AI in the Blue Economy: A Force for Good?
Written by: Nicholas LePage, Guest Writer
Few words seem to echo more in daily conversation than artificial intelligence, which is now impacting every sector – on and offshore. The Canadian government’s AI for All Strategy seeks to boost AI adoption across the economy from 12% today to 60% in 2034. The economic imperative underlying the strategy is resoundingly clear.
In fact, one of the key takeaways from the SOI Foundation’s Blue Futures Pathways learning module on Digital Technology & AI in the Sustainable Blue Economy, is that by 2030, AI is projected to contribute a staggering $15.7 trillion (USD) to the global economy. Though the plan does not mention the ocean and water sectors specifically, ( known as the Blue Economy), we can be sure that developments in AI will have deep and resounding impacts across these sectors too.
Few technologies seem to court as much controversy as AI, given that the expansion of its infrastructure has massive ramifications in terms of resource use and energy requirements. Networks of data centres and corresponding energy infrastructure are currently spreading like river systems across Canada, with nearly 100 hyperscale facilities announced or currently under construction.
As discussed in learning module 9, data centres require immense amounts of fresh water to cool servers. Though water use varies according to different data center designs, for most, roughly 80% of this water is then lost due to evaporation, leading to intense conflicts between developers and communities, especially in water-stressed regions.
Shortly after the release of the Canadian Government’s AI for All Strategy, a report by the United Nations University Institute for Water, Environment and Health projected that data centre expansions could consume enough freshwater to meet the minimum daily residential needs of Sub-Saharan Africa’s 1.3 billion residents.
Herein lies the paradox of the role of AI. How can a technology that requires such massive inputs of freshwater and energy be sustainably utilized within a truly Sustainable Blue Economy?
Using AI to Improve Ocean Management
One answer is tied to the challenges of ocean management. Given that the ocean is a highly complex environment that human observers are seldom able to visit directly, digital twin technologies are incredibly useful in furthering our understanding of the geography of the ocean floor and the dynamics of ocean currents.
The Digital Twin of the Ocean is an ambitious project led by the European Union to develop a virtual replica of the ocean. Using real time and historical continuous data, the project will ultimately create a more accurate model of the ocean than those currently available. By creating virtual models, the project can help the Sustainable Blue Economy anticipate human and environmental impacts. Crucially, this predictive modeling allows fishers and maritime workers to visualize and prepare for hazards like storm surges and sea level rise.
In doing so, policymakers, entrepreneurs, industry leaders and scientists will be better equipped to make ocean management decisions based on robust data that reflects the true complexity of ocean systems. The project shows how the strategic use of AI technology can foster international collaboration and improve our goals for the sustainable use of resources, including the ocean. Fusion skills, or the collaboration between AI and human operators are crucial to the success of these systems.
The immense scale of the ocean creates huge challenges for human observers to monitor and track human and non-human activity. Far from the eyes of law enforcement, the open ocean has long been lawless and nearly ungovernable, where environmental crimes and human rights abuses are seldom held to account. AI tools can be used to change that. The Nature Conservancy has developed an open-source and freely-available AI monitoring tool to bring new levels of transparency to fishing supply chains. The tool allows for near-real time monitoring of fishing catches to ensure that fishing vessels comply with regulations.
Tackling AI’s resource footprint
While AI use can certainly improve ocean management and conservation, the impact of AI in terms of resource use is multifaceted. For example, AI systems depend on critical mineral extraction both on land and at sea.
The rapid expansion of AI infrastructure depends on intensive, physical resource extraction that increasingly pushes into the world’s last remaining pristine wildernesses, the deep-sea bed. Environmental groups and researchers have long warned of the permanent ecological damage caused by rushing to harvest the rare earth metals and critical minerals required to manufacture advanced hardware. These extraction efforts frequently target highly fragile ecosystems that have taken millennia to form, raising deep concerns about irreversible biodiversity loss and the disruption of unstudied natural habitats.
Yet, a compelling paradox emerges as resource companies deploy AI itself to minimize the environmental footprint of these invasive operations. Instead of relying on traditional, destructive extraction methods that blindly clear-cut or dredge landscapes, modern operators are integrating AI-enhanced autonomous robotics and computer vision. These systems are trained to dynamically scan environments in real time, identifying local wildlife and sensitive micro-habitats to actively isolate and protect them from the extraction process. While this targeted approach seeks to reduce immediate surface disruption, debate remains over whether technology can ever fully neutralize the ecological costs of its own physical supply chain.
Once built, data centres also directly impact ocean ecosystems. Data centres built on the world’s coastlines use seawater to cool servers. But in the process they create ecological dead zones. Further resource extraction also leads to more e-waste, which often ends up at sea.
So much of AI’s environmental impact will depend on how we choose to deploy it. The rush to dramatically increase electricity generation has been a major driver to both renewable energy and the further expansion of fossil fuels. Many technology companies leading the charge on AI expansion initially promised facilities fully powered by renewables, but have since chosen instead to rely on natural gas facilities.
Still, there are means to ensure the AI boom can be sustainable. Adopting closed-loop water cooling systems can reduce the extraction of fresh water, demonstrating that AI’s impressive capabilities need not lead to a dystopian future. We can deploy AI for a better future, if we focus on “purpose built, efficient, and environmentally conscious systems” over raw scale.
Mapping a better future for AI in the Blue Economy
If we are to harness the positive potential of AI in our designs for the Sustainable Blue Economy, we must ensure our actions are guided by sound principles of sustainable resource use and ecosystem management. We will need many more sustainable blue economy professionals to tackle tough questions around AI’s water, energy, and material footprint.
To learn more about the future of blue technology, explore the free learning module on Digital Tech and AI offered by SOI’s Blue Futures Pathways. UBC’s online micro-credential program, Leadership in the Sustainable Blue Economy, delivered in collaboration with Blue Futures Pathways and Mitacs is another fantastic resource for emerging leaders looking to integrate AI into the marine sector.
Ultimately, no AI system will benefit humans in a world where the Earth’s hydrosphere is pushed beyond ecological limits. A recent study beautifully illustrated this point: “Life on this planet is sustained by water. It is not sustained by data. We don’t need data the way we need water, and we in Canada have been pretty blithe about our natural resources.” – concerned Nanaimo, B.C. resident (Montpetit & Brend, 2025).
We absolutely must keep this fundamental fact at the forefront of our minds as we continue to design our future alongside the potential of AI.