The global AI market could require $6 trillion in annual revenue by 2031 to fund AI infrastructure investment, with much of the value expected to come from new innovation beyond employee productivity, Bain & Company said in its seventh annual Global Technology Report.
Existing applications of AI will grow, with consumer AI through subscriptions and advertisements, and enterprise AI through software development, sales, marketing, customer service and IT operations, potentially generating between $1.2 trillion and $1.8 trillion in revenue.
Bain’s research identified four key categories that could fund the remaining $4.2 trillion of new revenue.
Model providers are replacing search engines and integrating advertisements to generate new revenue. Autonomous applications, particularly in automobiles, trucks and drones, as well as other industrial automation, will create new products and services.
Physical AI, including simulations, digital twins and robotics, will unlock applications in research and development and manufacturing. New products and uses that do not exist today could create new markets and opportunities 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.
Hardware strikes back
The massive demand for AI compute has revived the hardware industry.
Hardware and semiconductor stocks grew at a 24% compound annual rate from 2020 to 2026, compared with 6% for software.
Three of the fastest-growing segments include high-bandwidth memory, advanced packaging and custom silicon, with application-specific integrated circuits scaling rapidly.
The co-development of dynamic random-access memory with logic silicon means switching is more difficult. With major players focusing on high-bandwidth memory 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.
As a result, custom chips are grabbing a larger share of the data center compute market. The turning point is the emergence of grand-scale, homogeneous workloads — training, inference and agentic — that now run well past the volume needed to amortize a custom design.
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,” said Anne Hoecker, global head of Bain’s Technology practice.
“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,” Hoecker said.
Cybersecurity faces watershed moment
High-profile incidents, notably involving frontier AI model tests, have put cybersecurity at the front and center of every CISO’s agenda, Bain said.
AI has massively compressed the time required by a typical cyberattack from an estimated four weeks to about 18 hours, while the proliferation of AI agents expands its impact.
Poor agentic housekeeping is a glaring weakness. The lack of a silver-bullet solution from vendors is leading many firms to hesitate when they should be proactively creating a pragmatic and flexible solution through a build, buy or partner approach.
The Bain CISO survey also highlighted supply chain risk as an issue.
Companies must understand and control how vendors deploy AI through the whole life of a contract, including midcycle changes and fourth-party exposure. But with vendors shipping changes to models and software daily, oversight systems based on infrequent questionnaires are not coping.
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, the report found.
DevOps teams are also pushed to find and mitigate vulnerabilities.
Leaders are also prioritizing action in the most concentrated and hard-to-fix AI risks, including those posed by legacy platforms, network layers and software-as-a-service vendors.
AI absorption speed becomes new competitive advantage
Absorption speed, the pace at which companies can put AI to work, has become the new competitive variable, Bain reported.
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 that act as harnesses connecting AI to the enterprise and turning model intelligence into business outcomes.
