AI and Animation
The environmental promise of AI in animation is real. AI-assisted denoising and upscaling can significantly cut rendering time by producing high-quality images with fewer computational samples, directly reducing energy at the most intensive stage of production. AI tools for rigging, rotoscoping, and texture generation can reduce iteration cycles and rework. At the pipeline level, AI can optimize render farm scheduling, route workloads to lower-carbon infrastructure, and identify redundant data storage.
AI can also be a tool for inclusion, opening doors for artists with physical disabilities to express themselves in new and facilitated ways.
And yet, the environmental concerns about large scale AI infrastructure are important and well-founded. So is AI good or bad? The answer is complicated.
The Rebound Effect
While AI has some potential to improve the environmental impacts on a production level, there is a countervailing force that the industry needs: the Rebound Effect.
When AI tools make tasks faster and cheaper — generating assets, iterating on shots, rendering previews — teams tend to do more of those tasks, not fewer. If rendering becomes faster, artists run more variations. If asset generation becomes easier, more assets get generated. The efficiency gain per task does not automatically translate into a reduction in total compute. It can just as easily translate into more total work.
Training large AI models adds another layer of energy demand. A single large model training run can consume over 1,000 megawatt-hours of electricity before it is ever deployed to market (that’s equal to the energy needed to power about 1,200 average Canadian homes for an entire year). The ongoing use of AI tools — inference, in technical terms — creates a continuous distributed energy demand across every team member who uses them. And unlike rendering, which is at least centralized and measurable, AI inference is often invisible in emissions accounting.
From a carbon emissions perspective, AI adoption simply redistributes a production’s footprint— from local render farms to global data infrastructure — and potentially increases it if total compute grows faster than efficiency improves. Studios that adopt AI tools need to account for these energy implications in environmental reviews and reporting.
What Responsible AI Use Looks Like
The question for Canadian producers is not whether to use AI tools. It is how to use them in a way that captures the efficiency gains without triggering the rebound — and without obscuring the human and environmental costs.
Some practical starting points:
Measure before you adopt. Before adding a new AI tool to your pipeline, understand its energy footprint. The Directors Guild of Canada's new Low-Carbon GenAI Toolkit — developed with digital sustainability agency Decarbonade and supported by Telefilm Canada — provides a practical carbon calculator specifically designed for screen industry AI use. It estimates energy use and emissions associated with common generative AI tasks including text, image, audio, and video generation, using a Life Cycle Assessment methodology. It is free, bilingual, and built for Canadian productions. (genaitoolkit.dgcgreen.ca)
Ask where it runs. Not all AI tools are equal from an emissions perspective — and the difference is largely determined by where the computation happens. A tool running on servers in a hydro-powered Canadian data centre has a very different carbon footprint than the same tool running on fossil-fuel-heavy infrastructure elsewhere. Ask your AI vendors where their computer lives and what their energy sourcing looks like.
Watch the rebound. Set intentional limits on iteration. The ease of AI-generated variations is not an invitation to generate unlimited versions. Establish production norms that capture the time savings of AI without multiplying total compute. Fewer renders, not just faster ones.
Protect the work that matters. Identify the creative roles in your production where AI assistance genuinely adds value without displacing irreplaceable craft — and protect the roles where human authorship is the point. Not every efficiency gain is worth taking.
Track it. AI use is currently the least measured part of most studios' environmental footprints. That is not a reason to avoid it — it is a reason to start measuring now, while the norms are still being established and while your baseline data will actually mean something.
The environmental and technical questions around AI cannot be separated from the human ones. While AI has the potential to reduce render time, streamline workflows, and improve production efficiency, its adoption also raises important questions about the role of the artists, riggers, technical directors, and other creative professionals who have built Canada's animation industry over decades. A thriving workforce is part of what makes this sector worth sustaining. .
As we all consider the future of these transformative technologies, thinking sustainably means being mindful of all impact dimensions - positive and negative. Sustainable practices open the door for us to consider the complex impacts on people and planet into our technology and creative conversations, ensuring that the choices we make today help enable a sustainable and fair industry for everyone.
