AI Energy Inflation: Why CIOs Need New Efficiency Standards as Model Sizes Explode

Naveed AnjumPosted on December 30, 2025

11-12 Min Read Time

Artificial intelligence is scaling faster than any enterprise technology before it. Model sizes are growing. Training runs are becoming more compute-intensive. Inference volumes are exploding. Alongside performance gains, a quieter consequence is emerging: AI energy inflation.

Energy inflation in AI is not a future risk. It is already visible in electricity consumption, water usage, infrastructure strain, and rising operational costs.

As data scientist and environmental researcher Alex de Vries-Gao put it plainly:

“AI is on track to become one of the largest energy consumers in the digital world.”

For CIOs and CFOs, this marks a turning point. AI can no longer be evaluated on performance alone.

Model Scale and Compute Growth are Inseparable

Modern AI capability is fundamentally tied to scale. The most capable systems are also the largest, and that size directly translates into compute and energy demand.

Jesse Dodge, Research Scientist at the Allen Institute for AI, explained this clearly:

“The models that are able to write a poem for you, or draft an email, those are very large.”

He followed with an unambiguous clarification:

“Size is vital for them to have those capabilities.”

This matters because scale is not a neutral design choice. Larger models require more training cycles, denser hardware, and sustained inference capacity. For enterprises, that means persistent energy exposure, not a one-time cost.

Energy Inflation is Now Reshaping Real Infrastructure

For a long time, AI-related energy use sat quietly inside IT budgets. That is no longer the case.

At a sufficient scale, electricity demand from AI workloads begins to interact with the grid itself. It affects which power plants stay online, how markets price electricity, and where stress shows up first. The shift is subtle, but it’s already underway.

In a December 23, 2025 report, Reuters energy correspondent Laila Kearney captures the change in plain terms:

“AI data center electricity demand revives peaker power plants.”

That line reflects a broader pattern. Data centers supporting AI are now drawing enough power to alter decisions that were previously moving in the opposite direction. Plants that were rarely used, or scheduled for retirement, are being pulled back into service.

Reuters describes why this matters by pointing to the nature of these facilities:

“These often decades-old, fossil-fueled facilities emit more pollution when they are running and cost more to produce electricity than continuous power plants.”

This isn’t an abstract environmental concern. Peaker plants are expensive to run. They were designed for short bursts, not sustained demand. When they re-enter the picture, costs rise and efficiency falls.

For CIOs, this marks a structural change. AI workloads are no longer just another consumer of infrastructure capacity. At scale, they shape infrastructure outcomes. Energy pricing, grid reliability, and cost exposure are increasingly influenced by how AI systems are deployed and used.

That is why efficiency standards stop being optional at this point. When model scale and inference volume reach grid-relevant levels, informal optimization gives way to real economic and operational consequences.

Transparency Gaps Hide the True Cost of AI

One reason AI energy inflation catches enterprises off guard is the absence of consistent, standardized disclosure. While AI systems are rapidly embedded into products and services, their environmental impacts remain largely unclear to users, enterprises, and regulators.

That lack of visibility is stated directly in Yale Environment 360’s reporting on AI energy use. Writing about the current state of accountability, David Berreby notes:

“Right now, it’s not possible to tell how your A.I. request for homework help will affect carbon emissions or freshwater stocks.”

This opacity is not accidental. As the article explains, companies disclose AI-related impacts selectively, often without a consistent methodology or comparability.

The depth of this problem is reinforced by research cited in the same piece. Discussing the lack of usable emissions data, the article quotes a 2022 conference paper authored by a group of 10 prominent AI researchers, stating:

“Data scientists today do not have easy or reliable access to measurements of [greenhouse gas impacts from A.I.], which precludes development of actionable tactics.”

Together, these statements define the transparency problem clearly. Individual users cannot see the environmental impact of their AI interactions, and even experts lack reliable access to system-level emissions data. Without standardized reporting, AI energy costs remain invisible until they surface indirectly through higher electricity bills, strained infrastructure, or regulatory intervention.

For CIOs and CFOs, this reinforces a central point: new efficiency standards must begin with disclosure. You cannot govern what you cannot measure, and today’s AI ecosystem still operates largely without agreed-upon norms for reporting energy use, water consumption, and emissions impact.

Energy and Water Costs Extend Beyond Electricity Bills

AI energy inflation is not limited to electricity consumption. At scale, AI infrastructure places significant demands on water resources, especially for cooling data center equipment — an issue that is increasingly flagged by experts and analysts.

Reporting on the water footprint of major cloud and AI operators, The Guardian cites investigative reporting by Luke Barratt, Costanza Gambarini, and Andrew Witherspoon, noting:

“Amazon, Microsoft and Google are operating datacentres that use vast amounts of water in some of the world’s driest areas…”

Reporting on where and why these facilities are being built, The Guardian quotes Lorena Jaume-Palasí, founder of the Ethical Tech Society:

“It’s no coincidence they are building in dry areas,” as datacentres have to be built inland, where low humidity reduces the risk of metal corrosion, while seawater also causes corrosion if used for cooling.”

Local communities have begun raising alarms about this trend. Writing on the environmental impact of the data center boom, The Guardian reporter Oliver Milman observes:

“…people have started voicing concerns about the billions of gallons of water they are sucking up to cool their computer hardware.”

For CFOs and CIOs, this broadens the definition of AI cost. Energy inflation now includes not just electricity, but also water sourcing, cooling infrastructure, and the reputational and regulatory pressures that come with resource competition. Efficiency standards must therefore address both energy and water efficiency to ensure AI deployments are sustainable in resource-constrained environments.

Why Efficiency Must Become a CIO-Level Standard

As AI systems expand beyond text into multimodal and real-time use cases, the cost of compute is no longer linear. Larger models demand more energy exponentially during both training and inference, making efficiency a governance issue rather than a purely technical concern.

This escalation is highlighted by Vijay Gadepally, senior scientist and principal investigator at MIT Lincoln Laboratory, in MIT Sloan Management Review. Describing how capability gains are tied to scale, Gadepally notes:

“As we move from text to video to image, these AI models are growing larger and larger, and so is their energy impact.”

The implication for enterprise leaders is that efficiency cannot be evaluated abstractly. Energy use must be assessed relative to business value. Gadepally illustrates the tangible cost of compute at scale with a concrete comparison:

“Processing a million tokens, constituting a dollar’s worth of compute time, emits an amount of carbon similar to that produced by a gas-powered vehicle driven five to 20 miles.”

For CIOs and CFOs, these observations reinforce a structural reality: AI efficiency can no longer be treated as an optimization after deployment. It must be formalized through standards that govern model selection, inference scale, and acceptable cost-to-compute ratios. Without such standards, organizations risk locking in escalating energy and carbon costs as model sizes continue to grow.

What New Efficiency Standards Must Address

New efficiency standards for AI cannot be aspirational or symbolic. They must be concrete, measurable, and enforceable across infrastructure, models, and vendors. Without this rigor, efficiency remains a secondary concern while energy costs continue to scale unchecked.

The scale of the challenge is clear when looking at projected data center electricity consumption. Gartner estimates that energy use across data centers will accelerate sharply in the coming years:

“Their electricity usage is set to rise nearly fivefold, from 93 TWh in 2025 to 432 TWh in 2030.”

This trajectory suggests that incremental efficiency gains will not be enough. As AI workloads scale, enterprises need standards that actively constrain unnecessary compute growth rather than merely optimize around it.

The International Energy Agency (IEA) places this challenge in a global context, highlighting the systemic impact of unchecked data-center expansion:

“Global electricity demand from data centres is set to more than double over the next five years, consuming as much electricity by 2030 as the whole of Japan does today.”

For CIOs, the implication is operational. Efficiency standards only work if ownership and measurement are explicit. In practice, this means:

What Must Be Measured:

• Model-level energy consumption during training and inference
• Inference cost per transaction or request, not just aggregate spend
• Infrastructure utilization efficiency, including idle capacity
• Water and cooling intensity for AI workloads, where applicable

Who Owns The Measurement:

• CIO / Head of Platform Engineering: model and infrastructure efficiency metrics
• Finance (CFO org): cost-to-compute ratios and budget impact
• Sustainability / Risk teams: emissions, water use, and regulatory exposure
• Procurement: vendor-reported efficiency and transparency benchmarks

How Often It Must Be Reviewed:

• At deployment: before approving new models or scaling inference volumes
• Quarterly: as part of infrastructure, cloud, and AI spend reviews
• Annually: tied to capacity planning, vendor renewal, and sustainability reporting

Without this level of specificity, efficiency remains voluntary. With it, efficiency becomes governable. Standards that define what is measured, who is accountable, and how often decisions are revisited are what prevent AI energy inflation from becoming embedded into the enterprise cost base.

Why Delay Turns Energy Inflation into an Executive Liability

Failing to address AI energy inflation is more than a sustainability foul-up — it is a strategic risk that can impact long-term operational costs, energy supply dependencies, and even public trust in technology. Without enforceable efficiency standards, organizations risk normalizing energy-intensive practices that could trigger regulatory backlash, grid stress, and reputational harm.

For example, Rene Haas, CEO of Arm Holdings, has warned about the broader systemic risk of AI’s energy appetite:

“It's going to be difficult to accelerate the breakthroughs that we need if the power requirements for these large data centers for people to do research on keeps going up and up and up.”

Haas emphasized that the industry’s “insatiable” energy consumption — potentially rising to 20–25% of U.S. power demand by the end of the decade — could slow innovation if left unchecked. 

This concern aligns with broader industry recognition that AI’s energy footprint is not trivial and that efficiency cannot be left to voluntary measures. Satya Nadella, CEO of Microsoft, has made a related point from a social-license perspective:

“At the end of the day, I think that this industry — to which I belong — needs to earn the social permission to consume energy, because we’re doing good in the world.”

Nadella’s framing signals that energy intensity is now a governance issue, not just a technical or sustainability objective.

From a regulatory and economic perspective, national grid operators and energy policymakers are already reacting to AI’s growth — a situation that can translate into compliance and cost risks for enterprises that fail to anticipate these shifts. As AI becomes a more prominent driver of data-center energy use, companies cannot assume that infrastructure cushions or market growth will absorb inefficiencies indefinitely.

For executives, the takeaway is clear: efficiency matters strategically. Standards that mandate transparency, model-level energy reporting, and alignment between AI performance and energy cost are not optional extras — they are essential risk management tools. Organizations that treat them as such will be better positioned to navigate energy markets, regulatory scrutiny, and public expectations in the decade ahead.

How CIOs Translate Efficiency Standards Into Practice

Defining efficiency standards is only the first step. The harder challenge for CIOs is operationalizing them across data, models, infrastructure, and vendors without slowing innovation.

In practice, this requires cross-functional capabilities that many organizations do not have in-house:

• Model-level energy and cost instrumentation
• Data pipelines that link inference volume to infrastructure cost
• Governance frameworks that align AI teams, finance, and sustainability
• Vendor-neutral evaluation of architectures, cloud services, and deployment patterns

This is where experienced engineering and advisory partners become essential — not to “run AI,” but to make AI governable at scale.

At Arbisoft, this work typically sits at the intersection of data engineering, AI systems design, and enterprise data governance — helping technology leaders turn abstract efficiency goals into measurable, enforceable standards embedded directly into production systems.

Final Thought for CIOs and CFOs

AI is now a physical system. It depends on electricity, water, cooling capacity, and grid infrastructure to operate at scale.

As AI adoption expands, energy and infrastructure costs become an ongoing operational reality rather than a secondary consideration. Evaluating AI purely on performance leaves these costs implicit and unmanaged.

Efficiency standards provide a way to make AI energy use visible, measurable, and governable. When established early, they support clearer budgeting, more predictable infrastructure planning, and better alignment with sustainability objectives.

AI energy inflation is already underway. The remaining task for CIOs and CFOs is to address it through deliberate governance before rising costs and constraints begin to dictate outcomes.