By Rachel Samson

Big changes are coming to the global economy and the stakes are high for Canada. Over many decades, we have built a successful, export-oriented economy where our largest-value goods exports are oil and gas, metals and minerals, and autos – all of which are primarily destined for the U.S. market.

But these sectors and our entire trading relationship with the United States face significant uncertainty over the next decades as the world undergoes a reordering of trade patterns and a realignment of markets in the face of the rise of artificial intelligence, a changing climate, the global energy transition and U.S. tariffs.

How Canada fares will depend largely on private companies. Those that anticipate, innovate, adapt and prepare will thrive, while those that stagnate will struggle.

Governments can help by encouraging choices that are in our collective national strategic interest. Industrial policy – where governments deliberately support certain economic activities and outcomes – is an increasingly important tool.

However, to achieve results and maintain public trust, industrial policy needs to be designed and executed using the highest standards of excellence. Fortunately, we have years of experience and a growing body of research to give us a better idea of what to do. That’s critical because for every success story, there is an example where industrial policy went wrong.

Significant structural change in the global economy

The rise of artificial intelligence will increasingly influence the global competitiveness of companies and countries. While there are growing opportunities in the development of AI products, the overall adoption of AI is equally important, including in stalwart Canadian economic sectors such as agriculture, oil and gas and auto manufacturing.

As Evan Solomon, Canada’s first minister of artificial intelligence and digital innovation, said at an event in Montreal this summer, “Countries that master AI will dominate the future. You’re either part of the bulldozer or you’re part of the road.”

IRPP study: How Industrial Policy Can Strengthen Canada’s Economy and Sovereignty

Only targeted industrial policy will fulfil Canada’s value-adding potential 

Industrial policy and Canada’s uncertain future

AI isn’t the only area where the global economy will face disruption. Climate change is set to play a growing role in supply chains and goods production. As drought, floods, fires and storms increase in frequency and intensity, companies will face growing challenges with their operations and with securing feedstocks, water and other supplies.

Forestry, pharmaceuticals, chemicals and mining top the list for exposure to physical climate risks. If the pace of action to reduce emissions doesn’t accelerate, losses could reach eight per cent of global GDP by 2050. Companies and countries with strong adaptation strategies will fare better than those that don’t.

The global energy transition also remains underway, despite backtracking in the U.S. While there is debate about the pace at which demand for fossil fuels will decline, there is general agreement on a downward trajectory over the rest of this century.

Change may not come with a gradual price decline as some are envisioning. Instead, it could involve an extended period of volatility with mismatches between supply and demand leading to sudden price spikes and crashes that disrupt oil and gas producers as well as consumers.

Clean energy and electric vehicles may also face periods of volatility as companies struggle to predict demand in an environment of shifting government policies.

The energy transition continues to drive change in other markets as well, such as growing demand for the critical minerals needed for batteries and renewable energy. Making the right bets with the right timing will be critical, given the role energy and mineral sectors play in our economy.

The cost of underinvestment 

Understanding trends underway is one thing, but doing something about them is another. Unfortunately, Canada has lackluster private sector investment in critical areas, such as AI adoption, clean technology adoption, technology commercialization, adaptation to climate change and critical mineral development.

Without significant action, Canada could slip out of the Top 10 global economies and see a declining standard of living in the coming decades.

It would be easy to blame the private sector, but companies often have good reasons to delay investment. Those facing an uncertain trading relationship with the U.S. may be making the right call in putting off investments in automation or AI adoption. China’s dominance of critical mineral markets keeps prices too low or uncertain to spur major investments in mining or processing.

What can be done to overcome near-term barriers to private investments that are in Canada’s long-term national interest? That’s where smart industrial policy can play a key role.

Private investment in the national interest

Canadian governments are already actively using industrial policy to drive private investment toward certain activities, such as trade infrastructure, technology commercialization, critical mineral development or agricultural technology adoption.

The governments are using tools such as investment tax credits, targeted procurement, contracts for difference, offtake agreements, below-market loans and more. The goal is to reduce the barriers holding back private investment in strategic areas and to design a policy that works at the least cost to the taxpayer.

The use of industrial policy is likely to increase as governments seek to secure a foothold in new global markets, retain high-potential companies and improve lagging productivity. But given the stakes involved, it will be critical that governments do more to ensure they get industrial policy right.

Canadian governments could learn from Australia’s approach.

For example, when it introduced its critical minerals production tax incentive in 2024, the government published an impact analysis that analyzed the policy problem which the tax was aimed at solving; the barriers the private sector faced; the sufficiency of government intervention; and the need for additional government intervention, with a cost-benefit analysis of three policy options.

It also listed a series of success metrics to ensure that the policy has the intended long-term effect.

This type of analytical rigour and transparency could go a long way toward delivering industrial policy excellence and improving public confidence in the ability of Canadian governments to deliver results.

Choosing to be the bulldozer requires a smart strategy

Structural changes to the global economy are coming. If Canada wants to maintain its place as a Top 10 global economy, governments will need bold industrial policies that enable Canadian companies to seize opportunities.

But those policies need to be smart, with careful design and implementation that achieve results without breaking the bank. Now is not the time for big announcements with little follow-through. Canada needs thoughtful, deliberate policy interventions backed by analysis and skilled teams to ensure smooth implementation.

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By Chris Bonasia

The post More gas turbines for data centres may signal lost opportunity for solar appeared first on Thoughtful Journalism About Energy's Future.

The relationship between digitalization and energy transition took prominence during the IRENA Innovation Week 2025, where discussions centred on the principle that there is no digitalization without energy and no energy transition without digitalization. With smart devices (e.g. phones, tablets) and the internet becoming the norm worldwide, more electricity is required to power the cloud services for the virtual world.

To ensure the electricity supplying this high demand comes from renewable energy sources, higher integration of renewable energy is needed in the electricity grids. With digital technologies, variable renewable energy (VRE) such as solar and wind – known for their intermittent nature – can supply power more consistently and sustainably. For solar photovoltaics (PV) and onshore wind particularly, digitalization optimizes performance and increases market competitiveness.

From demand and pricing forecasting, flexibility and smart grid management, to informing predictive maintenance of energy infrastructure, digital technologies are becoming indispensable for the global energy transitions. They enhance grid services by intelligently managing consumer electricity use, energy storage, and balancing supply and demand in different locations in real-time, achieving smooth grid operation that guarantees consistent supply of clean electricity at all time.

Enabled by AI, real-time performance monitoring and smart maintenance based on weather forecast and analysis allow operators to anticipate generation patterns and grid requirements with precision. This in turn can reduce operations and maintenance costs.

On the demand side, consumers also benefit from cost efficiency. Smart meters, dynamic pricing systems and Internet of Things (IoT)-enabled appliances support demand response programmes, allowing consumers to shift or reduce their electricity use in response to price signals.

Building on these demand-side innovations, advanced grid management systems can utilize digital twins and AI algorithms to forecast congestion, coordinate distributed energy resources, and optimize dispatch in near real-time. This coordination changes the traditional paradigm of renewables as unpredictable power sources, and position them instead as active contributors offering essential, sustainable grid services.

However, this synergy presents both opportunities and challenges. Rising energy demands from data centres that support AI introduces complexities for grid planning and expansion, requiring careful coordination between digital innovation and energy infrastructure development.

And the deployment of digital solutions remains uneven globally, with emerging markets lagging due to limited smart metres, low investment in infrastructure, and regulatory barriers preventing virtual power plants and behind-the-metre systems from market participation. Additionally, the lack of common technical standards and growing cybersecurity risks create significant barriers to widespread adoption. Addressing these gaps remains crucial to ensuring digitalization can support a secure and rapid energy transition.

To understand the full scope of this challenge and potential solutions, Adrian Gonzalez, Program Officer in Innovation and End-Use Sectors at IRENA, explores the energy implications of AI data centres in detail.

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By Molly Taft, WIRED

AI is “not my thing,” President Donald Trump admitted during a speech in Pittsburgh on Tuesday. However, the president said during his remarks at the Energy and Innovation Summit, his advisers had told him just how important energy was to the future of AI.

“You need double the electric of what we have right now, and maybe even more than that,” Trump said, recalling a conversation with “David”—most likely White House AI czar David Sacks, a panelist at the summit. “I said, what, are you kidding? That’s double the electric that we have. Take everything we have and double it.”

At the high-profile summit on Tuesday—where, in addition to Sacks, panelists and attendees included Anthropic CEO Dario Amodei, Google president and chief investment officer Ruth Porat, and ExxonMobil CEO Darren Woods—companies announced $92 billion in investments across various energy and AI-related ventures. These are just the latest in recent breakneck rollouts in investment around AI and energy infrastructure. A day before the Pittsburgh meeting, Mark Zuckerberg shared on Threads that Meta would be building “titan clusters” of data centers to supercharge its AI efforts. The one closest to coming online, dubbed Prometheus, is located in Ohio and will be powered by onsite gas generation, SemiAnalysis reported last week.

For an administration committed to advancing the future of fossil fuels, the location of the event was significant. Pennsylvania sits on the Marcellus and Utica shale formations, which supercharged Pennsylvania’s fracking boom in the late 2000s and early 2010s. The state is still the country’s second-most prolific natural gas producer. Pennsylvania-based natural gas had a big role at the summit: The CEO of Pittsburgh-based natural gas company EQT, Toby Rice—who dubs himself the “people’s champion of natural gas”—moderated one of the panels and sat onstage with the president during his speech.

All this new demand from AI is welcome news for the natural gas industry in the US, the world’s top producer and exporter of liquefied natural gas. Global gas markets have been facing a mounting supply glut for years. Following a warm winter last year, Morgan Stanley predicted gas supply could reach “multi-decade highs” over the next few years. A jolt of new demand—like the demand represented by massive data centers—could revitalize the industry and help drive prices back up.

Natural gas from Pennsylvania and the Appalachian region, in particular, has faced market challenges both from ultra-cheap natural gas from the Permian Basin in Texas and New Mexico as well as a lack of infrastructure to carry supply out of the region. These economic headwinds are “why the industry is doing their best to sort of create this drumbeat or this narrative around the need for AI data centers,” says Clark Williams-Derry, an energy finance analyst at the Institute for Energy Economics and Financial Analysis. It appears to be working. Pipeline companies are already pitching new projects to truck gas from the northeast—responding, they say, to data center demand.

The industry is finding a willing partner in the Trump administration. Since taking office, Trump has used AI as a lever to open up opportunities for fossil fuels, including a well-publicized effort to resuscitate coal in the name of more computing power. The summit, which was organized by Republican senator (and former hedge fund CEO) Dave McCormick, clearly reflected the administration’s priorities in this regard: No representatives from any wind or solar companies were present on any of the public panels.

Tech companies, which have expressed an interest in using any and all cheap power available for AI and have quietly pushed back against some of the administration’s anti-renewables positions, aren’t necessarily on the same page as the Trump administration. Among the announcements made at the summit was a $3 billion investment in hydropower from Google.

This demand isn’t necessarily driven by a big concern for the climate—many tech giants have walked back their climate commitments in recent years as their focus on AI has sharpened—but rather pure economics. Financial analyst Lazard said last month that installing utility-scale solar panels and batteries is still cheaper than building out natural gas plants, even without tax incentives. Gas infrastructure is also facing a global shortage that makes the timescales for setting up power generation vastly different.

“The waiting list for a new turbine is five years,” Williams-Derry says. “If you want a new solar plant, you call China, you say, ‘I want more solar.’”

Given the ideological split at the summit, things occasionally got a little awkward. On one panel, Secretary of Energy Chris Wright, who headed up a fracking company before coming to the federal government, talked at length about how the Obama and Biden administrations were on an “energy crazy train,” scoffing at those administrations’ support for wind and solar. Speaking directly after Wright, BlackRock CEO Larry Fink admitted that solar would likely support dispatchable gas in powering AI. Incredibly, fellow panel member Woods, the ExxonMobil CEO, later paid some of the only lip service to the idea of drawing down emissions heard during the entire event. (Woods was touting the oil giant’s carbon capture and storage business.)

Still, the hype train, for the most part, moved smoothly, with everyone agreeing on one thing: We’re going to need a lot of power, and soon. Blackstone CEO Jonathan Gray said that AI could help drive “40 or 50 percent more power usage over the next decade,” while Porat, of Google, mentioned some economists’ projections that AI could add $4 trillion to the US economy by 2030.

It’s easy to find any variety of headlines or reports—often based on projections produced by private companies—projecting massive growth numbers for AI. “I view all of these projections with great skepticism,” says Jonathan Koomey, a computing researcher and consultant who has contributed to research around AI and power. “I don’t think anyone has any idea, even a few years hence, how much electricity data centers are gonna use.”

In February, Koomey coauthored a report for the Bipartisan Policy Center cautioning that improvements in AI efficiency and other developments in the technology make data center power load hard to predict. But there’s “a bunch of self-interested actors,” Koomey says, involved in the hype cycle around AI and power, including energy executives, utilities, consultants and AI companies.

Koomey remembers the last time there was a hype bubble around electricity, fossil fuels, and technology. In the late 1990s, a variety of sources, including investment banks, trade publications, and experts testifying in front of Congress began to spread hype around the growth of the internet, claiming that the internet could soon consume as much as half of US electricity. More coal-fired power, many of these sources argued, would be needed to support this massive expansion. (“Dig More Coal—The PCs Are Coming” was the headline of a 1999 Forbes article that Koomey cites as being particularly influential to shaping the hype.) The prediction never came to pass, as efficiency gains in tech helped drive down the internet’s energy needs; the initial projections were also based, Koomey says, on a variety of faulty calculations.

Koomey says that he sees parallels between the late 1990s and the current craze around AI and energy. “People just need to understand the history and not fall for these self-interested narratives,” he says. There’s some signs that the AI-energy bubble may not be inflating as much as Big Tech thinks: in March, Microsoft quietly backed out of 2GW of data center leases, citing a decision to not support some training workloads from OpenAI.

“It can both be true that there’s growth in electricity use and there’s a whole bunch of people hyping it way beyond what it’s likely to happen,” Koomey says.

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By E. Richard Gold, Cristina Vanberghen

Artificial intelligence (AI) is rapidly transforming sectors from health care to climate science. But amid the global scramble to lead this technological revolution, one truth is becoming clearer: data, its platforms and its circulations, have become critical infrastructure. And Canada, poised to host this year’s G7 Leaders Summit, has a rare opportunity to shape the rules that will govern AI globally.

Under the leadership of Prime Minister Mark Carney, the federal government elevated AI and digital innovation to a central pillar of national policy, and appointed Evan Solomon as minister of artificial intelligence and digital innovation. But ambition is not enough — Canada must now back its rhetoric with action that resonates at home and abroad.

Infrastructure intelligence

While AI headlines often focus on breakthroughs in generative models and robotics, the real engine of progress lies in less glamorous terrain: computing infrastructure and data systems.

Canada’s proposal to build “next-generation data centres” is about creating the backbone for globally competitive and ethically governed AI. Without these facilities, modern AI systems cannot be trained, validated or deployed responsibly.

AI models — like those used in medicine for developing new drugs and health services, clean technologies such as clean energy and carbon-capture or materials science — require enormous computational power and massive datasets. That data must be structured, validated and — to the extent possible — open to those who can use it.

Quality assurance

Our recent study underscores that the future of AI depends less on algorithmic cleverness and more on data quality and accessibility. Poorly labelled or fragmented datasets can introduce bias, reduce model performance or even endanger lives when used in health or safety applications.

Yet across many domains, useful data remains siloed and locked in proprietary formats, lacking documentation or inaccessible due to legal and technical barriers. This status quo serves monopolies, not society.

Canada holds the G7 presidency in 2025, and can provide leadership in data governance and AI innovation. A central priority should be to rally partners around a framework for ethical, accessible and well-designed datasets, especially in fields like health, climate science and materials research.

Tailored data

Our call for open data isn’t one-size-fits-all. It must be tailored to the needs of specific sectors:

• Health-care AI requires anonymized patient data, genomic sequences, protein structure data, toxicology and carcinogen data, and drug response datasets.
• Climate AI needs long-term environmental records, satellite imagery, power and water use information and real-time emissions data.
• Materials science AI demands chemical interaction data, physical testing results, structural data and thermodynamic properties.

What binds these fields is a common challenge: ensuring data is ethically sourced, high-quality, and useable across borders and institutions. Canada’s role should be to help build the platforms — digital, legal and diplomatic — that make this possible.

A G7 mandate

As host of the G7 in June, Canada can push for a transformative international commitment. At a minimum, this should include:

1. Common standards for open datasets, co-designed with input from AI developers, health professionals, climate researchers, materials scientists and legal experts.
2. Trusted data hubs, managed by public-private or non-profit entities, ensuring secure storage, privacy safeguards and public access.
3. Legal and diplomatic co-ordination, addressing cross-border data sharing, intellectual property constraints and ethical governance frameworks.

These steps would position the G7 — and Canada in particular — as a champion of AI that serves democratic values on top of commercial and geopolitical interests.

Canada’s risks and opportunities

Canada is not starting from scratch. The country boasts leading AI research institutions, including the Vector Institute and Mila, and has pioneered open science partnerships such as the Montreal Neurological Institute’s Tanenbaum Open Science Institute and the Toronto labs of the Structural Genomics Consortium.

Dataset platforms such as AIRCHECK(for AI-based chemical knowledge) and the CACHE competition (evaluating drug discovery models using open data), show how Canada is already putting together the building blocks of responsible AI. But the country risks squandering this advantage if it cannot scale these efforts or retain innovation domestically.

The stalled Artificial Intelligence and Data Act is a case in point. While the European Union moved forward with its AI Act, the General Data Protection Regulation and the European Health Data Space Regulation, Canada’s legislative framework remains in flux.

Without clear domestic rules, and a proactive global agenda, Canada could end up as an incubator for innovations that end up developed and applied elsewhere.

Global stakes

The AI race is not just about who builds the most powerful models. It’s about who defines the technical, ethical and geopolitical standards that shape the digital future.

The G7 offers Canada a moment of strategic clarity. By investing in AI infrastructure and leading an international agenda on open, trustworthy AI, Canada can lead in shaping the rules.

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By Matt Simon

The world is grappling with an energy crisis — not one of scarcity, but one created by overwhelming demand. More energy-hungry data centres and AI algorithms are coming online. Developing countries are using more energy to support their people and industries. And as the world electrifies — replacing gas cars with electric vehicles, for instance — it will use ever more power. So the electrical grid doesn’t just need renewables (and batteries to store their energy) to reduce greenhouse gas emissions, but also to meet growing demand.

A new analysis from the Paris-based International Energy Agency puts some hard numbers to the challenge, finding that in 2024, electricity consumption jumped by 4.3 per cent worldwide, almost double the annual average over the last decade. Power use in buildings accounted for nearly 60 per cent of the growth last year, with other drivers including the ballooning of energy-intensive industries and the electrification of transportation.

“What is certain is that electricity use is growing rapidly, pulling overall energy demand along with it to such an extent that it is enough to reverse years of declining energy consumption in advanced economies,” said Fatih Birol, the IEA’s executive director, in a press release announcing the findings. “The result is that demand for all major fuels and energy technologies increased in 2024, with renewables covering the largest share of the growth, followed by natural gas.”

The good news is that the installation of renewables like wind and solar hit a record in 2024 for the 22nd consecutive year, according to the analysis, while 33 per cent more nuclear capacity came online compared to 2023. Renewables and nuclear power combined for 80 per cent of the increase in worldwide electricity generation. Together, the two sources handled 40 per cent of overall generation for the first time, which meant energy-related carbon dioxide emissions rose by just 0.8 per cent last year, compared with 1.2 per cent in 2023.

At the same time, the global economy grew by more than 3 per cent in 2024. Carbon dioxide emissions, in other words, didn’t keep up with economic growth, so CO2 emissions and economic growth are increasingly “decoupled,” the report notes. Beneath the headline numbers, however, the story varies region to region. While countries like the U.S. can easily deploy more renewables to reduce their emissions and still maintain economic growth — renewables actually encourage that growth — in 2024 the bulk of the rise in emissions came from developing economies.

“We can have more energy and less emissions — we need to have more energy and less emissions,” said R. Max Holmes, president and CEO of the Woodwell Climate Research Center, who wasn’t involved in the analysis. “There are encouraging signs in this report that that decoupling is starting to take place.”

Still, no matter the country, renewables aren’t growing fast enough to displace fossil fuels: Oil demand rose by 0.8 per cent in 2024 and coal by 1 per cent. Natural gas demand went up 2.7 per cent, far above the annual growth rate of 1 per cent between 2019 and 2023. That was thanks to the growth of heavy industries along with brutal heat waves, especially in China and India. The hotter the world gets, the more people switch on their air conditioners, creating demand that power plants have to meet by burning fossil fuels, leading to even more warming and more AC use.

Even so, the report reveals that the world is making some progress in weaning itself off fossil fuels. In 2024, EVs accounted for a fifth of all car sales around the world. In the U.S., sales of electric heat pumps — which move heat from outdoor air into a home — jumped 15 per cent last year, and now outsell gas furnaces by 30 per cent. All told, since 2019, the deployment of solar and wind energy, nuclear power, EVs, and heat pumps now prevents the release of 2.6 billion metric tons of CO2 each year.

“That’s about half the U.S. economy’s worth of emissions, and that’s just five solutions in five years,” said Jonathan Foley, executive director of Project Drawdown, a Minnesota-based climate nonprofit that wasn’t involved in the report. “We’re still far behind. All the bad news is still true — climate change is still happening, it’s bad, it’s ugly, we’re not doing enough. But I’m seeing an inflection point here.”

The big question in the U.S. is whether the new Trump administration, which has been aggressively dismantling climate progress in its first two months in office, can kneecap this shift to clean energy. Experts say that there are fundamental market forces beyond the control of the federal government, namely that renewables are now cheaper to deploy than more fossil fuel infrastructure.

“The world is transitioning away from fossil fuels and toward renewable and non-greenhouse-gas-emitting energy sources, period,” Holmes said. “It is going to happen. What the Trump administration right now is doing can slow that transition, but it certainly can’t stop that transition.”

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By Jody MacPherson

As electricity grid operators across North America face an onslaught of new data centres demanding reliable power, two new reports warn better planning is needed to meet that demand, reduce emissions, and avoid negative consequences for consumers.

Demand for electricity will grow 15.8 per cent over the next five years compared to earlier predictions of 4.7 per cent, mostly driven by the surge in data centres for artificial intelligence (AI), predicts RMI, formerly known as the Rocky Mountain Institute.

A ‘Power Couple’ Strategy for Renewables

To handle this surge, new data centres should be built at existing gas interconnection points—such as infrequently used peaker plant sites—where renewables can be added “in ways that don’t affect the rest of the grid, that don’t force you to do upgrades,” Uday Varadarajan, senior principal with RMI, told NBC News

“If you have to build a whole lot of new wires and a whole lot of new generation then everybody would have to pay for that,” he added.

Vardarajan is one of three authors of an RMI study that calls this co-location strategy a “power couple”—where a large electricity consumer is paired with new solar, wind, and battery resources located near an existing generator with an already approved interconnection. This matchmaking approach could quickly add more than 50 gigawatts (GW) of new data centre loads with 88 per cent carbon-free energy on average, they report.

“Moreover, they can do this while reducing system-wide emissions, improving affordability, and maintaining grid reliability,” the authors write.

“The thing that makes this so compelling is that this clean energy can be paid for by the data centre providers, and what they’re exporting to the grid is largely very cheap excess clean energy that they don’t need,” Varadarajan said.

Data Centres as Grid Assets

The American Council for an Energy-Efficient Economy (ACEEE) released a report late last year suggesting that data centres offer a solution for growing power demand. They can become assets rather than threats—if regulators encourage data centre developers to invest in battery storage and install smart meters to forecast and manage energy supply.

Data centres require “highly reliable grids” or access to stable energy sources off the grid to meet their requirements for 99.999 per cent availability—known as the “five-nines” standard, writes research and analytics firm Rystad Energy in a recent report. A data centre can only be down for less than five minutes and fifteen seconds annually to meet reliability requirements—about half the time it takes to brew a pot of coffee.

Gas is emerging as a critical power source for firm and flexible power, write report authors Marina Domingues, Rystad vice president and head of new energies research in the United States, and analyst Surya Hendry. Integrated oil companies are building gas plants to meet the moment, but renewables will still play “an integral role in meeting rising demand.”

“Over two terawatts of potential energy projects, mostly made up of renewable resources, await grid interconnection approval,” say Domingues and Hendry, but approvals have historically been unable to keep up.

Rising Energy Costs and Political Pressure

Meanwhile, electricity costs in the U.S. are heading for a 30-year high, with power-hungry AI data centres driving the trend, reports Politico. Depending on regional factors, clean power may be easier and cheaper to ramp up than gas, said Michelle Solomon of the research firm Energy Innovation. She added that 90 per cent of new resources added to the U.S. grid last year were solar or wind, with more waiting to connect to the grid, “whereas with a gas project, you’re going to have to be starting from scratch.”

On the policy front, Donald Trump had promised to cut energy prices in half while campaigning for the U.S. presidency, mainly by ramping up gas production. Some analysts question whether his administration is following the Project 2025 energy strategy devised by the conservative Heritage Foundation.

A Project 2025 tracker shows that seven of its 19 proposed objectives for the U.S. Department of Energy have already been completed, with two more in progress—including a push to eliminate the Office of Grid Deployment, which oversees grid modernization. U.S. News and World Report wrote in mid-February that 12 per cent of the office’s staff had been laid off. These employees were responsible for making the grid “resilient to extreme weather and able to transmit power from clean energy and fossil fuel-fired power plants.”

AI Power Demand Could Be a Bubble

Artem Abramov, head of new energies research, and Elliot Busby, vice president and head of media and communications for Rystad, wrote in January that surging power demand calls for strategic investments in grid infrastructure alongside a balanced reliance on various energy sources and expanded energy storage.

Constraints to this approach include the lack of a domestic semiconductor supply chain in the U.S., with supply further impacted by tariffs. Abramov and Busby say fibre optic cable availability, water for cooling, and diesel generators for backup power “will have to be maintained if buildouts are to continue uninterrupted in the U.S.”

But even as grid planners brace for soaring electricity needs, some analysts warn the AI boom may not last. In an interview with economist Paul Krugman, Jim Chanos—the investment analyst known for predicting the collapse of Enron—said an AI bubble is forming, and that “returns on invested capital are really now beginning to turn down pretty hard for these companies.”

“But the problem is that it’s not so much the data centres that depreciate, they do because of the air conditioning and all the guts of them. It’s the chips that you’re paying $50,000 a piece for that are being leapfrogged by the same company,” said Chanos.

Adding to the uncertainty, Reuters reported in February that Microsoft had cancelled plans for “a couple of hundred megawatts” of capacity with two private data centre operators, citing “supply chain checks.”

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By Mitchell Beer

Limited coordination with gas utilities, low estimates of future power demand, and a failure to put demand-side resources on a par with new supply are all impeding Canada’s electric utilities from preparing for a net-zero future, Efficiency Canada warns in a scathing new analysis released this week.

“Utility resource planning is central to Canada’s ability to achieve its climate and energy commitments,” the Ottawa-based think tank writes in an online introduction to the report. Without it, “the country risks falling behind its net-zero goals, increasing consumer costs, and missing opportunities to leverage demand-side solutions for a more efficient and resilient energy system.”

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By Mitchell Beer

A cheaper AI system that reportedly uses a small fraction of the resources consumed by competing U.S. models upended assumptions about future electricity demand late last month when it was announced by Chinese startup DeepSeek.

The surprise news of DeepSeek’s R1 model drove down expectations or share values for electricity generation, small modular nuclear reactor, uranium, gas, and tech companies, among others, temporarily knocking nearly 17 per cent off the stock price of Nvidia, one of the main tech companies at the centre of the AI craze.

“AI’s energy needs have led companies such as OpenAI, Alphabet Inc., and Microsoft Corporation to seek new sources of power, such as shuttered nuclear plants. It has also complicated their ambitious climate goals,” Bloomberg reported at the time. “DeepSeek’s model appears to be more efficient and can achieve the same results for a fraction of the energy use, which may mean AI will have a smaller climate impact than thought.”

Just days after Donald Trump unveiled his US$500-billion Stargate AI initiative—reportedly enraging “first pal” and wannabe co-president Elon Musk by leaving him off the podium for the big reveal—DeepSeek “unveiled a large language model that can compete with U.S. giants but at potentially a fraction of the cost,” The Associated Press reported. “DeepSeek had already hit the top of the chart for free apps on Apple’s App Store by Monday morning [January 27], and analysts said such a feat would be particularly impressive given how the U.S. government has restricted Chinese access to top AI chips.”

The pushback came a scant four days later, with the New York Times reporting that the DeepSeek chatbot’s answers “reflect China’s view of the world” on topics that included late United States president Jimmy Carter’s position on Taiwan, China’s repression of Uyghurs in Xinjiang, the country’s handling of the COVID-19 pandemic, and Russia’s war in Ukraine. Some of that critical analysis came from NewsGuard, a team of online misinformation specialists that “found a similar propensity for disinformation and conspiratorial ideas in ChatGPT after it became public in 2022,” the Times said.

DeepSeek also mirrors other chatbots’ tendency to “hallucinate” with search responses that are “inaccurate, irrelevant, or nonsensical”, and follows the Chinese government’s censorship rules with some of the topics it avoids, the news story stated.

Scrambled Demand Forecasts

But those qualms didn’t stop DeepSeek from scrambling predictions of a massive surge in future energy demand. Ontario, for one, has been planning for a 75 per cent increase in power generation by 2050.

“For months, energy analysts and electric grid operators have been projecting a massive rise in the amount of power [the United States] will need to support the energy-guzzling artificial intelligence industry without causing widespread blackouts,” Politico reported. “That has led to proposals to build long-range power lines and power plants of all kinds. It drove up the stock of energy companies promising to fuel AI data centres. And it kicked off arguments about who will pay for such massive investments and whether [the United States] can afford to support AI’s energy habits without releasing more of the carbon pollution driving climate change.”

Those assumptions crashed and burned “seemingly overnight” with the arrival of a ChatGPT-like AI that is said to run far more efficiently and cheaply. The news prompted OpenAI CEO Sam Altman to declare DeepSeek’s model “impressive, particularly around what they’re able to deliver for the price,” vowing on social media that his company “will obviously deliver much better models, and also it’s legit invigorating to have a new competitor!”

A New Set of Assumptions

But in an in-depth thread on Bluesky, veteran AI journalist Karen Hao traced the deeper implications of DeepSeek’s announcement, far beyond a top-line narrative focused on tech competition between China and the U.S. “The biggest lesson to be drawn from DeepSeek is the huge cracks it illustrates with the current dominant paradigm of AI development,” she wrote. With an approach that requires 1/50 the resources of the current market leaders, “DeepSeek has demonstrated that scaling up AI models relentlessly, a paradigm OpenAI introduced and champions, is not the only, and far from the best, way to develop AI.”

Until DeepSeek burst their bubble, OpenAI and their “peer scaling labs” had sold the idea that constantly scaling up their systems was the best route to artificial general intelligence (AGI), the point where an AI can match or exceed human intelligence. But “this has always been more of an argument based in business than in science,” Hao said.

“There is empirical evidence that scaling AI models can lead to better performance. For businesses, such an approach lends itself to predictable quarterly planning cycles and offers a clear path for beating competition: Amass more chips,” she explained.

But “there are myriad huge negative externalities of taking this approach—not least of which is that you need to keep building massive data centres, which require the consumption of extraordinary amounts of resources”—distorting power supplies, consuming drinking water, extending the lives of gas and coal plants, worsening air quality, accelerating the carbon pollution that worsens the climate crisis, and with the more recent hype around Stargate, “ceding more and more control over critical energy and water infrastructure to Silicon Valley.”

But with DeepSeek, all of that may have changed.

“Scientifically, there’s no law of physics that says AI advancements must come from scaling rather than approaches using the same or fewer resources. Scaling is just an incredibly easy-to-follow formula,” Hao wrote. “OpenAI has been burning through staggering sums of cash to keep up its scaling paradigm and has yet to figure out how to balance its chequebooks—and it turns out it didn’t need to spend so much cash.”

So “it doesn’t matter if you’re a company in the U.S., China, or elsewhere. DeepSeek should be a cue to pivot hard toward investing in far more efficient methods of AI development. Even if you care nothing about community and climate impacts, it’s just better business.”

We’ve Been Here Before

While Hao had by far the most detailed assessment of DeepSeek’s advantage and OpenAI’s assumptions, energy modellers reacted swiftly to the news. The Financial Times headlined that DeepSeek had exposed the “guesswork” in the massive push for more electricity generation to meet AI demand.

“This abrupt reaction highlights that the market currently does not yet have adequate tools and information to assess the outlook for AI-driven electricity demand,” said Thomas Spencer, a lead energy and AI analyst at the International Energy Agency.

Investment analysts at Citi said robust growth projections for companies that supply power to data centres had been based on the sky-high growth projections, and “more computationally efficient AI could bring these trends into question.”

The recent history also shows that “one simply cannot rely on electricity demand forecasts that come from the electricity generation industry and their entourage,” Corporate Knights Director of Research Ralph Torrie wrote on LinkedIn. “Hopefully the hype around AI will get sorted out before the wave of malinvestment it might otherwise have triggered. Will we ever learn?”

“Many researchers had already believed the AI electricity demand forecasts were inflated,” agreed Joe Romm, a former acting assistant secretary of efficiency and renewables in the U.S. Department of Energy and long-ago editor of the popular Climate Progress newsletter. More specifically, “most of the hype around a nuclear renaissance and small modular reactors (SMRs) has been built around these gargantuan (over)estimates of power demand growth for AI,” even though “SMRs are particularly ill suited for data centres.”

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By Jody MacPherson

A First Nation in northern Alberta is telling the provincial government to “cease and desist” with plans for a C$70 billion artificial intelligence data centre proposed by celebrity investor Kevin O’Leary on its traditional territory.

On January 13, Sturgeon Lake Cree Nation wrote [pdf] an open letter to Premier Danielle Smith, stating it first learned about the proposed project—described by O’Leary Ventures as “the largest AI data centre industrial park in the world”—through [pdf] a press release.

The post First Nation demands Alberta halt O’Leary’s $70B data centre project appeared first on Thoughtful Journalism About Energy's Future.