Infrastructure adjacencies

Pathways

From prompts to power: Generative AI and implications for data centre demand

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Is data centre capacity being built ahead of AI demand that will never arrive? Most projections cannot say, because they track chips shipped and projects announced. We take a different approach: modelling demand from how generative AI is actually used, who uses it, how heavily, and at what energy cost.

Demand is real, and it compounds

In our base case, generative AI’s electricity use rises from about 91 terawatt-hours (TWh) in 2025 to roughly 857 TWh by 2040.1 That is a more-than-ninefold increase, or about 16 per cent a year. Even if adoption stalls and use stays light, demand may still rise around fivefold. At the top of our range, it reaches thirteen times today’s level.

Efficiency does a great deal of work – the energy behind an average token falls by more than 95 per cent by 2040. Volume still outruns it. Building the data centres and the power to serve these will take an estimated $US4.5 to $US5.6 trillion by 2040, or roughly $US300 to $US375 billion a year.2

Figure 1: Electricity demand in different scenarios

Source: Macquarie Asset Management analysis.

Key takeaway: Efficiency gains are real, but in every scenario modelled, volume grows even faster.

Intensity, not headcount, drives growth

The number of people using AI matters far less than how it is used. Of the 399-fold rise in tokens between 2025 and 2040 in our base case, a wider user base explains a factor of 4.8. The factor of 83 comes from each user generating more.3

Behind that sits agentic AI: models are given a goal and tools, and work through tasks on their own. Their output is no longer bounded by what a person can read. By 2040, a heavy user in our base case generates around 3.9 million output tokens a day, roughly 190 hours of reading.4 Early signs are already here: Microsoft reports a 28-fold rise in pull requests associated with AI agents on GitHub in ten months.5

Figure 2: Heavy users are a quarter of users by 2040 and account for about 70% of inference energy

Source: Macquarie Asset Management analysis.

Key takeaway: A small, increasingly machine-driven group carries most of the load. For investors, the question is less whether AI is widely adopted and more how demanding each user turns out to be.

A more predictable load, but a less flexible one

Inference, generative AI at work, rises from under 60 per cent of demand today to more than 85 per cent by 2040 in our base case. It follows its users, answers on demand, and tracks the working day and week. Training can be queued, but draws more power erratically. As the load grows, it becomes predictable, but less flexible.

That shifts the investment question. Generating enough electricity is achievable: generative AI reaches about 2.2 per cent of global electricity in our base case. The harder part is where the load lands, how fast it can connect, and whether firming and transmission arrive with it. Inference also follows users and data sovereignty rules, so more of it is likely to land in Europe and other regulated markets. Where load cannot be steered, flexibility can be built around it: on-site generation, storage, or connections sized for the swings rather than the average.

Figure 3: Inference rises from 57% to 87% of generative AI electricity demand

Source: Macquarie Asset Management analysis.

Key takeaway: Training is a fixed cost. Inference recurs with every request, so the balance keeps tipping towards the load that can't wait.

The demand is real, and meeting this will take substantial capital. The more valuable question for infrastructure investors is whether power can be delivered both where and when the load asks for it.

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Terawatt-hour (TWh) is a unit of energy equal to one trillion watt-hours. Gigawatt (GW) is a unit of power equal to one billion watts. Average continuous draw is annual electricity use divided by the 8,760 hours in a year.

Behind-the-meter generation is electricity produced on the customer's side of the utility meter and used on site, rather than delivered through the grid.

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Figures are numbered in the order they appear on this page and do not correspond to the figure numbers in the full Pathways paper.

  1. Macquarie Asset Management analysis. Base case, 2025 to 2040.
  2. Macquarie Asset Management calculations, with the capacity and cost definition from the paper’s endnote 19.
  3. Macquarie Asset Management analysis. Base case, 2025 to 2040.
  4. Macquarie Asset Management analysis, plus OpenAI, “What are tokens and how to count them?”, accessed August 2026 (endnote 4).
  5. Microsoft, “Global AI Diffusion Q1 2026 Trends and Insights”, accessed August 2026 (endnote 14).

 

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