Net Zero AI
Can Compute Ever Be Truly Carbon Neutral?
Balancing Innovation and Impact
As artificial intelligence becomes embedded in creative, commercial, and public services, the question of sustainability has moved from the margins to the mainstream. Every AI model, from large language systems to 3D reconstruction tools, relies on compute power. That power has a carbon cost.
In recent years, major AI developers have announced net zero or carbon neutral ambitions. But what does that really mean in practice? Can compute-heavy processes ever be truly sustainable, or are we simply offsetting the inevitable?
For SMEs and cultural organisations, understanding the nuance behind these claims is essential to making informed choices about infrastructure, partnerships, and procurement.
What “Net Zero AI” Really Means
“Net zero” in AI refers to balancing carbon emissions produced during training, deployment, and use with equivalent offsets or renewable energy investments.
However, not all net zero claims are equal. Some are based on purchasing offsets (such as reforestation or carbon credits) rather than reducing the actual energy consumed. Others focus on narrow stages of the AI lifecycle (for example, data centre emissions) while ignoring the embodied carbon in hardware production or disposal.
True net zero AI must therefore account for:
Direct emissions: Energy used in training, inference, and hosting.
Indirect emissions: The manufacture and transport of computing equipment.
Offset quality: Whether carbon credits genuinely neutralise the emissions claimed.
A more transparent approach and one that SMEs can influence lies in reducing absolute emissions through design, not just balancing the books after the fact.
The Role of Renewable Compute
Cloud providers increasingly promote renewable-powered data centres, but “green hosting” is not uniform.
While many regions use low-carbon grids, others still rely heavily on fossil fuels. Choosing where to host workloads can make a measurable difference.
SMEs can act by:
Selecting data centres in regions with higher renewable energy mixes.
Scheduling compute-heavy tasks during off-peak hours when grid intensity is lower.
Using dynamic scaling to ensure systems only consume power when active.
Exploring emerging micro-data centre networks that co-locate compute near renewable sources.
In heritage and creative applications, where datasets are often modest and workflows intermittent, these small operational adjustments can produce outsized sustainability gains.
Beyond Offsetting: Designing Out Waste
Offsetting remains a valuable tool, but the most effective sustainability strategy is waste prevention, designing systems that use less energy to begin with.
Techniques such as hybrid AI, transfer learning, and domain-informed models minimise redundant computation by embedding prior knowledge directly into the algorithm.
At Aralia Systems, we’ve seen that careful optimisation, from dataset management to model deployment, can reduce energy use by an order of magnitude without sacrificing accuracy.
These gains are particularly meaningful for SMEs, who can’t absorb the cost of sprawling infrastructure but can adopt frugal AI methods that deliver both environmental and economic efficiency.
Net Zero as Cultural Responsibility
For cultural and heritage organisations, AI sustainability carries a symbolic dimension.
Institutions tasked with preserving human history cannot ignore the environmental cost of the technologies they use to do so. Sustainable AI becomes part of a wider ethical narrative, one that connects stewardship of the past with responsibility for the future.
By openly communicating how energy and data are managed, organisations strengthen public trust and demonstrate that digital transformation need not come at ecological expense.
This transparency also sets the stage for a new kind of storytelling — one rooted in evidence, integrity, and accountability.
Final Thought
From Carbon to Clarity
Achieving genuine net zero AI may remain aspirational for now, but meaningful progress begins with clarity: knowing where emissions arise and reducing them through design rather than rhetoric.
The next frontier isn’t only technical it’s communicative. How we explain sustainability, data, and ethics to the public will define AI’s legitimacy as much as its performance.
That’s where our next discussion begins: Storytelling with Data, exploring how heritage bodies can engage audiences through transparent, evidence-based digital narratives that build understanding and trust.
