AI agents are scaling fast and so is their energy footprint

AI agents are being deployed at scale across corporate Australia, but almost no one is asking: at what cost to our environment? Professorial Fellow Jon Whittle discusses.

According to Deloitte, 74% of organisations are expected to use agentic AI at least moderately within the next two years. Use cases are expanding rapidly: agents handling end-to-end customer service, reviewing supplier contracts, monitoring compliance across jurisdictions, and generating code without human intervention.

The risks are rising too. Only one in five companies has mature governance in place for autonomous agents. Agents are most powerful when operating with little human oversight which is precisely when agents are riskiest. They introduce cybersecurity vulnerabilities, make costs harder to predict and raise questions about how automated decisions align with organisational values.

One risk that has received far less attention is the environmental footprint of AI agents.

There is already vigorous debate about the sustainability of AI more broadly. Critics point to rising energy use from data centres, including in Australia, where some operators have doubled carbon emissions in the last five years. Proposed data centres in our major cities are seeking daily water volumes equivalent to 80,000 homes.

Supporters counter that data centre energy use is relatively small compared to heating, ventilation, and air conditioning.

Water use is also minor compared to agriculture or even golf courses. Both sides are missing how rapidly AI use is changing and how little current energy and water projections account for it.

A recent study found agentic AI consumes between 62 and 136 times more energy per query than a conventional single AI inference. AI agents don’t just respond to prompts; they plan and iterate, making multiple calls to large language models to complete a single task. These “true” agents are the most energy intensive. With companies racing to implement AI agents – some have already deployed thousands of agents – the numbers add up quickly and have the potential to significantly change the AI sustainability equation.

Things might not be as bad as they seem, however. Not all agents are equal. Most AI agents deployed today are simple automations, many of which are not intelligent at all. But as organisations pursue more capable, autonomous systems, energy consumption is likely to rise.

There is also a countervailing effect. In some contexts, AI agents have the potential to be more efficient than humans at the same task. In the early days of generative AI, a widely cited study showed that a GenAI query consumed ten times the energy of an equivalent web search. That’s because GenAI does a lot of searching and synthesis that a web search doesn’t. But this extra work is exactly what makes GenAI more efficient than a human. Without GenAI, humans would need to do all that searching and synthesis by hand. This point was illustrated in a 2024 study that showed AI systems emit up to 1500 times less CO2e per page of text compared to human writers.

Australian companies are not well prepared. As mandatory climate disclosure processes gradually rollout across corporate Australia, the gap between adoption and governance becomes stark. Most sustainability frameworks are not designed to capture the complexity of AI workloads, let alone the iterative and dynamic nature of agentic systems. Boards are approving AI investments without clear visibility on their downstream energy implications. Technology teams are optimising for capability and speed, while sustainability teams are left without the tools to quantify impact. The result is a blind spot that is likely to widen as adoption accelerates.

AI agents offer capabilities that are hard to ignore and, in many contexts, hard to match through other means. But deploying them thoughtfully requires asking questions that are rarely raised: What does this agent cost to run at scale — in tokens, in compute, in energy? Are the efficiency gains at the business layer being partially consumed at the infrastructure layer? How does agent deployment sit alongside the sustainability commitments already on the public record?

These are not reasons to avoid agents. They are reasons to think about them more carefully than the current pace of deployment suggests most organisations are doing. The technology is advancing quickly. The frameworks for understanding its full cost are not. Closing that gap will define what responsible AI adoption actually means in 2026.

Jon Whittle is a Professorial Fellow at Melbourne Business School Institute for Digital Innovation and AI and the new Sustainable Value Creation Institute, helping businesses create shareholder value whilst navigating the sustainability transition. Jon is one of Australia’s leading voices on AI for business. He is a former technical lead at NASA, director of CSIRO’s AI capability and founder of Australia’s National AI Centre.