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AI can get cheaper per task while total compute demand rises because lower costs make it practical to use AI more often and for more kinds of work. The key distinction is between the resources used by one task and the resources used by all tasks combined: efficiency lowers the first, but wider adoption and more demanding workloads can raise the second.
What “cheaper AI” measures—and what it does not
“Compute demand” can mean several things: processor operations, accelerator-hours, inference tokens, installed computing capacity, or electricity. A claim that one AI task has become cheaper or more efficient is a unit measure; it does not by itself say whether total computing or electricity use is falling.
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For a specific benchmark, Stanford HAI’s 2025 AI Index found that the inference cost of a system performing at GPT-3.5 level fell more than 280-fold between November 2022 and October 2024. That is a striking reduction for a defined performance level and period—not a universal price change for every model, provider, workload, or user’s bill.
The IEA’s 2026 executive summary says energy use per AI task fell by at least an order of magnitude annually in recent years. That is the IEA’s summary of recent trends, not a measured rate that applies to every kind of AI task. Unit efficiency can improve even as the total number and complexity of tasks grow.
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Why lower costs can lead to more total use
More uses become worth building
When a query becomes less expensive, companies may find it practical to add AI features to more products or make existing features available to more people. Users may also turn to AI more frequently when the service is faster, cheaper, or built into tools they already use. This is a plausible economic mechanism, not a quantified explanation of how much of data-centre growth came from lower prices.
The number of tasks can outweigh savings per task
A simple way to think about the relationship is: total resource use depends on both the resources required per task and the number and kind of tasks performed. If unit use falls but volume rises sufficiently, the aggregate can still increase. There is no comprehensive global count of how often and how deeply people use AI, so the IEA does not establish a precise worldwide query-growth rate.
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New workloads may be heavier than the tasks they replace
Not every AI request is comparable. The IEA identifies video generation, reasoning, and agentic tasks as applications that can use hundreds or thousands of times more energy per query than simple text generation. An increase in these workloads can raise total electricity use even while basic text tasks become more efficient.
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So, if each AI query takes less compute, why are data centres still using more power? Because “each query” is not one fixed workload, and the total includes all queries and other data-centre services. More users, more frequent use, and a shift toward more resource-intensive tasks can outweigh lower energy use for a basic request.
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What the data-centre electricity figures show
Electricity figures give one concrete measure of the aggregate trend, but they cover data centres—not AI alone. Data centres support many digital services, and the IEA identifies AI as their most important growth driver alongside other services.
| Measure | Figure | How to read it |
|---|---|---|
| Global data-centre electricity use in 2024 | Estimated 415 TWh, about 1.5% of global electricity | Historical estimate for all data-centre workloads, not an AI-only total. The IEA estimated data-centre electricity use had grown about 12% per year since 2017. |
| Global data-centre electricity use in 2030 | Around 945 TWh | The IEA’s 2025 base-case projection: more than double the 2024 estimate, not a measured outcome. AI was identified as the leading growth driver alongside other digital services. |
| Change in data-centre electricity demand in 2025 | 17% growth | The IEA’s 2026 update reports this as a change in electricity demand, not a direct measure of all AI compute. |
| Change in electricity use by AI-focused data centres in 2025 | 50% growth | The IEA’s 2026 update reports this for AI-focused data centres; it is not the same as a global total for all AI-related computing. |
The 2024 estimate and 2030 projection come from the IEA’s 2025 analysis of energy demand from AI. The 2025 growth figures are from the IEA’s 2026 update. They use different report vintages and describe distinct time periods; the 2030 number remains a forecast.
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Training and inference are different kinds of demand
Training is the computing used to build or update a model. Inference is the computing used when a trained model responds to users or performs a task. A reduction in the cost of inference does not erase the resources involved in training, and training-cost estimates should not be mistaken for current per-query prices.
Stanford HAI’s 2024 AI Index estimated compute costs of $78 million to train GPT-4 and $191 million to train Gemini Ultra. These are dated estimates for those models’ training, not current quotes for training a model or running inference.
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Does efficiency guarantee a rebound in demand?
No. Lower costs can encourage greater use, and more capable applications can require more resources, but those mechanisms do not prove that efficiency gains will always be outweighed. The IEA says comprehensive global statistics on AI usage frequency and depth are not available, limiting certainty about how use is changing and how much of the aggregate increase is attributable to it.
It is therefore more accurate to say that efficiency and rising demand can coexist—and that expanding use and heavier workloads help explain how—than to claim that every efficiency improvement automatically causes total consumption to rise. Global electricity totals also do not reveal where new data centres are built or how their power demand affects a particular local grid.
Quick Recap
How to read claims about AI’s resource use
- Check whether a figure describes cost or energy per task, or total compute, capacity, or electricity use.
- Look for the workload: simple text, video, reasoning, and agentic tasks can have very different energy needs.
- Separate model training from inference, which serves requests after a model is built.
- Distinguish measured or estimated historical figures from forecasts, and check the year and scope.
- Do not treat all data-centre electricity as AI electricity; data centres run other digital services too.
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