This article was first published by Canada's National Observer.
Every great river begins not as a torrent but as a meeting. In the high Himalayas, for example, the Alaknanda and Bhagirathi rivers join, and yet their flow does not merely widen; it accelerates and reshapes everything downstream. Each of these rivers is formidable, yet their convergence is what inspires their titanic power as the mighty Ganges. Canada and India face a similar moment of convergence. Both nations are already consequential actors, yet far greater power lies in what their collaboration could unlock: a “third path,” to use Canadian Prime Minister Mark Carney’s words at Davos.
While the Canada-India relationship has experienced its share of ups and downs over the past decade, it appears re-energized under the governments of Carney and Narendra Modi. This comes at a moment when an increasingly unstable geopolitical order is pushing middle powers to seek dependable partners and clearer pathways to strategic autonomy. Climate cooperation and artificial intelligence (AI) offer precisely such a domain. Canada brings advanced AI and climate science capacity; India brings scale, deployment pathways and urgent real-world use cases that can rapidly translate models into impact. In short, each holds what the other seeks.
AI, climate and the new distribution of power
Climate intelligence is no longer governed primarily by observation. It is driven by prediction. It uses data, models and analytical tools to turn observations of weather, land and water into forward-looking estimates of climate risk and guidance for decision-makers.
As such, governments, businesses and civil society have become increasingly dependent on algorithms to forecast floods, model wildfires, guide harvests and plan industrial development. Machine-learning models determine not only the visible risks but also how uncertainty is hedged against and what those decision-makers prioritize.
But much of AI-driven climate intelligence and AI more broadly — data pipelines, model architectures, computational infrastructure — is controlled by the United States and China.
When AI models that guide climate adaptation are trained and deployed on their platforms, public decision-making begins to reflect their incentives. This creates a familiar pattern of dependence for middle powers.
India’s exposure to floods, heatwaves and monsoon variability has made it increasingly reliant on foreign climate-intelligence models for early warning and risk prediction. Canada, meanwhile, increasingly depends on cloud-based modelling tools and proprietary risk platforms for climate adaptation and lacks the scale and deployment pathways needed to fully develop and realize sovereign alternatives.
Canada and India have already committed to cooperating on climate and technology. The 2025 India-Canada Joint Statement reaffirmed collaboration on environmental resilience, disaster response and digital infrastructure around AI. Further, both countries participate in global climate-intelligence sharing through the International Charter: Space and Major Disasters, which uses shared satellite imagery to support flood and cyclone response.
These efforts matter. They demonstrate environmental data can function as shared public infrastructure rather than as a proprietary service. But these collaborative junctures remain fragmented and surface-level rather than converging as shared, bilateral architecture. Cooperation on AI-driven climate intelligence offers a pilot of deeper AI cooperation where both nations can leverage each other’s capabilities to address one another’s deficiencies. This domain could offer a test bed for what technological decoupling from the United States and China may look like.
AI-driven climate intelligence as a “third path”
As Carney has argued, sovereignty is increasingly eroded through dependence on great-power infrastructure. Addressing this requires a break from the postwar order and a third path that is strategic, not rhetorical — one that AI-enabled climate intelligence uniquely makes possible.
Rather than each flowing separately into dominant AI ecosystems, Canada and India could build a shared current: co-developing climate models, aligning data standards, pooling training data sets and coordinating governance over how AI systems are deployed in moments of crisis. For Canada, this would anchor AI leadership in trusted public-interest infrastructure rather than extractive platforms. For India, it would enable large-scale deployment of climate AI without deepening dependence on opaque external systems. For both, it offers cooperation without defaulting to a geopolitical alignment or dependence.
Canada and India should translate this complementarity into concrete infrastructure. Specifically, they could create a distributed Climate Intelligence Network: shared computer infrastructure, co-located research teams (rotating between Vancouver/Montreal and Delhi/Bangalore) and integrated data pipelines. Rather than a single physical facility, this would link existing capacity in both countries while creating dedicated nodes for joint model development and testing.
Equally important is governance. A bilateral Climate Intelligence Governance Council that is co-chaired by senior officials from both countries could establish binding standards for transparency, auditability and democratic oversight of AI-driven climate intelligence systems.
Two geopolitical questions
Two obvious questions emerge that may offer pushback: First, why bilateral cooperation when Carney, Modi and Anthony Albanese — Australia’s prime minister — recently signed a trilateral partnership to collaborate on AI, energy and crucial minerals? Second, can Canada pursue deep technological cooperation with India, given New Delhi’s closer alignment recently with Russia on energy and AI?
Climate intelligence offers a domain where bilateral cooperation makes more strategic sense. Australia competes with Canada on similar value propositions to India, including in both AI research capacity and climate science expertise, rather than adding a distinct third capability.
And while India’s deepening technological cooperation with Russia, including around AI, raises understandable concerns, it is also precisely why climate intelligence matters as a domain for Canada-India partnership. It demonstrates that strategic autonomy requires options instead of exclusive alignment. India’s shift toward Russia in defence and energy is partly a function of lacking alternatives; climate cooperation with Canada offers a different model. The question isn’t whether India will abandon other partnerships — it won’t, and it shouldn’t — but whether Canada can offer cooperation based on shared values of democratic governance, transparency and public interest.
Creating a shared current
To move from concept to implementation, Canada and India should leverage touchpoints at the India AI Impact Summit taking place February 19 and 20 in New Delhi and during upcoming talks to convene a bilateral Climate Intelligence Round Table within the next year. Participants would range from AI researchers and climate scientists to data governance and ethics experts — and include representation from Indigenous communities and regions most vulnerable to climate impacts, as well as private-sector partners. The round table should aim to produce specific recommendations on concrete points such as data-sharing protocols, joint model development frameworks, governance structures and institutional architecture.
When rivers meet, they do not surrender their identity. Distinct currents remain visible even as the combined flow becomes harder to divert. Confluence does not dilute sovereignty; it concentrates influence.
