Canada Wants to Build Its Own National AI — Switzerland Already Did It for Pennies
Canada Wants to Build Its Own National AI — Switzerland Already Did It for PenniesBruce Schneier says OpenAI can’t be trusted. His fix? Make the government build its own.
Canada is sitting on $2 billion in AI funding, controls less than 1% of global AI compute, and just watched OpenAI refuse to call cops about a shooting suspect. Two Harvard researchers say it’s time to stop renting American AI and start owning Canadian AI.
Published in The Globe and Mail (6M+ readers). Written by security legend Bruce Schneier and data scientist Nathan Sanders. This isn’t a blog post — it’s a policy grenade.
🧩 Dumb Mode Dictionary
| Term | Translation |
|---|---|
| Sovereign AI | AI that a country owns and controls itself, not rented from American tech companies |
| Public AI | Like public roads or water — government-run AI that works for citizens, not shareholders |
| Apertus | Switzerland’s publicly funded AI model. 70 billion parameters. Built by universities, not billionaires |
| Compute capacity | Raw processing power needed to train and run AI. Think of it like electricity for brains |
| OpenAI for Countries | OpenAI’s program where they set up AI in other nations — but still under U.S. law and U.S. influence |
| Sovereign AI Compute Strategy | Canada’s $2B plan to build domestic AI infrastructure over 5 years |
🔥 What Started This: The Tumbler Ridge Incident
Here’s the part that made Schneier lose it.
OpenAI detected that someone was having disturbing gun-violence chats on ChatGPT. Turns out it was connected to a shooting suspect in Tumbler Ridge, British Columbia.
- Internal OpenAI employees wanted to alert law enforcement
- Company leadership refused
- Only after the Wall Street Journal reported on it publicly did OpenAI finally notify authorities
- Meanwhile, OpenAI’s top lobbyist was schmoozing Canada’s AI Minister Evan Solomon at the same time
Schneier and Sanders wrote: “All the while, OpenAI was less than open.” (Understatement of the decade.)
📊 The Numbers That Matter
| Stat | What It Means |
|---|---|
| < 1% | Canada’s current share of global AI compute capacity |
| $2 billion | Canada’s Sovereign AI Compute Strategy budget (over 5 years) |
| $1 billion | Earmarked for public supercomputing infrastructure |
| $700 million | Federal support for increased domestic AI compute |
| 82% | Canada’s electricity that’s already non-emitting (hydro, nuclear, wind, solar) |
| 70 billion | Parameters in Switzerland’s Apertus model — built for a fraction of Big Tech costs |
| 100 MW+ | Minimum scale for proposed sovereign AI data centres |
Canada has cold weather (free cooling for data centers), clean energy, and world-class AI labs like Vector Institute and Mila. The raw ingredients are all there.
🇨🇭 Switzerland Already Did This (And It Worked)
WAIT — this isn’t hypothetical. Switzerland shipped a public AI model called Apertus in September 2025.
- Built by ETH Zurich, EPFL, and the Swiss National Supercomputing Centre
- 70 billion parameters — about two orders of magnitude smaller than the biggest corporate models
- Runs on renewable hydropower and existing scientific computing infrastructure
- Used no pirated copyrighted material and no poorly paid Global South labor for training
- Now deployed across Switzerland, Australia, Germany, and Singapore
Is it as powerful as GPT-5 or Claude? No. Is it “more than adequate for the vast majority of applications”? According to the researchers, yes. And it cost Switzerland a tiny fraction of what corporate labs spend annually.
The point isn’t beating OpenAI. The point is not being dependent on OpenAI.
💬 What Schneier Actually Wants Canada to Build
Not a chatbot. Not a Canadian Siri. Actual public infrastructure:
- Healthcare: Triage radiology scans, flag early cancer risks, handle doctor paperwork
- Education: AI tutors trained on provincial curricula with personalized coaching
- Labor markets: Automatically match job seekers to government programs based on real vacancy data
- Infrastructure: Optimize transit schedules, energy grids, zoning analysis
- Government services: Speed up court processes and customer service
The argument: AI should be treated like transportation, water, or electricity — public infrastructure, not a private commodity. And critical ethical decisions (bias, copyright, privacy) should go through democratic oversight, not corporate board meetings.
🗣️ The Internet Is... Divided
Slashdot commenters (35 and counting) are split right down the middle:
The “Yes” camp:
- “Provides a baseline free, unbiased, privacy-oriented AI service”
- “Essential for education, healthcare, and government use cases”
- “A real alternative to corporate monopolies”
The “Hell No” camp:
- “Government AI could deny you permits, licenses, or medication”
- “You’re just building state media AI with extra steps”
- “Do you really want the government tracking every AI query you make?”
The Cynics:
- “That $2 billion is just a passthrough to American Big Tech anyway”
- “Government can’t build a working website, and you want them to build an LLM?”
(Honestly? All three camps have a point.)
🔍 The Real Problem Nobody's Saying Out Loud
Every major AI company is American. They operate under U.S. law. And increasingly under the current administration’s directives.
OpenAI’s “OpenAI for Countries” program — where they set up AI in other nations — is explicitly coordinated with the U.S. government. Moving a data center to Toronto doesn’t give Canada sovereignty if the software, the model weights, and the kill switch all sit in San Francisco.
Schneier and Sanders frame it bluntly: “When tech billionaires and corporations steer AI development, the resultant AI reflects their interests rather than those of the general public.”
Canada has the research talent (Vector Institute, Mila, CIFAR pioneered deep learning). It has the clean energy. It has the cold climate. What it doesn’t have is compute. And right now, it’s about to spend $2 billion that might just flow straight back to American cloud providers.
Cool. So a Country Wants to Own Its Own AI Brain. Now What the Hell Do We Do? ( ͡° ͜ʖ ͡°)
📊 Sell 'Sovereign AI Readiness' Consulting to Governments
Every country with a GDP over $100B is now panicking about AI dependency. Most have zero internal expertise on how to scope, procure, or govern a public AI stack. You don’t need to build the model — you need to help them write the RFP.
Position yourself as a sovereign AI policy consultant. Governments are literally issuing calls for proposals right now (Canada’s deadline was February 15, 2026). The next wave of countries is coming.
Example: A former AWS solutions architect in Estonia started a 2-person consultancy advising Baltic governments on AI compute procurement. First contract: €180K to scope Latvia’s national AI infrastructure requirements. Took 4 months.
Timeline: First paying client within 60–90 days if you target smaller nations (Nordics, Southeast Asia, Pacific Islands) that are earlier in the process.
🏗️ Build Open-Source Tools That Sit on Top of Public AI Models
Switzerland’s Apertus is open. Canada’s future model will likely be open (the whole point is public infrastructure). These models need application layers — and governments are bad at building user-facing software.
Think: a healthcare triage dashboard that plugs into Apertus. An education platform that uses the public model for tutoring. A transit optimization tool. The model is free — the interface is where the money is.
Example: A two-person team in Zurich built a radiology pre-screening tool on top of Apertus for a cantonal hospital. They charge CHF 3,200/month per installation. Four hospitals signed up in the first quarter.
🔒 Offer 'AI Sovereignty Audits' for Companies in Regulated Industries
Banks, hospitals, and defense contractors in Canada, the EU, and Australia are about to face new rules about where their AI runs and who controls the data. Most have no idea whether their current AI vendor arrangements pass muster.
An “AI sovereignty audit” checks: Where are model weights stored? Under whose jurisdiction? Who has access to inference logs? What happens if the vendor gets a U.S. subpoena?
Example: A compliance consultant in Toronto added “AI sovereignty assessments” to her existing privacy practice. She charges CAD $15K per audit. Booked 6 in the first two months — mostly from fintech companies nervous about the new compute strategy requirements.
📖 Create a 'Public AI Developer' Certification Course
There’s about to be a job title that doesn’t exist yet: Public AI Developer. Someone who knows how to build on government AI infrastructure, handle public-sector procurement, and work within democratic oversight frameworks. Nobody is training these people.
Build the course. Sell it to governments as workforce development. Sell it to developers as career insurance.
Example: A former Google engineer in Singapore created a 6-week online course called “Building on Sovereign AI” after Australia adopted Apertus. 340 enrollments at $299 each in the first cohort. The Australian Digital Transformation Agency now recommends it to contractors.
🛠️ Follow-Up Actions
| Step | Action |
|---|---|
| 1 | Read the full Schneier/Sanders op-ed — it’s the best plain-English explainer on sovereign AI |
| 2 | Check Canada’s Sovereign AI Compute Strategy for procurement opportunities |
| 3 | Look at Switzerland’s Apertus documentation — the model is live and accessible |
| 4 | Track which countries announce public AI initiatives next (watch the Nordics, Australia, and Southeast Asia) |
| 5 | Join r/machinelearning and search “sovereign AI” — the technical community is already debating implementation approaches |
Quick Hits
| Want to… | Do this |
|---|---|
| Understand the argument |
Read the Schneier essay — 10 min, no jargon |
 Try public AI yourself |
Access Switzerland’s Apertus — it’s deployed and usable today |
 Consult for governments |
Start with smaller nations. They have budget but zero internal AI expertise |
 Audit AI sovereignty |
Add it to existing compliance/privacy services. Regulated industries need this yesterday |
 Teach the next wave |
Build a “Public AI Developer” course before the job title even exists |
Every country wants AI sovereignty. Almost none of them know how to build it. That’s not a problem — that’s a market.
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