Artificial intelligence now underpins everything from fraud detection to code assistants and grid operations. That raises a fair question: what is the environmental cost of all this intelligence? The answer is nuanced. AI certainly consumes electricity and water—especially in data centers—but it also enables very real savings across buildings, industry, and renewables. Getting the accounting and the trade‑offs right is the first step toward shipping AI that’s both useful and resource‑efficient.
This article clarifies what exactly we’re measuring, summarizes the latest per‑prompt figures from Google’s new methodology, shows how public leaderboards help you choose efficient models, and closes with practical steps for greener deployments.
What Exactly Are We Measuring?
When people ask “How much energy does AI use?”, they can mean several different things. Scope and system boundaries matter:
- Training vs. inference. Training a foundation model (weeks to months) is power‑hungry but episodic. Inference—the day‑to‑day act of answering prompts—dominates ongoing use.
- Modalities. Text generation is typically less energy‑intensive than image or video generation for the same latency targets.
- System boundaries. Rigorous accounting should include accelerators (GPU/TPU), host CPU/RAM, idle capacity, data‑center overhead (PUE), and cooling water—not just the chip in isolation.
- Carbon accounting method. Location‑based vs. market‑based assumptions can shift the same electricity use to very different emissions numbers depending on the grid.
- What’s out of scope here. The figures below focus on median text inference in a large consumer assistant. They do not include model training, embodied emissions of hardware, most networking, or client‑side energy.
Being explicit about these boundaries prevents apples‑to‑oranges comparisons and makes optimization work legible.
What Google’s New Data Shows (for Text Inference)
Google recently published a comprehensive methodology for measuring the environmental footprint of Gemini Apps median text prompts. It factors in achieved chip utilization, idle capacity, CPU/RAM, and data‑center overhead and water—rather than just measuring a single accelerator under idealized load. Key takeaways:
- Energy per prompt: ~0.24 Wh (≈ watching TV for under nine seconds).
- Carbon per prompt: ~0.03 g CO₂e, using their fleet‑average electricity mix.
- Water per prompt: ~0.26 mL (≈ five drops), tied to cooling demand.
- Year‑over‑year efficiency: 33× lower energy per median prompt and 44× lower carbon intensity per prompt than one year prior, attributed to model/serving efficiency and cleaner procurement.
- Hardware & data centers: Latest‑gen TPU Ironwood claims ~30× energy efficiency vs. the first TPU; Google reports a fleet‑wide PUE ≈ 1.09 and a water‑replenishment ambition of 120%.
Two cautions keep this grounded:
- These are median figures for text in Gemini Apps; other modalities and products can differ materially.
- Experts note that location‑based carbon or broader water accounting can raise the footprint. Even so, Google’s fuller, system‑wide measurement is a major step toward comparable numbers.
Choosing Efficient Models: Open Measurements & Simple Labels
Selecting a model is no longer just about quality and latency. Public tools now expose energy per output to help you stay on the Pareto frontier:
- ML.ENERGY Leaderboard. Developed by researchers at the University of Michigan and collaborators, this benchmark runs models on fixed hardware and reports energy (Wh), latency, and quality. It makes a core truth visible: energy scales with tokens processed and generated. Models that produce longer answers (or use longer contexts) often consume more energy for the same task. The leaderboard lets you compare like‑for‑like and see when a smaller or distilled model gets you 95% of the quality at a fraction of the joules.
- AI Energy Score (1–5 stars). For a quick, high‑level signal, the AI Energy Score provides star ratings of relative energy efficiency per task (text, image, ASR, etc.). It’s a label, not an LCA—use it to shortlist candidates before you run your own measurements.
- Measure locally, too. If you operate your own stack, pair leaderboard insights with power telemetry (e.g., NVML, PMBus) or open tooling such as Zeus to report Wh/request in your environment.
Where AI Pays Its Own Energy Debt
The environmental story is two‑sided: AI also saves energy—often dramatically—when embedded in the physical economy.
- Buildings (HVAC). Reviews of AI‑controlled building energy management report double‑digit savings and up to ~37% in offices when AI controls HVAC scheduling, setpoints, and ventilation based on occupancy and weather. Residential gains are typically lower but still meaningful.
- Industry. In a Widespread Adoption case, analyses project ~8% energy savings by 2035 in light industry from AI‑enabled process optimization and predictive maintenance.
- Power systems & renewables. AI improves wind/solar forecasting, curtails preventable losses, time‑shifts demand response, and extends battery lifetime through smarter charge cycles. It also aids grid resilience by anticipating faults from weather or cyber anomalies.
Taken together, these systems‑level efficiencies can outweigh the footprint of inference when deployments are designed thoughtfully.
The Local Revolution: Small Models at the Edge
A growing share of inference is moving on‑device to phones, PCs with NPUs, and embedded controllers. Benefits:
- Lower end‑to‑end energy by eliminating constant round‑trips to the cloud.
- Latency & privacy improvements that let you shrink prompts and outputs, often the biggest levers for energy.
- Resilience and locality: Run in low‑carbon regions—or off‑grid—when needed.
How to get there:
- Prefer task‑specific or distilled models over jumbo generalists.
- Apply quantization (8‑/4‑bit) and compiler optimizations (Flash‑attention, graph fusion).
- Exploit speculative decoding and dynamic batching on servers; autoscale aggressively to avoid idle.
Back‑of‑the‑envelope. At 0.24 Wh per median text prompt, 1 million prompts ≈ 240 kWh. For context, a typical U.S. household uses on the order of ~855–900 kWh per month. So a million prompts is roughly a quarter to a third of a household‑month of electricity—not counting training or embodied emissions. The global picture depends on total volume and where/when workloads run.
How to Ship Greener (A Practical Checklist)
- Right‑size the model. Start small; move up only if quality demands it. Prefer MoE or distilled variants.
- Constrain tokens. Cap context and max‑tokens. Encourage concise outputs; paginate long answers.
- Quantize & compile. Use 8‑/4‑bit inference, kernel fusions, and attention optimizations.
- Batch or burst. Batch opportunistically; otherwise autoscale to zero to kill idle energy.
- Cache & reuse. Cache embeddings, retrieval results, and chain‑of‑thought planning artifacts.
- Pick low‑carbon regions & time‑shift. Run non‑urgent jobs when/where the grid is cleanest.
- Measure what you run. Report Wh/request using GPU power APIs or tools like Zeus; publish methods.
- Watch water. Prefer air/immersion‑cooled regions or recycled water where possible.
- Design for edge. Where feasible, run on‑device to cut network energy and protect privacy.
Wrapping Up
The footprint of AI is real—but getting more measurable. Google’s new methodology brings long‑needed clarity for text inference, and public benchmarks now surface energy–latency–quality trade‑offs so teams can choose wisely. Meanwhile, the biggest environmental wins often come from using AI to optimize buildings, factories, and grids.
The practical path forward is not “AI or the planet,” but AI that’s built and operated with efficiency as a first‑class objective—from model selection and system design to region choice and on‑device deployments. Measure honestly, optimize relentlessly, and aim for the Pareto frontier where quality, latency, and joules are jointly minimized.