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Artificial Intelligence

A Frisson of Fission: Why Nuclear Power Won’t Replace Natural Gas as North America’s Critical Fuel

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From the C2C Journal

By Gwyn Morgan
The recent collapse of the power grid in Cuba, plunging the island nation into darkness and grinding its meagre economy to a halt, served as a reminder of electricity’s centrality to modern civilization. That dependency is only expected to increase as more electric vehicles take to the road – and, writes Gwyn Morgan, as the tech sector’s voracious appetite for electrons expands unabated. Morgan pours a pail of cold water on the much-mooted “nuclear revival” that has yet to deliver any actual new electricity. He argues instead that what’s needed is clear-eyed recognition that the most reliable, most abundant, most flexible and most affordable energy source is a fossil fuel located in vast quantities right beneath North Americans’ feet.
Three Mile Island: now there’s a name only us retired folk will remember. On March 28, 1979 the Unit 2 reactor in the Three Mile Island Nuclear Generating Station near Middletown, Pennsylvania incurred a partial melt-down. This was and remains the most serious accident in U.S. nuclear power-plant operating history. Although nobody was killed or injured, the near-catastrophe gripped Americans for months (that was when the term “melt-down” entered the public lexicon). It further energized the powerful anti-nuclear movement – eerily, the movie The China Syndrome concerning a fictional reactor melt-down had been released just 12 days before the actual Three Mile Island event – and shifted public opinion further against generating electricity by splitting the atom. Construction of new facilities slowed dramatically and eventually the number of cancellations – 120 – exceeded the approximately 90 nuclear plants that actually operate; not one was built for 30 years.

Now, 45 years later, comes announcement of a deal by tech giant Microsoft Corporation with Constellation Energy, owner of the infamous Three Mile Island facility, to restart the mothballed nuclear plant’s sister reactor, Unit 1. It will be the first such restart in the U.S.

Nuclear revival? Forty-five years after the infamous partial reactor core melt-down at Three Mile Island (pictured at top left and centre) and release of the sensationalistic anti-nuclear movie The China Syndrome (starring Jane Fonda, pictured at bottom left), the plant’s sister reactor is set for a US$1.6 billion restart to power data centres supporting artificial intelligence (AI). Shown at top right, Nuclear Regulatory Commission staff during Three Mile Island crisis; bottom right, U.S. President Jimmy Carter’s motorcade leaves Three Mile Island nuclear power station. (Sources of photos: (top left) zoso8203, licensed under CC BY 2.0; (top centre) AP Photo/Carolyn Kaster; (top right) NRCgov, licensed under CC BY-NC-ND 2.0; (bottom left) Everett Collection/The Canadian Press; (bottom right)  NRCgov, licensed under CC BY 2.0)

After all these years, why now? The answer is electricity demand for artificial intelligence (AI). Like many things in the tech realm, AI is a sneakily prodigious consumer of electricity, and AI’s use is exploding. The Microsoft/Constellation project is one of several such deals recently unveiled by tech giants.

A Goldman Sachs report from May of this year illuminates the issue, observing that, “On average, a ChatGPT query needs 10 times as much electricity to process as a Google search.” ChatGPT is a popular AI tool for information research and content creation (college kids particularly love it); a related and even more power-hungry tool spits out sophisticated digital imagery. And ChatGPT is only one of the burgeoning AI applications, which include everything from order processing and customer fulfillment to global shipping, generating sales leads, and helping operate factories and ports. Consequently, says Goldman Sachs, “Our researchers estimate data center power demand will grow 160% by 2030” – representing a remarkable one-third of all growth in U.S. electricity demand. “This increased demand will help drive the kind of electricity growth that hasn’t been seen in a generation,” says the report, which it pegs at a robust 2.4 percent per year during this period.

Power-hungry tech: The rise of AI tools like ChatGPT is forecast to increase power demand from data centres by 160 percent over the next six years, part of a robust expected increase in overall electricity consumption. Shown at bottom, Google data centre for the company’s Gemini AI platform. (Sources of photos: (top) Ju Jae-young/Shutterstock; (bottom) Google)

That’s a lot of juice. So where will all this additional power come from? In the U.S., 60 percent of electricity comes from natural gas and coal. Nuclear energy supplies 19 percent, hydroelectric facilities 6 percent, while wind and solar provide the remaining 14 percent. But wind and solar are intermittent, difficult to scale quickly, geographically limited – and, above all, cannot be counted on for the large-scale, uninterrupted, secure “base load” that AI requires.

The small modular reactor – a digital rendering of which is shown here – is said to offer great potential for adding nuclear power in manageable increments; the technology remains in testing, however, and is unlikely to hit the ground in Western Canada before 2034. (Source of image: OPG)

And while there is something of a nuclear revival happening in the U.S. and around the world, it will be four years before Three Mile Island comes back on-stream (at an anticipated cost of US$1.6 billion). Such a time-frame even to restart an existing facility underscores the long lead times afflicting the design, construction and commissioning of any technically complex, large-scale and politically controversial infrastructure. There’s a lot of talk about shortening that cycle by focusing on a new generation of “small modular reactors” (SMR), which generate about one-quarter the power of the regular kind. But SMRs remain largely untested and, here too, their lead times are long. Alberta and Saskatchewan, for example, have been talking with other provinces for the last four years about the concept, but haven’t even begun writing the governing regulations, let alone holding public hearings. The most optimistic scenario has the first SMR coming online in 2034.

Realistically, then, most of the growth in power demand for AI will have to be met by fossil fuels, however distasteful this will be to America’s tech moguls, who want to be seen as hip and earth-friendly even if not all of them are actually left-leaning. (A laughable detail of the recent Constellation/Microsoft deal is that Three Mile Island is being renamed the “Crane Clean Energy Center”, as if it’s some kind of Google-style campus.)

Those tech moguls will have to come to terms with natural gas. Natural gas is by far the lowest-emission fossil fuel. It is readily transportable by pipeline around North America. Large-scale gas-fired generating facilities can be built quickly, at reasonable cost and at low risk using mature technology, and can be located almost anywhere. And, fortunately for Americans, natural gas is in robust supply, with production setting new records nearly every year, and is currently cheaper than dirt. Indeed, the Goldman report itself forecasts (too conservatively, in my view) that the growth in electricity demand will in turn trigger “3.3 billion cubic feet per day of new natural gas demand by 2030, which will require new pipeline capacity to be built.”

In Canada, 60 percent of our electricity comes from hydro power, but very few viable new dam sites are left (Quebec recently commissioned a new dam after years of delay, and does have a few additional candidate sites, but these are the rare exceptions). Ontario’s nuclear plants supply 16 percent. Expansion of this is under consideration but, as noted, any new capacity is many years away. Coal and coke supply 8 percent (and are being further scaled back), natural gas 8 percent, and solar and wind 6 percent. So Canada’s growing electricity demand, much of it driven by AI and other tech requirements, will also need to be fuelled by natural gas. Fortunately, Canada too has enormous untapped natural gas reserves, and is also setting new production records.

Plentiful, flexible, transportable, cheap: The lowest-emission fossil fuel, natural gas offers the best way to meet growing global energy demand, representing an enormous export opportunity for Canada and the U.S. Shown at top left, Freeport LNG Liquefaction facility, Freeport, Texas; top right, LNG Canada project under construction in Kitimat, B.C. (Sources: (top left photo) Freeport LNG; (top right photo) The Canadian Press/Darryl Dyck; (graph) Canadian Energy Regulator)

In contrast to the United States and Canada, Europe is struggling just to meet existing electricity demand after natural gas imports from Russia dropped from 5.5 trillion cubic feet in 2021 to 2.2 trillion cubic feet last year. Europe’s only option is importing liquefied natural gas (LNG). Germany, previously the largest importer of Russian gas – and which in the face of the resulting energy shortage chose to shut down the last of its nuclear plants – is constructing LNG import/regasification terminals on an urgent basis. Regrettably, the situation could get even worse for Europe; China is in talks with Russia that could lead to complete stoppage of remaining gas flows, further escalating Europe’s need for LNG.

That makes meeting the electricity demands of the EU’s smaller but also growing AI sector even more challenging. Moreover, Europe’s power grid is the oldest in the world at 50 years, so it needs both modernization and expansion. The above-quoted Goldman Sachs report states that, “Europe needs $1 trillion [in new investment] to prepare its power grid for AI.” Goldman’s researchers estimate that the continent’s power demand could grow by at least 40 percent in the next ten years, requiring investment of US$861 billion in electricity generation on top of the even higher amount to replace those old transmission systems. The situation is complex and challenging, but one thing is clear: the electricity Europe requires for AI can be fuelled in large part only by natural gas imported from friendly countries.

The AI frenzy may still seem incomprehensible to most Canadians, so it’s important to understand how its applications are spreading through more and more of the economy. Toronto-based Thomson Reuters is a well-known company that provides data and information to professionals across three main industries: legal, tax & accounting, and news & media. A recent Globe and Mail article about Thomson Reuters’ journey from reticence to embrace of the AI world provides helpful perspective. After spending a year of assessment, management concluded that AI was key to the company’s future. Thomson Reuters pledged to spend US$100 million annually to develop its AI capacity. Knowing that this is the cost for just one medium-sized Canadian company puts into perspective the potential scale of AI’s electricity-hungry global growth.

More juice needed: As many more companies – like Toronto-based information conglomerate Thomson Reuters – come to understand the need to embrace AI technology, the global appetite for electricity will continue to grow, demand that will only increase with the further advancement of cryptocurrencies and electric vehicles. (Sources of photos: (left) The Canadian Press/Lars Hagberg; (right) Shutterstock)

Almost forgotten in the electricity-devouring list are cryptocurrencies. In 2020-21 Bitcoin “mining” (the data centres that compete to solve the encrypted blockchains as quickly as possible) consumed more electricity than the 230 million people of Pakistan. Meeting the tech sector’s voracious and – if the growth forecasts are accurate – essentially insatiable demand for electricity will be challenging enough, but there’s another major source of electricity demand growth: electric vehicles (EVs). An International Energy Agency report estimates that EV power needs in the U.S. and Europe will rise from less than 1 percent of electricity demand today to 14 percent in 2030 if electric vehicle mandates are to be met. This C2C article examines the specific implications for Canada.

Who could have imagined that these celebrated new technologies – billed as clean, green and “sustainable” – would end up being the biggest drivers of fossil fuel growth! With our incredible endowment of accessible natural resources, our nation should seize this enormous natural gas export opportunity by getting rid of the bureaucratic time-consuming processes and other roadblocks that have so long discouraged getting new LNG export terminals built and operating.

Gwyn Morgan is a retired business leader who was a director of five global corporations.

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Artificial Intelligence

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

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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.
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

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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.

Paige MacPherson

Senior Fellow, Education Policy, Fraser Institute
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