Sustainability

Introduction TL;DR: The exponential growth of AI models is colliding with physical reality: power grids are tapped out, and water resources are depleting. The root cause lies not just in demand, but in the fundamental inefficiency of current hardware architectures. Emerging non-von Neumann designs offer a path forward. As of late 2025, the AI industry is facing a reckoning. While model capabilities continue to soar, the infrastructure required to run them is hitting a hard ceiling. Reports indicate that AI data centers now consume water equivalent to the global bottled water supply and generate carbon emissions rivaling major cities. The era of “compute at all costs” is ending; the era of “sustainable compute” must begin. ...

Introduction TL;DR: AI models’ energy demand is rising fast enough to visibly reshape power systems in several countries. Global data center electricity use reached around 415 TWh in 2024 (about 1.5% of global demand) and is expected to more than double by 2030. In the US, data center power use has climbed to roughly 4.4% of total electricity consumption and could reach 10–12% by 2028 under high-growth scenarios. Local grids in Ireland, Texas, and Northern Virginia are already facing real constraints, forcing costly upgrades and new regulatory approaches. At the same time, hyperscalers are signing multi‑GW renewable PPAs and pushing efficiency hard, yet Scope 3 emissions and local grid bottlenecks remain unresolved. The real question is how to balance AI progress with sustainability through grid upgrades, clean energy, demand flexibility, and smarter siting — not whether to stop AI. ...