Deutsche Bank has addressed recent findings indicating that the swift growth of artificial intelligence could encounter a funding gap of US$800 billion, primarily due to the immense infrastructure and data center investments required to support anticipated revenue increases.
Scaling Infrastructure for AI
New analysis from the bank reveals that continued growth in AI hinges not on software innovation but on massive capital spending across data centre capacity, computing hardware and power systems.
New research from consulting firm Bain & Company warns that the AI sector faces a major challenge in meeting demand for compute capacity by the end of the decade.
It estimates that the industry will need to generate US$2tn annually in revenue by 2030 to keep up with demand for computing infrastructure. Even after factoring in cost savings across sectors using AI, Bain calculates that the shortfall remains at US$800bn.
AI Growth Depends on Data Centre Scale
Although AI promises long-term productivity gains, Deutsche Bank identifies infrastructure spending – not the technology itself – as the main source of economic activity at present. Companies are funnelling funds into expanding data centre real estate, procuring specialised hardware and scaling energy provision to support AI workloads.
With capital expenditure on AI already reaching US$368bn through August, the future of AI’s economic role may rest on whether hyperscale investment can continue at this pace. Amazon, Microsoft and Google are leading this charge, but infrastructure projects of this scale demand vast energy input, physical space and hardware procurement.
Data Centres at Unprecedented Scale
The deployment of 10GW of GPU-powered systems represents one of the largest AI infrastructure projects to date. These facilities, which will host millions of GPUs, are being designed to deliver the compute power required for GenAI’s future models and AI-driven services.
Reference previous article on Global Data Centre Trends 2025.
Investment and Innovation Outlook
Bain’s report underlines that AI compute demand is growing at more than twice the rate of Moore’s Law. The US is expected to account for half of the 200GW required, straining not only capital budgets but also electricity grids.
However, the infrastructure question remains unresolved. Bain & Company concludes: “Two trillion dollars in annual revenue is what’s needed to fund computing power needed to meet anticipated AI demand by 2030. However, even with AI-related savings, the world is still US$800bn short to keep pace with demand.”
The Reason Behind AI Growth
Bain notes that leading enterprises are pushing into agentic AI, developing platforms that enable autonomous workflows across multiple systems. These require data centres capable of supporting high levels of virtualisation, low-latency interconnections and seamless access to real-time data.
The consultancy identifies four stages of agentic AI maturity, from single-task workflows to multi-agent constellations. The middle levels, where capital investment and innovation are converging, will be especially demanding for data centre infrastructure.
Goldman Sachs economist Manuel Abecasis explains: “We expect productivity gains from AI to boost GDP significantly, by about 0.4% through the next few years and 1.5% cumulatively as adoption rises over the long run.” He argues that “once it is widely adopted, AI is likely to allow workers and firms to produce more output for a given set of inputs, which will raise total factor productivity growth.”