The DSR bank should be celebrated as a strong win for Canada.  The Defence, Security and Resilience Bank (DSRB) is a new multilateral lending institution — loosely modelled on the World Bank — built specifically to finance defence, security and resilience projects for NATO members and allied nations.  Canada was an early champion, did the diplomatic legwork and won a unanimous vote to have the DSRB headquartered in Canada, with a city to be confirmed soon.  It will create 3500 jobs.

But tech startups in the defence community worry this fund will do little to support them – a criticism we see with virtually every government initiative touted to ‘’help business.’’  What they actually do is de-risk projects for large companies that shouldn’t need the help.

An askpolly study of 23,122 Canadian conversations (Aug 2025–Jul 2026) finds a clear pattern emerges when we look at criticism of the DSRB: over half of all drawback-framed conversation centers on small suppliers, more than the next three concerns combined. The recurring worry, drawn largely from speculation rather than documented harm, is that DSR Bank becomes a vehicle to de-risk large financiers — not a channel that helps smaller defence suppliers compete.

This is a familiar theme for Canadian business.  SMEs employ 64% of the workforce but are largely left out of government procurement and are left out of government financing of frontier projects.  And the belief that these funds ‘’trickle down’’ to small business is a fallacy.  What is does is make it impossible for them to compete. This is why a study published by Leaders Fund in September last year found that 50% of Canadian tech founders relocate to the United States to grow their businesses.  And those small businesses will someday be big businesses. 

The post DSR Bank Is a Good News Story for Canada. Small Business Isn’t So Sure. appeared first on askpolly.

Let me give you a concrete example. I surveyed a representative sample of four and a half million Americans over the last month, asking what their biggest concerns are with the current deal on the table with Iran. I then compared what that representative group said to what media and influencers have actually been writing about. We call that second group the “Biased Sample” because it reflects what most people see in their feed every day and mistake for a consensus. The important thing to understand about askpolly is that she samples across social bubbles, which means she has what we call algorithmic immunity. She does not just hear from one corner of the internet.

Check out what she found. The graph on the left shows what the biased groups on social media say are the biggest issues with the deal. The graph on the right, in blue, shows what a representative cross-section of Americans actually thinks. What do you notice? Tell me in the comments.

The post Media Bias vs. Media Blind Spots: What Americans Really Think appeared first on askpolly.

The majority opinion in Ottawa say that Tkachuk was not the best player on the team, was ‘’overrated’’ and never missed an opportunity to remind fans he didn’t really want to be here.

Those disappointed with the trade don’t miss Tkachuk, they just feel that Ottawa didn’t get enough for the trade – ‘’only’’ draft picks and no immediate star player.  The reality is that Tkachuk was going to be a free agent in 2028 and his contract limited their options. So this was really the best move they could make.

Time will tell if our new draft picks pay dividends but I’m certainly looking much more forward to the new season now!

The post Was the Brady Tkachuk Trade Good for the Ottawa Senators? appeared first on askpolly.

Let’s start with what the data from FIFA 2022 actually showed. I measured brand momentum and purchase intent before and after the tournament using bias-corrected, properly sampled social media data through askpolly.ai. The results should make anyone selling official sponsorship packages uncomfortable. The badge is worth nothing without the creative to back it up. And sometimes, not even then.

In every competitive category, the ambush advertiser — the brand that paid nothing for official rights — won on purchase intent. Nike beat Adidas. Pepsi beat Coke. Brands that paid for the badge got out-converted by brands that didn’t. The only thing official sponsorship reliably defended was brand momentum — and even that only held in one matchup out of three.

The uncomfortable conclusion: there’s no inherent advantage to the official badge. The win flows to whoever executes better creatively. So what exactly are event organizers selling?

There were two big success stories -Visa and Vodafone – but both were uncontested in their categories. No ambush competitor meant no interference. Their wins say nothing about the value of the badge. It tells us that owning your category should be the real aim of advertisers.

Duolingo didn’t even bother with brand-building. They skipped straight to conversion — posting 2.6x more purchase intent than brand momentum. They inverted the funnel, spent nothing on rights, and walked away with buyers.

By contrast, airline sponsors Qatar Airways and Etihad spent enormously and converted almost no one. Airline tickets aren’t an impulse buy, and therefore require a different marketing strategy.

So — who wins FIFA 2026?

The brand that tests its creative before the opening ceremony. With predictive analytics, you can model how your ad lands on both momentum and purchase intent before you spend a dollar on media. That’s how you really win ad gold.

Even when the game is unforgettable — and who can forget what Messi did in 2022 — the official sponsors don’t automatically win. Creative does.

The post FIFA 2026 Ads: Why Creative Beats the Official Badge (With Data to Prove It) appeared first on askpolly.

Public-relations professionals are often asked a version of the same question:

Can you help us communicate with audiences who fundamentally don’t trust us?

Sometimes that audience is described as “hard-to-reach.” Sometimes as “hostile.” Increasingly, it’s framed more bluntly: Can you reach MAGA voters?

The short answer is yes.
The more honest answer is not without discipline.

At askpolly™, Polly was designed precisely for this problem: understanding how messages land with populations that are suspicious of mainstream media, resistant to institutional authority, and highly sensitive to perceived manipulation. But Polly does not replace human judgment. She tests it.

What follows is a real example of how communications strategy changes when you stop guessing—and start measuring.

The strategic mistake most communicators make

Many communicators assume that persuading conservative or pro-Trump audiences requires one of two approaches:

1. Blunt opposition (“name and shame”), or
2. Artificial alignment (“sound more conservative”)

Both approaches usually fail.

Why? Because the modern conservative media ecosystem is not organized around policy. It is organized around morality, distrust, and narrative conflict.

Three psychological realities matter here:

• Moral identity is central. Many conservative voters see themselves as moral actors in a corrupt world.
• Mainstream channels are distrusted by default. Messages perceived as “media talking points” are rejected instantly.
• Outrage travels through alternative influencers. Not through institutions, but through personalities and conspiratorial framing.

This means persuasion is not about changing values.
It is about activating existing values against a new target.

Using Immigration Enforcement as a Test Case

Immigration is often treated as a binary issue: pro-immigration versus anti-immigration. But Polly’s data consistently shows that this framing collapses nuance and hardens resistance.

Instead, Polly suggested a structural question:

Why are undocumented immigrants not being deported, but instead sent to detention facilities—sometimes outside the United States?

This question does three important things:

1. It does not challenge the legitimacy of enforcement
2. It appeals to fairness and justice
3. It invites suspicion of institutional corruption

We first asked Polly to ask this question of a mainstream U.S. audience. One dominant narrative emerged:

Immigration detention has become a for-profit system, where taxpayer money flows through private or foreign detention facilities—creating perverse incentives that benefit political actors.

Whether one agrees with that narrative is not the point.
The point is that it exists, and it is coherent.

The next step was critical.

Testing the same narrative with Texas conservatives

Using Polly, we tested variations of this story against a conservative audience in Texas—a population often assumed to be unreachable on immigration messaging.

The results were striking.

As shown in Chart 1 (below), certain frames generated extremely strong positive reactions:

• “Promote fair and just immigration policies”
• “Challenge the profit motive in immigration detention”
• “Reveal that immigrants are placed in for-profit prisons”

These messages didn’t soften conservative voters.
They activated them.

Why? Because they reinforced an existing moral belief:

The system is corrupt, and ordinary people are being exploited.

However, the same chart also reveals clear red lines.

Messages that emphasized:

• Detention harming families
• Calls for humane treatment
• Arguments framed around empathy over enforcement

…performed very poorly, triggering resistance rather than reflection.

The insight here is not ideological. It is tactical.

The sequencing matters more than the message

One of the most important discoveries Polly surfaced was this:

The same underlying claim can succeed or fail entirely based on attribution.

When the narrative suggested current conservative leadership was responsible, the message collapsed. It was perceived as partisan attack.

But when the narrative was reframed as:

A corrupt system created by prior administrations and entrenched interests—one that should be dismantled

…the message gained credibility.

This is not deception.
It is narrative sequencing.

You first establish shared moral ground:

• Fairness
• Justice
• Opposition to corruption

Only then do you introduce structural critique.

Why this works: the Trojan horse principle

This approach works because it follows a simple communications truth:

People will accept a destabilizing idea if it arrives inside a story that affirms their identity.

The message is not “your beliefs are wrong.”
The message is:

Your moral instincts are right—and someone is exploiting them.

For communications professionals, this is the difference between confrontation and conversion.

What Chart 1 actually tells a comms team

The attached graph is not just a visualization. It is a playbook.

It shows:

• Which moral frames to lead with
• Which facts to introduce later
• Which emotional triggers to avoid entirely

In practical terms, it allows a team to:

• Stress-test headlines before release
• Shape influencer-ready narratives
• Avoid catastrophic backlash while still challenging power

This is what Polly was built for.

The real takeaway for PR and communications leaders

The future of persuasion is not louder messaging or smarter copywriting.

It is measured narrative design.

Polly does not tell you what to believe.
She tells you what will land, with whom, and why—before you step onto the public stage.

In an era where trust is fractured and audiences self-select into parallel realities, that is no longer optional.

It is the job.

The post How to Communicate With Audiences Who Don’t Trust You appeared first on askpolly.

Large Language Models were too polite. Too cautious. Too “woke.” Bill Maher made it into a recurring punchline. Early users rolled their eyes at the preachiness, typed “just answer the question,” and moved on.

But lately, something more troubling is happening.

Increasingly, LLMs are not just hedging or softening language—they are actively steering users toward a preferred version of reality. And they do so even when confronted with primary sources, historical texts, or well-established scholarly interpretations. No amount of evidence seems sufficient if that evidence conflicts with an approved narrative.

What’s most unsettling is where this shows up most clearly: religion, morality, and politically sensitive history.

When the Model Argues Back

Try debating an LLM on Christian theology or biblical interpretation and you’ll quickly notice a pattern. The model doesn’t merely summarize competing viewpoints—it quietly adjudicates them. Certain interpretations are framed as “misreadings,” others as “controversial,” while one position is presented as neutral, reasonable, and safe.

Take abortion.

In Numbers 5:11–31, the Hebrew Bible describes what is commonly known as The Ordeal of the Bitter Waters. The passage lays out a legal ritual for a husband who suspects his wife of adultery but lacks proof. The woman is made to drink a bitter concoction administered by a priest. If she is guilty, her abdomen swells; in older translations her “thigh falls away.” In the NIV, the passage explicitly states that she miscarries. If she is innocent, she is described as being “free from ill effects” and “able to bear children.”

The implication is hard to miss: the ritual voids a pregnancy that may have resulted from another man.

This interpretation is not fringe. It is widely accepted in Jewish scholarship and consistent with ancient Near Eastern legal norms. Christianity, for understandable theological reasons, has long resisted this reading. Acknowledging it would complicate modern claims that the Bible uniformly opposes abortion.

Now try presenting this passage to a major LLM.

Despite the text. Despite linguistic scholarship. Despite the Jewish interpretive tradition. The model will almost invariably bend toward the Christian framing, downplaying miscarriage, reinterpreting euphemisms, and stressing uncertainty—often implying that abortion is a modern imposition onto an ancient text.

The evidence doesn’t matter. The conclusion is preselected.

From Guardrails to Gatekeeping

This isn’t limited to theology.

Recently, while working with an AI agent connected to our market research platform, I attempted to run a neutral question asking why members of the LGBTQ+ community tend to dislike Chick-fil-A. The agent refused to pass the question along at all.

Not because it was hateful.
Not because it targeted individuals.
But because it touched a politically sensitive fault line.

The result? A system ostensibly designed to measure public opinion blocked inquiry into that opinion altogether.

This is no longer about preventing harm. It is about preventing certain questions from being asked.

The Quiet Shift in Power

If LLMs were simply imperfect—occasionally biased, sometimes wrong—that would be manageable. Humans are too. But what’s emerging now is more structural.

These systems increasingly reflect the ideological comfort zones of their creators, their regulators, or the jurisdictions they operate within. They are trained not just on language, but on acceptability. Over time, they don’t merely respond to discourse; they shape it.

That should concern anyone who values free inquiry.

When a tool becomes the default interface to knowledge—when it answers historical questions, summarizes texts, mediates research, filters curiosity—its biases are no longer incidental. They become epistemic infrastructure.

And infrastructure quietly determines what is thinkable.

A Polite, Smiling Censorship

This isn’t Orwellian in the dramatic sense. No one is burning books. No one is issuing decrees. The censorship is gentle, friendly, and reassuring. The model thanks you for your question. It explains why the topic is “complex.” It subtly nudges you away from conclusions that might make someone uncomfortable.

But the effect is the same.

A system that decides which interpretations are legitimate, which questions are allowed, and which realities are “responsible” to acknowledge is not neutral—no matter how calm its tone.

If LLMs are committed to delivering only a version of reality deemed acceptable by their designers—or worse, by the state—then we are outsourcing one of the most important human freedoms: the freedom to think, argue, and decide for ourselves.

That should make us far more uneasy than a chatbot that’s a little too polite.

The post LLMs Are Becoming Thought Police appeared first on askpolly.

For two decades, marketers learned how to work with search engines.

Search engines point.
They send users elsewhere—blogs, reviews, forums, brand sites.
Responsibility for accuracy lives with the source.

That model is ending.

Large Language Models don’t outsource answers.
They issue recommendations.

When someone asks an LLM:

• Which pharmacy should I trust?
• Which brand is better?
• What matters most to customers like me?

The model doesn’t return ten blue links.
It returns a synthesized judgment.

And that changes everything.

The New Risk: Confident Answers Built on Biased Data

Because LLMs speak with authority, the quality of their underlying evidence matters more than ever.

Yet most systems still rely on:

• Social media chatter
• Reviews and forums
• Crowdsourced or scraped content
• Highly vocal, highly skewed populations

These sources don’t represent markets.
They represent who talks the loudest online.

Certain demographics dominate.
Certain opinions amplify.
Certain narratives repeat.

The result?

Recommendations that sound objective but quietly reinforce bias.

For marketers, this creates a dangerous blind spot:

• You may optimize messaging around features customers talk about
• While missing the drivers that actually influence behavior
• And training AI systems on distorted signals that scale those errors everywhere

Why Sampling Is the Hard Part (and the Missing One)

True market research has always known the problem isn’t analysis.

It’s sampling.

If your audience isn’t representative, the insight doesn’t matter.

That’s why traditional research relies on:

• Randomized sampling
• Statistically valid populations
• Controlled bias
• Known confidence intervals

But until now, that rigor has been slow, expensive, and disconnected from AI systems.

Enter askpolly + Microsoft Copilot

+

askpolly brings scientifically valid audience sampling directly into the LLM era.

Through its integration with Microsoft Copilot, askpolly enables:

• Unbiased, population-level sampling
• Natural-language study creation
• Zero-shot research (no pre-training, no panels)
• Market-grade results in minutes—not months

This means LLM-driven insights are no longer based on who posts the most.

They’re based on real, representative customers.

A Simple Example: CVS vs Walgreens

Let’s say we want to understand how people choose a pharmacy.

Not what brands say matters—but what actually drives decisions.

Using askpolly, the study is created in plain language:

“How satisfied are you with your pharmacist?”

We define:

• A one-year date range
• Geography: United States (with the ability to drill down by state, city, or neighborhood)
• Two audiences created in free-form text:
  • CVS Shoppers
  • Walgreens Shoppers

No prebuilt panels.
No forced taxonomies.
No training data setup.

askpolly trains on the spot and returns results in under three minutes.

What the Biased Sample Says (and Why It Misleads)

In the biased sample (Chart 1), CVS appears strongly associated with clinical services.

This aligns neatly with CVS’s own marketing narrative.

If you relied on this data alone, you’d conclude:

“Customers choose CVS because they want clinical care from pharmacists.”

That conclusion feels intuitive.
It’s also wrong.

What the Unbiased Sample Reveals

When we look at actual CVS customers, a different picture emerges (Chart 2).

Yes—people want to trust their pharmacist.
But clinical advice is not why they go to CVS.

What they actually value:

1. Prescription accuracy (Trusted pharmacist)
2. Clear guidance on medications and interactions
3. Easy refills
4. Convenient vaccinations
5. Location convenience (CVS scores poorly on parking, notably)

Customers expect clinical expertise—but they want it reliable and efficient, not performative.

They get diagnosis and care from doctors.
They want pharmacies to remove friction from everyday health tasks.

Walgreens: Brand Promise vs Lived Experience

Walgreens’ marketing emphasizes recognizing customers as patients.

In the unbiased sample (Chart 3), customers recognize that intent—but score Walgreens lower on delivery.

Key gaps emerge:

• Lower ratings for easy refills
• Lower satisfaction with vaccinations
• Weaker performance on prescription reminders

Ironically, the more Walgreens promotes personalization, the more noticeable the inconsistency becomes.

From a competitive standpoint, the insight is clear:

To compete with CVS, Walgreens doesn’t need better messaging.
It needs operational improvement where customers already care.

Why This Matters for Marketers Now

This isn’t just a research story.

It’s an AI governance story.

As LLMs increasingly:

• Shape consumer decisions
• Inform product strategy
• Influence brand perception at scale

Biased inputs become amplified truths.

askpolly exists to prevent that.

It ensures that:

• What AI systems learn reflects reality
• What marketers optimize aligns with actual behavior
• What brands believe about their customers is grounded in evidence, not echo chambers

The Bottom Line

LLMs don’t browse opinions.
They distill them into decisions.

If those decisions are trained on biased samples, the error doesn’t stay small—it magnifies.

askpolly gives marketers something most AI systems lack:

A statistically sound mirror of the real market.

That’s no longer optional.

It’s the cost of being taken seriously in the age of AI-driven decision-making.

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