What Sustainable AI Has to Prove

AI runs on physical infrastructure with growing demands for electricity, water, and hardware. Kiani examined those demands alongside AI’s potential environmental benefits, then developed and presented an argument for the evidence, local safeguards, and accountability that responsible growth requires.

Academic research project completed and presented for a sustainability class in 2026. An eight-slide research presentation with speaker notes.

Kiani began by defining the assessment boundary. A server’s energy efficiency captures only part of the resources required to deliver an AI service. His research connected facility electricity, cooling, and noise to the surrounding grid, watershed, and communities, and followed hardware from extraction and manufacturing through use, recovery, or disposal.

The central tension emerged in the electricity research. Energy use per AI task was falling, while wider adoption and more demanding applications drove total consumption upward. The IEA’s 2026 assessment made the scale of that tension clear: electricity consumption at AI-focused sites alone grew 50% in 2025.

All data centers worldwide used an estimated 485 TWh of electricity in 2025, projected to reach 950 TWh by 2030 (IEA 2026). The figure covers all workloads, not AI alone.

Kiani used this contrast to argue for evaluating total environmental impact alongside efficiency. Lower consumption per task matters, but expanding use changes the scale of the problem. Capacity planning and environmental commitments have to account for both.

The presentation also examined AI’s potential to improve building controls, traffic routing, weather forecasting, and grid operation. The IEA’s 2025 widespread-adoption scenario estimated that existing applications could avoid 1.4 gigatonnes of CO₂ in 2035. Sensors and AI-assisted grid management could unlock up to 175 GW of transmission capacity on existing lines.

Kiani connected those possibilities to their enabling conditions: investment, access to data, and adoption. His argument preserved the opportunity while making verified environmental benefit part of the assessment. A promising application earns its place through what it delivers.

Water made the relationship between computing and place concrete. Berkeley Lab estimated that all U.S. data centers consumed 66 billion liters directly in 2023, with nearly 800 billion liters consumed indirectly through their electricity supply. Kiani considered dry cooling and the importance of local drought and groundwater conditions when evaluating a facility.

He also examined noise as a social impact. Virginia’s legislative research commission found that the constant sound from some facilities affected nearby residents even when it rarely exceeded local noise limits. The example connected environmental performance to community well-being and the adequacy of existing rules.

For hardware, he used UNITAR’s estimate of 62 million tonnes of global electronic waste in 2022, of which 22.3% was documented as properly collected and recycled. These figures describe all electronics. The presentation placed AI hardware within that wider material system.

Kiani tested the meaning of a renewable-energy claim against a practical question: what powers the facility at 2 a.m.? The IEA’s 2025 outlook projected that renewables would meet nearly half of added data-center electricity demand through 2030, with gas and coal supplying more than 40%.

He argued for new low-carbon generation, storage, and transmission, matched to demand hour by hour. Nuclear offered a further source of steady low-carbon power, with cost, construction time, waste, and cooling-water considerations.

The conclusion brought the research into four questions:

Using the EU’s annual reporting requirement for data centers with at least 500 kW of installed IT power as a starting point, Kiani proposed public energy, water, emissions, and hourly power-source data, together with local water and noise limits.

For businesses expanding AI capacity, these questions connect sustainability to operating costs, resource availability, and community trust. Site selection, power contracts, and equipment choices determine whether environmental commitments remain credible as demand grows. His conclusion was conditional: AI data centers can become more sustainable when total impacts fall and environmental benefits hold up to verification.