Canada’s Scaling Problem isn’t Compute, it’s Coastlines
When your country is this big, AI solves a different kind of scale.
In November 2025, the federal government quietly launched something unusual: a public registry of every AI system used across federal departments. Over 400 AI use cases, all searchable, all documented.
I spent a few hours digging through it over the holiday break. If you’re looking for a dystopian surveillance state, you might be disappointed. The real pattern is more mundane and, honestly, more interesting:
Canada doesn’t need to scale AI. It needs AI to scale.
When you’re governing the world’s second-largest country, with the longest coastline on Earth and a population scattered across millions of square kilometers, you need machines to help watch what people can’t.
Here are the most interesting tools they’re building:
Examples of Canada Using AI For Scale:
1. Listening for "Dark" Drones (NRC)
The NRC is training AI to act like "Shazam for security" by recognizing the unique humming sounds of drone propellers, allowing them to spot silent, autonomous drones that are invisible to traditional radar and cameras.
2. The Fish Eavesdropper (DFO)
Fisheries and Oceans Canada uses AI to listen to underwater sounds and identify specific fish species, allowing scientists to track ocean health and biodiversity without ever having to use a net.
3. X-Ray Gun Detection (CBSA)
The CBSA is developing AI that can "see" through the clutter of mail packages, automatically spotting disassembled gun parts hidden inside other objects in X-ray scans.
4. Digitizing History (1931 Census)
Researchers used AI to read and transcribe 10 million handwritten names from the 1931 Census, turning decades of old cursive records into a searchable digital database in a fraction of the time it would take humans.
5. Wildfire Predictions (Satellite Imagery)
By analyzing satellite images from space, this AI can predict how and where a wildfire will move through remote forests, giving emergency teams a head start to protect nearby communities.
A few other use cases worth noting:
CANChat is the government’s private ChatGPT, built so public servants don’t leak sensitive data to OpenAI. It runs entirely on Canadian servers and is used for drafting briefing notes and summarizing legislation.
Immigration AI has now processed over 7 million routine applications. The system can approve straightforward cases without a human ever looking at the file, but it’s strictly forbidden from refusing one. If the AI flags a risk, a human takes over.
Arctic permafrost monitoring uses AI to predict where terrain will collapse before it happens, helping Northern communities move infrastructure in time.
Synthetic whales: One project uses AI-generated fake whale imagery to train models that spot endangered North Atlantic Right Whales from satellite, letting the Coast Guard redirect ships in real-time.
Election defence: After the 2024 federal election, the Communications Security Establishment reported a surge in AI-generated fake news sites mimicking CBC and CTV branding. The government now uses a tool called Assemblyline that scans over 1 billion files a year to catch state-sponsored malware and disinformation before it spreads.
The Autonomous Moon Arm : Because it takes too long for signals to travel between Earth and the Moon for manual control, the CSA is giving Canadarm3 its own “brain” to navigate and avoid collisions automatically during lunar missions.
The Pattern
The tools that stand out aren’t the chatbots or the administrative helpers. They’re the ones tackling problems that are either physically remote or buried in massive datasets. Satellites scanning the Arctic 24/7. Microphones listening to fish in the deep ocean. Algorithms sorting millions of postal packages.
Canada’s AI strategy isn’t really about efficiency. It’s about coverage. When your borders span a continent and your coastline wraps around three oceans, you need machines to watch the parts humans can’t reach. The registry makes that clear: AI isn’t replacing people here, it’s extending their reach into places they were never going to look in the first place.
The immigration use case is the most striking example of how the federal government is trying to automate the "boring" parts of the state. The Treasury Board actually updated its Directive on Automated Decision-Making recently to address these kinds of systems. While the AI can approve applications to speed up the backlog, the rules still require a human to sign off on any "high stakes" refusal. It is a massive shift from the 1990s, when Hansard records show MPs were constantly debating the backlog of paper files being mailed between regional offices. Now the bottleneck isn't the mail, it is ensuring the algorithms don't inherit the old manual biases.
I know Canada can’t be perfect, but this story warms me.