Connect with us

Artificial Intelligence

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

Published

12 minute read

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.

The Audit is a reader-supported publication.

To receive new posts and support my work, consider becoming a free or paid subscriber.

Todayville is a digital media and technology company. We profile unique stories and events in our community. Register and promote your community event for free.

Follow Author

Artificial Intelligence

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

Published on

 

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.

Dear Readers:

As a nonprofit, we are dependent on the generosity of our readers.

Please consider making a small donation of any amount here.

Thank you!

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.

Continue Reading

Artificial Intelligence

The App That Pays You to Give Away Your Voice

Published on

logo

By

What sounds like side hustle money is really a permanent trade of privacy for pennies

An app that pays users for access to their phone call audio has surged to the top of Apple’s US App Store rankings, reflecting a growing willingness to exchange personal privacy for small financial rewards.
Neon Mobile, which now ranks second in the Social Networking category, invites users to record their calls in exchange for cash.
Those recordings are then sold to companies building artificial intelligence systems.
The pitch is framed as a way to earn extra income, with Neon promising “hundreds or even thousands of dollars per year” to those who opt in.
The business model is straightforward. Users are paid 30 cents per minute when they call other Neon users, and they can earn up to $30 a day for calls made to non-users.
Referral bonuses are also on offer. Appfigures, a platform that tracks app performance, reported that Neon was ranked No. 476 in its category on September 18.
Within days, it had entered the top 10 and eventually reached the No. 2 position for social apps. On the overall charts, it climbed as high as sixth place.
Neon’s terms confirm that it records both incoming and outgoing calls. The company says it only captures the user’s side of a conversation unless both participants are using the app.
These recordings are then sold to AI firms to assist in developing and refining machine learning systems, according to the company’s own policies.
What’s being offered is not just a phone call service. It’s a pipeline for training AI with real human voices, and users are being asked to provide this data willingly. The high ranking of the app suggests that some are comfortable giving up personal conversations in return for small daily payouts.
However, beneath the simple interface is a license agreement that gives Neon sweeping control over any recording submitted through the app. It reads:
“Worldwide, exclusive, irrevocable, transferable, royalty-free, fully paid right and license (with the right to sublicense through multiple tiers) to sell, use, host, store, transfer, publicly display, publicly perform (including by means of a digital audio transmission), communicate to the public, reproduce, modify for the purpose of formatting for display, create derivative works as authorized in these Terms, and distribute your Recordings, in whole or in part, in any media formats and through any media channels, in each instance whether now known or hereafter developed.”
This gives the company broad latitude to share, edit, sell, and repurpose user recordings in virtually any way, through any medium, with no expiration or limitations on scope.
Users maintain copyright over their recordings, but that ownership is heavily constrained by the licensing terms.
Although Neon claims to remove names, phone numbers, and email addresses before selling recordings, it does not reveal which companies receive the data or how it might be used after the fact.
The risks go beyond marketing or analytics. Audio recordings could potentially be used for impersonation, scam calls, or to build synthetic voices that mimic real people.
The app presents itself as an easy way to turn conversations into cash, but what it truly trades on is access to personal voice data. That trade-off may seem harmless at first, yet it opens the door to long-term consequences few users are likely to fully consider.
Continue Reading

Trending

X