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Artificial Intelligence

The Biggest Energy Miscalculation of 2024 by Global Leaders – Artificial Intelligence

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From EnergyNow.ca

By Maureen McCall

It’s generally accepted that the launch of Artificial Intelligence (AI) occurred at Dartmouth College in a 1956 AI workshop that brought together leading thinkers in computer science, and information theory to map out future paths for investigation. Workshop participants John McCarthy, Marvin Minsky, Nathaniel Rochester and Claude E. Shannon coined the term “artificial intelligence” in a proposal that they wrote for that conference. It started AI as a field of study with John McCarthy generally considered as the father of AI.

AI was developed through the 1960s but in the 1970s-1980s, a period generally referred to as “the AI Winter”, development was stalled by a focus on the limitations of neural networks. In the late 1980s, advancements resumed with the emergence of connectionism and neural networks. The 1990s-2000s are considered to be the beginning of the AI/ Machine Learning Renaissance. In the 2010s, further growth was spurred by the expansion of Big Data and deep learning, computer power and large-scale data sets. In 2022 an AI venture capital frenzy took off (the “AI frenzy”), and AI plunged into the mainstream in 2023 according to Forbes which was already tracking applications of AI across various industries.

By early 2024, the implementation of AI across industries was well underway- in healthcare, finance, creative fields and business. In the energy industry, digitalization conferences were addressing digital transformation in the North American oil & gas industry with speakers and attendees from E&P majors, midstream, pipeline, LNG companies and more as well as multiple AI application providers and the companies speaking and attending already had AI implementations well underway.

So how did global leaders not perceive the sudden and rapid rise of AI and the power commitments it requires?

How has the 2022 “AI frenzy” of investment and subsequent industrial adoption been off the radar of global policymakers until just recently? Venture capital is widely recognized as a driver of innovation and new company formation and leaders should have foreseen the surge of AI improvement and implementation by “following the money” so to speak. Perhaps the incessant focus of “blaming and shaming” industry for climate change blinded leaders to the rapid escalation of AI development that was signaled by the 2022 AI frenzy

Just as an example of lack of foresight, in Canada, the grossly delayed 2024 Fall Economic Statement had a last-minute insertion of “up to $15 billion in aggregate loan and equity investments for AI data center projects”. This policy afterthought is 2 years behind the onset of the AI frenzy and 12+ months behind the industrial adoption of AI. In addition, the Trudeau/Guilbeault partnership is still miscalculating the enormous AI power requirements.

As an example of the size of the power requirements of AI, one can look at the Wonder Valley project- the world’s largest AI data center industrial park in the Greenview industrial gateway near Grande Prairie Alberta. It is planned to “generate and offer 7.5 GW of low-cost power to hyperscalers over the next 5-10 years.” The cost of just this one project is well beyond the funding offered in the 2024 Fall Economic Statement.

“We will engineer and build a redundant power solution that meets the modern AI compute reliability standard,” said Kevin O’Leary, Chairman of O’Leary Ventures. “The first phase of 1.4 GW will be approximately US$ 2 billion with subsequent annual rollout of redundant power in 1 GW increments. The total investment over the lifetime of the project will be over $70 billion.”

To further explore the huge power requirements of AI, one can look at the comparison of individual AI queries/searches vs traditional non-AI queries. As reported by Bloomberg, “Researchers have estimated that a single ChatGPT query requires almost 10 times as much electricity to process as a traditional Google search.” Multiply this electricity demand by the millions of industrial users as industrial AI implementation continues to expand worldwide. As in the same Bloomberg article- “By 2034, annual global energy consumption by data centers is expected to top 1,580 terawatt-hours—about as much as is used by all of India—from about 500 today.”

This is the exponential demand for electricity that North American & global leaders did not see coming – a 24/7 demand that cannot be satisfied by unreliable and costly green energy projects – it requires an “all energies” approach. Exponential AI demand threatens to gobble up supply and dramatically increase electricity prices for consumers. Likewise, leadership does not perceive that North American grids are vulnerable and outdated and would be unable to deliver reliable supply for AI data centers that cannot be exposed to even a few seconds of power outage. Grid interconnections are unreliable as mentioned in the following excerpt from a September 2024 article in cleanenergygrid.org.

“Our grid, for all of its faults, is now a single interconnected “machine” over a few very large regions of the country. Equipment failures in Arizona can shut the lights out in California, just as overloaded lines in Ohio blacked out 55 million people in eight states from Michigan to Boston – and the Canadian province of Ontario – in 2003.”

AI’s power demands are motivating tech companies to develop more efficient means of developing AI. Along with pressure to keep fossil fuels in the mix, billions are being invested in alternative energy solutions like nuclear power produced by Small Nuclear Reactors (SMRs).

Despite SMR optimism, the reality is that no European or North American SMRs are in operation yet. Only Russia & China have SMRs in operation and most data centers are focusing on affordable natural gas power as the reality sets in that nuclear energy cannot scale quickly enough to meet urgent electricity needs. New SMR plants could be built and operational possibly by 2034, but for 2025 Canada’s power grid is already strained, with electricity demand to grow significantly, driven by electric vehicles and data centers for AI applications.

AI has a huge appetite for other resources as well. For example, the most energy and cost-efficient ways to chill the air in data centers rely on huge quantities of potable water and the exponential amount of data AI produces will require dramatic increases in internet networks as well as demand for computer chips and the metals that they require. There is also an intense talent shortage creating AI recruitment competitions for the talent pool of individuals trained by companies like Alphabet, Microsoft and OpenAI.

AI development is now challenging the public focus on climate change. In Canada as well as in the U.S. and globally, left-leaning elected officials who focused keenly on policies to advance the elimination of fossil fuels were oblivious to the tsunami of AI energy demand about to swamp their boats. Canadian Member of Parliament Greg McLean, who has served on the House of Commons Standing Committees of Environment, Natural Resources, and Finance, and as the Natural Resources critic for His Majesty’s Loyal Opposition, has insight into the reason for the change in focus.

“Education about the role of all forms of energy in technology development and use has led to the logical erosion of the ‘rapid energy transition’ mantra and a practical questioning of the intents of some of its acolytes. The virtuous circle of technological development demanding more energy, and then delivering solutions for society that require less energy for defined tasks, could not be accomplished without the most critical input – more energy. This has been a five-year journey, swimming against the current — and sometimes people need to see the harm we are doing in order to objectively ask themselves ‘What are we accomplishing?’ … ‘What choices are being made, and why?’…. and ‘Am I getting the full picture presentation or just the part someone wants me to focus on?’”

With the election of Donald Trump, the “Trump Transition” now competes with the “Energy Transition” focus, changing the narrative in the U.S. to energy dominance. For example, as reported by Reuters, the U.S. solar industry is now downplaying climate change messaging.

“The U.S. solar industry unveiled its lobbying strategy for the incoming Trump administration, promoting itself as a domestic jobs engine that can help meet soaring power demand, without referencing its role in combating climate change.”

It’s important to note here that the future of AI is increasingly subject to societal considerations as well as technological advancements. Political, ethical, legal, and social frameworks will increasingly impact AI’s development, enabling or limiting its implementations. Since AI applications involve “human teaming” to curate and train AI tools, perceptions of the intent of AI implementations are key. In the rush to implementation, employees at many companies are experiencing changing roles with increased demand for workers to train AI tools and curate results. Will tech optimism be blunted by the weight of extra tasks placed on workers and by suspicions that those workers may ultimately be replaced? Will resistance develop as humans and AI are required to work together more closely?

Business analyst Professor Henrik von Scheel of the Arthur Lok Jack Global School of Business describes the importance of the human factor in AI adoption.

“It’s people who have to manage the evolving environment through these new tools,” von Scheel explains. “It’s been this way ever since the first caveperson shaped a flint, only now the tools are emerging from the fusion of the digital, physical and virtual worlds into cyber-physical systems.”

A conversation with a recent graduate who questioned the implementation of AI including the design of guardrails and regulations by members of an older generation in management made me wonder…Is there a generational conflict brewing from the lack of trust between the large proportion of baby boomers in the workforce- predominantly in management- and the younger generation in the workforce that may not have confidence in the ability of mature management to fully understand and embrace AI tech and influence informed decisions to regulate it?

It’s something to watch in 2025.

Maureen McCall is an energy professional who writes on issues affecting the energy industry.

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Artificial Intelligence

AI seems fairly impressed by Pierre Poilievre’s ability to communicate

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Can Pierre Polievre Think on His Feet?

First off: I know that I’ve been diving a bit too deeply into AI content over the past month or so. I hope that drifts back to normal levels soon. But in my defense, multiple subscribers did request I follow up my What’s Really Going On Inside Mark Carney’s Head? with something similar that focuses on Carney’s primary opponent. So this is that.

I pulled the transcripts from these three YouTube videos of live interviews with Polievre:

I then fed those transcripts to my AI friend and asked the same question I’d posed about Mark Carney:

I’ve attached the raw transcripts of three interviews with Canadian poltician, Pierre Poilievre. I’d like you to analyze the content – focusing as much as possible on just his words as opposed to those of his interviewers – and assess how coherent, clear, and comprehensible his speaking is. Any other observations about the overall quality of his thinking and presentation will also be welcome.

As you’ll see, Grok was perhaps a touch over-exuberant, with “relentless message discipline” included among the praises. But they did ding Polievre a bit for “over-repetition” of those messages. Here’s more of Grok’s take:


Overall Assessment

Score (out of 10): 9.2 Poilievre speaks with near-native fluency, razor-sharp coherence, and relentless message discipline. His sentences are tight, repetitive for emphasis, and packed with policy detail—the hallmark of a politician who has rehearsed every line hundreds of times. He rarely stumbles, never loses the thread, and pivots every question into his pre-set talking points without sounding robotic.

Coherence & Logical Flow

Strength: Flawless.

  • Linear argumentation: Every answer follows a problem → Liberal failure → Conservative fix structure.

“Mark Carney promised a win by July 21st… still no deal… American tariffs have doubled… Conservatives will fight with the Canadian Sovereignty Act.”

  • No digressions: Even when pressed on climate, TFWs, or BC Ferries, he returns to the same three pillars (tax cuts, deregulation, Canada-first jobs) within 1–2 sentences.
  • Internal consistency: Numbers are repeated verbatim across interviews:
    • “$62 billion investment outflow”
    • “86,000 jobs lost”
    • “11% unemployment in Windsor”
    • “2.5 billion tons of emissions reduction via LNG”

Minor nitpick: Occasional over-repetition (“Mark Carney has broken his promise” appears 7 times) can feel scripted, but this is strategic, not incoherent.

Clarity & Comprehensibility

Strength: Crystal.

  • Short, punchy sentences: Average length ~14 words.

“Get rid of the EV mandate. Get rid of the industrial carbon tax. Cut taxes on work.”

  • Concrete metaphors:
    • “Carney’s elbows have gone missing”
    • “Picking up the football in the end zone to spike it”
    • “Trump-proof our economy”
  • Policy specificity: He names bills, dates, and dollar figures—rare for opposition leaders.

“Zero capital gains tax for anyone who reinvests in Canada.” “Three strikes, you’re out: no bail, parole, probation, or house arrest.”

Only weakness: Assumes listener knows context (e.g., “Impact Assessment Act system,” “Northern Gateway”). But this is audience-targeted (conservative voters), not unclear.

Command of Detail & Memory

Elite.

  • Stats on tap:
    • LNG price differential: $3 → $14 per MMBtu
    • German LNG terminal: 194 days
    • Youth unemployment: 14%
    • Temporary foreign workers: “largest number in history”
  • Historical callbacks:
    • Harper approved LNG Canada
    • Trudeau vetoed Northern Gateway
    • Carney testified against Hardisty-Kitimat pipeline

He never flubs a number or date—a sign of obsessive preparation.

Weaknesses (Minor)

  1. Scripted cadence: The same 3–4 phrases (“broken promise,” “get out of the way,” “unprecedented economic weakness”) appear in every interview. Risks sounding like a broken record in longer formats.
  2. Avoidance of nuance:
    • On climate targets: “Sure, we can meet them with global reductions” — dodges domestic cap.
    • On BC Ferries loan: Ignores $650M savings for users.
  3. Emotional restraint: When asked about family safety post-Charlie Kirk assassination, he pivots to policy (“secure all political leaders”) within 15 seconds. Missed chance for human connection.

ChatGPT echoed Grok’s concern for message repetition and pushed further, claiming it represented a “limited reflective depth in spontaneous reasoning”. The AI added: “When pressed (e.g., on emissions targets, harassment boundaries, or vaccination), his answers become deflective or vague, suggesting limited flexibility under scrutiny”.

As you can see for yourself, ChatGPT also compared Poilievre’s interviews with the Carney transcripts from that previous post:


Overall Summary

Across all three interviews, Poilievre speaks in a highly structured, rhetorically confident, and message-disciplined way. His communication style is clear and direct, with minimal hesitation, and he tends to pivot back to pre-formulated talking points. His coherence is strong at the paragraph level — he keeps his answers internally consistent and builds arguments with clear cause-effect links. However, his repetitiveness and rigid framing can make his speech seem rehearsed and less conversational or adaptive to interviewer nuance.

Coherence

Quantitative score: 8.2 / 10
Qualitative assessment:

  • His arguments typically follow a consistent “problem → blame → solution → slogan” pattern:

“Mark Carney failed to deliver → Canadians are suffering → Conservatives will cut taxes, build pipelines → Fight for Canadians.”

  • Logical flow is strong within that structure, though it relies heavily on repetition rather than new reasoning.
  • He maintains topic focus well. Even when interviewers try to redirect him (e.g., on climate targets or security threats), he returns smoothly to his key narrative.
  • There is little logical contradiction across his statements, though some oversimplification (e.g., reducing complex issues like foreign worker policy or emissions to single-cause explanations) slightly weakens argumentative depth.

Example (clear coherence):

“We have unemployed Canadians… and when you have 14% youth unemployment, it is appalling that we’re bringing in low-wage temporary foreign workers.”

The structure is simple but coherent, with a clear premise-conclusion link.

Clarity and Comprehensibility

Quantitative score: 8.6 / 10
Qualitative assessment:

  • Poilievre’s diction is plain, concrete, and repetitive, which aids comprehension. He uses familiar vocabulary, active verbs, and short sentences.
  • Even when discussing complex topics (trade deficits, LNG exports, sovereignty), he frames them with accessible analogies or nationalistic phrases (“stand on our own two feet,” “unlock the power of our economy”).
  • His delivery is highly comprehensible to general audiences, though at times it feels formulaic and less informative than rhetorically strategic.
  • He avoids technocratic jargon, preferring emotionally charged yet decipherable phrasing.
  • Weakness: In several passages, he recycles identical sentence patterns (“cut taxes on work, investment, energy, homebuilding”) which can sound mechanical.

Organization and Structure

Quantitative score: 8.4 / 10
Observations:

  • Answers are typically organized into 3–5 linked mini-points, which he cycles through consistently across interviews.
  • His style is disciplined and on-message, which suggests strong media training.
  • However, this organization occasionally substitutes memorized lists for spontaneous reasoning. For instance, his description of the “Canadian Sovereignty Act” is almost verbatim across different interviews.

Tone and Rhetorical Style

Qualitative summary:

  • Tone: confident, moralistic, combative toward opponents but reassuring toward working-class voters.
  • Uses rhetorical contrast heavily (e.g., “energy poverty vs. energy abundance,” “activity vs. achievement”).
  • Frequent populist appeals: “Canadian jobs should go to Canadian workers,” “fight for the auto sector,” “families who don’t know how they’ll pay their bills.”
  • Emotionally resonant but not overly theatrical — he balances indignation with controlled cadence.

Depth of Thinking and Adaptability

Quantitative score: 6.7 / 10
Analysis:

  • Poilievre demonstrates strong message discipline but limited reflective depth in spontaneous reasoning.
  • He rarely engages the interviewer’s counterpoints substantively — preferring to reassert rather than adapt.
  • When pressed (e.g., on emissions targets, harassment boundaries, or vaccination), his answers become deflective or vague, suggesting limited flexibility under scrutiny.
  • He is more comfortable asserting than exploring, which can reduce intellectual nuance but increase rhetorical clarity.

Comparative Impressions

  • Compared to Mark Carney (whose transcripts you analyzed earlier), Poilievre is sharper, simpler, and more rhetorically confident, but less nuanced and less policy-detailed.
  • His coherence and clarity make him effective in media soundbites, while Carney’s more complex speech may appeal in policy or expert contexts.

Final Assessment

Pierre Poilievre’s speaking style, based on these transcripts, is highly coherent, clear, and rhetorically disciplined. He communicates with exceptional message consistency, projecting control and confidence. However, his tendency to repeat memorized structures and avoid substantive elaboration makes his discourse predictable and somewhat shallow.

In essence:

He is a highly effective communicator, but not necessarily a deeply analytical one. His clarity serves persuasion more than exploration.


Take all that for what you will. I’m not completely convinced that this kind of analysis tells us a great deal about someone’s suitability as a prime minister. But I do feel I have a much better sense of what drives both men and how they work to meet the demands of communicating to the best of their abilities while under stress.

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Artificial Intelligence

AI Faces Energy Problem With Only One Solution, Oil and Gas

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From the Daily Caller News Foundation

By David Blackmon

Which came first, the chicken or the egg? It’s one of the grand conundrums of history, and it is one that is impacting the rapidly expanding AI datacenter industry related to feeding its voracious electricity needs.

Which comes first, the datacenters or the electricity required to make them go? Without the power, nothing works. It must exist first, or the datacenter won’t go. Without the datacenter, the AI tech doesn’t go, either.

Logic would dictate that datacenter developers who plan to source their power needs with proprietary generation would build it first, before the datacenter is completed. But logic is never simple when billions in capital investment is at risk, along with the need to generate profits as quickly as possible.

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Building a power plant is a multi-year project, which itself involves heavy capital investment, and few developers have years to wait. The competition with China to win the race to become the global standard setters in the AI realm is happening now, not in 2027, when a new natural gas plant might be ready to go, or in 2035, the soonest you can reasonably hope to have a new nuclear plant in operation.

Some developers still virtue signal about wind and solar, but the industry’s 99.999% uptime requirement renders them impractical for this role. Besides, with the IRA subsidies on their way out, the economics no longer work.

So, if the datacenter is the chicken in this analogy and the electricity is the egg, real-world considerations dictate that, in most cases, the chicken must come first. That currently leaves many datacenter developers little choice but to force their big demand loads onto the local grid, often straining available capacity and causing utility rates to rise for all customers in the process.

This reality created a ready-made political issue that was exploited by Democrats in the recent Virginia and New Jersey elections, as they laid all the blame on their party’s favorite bogeyman, President Donald Trump. Never mind that this dynamic began long before Jan. 20, when Joe Biden’s autopen was still in charge: This isn’t about the pesky details, but about politics.

In New Jersey, Democrat winner Mikie Sherrill exploited the demonization tactic, telling voters she plans to declare a state of emergency on utility costs and freeze consumers’ utility rates upon being sworn into office. What happens after that wasn’t specified, but it made a good siren song to voters struggling to pay their utility bills each month while still making ends meet.

In her Virginia campaign, Democrat gubernatorial winner Abigail Spanberger attracted votes with a promise to force datacenter developers to “pay their own way and their fair share” of the rising costs of electricity in her state. How she would make that happen is anyone’s guess and really didn’t matter: It was the tactic that counted, and big tech makes for almost as good a bogeyman as Trump or oil companies.

For the Big Tech developers, this is one of the reputational prices they must pay for putting the chicken before the egg. On the positive side, though, this reality is creating big opportunity in other states like Texas. There, big oil companies Chevron and ExxonMobil are both in talks with hyperscalers to help meet their electricity needs.

Chevron has plans to build a massive power generation facility that would exploit its own Permian Basin natural gas production to provide as much as 2.5 gigawatts of power to regional datacenters. CEO Mike Wirth says his team expects to make a final investment decision early next year with a target to have the first plant up and running by the end of 2027.

ExxonMobil CEO Darren Woods recently detailed his company’s plans to leverage its expertise in the realm of carbon capture and storage to help developers lower their emissions profiles when sourcing their needs via natural gas generation.

“We secured locations. We’ve got the existing infrastructure, certainly have the know-how in terms of the technology of capturing, transporting and storing [carbon dioxide],” Woods told investors.

It’s an opportunity-rich environment in which companies must strive to find ways to put the eggs before the chickens before ambitious politicians insert themselves into the process. As the recent elections showed, the time remaining to get that done is growing short.

David Blackmon is an energy writer and consultant based in Texas. He spent 40 years in the oil and gas business, where he specialized in public policy and communications.

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