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Solar PV accounted for 83% of all new electricity generation capacity added globally in 2025, and for the first time, renewables surpassed coal in total global electricity generation. The challenge now isn't building clean energy — it's using it efficiently.
A UN University report warns AI data centers consumed 4.5 trillion litres of water in 2025 and could hit 9.3 trillion by 2030 — rivaling global drinking water needs.
The European Commission launched AI.grids, a coordinated initiative to develop AI models specifically for grid management and planning, estimating demand-side flexibility could cut EU electricity costs by more than 71 billion euros per year.
China added 430 gigawatts of renewable energy in 2025 alone and is co-locating AI data centers in surplus clean-power zones, creating a structural electricity cost advantage over US AI infrastructure that export controls and chip restrictions cannot address.
Early adopters of AI-managed commercial buildings are reporting 20-30% energy savings, while AI tools reshape construction from pre-design cost modeling to permitting to long-term building operations.
Grid-scale battery storage is filling the gap between AI data centers' round-the-clock power demands and intermittent renewables — reshaping how hyperscalers structure power agreements.
The world's largest commercial real estate firm didn't set out to become an AI infrastructure gatekeeper. The data center boom didn't give it a choice.
NextEra Energy's $67 billion all-stock acquisition of Dominion Energy creates the world's largest regulated utility—explicitly built to meet surging AI data center power demand.
AI data centers are demanding power faster than the grid's seven-to-ten-year interconnection timeline can handle, forcing utilities and the DOE to promote battery-bridge connections and speed-first grid frameworks.
AI data centers scale in 12 to 18 months. Renewable energy takes three to five years. Carbon Direct's 2026 outlook shows the mismatch is forcing hyperscalers into natural gas while climate commitments quietly erode.
MIT published a method to estimate AI energy consumption from chip architecture specs alone — giving grid planners and policymakers a faster way to project AI's power demand before systems are deployed.
AI workload scheduling — not new power plants — is the fastest available solution to data center grid stress, as AI forecasting now predicts renewable output at 95%+ accuracy.
A World Economic Forum analysis finds that AI-enabled tools — not government policy — have become the primary driver of renewable energy deployment speed, as global renewables added 793 GW in 2025 and surpassed coal in electricity generation for the first time.
Google is in active negotiations with SpaceX to put AI compute infrastructure into orbit — a move that could reshape who controls the physical layer of AI.
AI's energy demand is accelerating renewable buildout through hyperscaler procurement commitments — but the feedback loop is policy-dependent and could reverse if clean-energy matching requirements aren't enforced.
AI data center demand is funding renewable energy projects at record pace — and simultaneously overwhelming the transmission infrastructure needed to deliver that power.
Community opposition to AI data center construction is spreading nationwide, driven by concerns over water use, grid strain, and industrial noise — and now it's reaching governors' desks.
Microsoft is weighing whether to abandon its 2030 goal of matching 100% of its electricity with carbon-free energy on an hourly basis, as AI data center demand outpaces renewable deployment.
Commercial buildings can detect energy waste and equipment failures with AI, but most lack automated controls to act on those signals — a gap eroding the ROI case for smart building deployments.
Microsoft is internally reviewing whether to abandon its 2030 clean energy commitment as AI data center expansion has pushed power demand far beyond what its renewable procurement pipeline can cover.