This data-file forecasts the energy consumption of the internet, rising from 900 TWH in 2025 to 1,800 TWH in 2030 and 4,000 TWH by 2050. The main driver is the energy consumption of AI, plus blockchains, rising traffic, and offset by rising efficiency. Input assumptions to the model can be flexed. Underlying data are from technical papers.
Our best estimate is that the internet accounted for 900 TWH of global electricity in 2025, which is 2.7% of all global electricity. Just 30TWH of this 900TWH was AI. Despite this area being an analytical minefield, we have attempted to construct a simple model for the future energy demands of the internet, which decision-makers can flex, based on data and assumptions.
There are 12,000 data centers in the world in 2025, and our forecasts also capture the number of data centers by region over time, the capacity of these data centers by region over time, and distributions for the sizes of data centers over time, as discussed in our report into AI data center growth.
Internet traffic has been rising at a CAGR of 30%, as shown by the data use of developed world households, rising to almost 3 TB per user per year by 2023. The scatter also shows a common theme in this data-file, which is that different estimates from different sources can vary widely.
Future internet traffic is likely to continue rising. By 2022 there were 5bn global internet users underpinning 4.7 Zettabytes (ZB) of internet traffic. Users will grow. Traffic per user will likely grow. We have pencilled in some estimates, but uncertainty is high.
The energy intensity of internet traffic spans across data-centers, transmission networks and local networking equipment. Again, different estimates from different technical papers can vary by an order of magnitude. But a first general rule is that the numbers have declined sharply, sometimes halving every 2-3 years.
The current energy intensity of the internet is thus estimated at 140 Wh/GB in our base case, broken down in the waterfall chart below, using our findings from technical papers and the spec sheets of underlying products (e.g., offered by companies such as Dell).
Energy intensity of internet processes will almost certainly decline in the future, as traffic volumes rise. Again, we have pencilled in some estimates to our models, which can be flexed.
However the energy needed for AI is now rising exponentially. Training Chat GPT-3 in 2020 used 1.3 GWH to absorb 175bn parameters. But training chat GPT-4 in 2023 used 50 GWH to absorb 1.8trn parameters. We find a 98% correlation between AI training energy and the total compute during training.
AI querying energy is also correlated with the complexity of the AI model, and thus will likely continue rising in the future. Average energy use was estimated at 3.6 Wh per query in 2022, which is 4x more than an email (1 Wh) and 10x more than a google search (0.3 Wh). However, by 2025, leading AI developers are quoting 0.3 Wh/query in mass inferencing.
Our forecasts for AI energy consumption were last updated in March-2026, and ultimately see querying comprising c80% of AI energy consumption after 2030. We have also evaluated scaling laws that potentially drive AI training energy to 250TWH pa by 2030. But most of our research in 2026 sees AI shifting to smaller-scale edge deployments.
Muting the impacts of larger data-processing volumes, we expect a 40-100x increase in future computer performance in GFLOPS per Watt (chart below), and increasing software-based jumps in the performance-vs-compute curve. This yields 800 TWH of AI demand around 2030. Revisions to our numbers are plotted above.
Please download the model to stress-test your own estimates for the energy intensity of the internet. It is not impossible for total electricity demand to ‘go sideways’ (i.e., it does not increase). It is also possible for the electricity demand of the internet to exceed our estimates by a factor of 2-3x if the pace of productivity improvements slows down.