AI is another reason why Canada needs to boost the energy supply

Massive energy levels are required to keep up with AI innovations, and Canada risks being unable to do that

Artificial Intelligence is already one of the most important technologies of our time, and its development has been pushing innovation at a breakneck pace across huge swathes of the economy. Smart assistants now operate, albeit in a limited fashion, as secretaries for those who need help in the office, while autonomous vehicle capabilities keep improving.

It is a remarkable and world-changing time.

Just as one plays a video game, turns on a light, or starts up their car, AI requires energy. To say that AI’s appetite for energy is ravenous is an understatement, and Canadian governments must understand the challenge that comes with that.

Energy shortages are a growing threat to Canada’s economic security and, yes, our standard of living. Failure to keep up with demand means importing more energy at a cost, or facing energy blackouts, in which case Canada will fall behind in far more than just AI.

New AI models are seemingly rolling out every month, especially in machine learning and generative AI. OpenAI’s ChatGPT and Google’s Bard require huge levels of computing power to work. To train ChatGPT-4, an advanced language model, consumes thousands of megawatt hours of electricity, not incomparable to the energy usage of urban centres.

A single query made to ChatGPT requires ten times the energy of making a search on Google, revealing the massive needs of AI technology. AI is not just another internet search extension or downloadable app, it is an entirely new industry.

AI models are trained and run in data centers, which are central to this energy dilemma. The sheer power consumption in data centers is ballooning, and some estimates warn that the world’s data center energy demand will surge by 160 percent by 2030.

The International Energy Agency (IEA) has reported that AI and data centers already consume 1 to 2 percent of global electricity, a figure expected only to climb as more companies embrace AI-driven technology. As much as AI is driving digital innovation, it is also consuming electricity at a rate we will have to match.

Canada’s energy security is being seriously challenged by rising demand, with or without AI. Historically, Canadians have enjoyed the fruits of abundant, cheap energy generated by hydroelectricity in BC and Quebec, or nuclear power in Ontario. Times, and weather, have unfortunately changed.

A large and growing population, electrifying economies, and the weakening of Canada’s legacy energy sources are pushing the country to its limits regarding power supply.

The current federal government wants Canada to achieve net-zero emissions by 2050, which means that electricity is going to have to double in the next 25 years. Canada is already dealing with electricity shortages, such as in British Columbia, where demand for hydroelectricity is expected to rise 15 percent over the next six years. Manitoba is projecting a shortfall by 2029, while Ontario races to put up new nuclear power plants to avert an energy crisis by 2029 as well.

AI can help Canadians craft solutions to its incoming energy problems as a valuable research aid that can help with modeling and processing data. However, that will mean more energy consumption as part of the rogue wave of energy consumption that AI innovation has created.

As evidenced by the constant developments in AI, it is obvious that the technology is going nowhere, and neither are Canada’s energy shortfalls.

If AI is going to contribute to the surge in energy demand, then it only makes sense that it becomes a vital tool in the search for solutions, and we need those solutions now.

Related Topics:#ResourceWorks AIEnergy CanadaEconomySecurity CanadaEnergySupply ComputingPower EnergyShortages EnergyUsage

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Artificial Intelligence

UK Police Pilot AI System to Track “Suspicious” Driver Journeys

Published on December 22, 2025

Todayville

By Ken Macon

AI-driven surveillance is shifting from spotting suspects to mapping ordinary life, turning everyday travel into a stream of behavioral data

Police forces across Britain are experimenting with artificial intelligence that can automatically monitor and categorize drivers’ movements using the country’s extensive number plate recognition network.

Internal records obtained by Liberty Investigates and The Telegraph reveal that three of England and Wales’s nine regional organized crime units are piloting a Faculty AI-built program designed to learn from vehicle movement data and detect journeys that algorithms label “suspicious.”

For years, the automatic number plate recognition (ANPR) system has logged more than 100 million vehicle sightings each day, mostly for confirming whether a specific registration has appeared in a certain area.

The new initiative changes that logic entirely. Instead of checking isolated plates, it teaches software to trace entire routes, looking for patterns of behavior that resemble the travel of criminal networks known for “county lines” drug trafficking.

The project, called Operation Ignition, represents a change in scale and ambition.

Unlike traditional alerts that depend on officers manually flagging “vehicles of interest,” the machine learning model learns from past data to generate its own list of potential targets.

Official papers admit that the process could involve “millions of [vehicle registrations],” and that the information gathered may guide future decisions about the ethical and operational use of such technologies.

What began as a Home Office-funded trial in the North West covering Merseyside, Greater Manchester, Cheshire, Cumbria, Lancashire, and North Wales has now expanded into three regional crime units.

Authorities describe this as a technical experiment, but documents point to long-term plans for nationwide adoption.

Civil liberty groups warn that these kinds of systems rarely stay limited to their original purpose.

More: London’s Surveillance Scheme Rakes in Millions While Failing the Community

Jake Hurfurt of Big Brother Watch said: “The UK’s ANPR network is already one of the biggest surveillance networks on the planet, tracking millions of innocent people’s journeys every single day. Using AI to analyse the millions of number plates it picks up will only make the surveillance dragnet even more intrusive. Monitoring and analysing this many journeys will impact everybody’s privacy and has the potential to allow police to analyse how we all move around the country at the click of a button.”

He added that while tackling organized drug routes is a legitimate goal, “there is a real danger of mission creep – ANPR was introduced as a counter-terror measure, now it is used to enforce driving rules. The question is not whether should police try and stop gangs, but how could this next-generation use of number plate scans be used down the line?”

The find and profile app was built by Faculty AI, a British technology firm with deep ties to government projects.

The company, which worked with Dominic Cummings during the Vote Leave campaign, has since developed data analysis tools for the NHS and Ministry of Defence.

Faculty recently drew attention after it was contracted to create software that scans social media for “concerning” posts, later used to monitor online debate about asylum housing.

Faculty declined to comment on its part in the ANPR initiative.

Chief constable Chris Todd, chair of the National Police Chiefs’ Council’s data and analytics board, described the system as “a small-scale, exploratory, operational proof of concept looking at the potential use of machine learning in conjunction with ANPR data.”

He said the pilot used “a very small subset of ANPR data” and insisted that “data protection and security measures are in place, and an ethics panel has been established to oversee the work.”

William Webster, the Biometrics and Surveillance Camera Commissioner, said the Home Office was consulting on new legal rules for digital and biometric policing tools, including ANPR.

“Oversight is a key part of this framework,” he said, adding that trials of this kind should take place within “a ‘safe space’” that ensures “transparency and accountability at the outset.”

A Home Office spokesperson said the app was “designed to support investigations into serious and organised crime” and was “currently being tested on a small scale” using “a small subset of data collected by the national ANPR network.”

From a privacy standpoint, the concern is not just the collection of travel data but what can be inferred from it.

By linking millions of journeys into behavioral models, the system could eventually form a live map of how people move across the country.

Once this analytical capacity becomes part of routine policing, the distinction between tracking suspects and tracking citizens may blur entirely.

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Alberta

Schools should go back to basics to mitigate effects of AI

Published on December 15, 2025

Todayville

From the Fraser Institute

By Paige MacPherson

Odds are, you can’t tell whether this sentence was written by AI. Schools across Canada face the same problem. And happily, some are finding simple solutions.

Manitoba’s Division Scolaire Franco-Manitobaine recently issued new guidelines for teachers, to only assign optional homework and reading in grades Kindergarten to six, and limit homework in grades seven to 12. The reason? The proliferation of generative artificial intelligence (AI) chatbots such as ChatGPT make it very difficult for teachers, juggling a heavy workload, to discern genuine student work from AI-generated text. In fact, according to Division superintendent Alain Laberge, “Most of the [after-school assignment] submissions, we find, are coming from AI, to be quite honest.”

This problem isn’t limited to Manitoba, of course.

Two provincial doors down, in Alberta, new data analysis revealed that high school report card grades are rising while scores on provincewide assessments are not—particularly since 2022, the year ChatGPT was released. Report cards account for take-home work, while standardized tests are written in person, in the presence of teaching staff.

Specifically, from 2016 to 2019, the average standardized test score in Alberta across a range of subjects was 64 while the report card grade was 73.3—or 9.3 percentage points higher). From 2022 and 2024, the gap increased to 12.5 percentage points. (Data for 2020 and 2021 are unavailable due to COVID school closures.)

In lieu of take-home work, the Division Scolaire Franco-Manitobaine recommends nightly reading for students, which is a great idea. Having students read nightly doesn’t cost schools a dime but it’s strongly associated with improving academic outcomes.

According to a Programme for International Student Assessment (PISA) analysis of 174,000 student scores across 32 countries, the connection between daily reading and literacy was “moderately strong and meaningful,” and reading engagement affects reading achievement more than the socioeconomic status, gender or family structure of students.

All of this points to an undeniable shift in education—that is, teachers are losing a once-valuable tool (homework) and shifting more work back into the classroom. And while new technologies will continue to change the education landscape in heretofore unknown ways, one time-tested winning strategy is to go back to basics.

And some of “the basics” have slipped rapidly away. Some college students in elite universities arrive on campus never having read an entire book. Many university professors bemoan the newfound inability of students to write essays or deconstruct basic story components. Canada’s average PISA scores—a test of 15-year-olds in math, reading and science—have plummeted. In math, student test scores have dropped 35 points—the PISA equivalent of nearly two years of lost learning—in the last two decades. In reading, students have fallen about one year behind while science scores dropped moderately.

The decline in Canadian student achievement predates the widespread access of generative AI, but AI complicates the problem. Again, the solution needn’t be costly or complicated. There’s a reason why many tech CEOs famously send their children to screen-free schools. If technology is too tempting, in or outside of class, students should write with a pencil and paper. If ChatGPT is too hard to detect (and we know it is, because even AI often can’t accurately detect AI), in-class essays and assignments make sense.

And crucially, standardized tests provide the most reliable equitable measure of student progress, and if properly monitored, they’re AI-proof. Yet standardized testing is on the wane in Canada, thanks to long-standing attacks from teacher unions and other opponents, and despite broad support from parents. Now more than ever, parents and educators require reliable data to access the ability of students. Standardized testing varies widely among the provinces, but parents in every province should demand a strong standardized testing regime.

AI may be here to stay and it may play a large role in the future of education. But if schools deprive students of the ability to read books, structure clear sentences, correspond organically with other humans and complete their own work, they will do students no favours. The best way to ensure kids are “future ready”—to borrow a phrase oft-used to justify seesawing educational tech trends—is to school them in the basics.