Bain & Company’s 6th Annual Global Technology Report: $2 Trillion Needed to Fund AI’s Scaling Trend
September 23, 2025 — San Francisco
As artificial intelligence (AI) continues its rapid expansion, Bain & Company’s latest research reveals that an estimated $2 trillion in new annual revenue will be necessary to fund the unprecedented growth in computing power required to meet AI demands by 2030. The findings are detailed in Bain’s 6th annual Global Technology Report released today.
The Scale of AI Compute Demand by 2030
Bain forecasts that by 2030, incremental global AI compute demands could reach 200 gigawatts, with the United States alone accounting for approximately half of that power consumption. This figure highlights the immense scale of the computing infrastructure that will be needed to support next-generation AI applications.
Even with cost savings driven by AI efficiencies, the world faces an $800 billion annual shortfall to profitably fund the data centers necessary to meet the anticipated compute needs. Bain’s analysis suggests that even if all on-premise IT budgets within U.S. companies were shifted entirely to cloud services and reinvested into new data center capacity, it wouldn’t close the financial gap.
The Growing Challenge Beyond Moore’s Law
AI’s demand for compute power is growing at more than twice the rate predicted by Moore’s Law — the historical norm for semiconductor performance improvements. David Crawford, chairman of Bain’s Global Technology Practice, explains:
“If current scaling laws hold, AI will increasingly strain supply chains globally. By 2030, technology executives will need to deploy about $500 billion in capital expenditures and generate roughly $2 trillion in new revenue to profitably meet AI demand. Compounded by semiconductor efficiency limits and grid power constraints, the scale of investment and infrastructure expansion is unprecedented.”
Power grids in many regions have not added significant capacity for decades, posing further challenges to meeting the electrical demands of AI-centric data centers. This scenario is complicated by an emerging arms race among nations and key industry players competing for AI leadership, raising the stakes for infrastructure planning amid risks of both overbuilding and underbuilding.
Agentic AI Innovation and Enterprise Adoption
The report highlights that while computational demand surges, many leading companies have moved beyond piloting AI. These organizations are now scaling AI integrations within core workflows, realizing EBITDA gains ranging from 10% to 25% over the last two years. Despite this progress, Bain notes that most companies remain in experimentation phases and have only achieved modest productivity improvements.
Tech-forward firms are focusing on agentic AI—intelligent systems capable of autonomous decision-making and multi-step tasks—which is accelerating innovation at an unprecedented pace. Over the next three to five years, Bain forecasts that 5% to 10% of global technology spending will be directed toward foundational AI capabilities such as agent platforms, communication protocols, and enabling real-time data accessibility for AI agents.
Intriguingly, up to half of overall corporate technology budgets could be devoted to running AI agents across enterprises in the near future.
Maturity Levels in Agentic AI Adoption
The report outlines four maturity levels for organizations adopting agentic AI:
- Large language model (LLM) powered information retrieval agents.
- Single-task agentic workflows.
- Cross-system agentic workflow orchestration.
- Multi-agent constellations.
Bain finds that investments and innovation are currently converging around levels 2 and 3, where AI workflows are increasingly orchestrated across systems. Leaders in AI maturity continue to pull ahead, while laggards fall further behind. A key obstacle remains enterprise IT architectures, which struggle to support secure, context-aware AI agents capable of collaborating autonomously across diverse applications and databases. The report emphasizes the importance of a “north star” architecture, though profit motives and security concerns will influence uneven adoption trajectories.
AI’s Impact on SaaS Providers
Software-as-a-Service (SaaS) businesses are poised for significant disruption from generative and agentic AI technologies. However, Bain cautions that AI does not necessarily mean obsolescence for SaaS providers. In many cases, AI adoption can expand the total addressable market (TAM), creating new growth opportunities.
To harness these benefits, SaaS companies should assess:
- The potential for AI to automate user tasks within their platforms.
- The extent to which AI can integrate across SaaS workflows.
Bain suggests that incumbents who strategically incorporate AI into their offerings may strengthen their market positions and capitalize on AI-driven efficiencies.
The Quantum Computing Horizon
Beyond AI, Bain’s report touches on the promise of quantum computing, projecting this technology could unlock up to $250 billion in market value by transforming industries from pharmaceuticals to logistics. While still emerging, quantum computing is positioned as a potential accelerant for future innovation and enterprise value creation.
As AI reshapes the technological landscape, Bain & Company’s Global Technology Report serves as a critical wake-up call for businesses and policymakers. Closing the $2 trillion revenue gap and overcoming infrastructure challenges will require coordinated investment, innovation, and strategic vision to successfully navigate AI’s transformative scaling trend through 2030 and beyond.
