Thank you very much.
Honourable members of the committee, thank you for convening this crucial study.
As an AI researcher based in Montreal, my career has been dedicated to measuring the environmental impacts of artificial intelligence, first in academia, working at the Mila institute with Dr. Yoshua Bengio, but now in industry, where I recently co-founded the Sustainable AI Group, a research and advisory company dedicated to helping organizations measure and reduce the environmental impacts of the AI that they use.
In the last decade, my research has increasingly exposed a critical, often-ignored reality. AI is a very physical technology. It does not exist in an ethereal cloud. It relies on massive physical infrastructure. It consumes large amounts of energy and water, and it leaves behind a significant carbon footprint.
As Canada reshapes its digital policy agenda following the legislative reset of Bill C-27, we must understand that digital sovereignty and environmental sustainability are two sides of the same coin. Canada is uniquely positioned to lead the global shift towards responsible, sustainable AI. We should not attempt to outspend foreign monopolies on bloated general-purpose models and gigawatt data centres. Instead, our competitive advantage lies in building transparent, green and clean AI.
The integration of AI into sectors like construction and manufacturing does offer immense productivity gains, but the current industry trend of relying on massive, generic, cloud-hosted models is both ecologically and operationally unsustainable. The large language models that many of us use today are trained at great cost to be general purpose, which definitely makes sense from the point of view of the tech companies that want to respond to any kind of query thrown at them, be it coming up with a chocolate chip cookie recipe or an itinerary for a family vacation in Italy. These companies can and do spend hundreds of millions of dollars on compute in order to develop these models, often using our own data to improve them and then selling it back to us.
Most of what businesses in Canada need and want to use AI for isn't general purpose at all. It is specific tasks that require robust, trustworthy technology without all the bells and whistles. In fact, querying generic, multi-billion-parameter generative AI models to optimize a manufacturing assembly line or analyze a construction blueprint is like taking a helicopter to do your groceries. It is an absurd waste of energy. My research has found that generating a high-quality image with AI can use as much energy as half of a cellphone charge and that generating videos requires thousands of times more. The gap between the most and least efficient models is growing with the rise of reasoning models and agentic AI.
However, Canada has an opportunity to incentivize on-device AI and small, task-specific models that run locally within Canadian facilities. This creates a direct, powerful synergy between operational security and environmental sustainability.
Also, we can and should mandate transparency to audit the environmental and operational costs of training and deploying AI models. When we know where models are running, along with details regarding hardware and energy, we can accurately measure sustainability. The difference between training a multi-billion-parameter model on a low-carbon grid, like those in Quebec and Ontario, versus a grid powered by behind-the-meter natural gas can be orders of magnitude fewer emissions. Therefore, future federal AI legislation should mandate that companies deploying AI models disclose their full life-cycle environmental footprint, including the energy grid mix and water usage of the hosting data centres.
Canada can foster a booming local ecosystem by supporting companies that use open-source tools like CodeCarbon, which I helped develop, to publicly document their models' energy efficiency. This establishes a green AI stamp of approval, appealing to a global market increasingly desperate for ESG-compliant technology, differentiating Canada's AI offerings and giving our companies a competitive advantage. The federal government can lead the way by requiring AI developers to provide this information when applying for tender offers for government contracts, setting a baseline for the entire field.
Also, just as we rely on Energy Star ratings for appliances, Canada can implement energy certifications for commercial AI models, disincentivizing the use of loaded models for simple tasks. This approach is practical and highly achievable. It has already been proposed by the AI energy score project, which I have co-led for the last two years. In our work, we've tested hundreds of open-source AI models across dozens of tasks, finding efficiency differences in the tens of thousands between models of different sizes and architectures.
Finally, we must align the government's current industry plan with these ecological limits. The federal government's allocation towards the sovereign compute infrastructure program, SCIP, is a vital step toward reclaiming our data sovereignty, but compute power cannot be decoupled from environmental boundaries. The government must ensure that any public supercomputing infrastructure built under the SCIP is powered, at least in the majority, by renewable energy and utilizes cooling systems that sustainably use a local power supply and that are developed in consultation with local residents, including indigenous communities.
Instead of copying the hyperscale, football field-sized data centres that are becoming the norm in the United States, Canada has the opportunity to fund more creative approaches to compute. For instance, we can build smaller data centres that are better integrated with existing infrastructure, allowing us to reduce resource consumption while reusing the generated heat for offices, residences and university campuses.
Furthermore, SCIP resources should explicitly prioritize Canadian researchers and open-source initiatives developing climate tech solutions and sustainable industrial applications. Each project that plans to use SCIP compute should be required to measure and report their energy and emissions, improving the transparency of the field as a whole.
True digital sovereignty is completely impossible without environmental sustainability. Canada must reject the current trajectory of AI, which is unsustainable from all perspectives, and define a better trajectory for Canadian AI.
By legally mandating—