Technology practice.
The massive demand for AI compute has revived the hardware industry. In a reversal, hardware and semiconductor stocks grew at a 24% compound annual rate from 2020 to 2026 vs. 6% for software. Three of the fastest growing segments include high-bandwidth memory (HBM), advanced packaging and custom silicon, with application-specific integrated circuits (ASICs) scaling rapidly.
The co-development of dynamic random-access memory (DRAM) with logic silicon means switching is more difficult. With major players focusing on HBM capacity, there is little investment in double data rate and NAND, which could worsen shortages and raise the prices of smartphones and PCs. Special-purpose, high-performance accelerators are moving from niche to mainstream as hyperscalers and AI-native firms design chips tuned to their workloads.
Pricing and supply risks from natural disasters, geopolitical disruption, and export controls are rewriting supply chain dynamics. Companies are now securing multiple supply locations with diversified vendors, while foundries are responding similarly, particularly in logic chips.
"This is a dynamic time for players in the hardware sector. Supply chain and procurement strategies are increasingly sources of competitive advantage as investing in supplier capacity, long-term agreements, equity investments in the supply chain, and multi-vendor, multi-geography sourcing are now very high on the C-suite agenda. Also, product strategy choices are changing. The old model where one vendor innovates and sells to everyone else is changing, and a wave of verticalization and semi-custom design are becoming more prevalent," said Anne Hoecker, global head of Bain's Technology practice.
Bain finds AI has massively compressed the time required by a typical cyberattack from an estimated four weeks to about 18 hours, and the proliferation of AI agents expands its impact. Poor agentic housekeeping is a glaring weakness, with many firms hesitating due to lack of a silver-bullet solution from vendors.
Leading companies are strengthening their remediation operating model. Leaders have increased remediation budgets, typically by a double-digit percentage, while redirecting as much as 20% to 25% of their cybersecurity human resources from other work to remediate alerts from AI-powered scans. DevOps teams are also pushed to find and mitigate vulnerabilities, with priority on legacy platforms, network layers and software-as-a-service vendors.
Absorption speed, the pace at which companies can put AI to work, has become the new competitive variable. To address this, leading labs are investing upwards of $9.75 billion in forward-deployed engineering models to help companies assimilate faster. Vendors are also building out the application and infrastructure layers which act as harnesses that connect AI to the enterprise.
Bain does not see commoditization of large language models despite rising model competition and falling token prices.
"We see the more likely scenario as a continuum of frontier and mature models. New and unproven cases will initially favor frontier models, then likely migrate to lower-cost alternatives as the cases mature. Frontier providers will push toward more fundamental challenges, capturing value where superior intelligence matters most. Meanwhile, lower cost, specialized, and optimized models will efficiently serve more common tasks, providing the services and support that enterprise customers need," said Crawford.
"The industry end state is far from settled. We are likely headed into a segmentation between frontier models and trailing models or an expanding definition of models rather than a classic commoditization pattern."
As companies become more adept at deploying the right model for the right task, building engineering management systems becomes critical. Bain's Tech and Engineering Survey of 293 senior technology leaders found respondents anticipated a 148% improvement in release-cycle speed and a 95% uplift in software developer productivity within one to two years, much higher than the 20% to 27% gains captured today.
To meet this, leading companies are structuring knowledge for AI agents, engineering quality and trust into workflows, and managing the software development lifecycle (SDLC) as a continuously improving product.
Bain's Global Technology Report underscores that sustainable AI growth will depend not just on infrastructure investment, but on unlocking entirely new markets, rearchitecting cybersecurity, and mastering AI absorption across the enterprise.
About Bain & Company
Bain & Company works with leaders worldwide to solve their toughest challenges and deliver enduring results. Since 1973, we've partnered with clients, including private equity and portfolio companies, to build the capabilities they need to stay ahead of change and help them redefine their industries. We measure our success by our clients' success, and we proudly hold the highest levels of client advocacy in our field.
Bain is consistently recognized globally as one of the best places to work. We operate as one global team, uniting strategists, industry and functional experts, technologists, and advisors with a vibrant ecosystem of technology partners.
