Five Gold Rings (of Energy Consumption): AI Energy Costs Hit the P&L
- William Beresford
- 3 days ago
- 3 min read
12 Days of Christmas Predictions for 2026 — Beyond’s View of What’s Next
Welcome to Day 5 of our Christmas Predictions for 2026: a series exploring the real, practical shifts shaping organisations over the next two years.
Today’s prediction touches on a reality many leaders have quietly suspected, but few have yet quantified: AI consumes a lot of energy. By 2026, that will become a board-level issue.
AI’s operational footprint is no longer something abstract, academic or “for the sustainability team to look at”. It is fast becoming a line item in both cost and carbon that organisations cannot ignore.
Prediction: AI Energy Costs Hit the P&L
As enterprises scale AI including models, agents, automations and copilots, the reality becomes unavoidable: AI is expensive to run and the biggest driver of that cost is energy.
In 2026, energy-related AI expenditure will move from an invisible technical detail to a visible governance, financial and strategic concern.
Why AI’s Energy Impact Is Becoming Impossible to Ignore
1. AI models are growing faster than infrastructure efficiency
According to the International Energy Agency (IEA), global electricity demand from data centres, AI and cryptocurrency could double by 2026, with AI being the primary driver of that increase. While hardware improvements help, they are not keeping pace with the scale of enterprise adoption.
As McKinsey puts it: “Energy will become one of the defining constraints of AI deployment.”
2. GPU intensity is directly tied to cost curves
Training and inference for models consumes vast GPU cycles. Analysis by Stanford’s AI Index shows that training frontier models can require multiple gigawatt-hours, equivalent to powering thousands of homes for days. Even inference, run across millions of enterprise users, adds up fast.
A CFO may not know how a transformer model works — but they will notice the cloud bill!
3. Regulators and investors are watching closely
ESG reporting mandates in Europe and emerging disclosure frameworks globally are beginning to include digital emissions. Harvard Business Review warns: “Digital carbon will be one of the most scrutinised components of sustainability reporting.” Boards cannot claim sustainability leadership if their AI footprint is opaque.
4. Cloud providers are shifting strategy
AWS, Google and Microsoft have all released new commitments around:
carbon-aware workload routing
greener data centre regions
renewable purchasing
model efficiency improvements
Enterprises will increasingly be asked to make conscious choices about where workloads run and at what environmental (and financial) cost.
What This Means for Organisations in 2026
1. AI architecture decisions become financial decisions
Choosing a model isn’t just about performance anymore. It’s about:
inference cost per 1,000 calls
energy draw per query
region-level carbon intensity
hardware requirements
utilisation efficiency
In many cases, a smaller, domain-specific model will outperform a larger model on total ROI.
2. Sustainability teams and IT teams must collaborate
This is one of 2026’s biggest organisational shifts. The people measuring emissions must work directly with the people deploying models. And as a result carbon accounting will become a real-time activity, not an annual chore.
3. Workload redesign becomes a competitive advantage
Leaders start asking:
Which tasks actually need an LLM?
Which can be handled by retrieval-based systems?
Which require high precision vs. high volume?
How can inference be cached, batched or throttled?
Optimised workloads save money and cut emissions.
4. “Green regions” become standard practice
Cloud providers already publish carbon intensities by region. But by the end of 2026, organisations will route workloads to greener jurisdictions the same way they route traffic to more available servers. Sustainability will become an architectural principle.
Case Study Signals
Microsoft reports that AI workloads are one of the fastest-growing contributors to its Scope 2 emissions.
Google openly acknowledges that AI’s expansion is slowing progress toward its carbon-neutral targets.
Hugging Face and Meta have launched model efficiency benchmarking to encourage responsible deployment.
DeepMind reduced model inference energy by up to 70% through algorithmic optimisation.
Beyond: Putting Data to Work
AI sustainability is about energy consumption, efficiency, architecture and intelligent design. The organisations that win in 2026 will deploy AI thoughtfully, strategically and measurably.
At Beyond, we help businesses:
analyse the true cost of AI workloads
design efficient hybrid models (LLMs + retrieval + domain systems)
route inference to greener, lower-cost regions
model the carbon impact of AI usage across the enterprise
implement governance frameworks linking AI deployment to sustainability goals
build data pipelines that reduce redundant compute
AI should create value not runaway cost curves.
If you want 2026 to be the year your AI is powerful and responsible, get in touch!
