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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

DeepSeek: The Rise of China’s Open-Source AI Amid US Regulatory Shifts and Privacy Concerns

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DeepSeek offers open-source generative AI with localized data storage but raises concerns over censorship, privacy, and disruption of Western markets.

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A recent regulatory clampdown in the United States on TikTok, a Chinese-owned social media platform, triggered a surge of users migrating to another Chinese app, Rednote. Now, another significant player has entered the spotlight: DeepSeek, a Chinese-developed generative artificial intelligence (AI) platform, which is rapidly gaining traction. The growing popularity of DeepSeek raises questions about the effectiveness of bans like TikTok and their ability to curtail the use of Chinese digital services by Americans.

President Donald Trump has called attention to a recent Chinese AI development, describing it as a “wake-up call” for the US tech industry.

Speaking to Republican lawmakers in Florida on Monday evening, the president emphasized the need for America to strengthen its competitive edge against China’s advancements in technology.

During the event, Trump referenced the launch of DeepSeek AI, highlighting its potential implications for the global tech landscape. “Last week, I signed an order revoking Joe Biden’s destructive artificial intelligence regulations so that AI companies can once again focus on being the best, not just being the most woke,” Trump stated. He continued by explaining that he had been closely following developments in China’s tech sector, including reports of a faster and more cost-effective approach to AI.

“That’s good because you don’t have to spend as much money,” Trump remarked, adding that while the claims about this Chinese breakthrough remain unverified, the idea of achieving similar results with lower costs could be seen as an opportunity for US companies. He stressed, “The release of DeepSeek AI from a Chinese company should be a wake-up call for our industries, that we need to be laser-focused on competing to win because we have the greatest scientists in the world.”

Trump also pointed to what he views as a recognition by China of America’s dominance in scientific and engineering talent. “This is very unusual, when you hear a DeepSeek when you hear somebody come up with something, we always have the ideas,” he said. “We’re always first. So I would say that’s a positive that could be very much a positive development.”

DeepSeek, created by a Chinese AI research lab backed by a hedge fund, has made waves with its open-source generative AI model. The platform rivals offerings from major US developers, including OpenAI. To circumvent US sanctions on hardware and software, the company allegedly implemented innovative solutions during the development of its models.

DeepSeek’s approach to sensitive topics raises significant concerns about censorship and the manipulation of information. By mirroring state-approved narratives and avoiding discussions on politically charged issues like Tiananmen Square or Winnie the Pooh’s satirical association with Xi Jinping, DeepSeek exemplifies how AI can be wielded to reinforce government-controlled messaging.

This selective presentation of facts, or outright omission of them, deprives users of a fuller understanding of critical events and stifles diverse perspectives. Such practices not only limit the free flow of information but also normalize propaganda under the guise of fostering a “wholesome cyberspace,” calling into question the ethical implications of deploying AI that prioritizes political conformity over truth and open dialogue.

While DeepSeek provides multiple options for accessing its AI models, including downloadable local versions, most users rely on its mobile apps or web chat interface.

The platform offers features such as answering queries, web searches, and detailed reasoning responses. However, concerns over data privacy and censorship are growing as DeepSeek collects extensive information and has been observed censoring content critical of China.

DeepSeek’s data practices raise alarm among privacy advocates. The company’s privacy policy explicitly states, “We store the information we collect in secure servers located in the People’s Republic of China.”

This includes user-submitted data such as chat messages, prompts, uploaded files, and chat histories. While users can delete chat history via the app, privacy experts emphasize the risks of sharing sensitive information with such platforms.

DeepSeek also gathers other personal information, such as email addresses, phone numbers, and device data, including operating systems and IP addresses. It employs tracking technologies, such as cookies, to monitor user activity. Additionally, interactions with advertisers may result in the sharing of mobile identifiers and other information with the platform. Analysis of DeepSeek’s web activity revealed connections to Baidu and other Chinese internet infrastructure firms.

While such practices are common in the AI industry, privacy concerns are heightened by DeepSeek’s storage of data in China, where stringent cybersecurity laws allow authorities to demand access to company-held information.

The safest option is running local or self-hosted versions of AI models, which prevent data from being transmitted to the developer.

And with Deepseek, this is simple as its models are open-source.

Open-source AI stands out as the superior approach to artificial intelligence because it fosters transparency, collaboration, and accessibility. Unlike proprietary systems, which often operate as opaque black boxes, open-source AI allows anyone to examine its code, ensuring accountability and reducing biases. This transparency builds trust, while the collaborative nature of open-source development accelerates innovation by enabling researchers and developers worldwide to contribute to and improve upon existing models.

Additionally, open-source AI democratizes access to cutting-edge technology, empowering startups, researchers, and underfunded regions to harness AI’s potential without the financial barriers of proprietary systems.

It also prevents monopolistic control by decentralizing AI development, reducing the dominance of a few tech giants.

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Artificial Intelligence

Everyone is freaking out over DeepSeek. Here’s why

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From The Deep View

$600 billion collapse

Volatility is kind of a given when it comes to Wall Street’s tech sector. It doesn’t take much to send things soaring; it likewise doesn’t take much to set off a downward spiral.
After months of soaring, Monday marked the possible beginning of a spiral, and a Chinese company seems to be at the center of it.
Alright, what’s going on: A week ago, Chinese tech firm DeepSeek launched R1, a so-called reasoning model, that, according to DeepSeek, has reached technical parity with OpenAI’s o1 across a few benchmarks. But, unlike its American competition, DeepSeek open-sourced R1 under an MIT license, making it significantly cheaper and more accessible than any of the closed models coming from U.S. tech giants.
  • But the real punchline here doesn’t have to do with R1 at all, but with a previous language model — called V3 — that DeepSeek released in December. DeepSeek was reportedly able to train V3 using a small collection of older Nvidia chips (about 2,000 H800s) at a cost of about $5.6 million.
  • Still, training is only one cost of many tied to AI development/deployment; while the costs associated with researching, developing, training and operating both R1 and V3 remain either unknown or unconfirmed, DeepSeek’s apparent ability to reach technical parity at a far reduced cost, without state-of-the-art GPU chips or massive GPU clusters, has a lot of implications for America’s now tenuous position in AI leadership. (Though DeepSeek says it is open-sourced, the company did not release its training data).
Since the release of R1, DeepSeek has become the top free app in Apple’s App Store, bumping ChatGPT to the number two slot. In the midst of its spiking popularity, DeepSeek restricted new sign-ups due to large-scale cyberattacks against its servers. And, as Salesforce Chief Marc Benioff noted, “no Nvidia supercomputers or $100M needed,” a point that the market heard loud and clear. 
What happened: Led by Nvidia, a series of tech and chip stocks, in addition to the three major stock indices, fell hard in pre-market trading early Monday morning. All told, $1.1 trillion of U.S. market cap was erased within a half hour of the opening bell.
  • Performance didn’t get better throughout the day. Nvidia closed Monday down 17%, erasing some $600 billion in market capitalization, a Wall Street record. TSMC was down 14%, Arm was down 11%, Broadcom was down 17%, Google was down 4% and Microsoft was down 2%. The S&P fell 1.4% and the Nasdaq fell 3.3%. An Nvidia spokesperson called R1 an “excellent AI advancement.”
  • This is all going into a week of Big Tech earnings, where Microsoft and Meta will be held to account for the billions of dollars ($80 billion and $65 billion, respectively) they plan to spend on AI infrastructure in 2025, a cost that Wall Street no longer seems to feel quite so good about.
It’s hard to miss the political tensions underlying all of this. The tail end of former President Joe Biden’s time in office was marked in part by an increasingly tense trade war with China, wherein both countries issued bans on the export of materials needed to build advanced AI chips. And with President Trump hell-bent on maintaining American leadership in AI, and despite the chip restrictions that are in place, Chinese companies seem to be turning hardware challenges into a motivation for innovation that challenges the American lead, something they seem keen to drive home.
R1, for instance, was announced at around the same time as OpenAI’s $500 billion Project Stargate, two impactfully divergent approaches.
What’s happening here is that the market has finally come around to the idea that maybe the cost of AI development (hundreds of billions of dollars annually) is too high, a recognition “that the winners in AI will be the most innovative companies, not just those with the most GPUs,” according to Writer CTA Waseem Alshikh. “Brute-forcing AI with GPUs is no longer a viable strategy.”
Wedbush analyst Dan Ives, however, thinks this is just a good time to buy into Nvidia — Nvidia and the rest are building infrastructure that, he argues, China will not be able to compete with in the long run. “Launching a competitive LLM model for consumer use cases is one thing,” Ives wrote. “Launching broader AI infrastructure is a whole other ballgame.”
“I view cost reduction as a good thing. I’m of the belief that if you’re freeing up compute capacity, it likely gets absorbed — we’re going to need innovations like this,” Bernstein semiconductor analyst Stacy Rasgon told Yahoo Finance. “I understand why all the panic is going on. I don’t think DeepSeek is doomsday for AI infrastructure.”
Somewhat relatedly, Perplexity has already added DeepSeek’s R1 model to its AI search engine. And DeepSeek on Monday launched another model, one capable of competitive image generation.
Last week, I said that R1 should be enough to make OpenAI a little nervous. This anxiety spread way quicker than I anticipated; DeepSeek spent Monday dominating headlines at every publication I came across, setting off a debate and panic that has spread far beyond the tech and AI community.
Some are concerned about the national security implications of China’s AI capabilities. Some are concerned about the AI trade. Granted, there are more unknowns here than knowns; we do not know the details of DeepSeek’s costs or technical setup (and the costs are likely way higher than they seem). But this does read like a turning point in the AI race.
In January, we talked about reversion to the mean. Right now, it’s too early to tell how long-term the market impacts of DeepSeek will be. But, if Nvidia and the rest fall hard and stay down — or drop lower — through earnings season, one might argue that the bubble has begun to burst. As a part of this, watch model pricing closely; OpenAI may well be forced to bring down the costs of its models to remain competitive.
At the very least, DeepSeek appears to be evidence that scaling is one, not a law, and two, not the only (or best) way to develop more advanced AI models, something that rains heavily on OpenAI and co.’s parade since it runs contrary to everything OpenAI’s been saying for months. Funnily, it actually seems like good news for the science of AI, possibly lighting a path toward systems that are less resource-intensive (which is much needed!)
It’s yet another example of the science and the business of AI not being on the same page.
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