Bain & Company’s Global Technology Report Highlights $2 Trillion Revenue Gap to Fund AI Expansion
September 23, 2025 – San Francisco
Bain & Company’s sixth annual Global Technology Report reveals that the rapid scaling of artificial intelligence (AI) technology will require an additional $2 trillion in annual revenue by 2030 to meet skyrocketing computing power demands. Despite cost savings generated through AI applications, businesses globally face an $800 billion shortfall in revenue necessary to profitably build and sustain the AI data centers of the future.
AI Compute Demand Surges Beyond Moore’s Law
According to Bain’s research, incremental AI computing needs could reach 200 gigawatts by 2030, with the United States accounting for roughly half of this power consumption. This surge in demand is growing more than twice as fast as improvements in semiconductor efficiency, outpacing the rate predicted by Moore’s Law.
David Crawford, Chairman of Bain’s Global Technology Practice, emphasized the magnitude of the challenge: “If current scaling trends persist, AI will increasingly strain global supply chains and energy grids, many of which have not expanded capacity in decades."
Capital Investment and Revenue Generation Challenges
Technology executives will need to deploy approximately $500 billion in capital expenditures to support this AI growth. However, due to the accelerating compute needs, companies must also generate about $2 trillion in new revenue streams in order to fund the expansion sustainably and profitably.
Even hypothetical scenarios where U.S. companies redirect their entire on-premises IT budgets to cloud infrastructure and reinvest AI-generated savings into new data centers fall short of the funding requirements. This funding gap illustrates the urgency for strategic investment and innovative solutions across the AI ecosystem.
Rapid Innovation in Agentic AI
While the computational demands increase, Bain’s report notes that leading companies are transitioning from experimentation with AI to scaling agentic AI capabilities—those that operate autonomously across workflows. These firms have already experienced earnings before interest, taxes, depreciation, and amortization (EBITDA) improvements ranging from 10% to 25% over two years.
Despite these advances, most organizations remain in early AI adoption stages and report only modest productivity gains. The report forecasts that 5% to 10% of technology budgets could soon be dedicated to foundational AI development, including agent platforms and real-time data access protocols. Bain estimates that AI agents may consume up to half of overall technology spending as enterprises increasingly deploy these tools in their operations.
Four Levels of AI Maturity Create a Growing Divide
Bain identifies four maturity levels virtually defining the AI adoption trajectory:
- Large Language Model (LLM)-powered information retrieval agents
- Single-task agentic workflows
- Cross-system agentic workflow orchestration
- Multi-agent constellations collaborating at scale
Most investment and innovation currently converges at levels 2 and 3, where capital infusion and deployment velocity are highest. The report warns of widening gaps as early adopters accelerate their AI capabilities, leaving laggards behind. Developing a “north star” IT architecture that securely enables collaboration among AI agents is crucial, yet progress will vary due to competing business priorities and security concerns.
AI’s Impact on SaaS Providers
Software-as-a-Service (SaaS) companies face disruption from generative and agentic AI, though Bain highlights that this disruption does not necessarily mean extinction. Instead, AI advances can expand the total addressable market for SaaS providers.
SaaS incumbents are advised to analyze AI’s potential to automate user tasks and its likelihood to penetrate existing workflows. Companies that proactively integrate AI will be better positioned to capitalize on emerging opportunities within their sectors.
Bain & Company’s findings underline the immense economic and technological effort required to harness AI’s transformative potential. Bridging the multi-trillion-dollar revenue gap and strategically allocating capital will be pivotal for companies and governments aiming to lead in the AI-driven era.
