Artificial intelligence is reshaping our world – accelerating discovery, optimising systems, and unlocking new possibilities across every sector. But with its vast potential comes a shared responsibility.
AI can be a powerful ally for transforming businesses and reducing cost. It can help organizations minimize carbon emissions, industries manage energy use, and scientists model complex climate systems in real time. Yet the way we design, deploy, and run AI also matters. Building software sustainably means making every stage of the digital journey – from architecture to inference – more efficient, transparent, and resilient.
Innovation that serves sustainability
At Google, we believe innovation and sustainability go hand in hand. The same intelligence that powers breakthrough…
Artificial intelligence is reshaping our world – accelerating discovery, optimising systems, and unlocking new possibilities across every sector. But with its vast potential comes a shared responsibility.
AI can be a powerful ally for transforming businesses and reducing cost. It can help organizations minimize carbon emissions, industries manage energy use, and scientists model complex climate systems in real time. Yet the way we design, deploy, and run AI also matters. Building software sustainably means making every stage of the digital journey – from architecture to inference – more efficient, transparent, and resilient.
Innovation that serves sustainability
At Google, we believe innovation and sustainability go hand in hand. The same intelligence that powers breakthroughs can also help us use resources more wisely.
Projects like Green Light, which uses AI to optimise traffic signals and reduce emissions, and Project Contrails, which helps airlines cut the warming effects of condensation trails, show what happens when technology serves both performance and planet.
Each example reveals a helpful truth – that sustainability doesn’t slow innovation but instead fuels it, enabling efficiency to become an engine of progress.
From footprint to framework
Every software system, including AI, has an environmental footprint – from the hardware and energy that powers data centers to the water used to cool them. Water is one of the planet’s most precious and increasingly scarce resources and protecting it must be part of any technology strategy. That’s why Google is investing in advanced cooling systems and water stewardship projects with the goal to replenish more than we consume, helping preserve local ecosystems and community supplies.
Understanding this footprint helps engineers and organisations make smarter choices, like selecting efficient accelerators, rightsizing workloads, and scheduling operations when the grid is cleanest.
Across Google Cloud, we’re continually improving efficiency. Our Ironwood Tensor Processing Units (TPUs) are nearly 30 times more energy-efficient than our first Cloud TPU from 2018, and our data centres operate at a fleet-wide Power Usage Effectiveness (PUE) of 1.09, which is amongst the best in the world.
By designing systems that consume less energy and run on more carbon-free power, we help close the gap between ambition and action – turning digital progress into tangible emissions reductions.
But this isn’t achieved through infrastructure alone. It’s the result of decisions made at every layer of the software lifecycle. That’s why we encourage teams to think Sustainable by Design, bringing efficiency, measurement, and responsibility into every stage of building software.
Sustainable by Design: a mindset for the AI era
Today’s sustainability questions aren’t coming just from sustainability teams; they are coming directly from executives, financial operations teams, technology leads and developers. And they are often asking sustainability questions using infrastructure language: “Are we building the most price-performant AND efficient way to run AI?” This is not a niche environmental question; it’s relevant across -industries, across-geo’s and it requires that leaders consider sustainability criteria when they are designing infrastructure. A Sustainable by Design infrastructure strategy makes AI training and operation dramatically more cost- and energy-efficient. It’s built around a set of principles known as the 4Ms which lay out powerful ways to embed efficiency into software:
- **Machine **- choose efficient computing resources that deliver more performance per watt.
- **Model **- use or adapt existing models rather than starting from scratch — smaller, fine-tuned models can be faster and more resource efficient.
- **Mechanisation **- automate data and AI operations through serverless and managed services to minimise idle compute.
- **Map **- run workloads where and when the energy supply is cleanest.
The 4Ms help turn sustainability into a design principle, and a shared responsibility across every role in tech.
A collective journey toward resilience
As we host the AI Days in the Nordics, the conversation about AI’s environmental impact is accelerating, and so is the opportunity to act. Every software team, cloud architect, and product manager has a role to play in designing a digital ecosystem that enables and fuels innovation without compromising environmental impact.
Building software sustainably is essential for business resilience –AI applications that use fewer resources are not only more energy efficient; they’re scalable, and cost-effective for the organisations that depend on them.
To learn more about how we can make the future sustainable by design, download our Build Software Sustainably ebook.
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