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Artificial Intelligence

Elon Musk is building the ‘most powerful Artificial Intelligence training cluster in the world’

Published

1 year ago

2 minute read

News release from The Deep View

Elon Musk’s xAI has ended talks with Oracle to rent more specialized Nvidia chips — in what could have been a $10 billion deal — according to The Information.
Musk is instead buying the chips himself, all to begin putting together his planned “gigafactory of compute.”
The details: Musk confirmed in a post on Twitter that xAI is now working to build the “gigafactory” internally.
  • Musk explained that the reason behind the shift is “that our fundamental competitiveness depends on being faster than any other AI company. This is the only way to catch up.”
  • “xAI is building the 100k H100 system itself for fastest time to completion,” he said. “Aiming to begin training later this month. It will be the most powerful training cluster in the world by a large margin.”
xAI isn’t the only one trying to build a supercomputer; Microsoft and OpenAI, also according to The Information, have been working on plans for a $100 billion supercomputer nicknamed “Stargate.”
Why it matters: The industry is keen to pour more and more resources into the generation of abstractly more powerful AI models, and VC investments into AI companies, as we noted yesterday, are growing.
But at the same time, concerns about revenue and return on investment are growing as well, with a growing number of analysts gaining confidence in the idea that we are in a bubble of high costs and low returns, something that could be compounded by multi-billion-dollar supercomputers.
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Artificial Intelligence

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

Published on December 22, 2025

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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.
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 20, 2025

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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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