News DeskDubai, UAE: Arthur D. Little’s Blue Shift Institute has released a new report warning that the accelerating adoption of artificial intelligence is creating significant hidden dependencies on energy, environmental resources, and compute infrastructure—posing growing systemic risks for businesses worldwide.
Titled AI’s Hidden Dependencies, the in-depth study draws on insights from more than 50 experts and examines how AI’s expanding use is placing unprecedented strain on electricity grids, water supplies, and global technology supply chains. As AI increasingly becomes a form of critical infrastructure, the report argues that these pressures will translate into economic, sustainability, and strategic vulnerabilities for companies.
The report identifies three major areas of dependency. First, AI’s environmental impact is rising sharply due to high energy consumption and the carbon-intensive manufacturing of specialized hardware. Second, growing electricity demand from data centers is putting pressure on energy systems, leading to grid congestion, connection delays, and even moratoria in major AI hubs. Third, reliance on concentrated compute infrastructure and dominant providers is creating supply-chain choke points and increasing the risk of strategic lock-in.
According to the study, AI energy demand could grow fivefold by 2030, pushing global data-center electricity consumption close to 1,000 terawatt-hours—around 3% of total global power demand. In some regions, data centers could account for up to 40% of local electricity use within the next decade. The report also highlights water stress, noting that a single hyperscale AI data center can consume as much water per day as a medium-sized city.
The findings suggest that AI inference—rather than training—is now the main driver of AI-related emissions, as AI systems shift toward continuous, always-on usage. At the same time, transparency is declining: fewer than 3% of newly released AI models disclose energy or emissions data, down from about 10% a year ago.
As these hidden dependencies surface, the report warns of three systemic business risks: rising economic instability as true AI costs emerge, growing sustainability exposure as companies lose control over their carbon footprint, and reduced competitiveness due to dependence on a small number of suppliers and jurisdictions.
To address these challenges, Arthur D. Little recommends a set of “no-regret” actions for businesses. These include aligning AI investments with clear business value, gaining visibility and control over the real environmental footprint of AI use, and building strategic resilience by preserving the ability to shift between providers and regions.
Commenting on the findings, Dr. Albert Meige, Global Director of the Blue Shift Institute, said: “AI feels cheap today because its real economic and environmental costs are essentially hidden. Once dependence sets in, those costs will surface. And companies should be strategically prepared.”
The report concludes that while AI remains a powerful driver of productivity and innovation, managing its underlying dependencies will be critical to ensuring long-term resilience, sustainability, and competitiveness.
