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Can LLMs Reason? A Practical Guide for Canadian Technology Magazine Readers

Sofia Alvarez
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One of the most heated and confusing debates in tech today is whether large language models can think, understand, or reason. If you follow Canadian Technology Magazine, you have likely seen passionate opinions on both sides: some insist LLMs are nothing but statistical parrots, others say they are already demonstrating reasoning capabilities. This article cuts through the noise and gives you a practical framework to answer that question for yourself, with examples, tests you can apply, and a level-headed view of what to expect next. As you read, keep Canadian Technology Magazine in mind as the type of audience that benefits from evidence-based, actionable analysis.

Table of Contents

Why Saying “They’re Just X” Is Not an Argument

A lot of people respond to the question “Can LLMs reason?” with a shorthand dismissal. You will see phrases like “they are just a next-token predictor” or “they are just a stochastic parrot.” Call this the justism fallacy: because something is made of components A, B, and C, it cannot do behavior Z. That logic is tempting but wrong.

Think about clocks. If you saw a digital watch made of a circuit board and a quartz crystal, would you say it cannot tell time because it is not a gear-driven clock? Of course not. The composition of a system does not automatically determine capability. The real question is observable: does it perform the function reliably within agreed rules? When we debate whether LLMs can reason, saying “they are just X” is a category error unless you also provide a testable claim about what reasoning is and how to measure it.

Readers of Canadian Technology Magazine should expect critiques to be framed as falsifiable hypotheses. If someone tells you a model cannot reason because it is trained by reinforcement learning or because it uses token prediction, ask them for a concrete, objective test. Without that, you are dealing with rhetoric and fear, not evidence.

How to Test Whether Something “Can” Do Something

When we ask whether an object or system has an ability, the sensible method is to design a test with clear pass/fail criteria. The test should include rules to prevent cheating, a measurement of performance, and a standard that indicates success.

Use the clock example. To call something a clock, you need:

A sundial, a gear clock, and a digital quartz watch meet those criteria in different ways. Saying the sundial is “just a stick” misses the point. The sundial passes the test for timekeeping in its context. Likewise, the right way to investigate LLM reasoning is to build clear tests that represent the phenomena we call reasoning.

Four-Minute Mile: A Useful Analogy

Another helpful analogy is the four-minute mile. For decades the idea that humans could not run a mile under four minutes was treated as a biological limit. People tried, many failed, and then once Roger Bannister broke the barrier, many others followed. If you ran a thousand people and none of them broke four minutes, that would not prove humans could never do it. On the other hand, one verifiable run under four minutes falsifies the claim that humans cannot.

If someone asserts “LLMs cannot reason,” ask them: what would count as evidence to change your mind? Would it have to synthesize novel arguments, solve multi-step problems reliably, or create scientific insights never before seen? Define the counterexample that would prove you wrong. That’s how scientific skepticism should work, and it is exactly the approach reporters and analysts at Canadian Technology Magazine should promote.

Counterexamples, Anecdotes, and the Broken-Clock Fallacy

Many critics wave around counterexamples: an LLM repeated instructions, failed to attach a file, or miscounted letters. Those anecdotes feel satisfying because they demonstrate obvious failure, but they do not falsify the hypothesis that LLMs can reason. A single failure is like showing a broken clock and claiming no clocks can tell time.

To be meaningful, test data should be systematic. Random examples of failures are useful for debugging and understanding limitations, but they do not by themselves prove an inability. If your standard for “reasoning” is perfection in every possible scenario, then by definition no system will qualify. A better approach is to define a realistic set of tasks and measure performance across many trials, with control groups and baseline human performance for comparison. That is the sort of coverage readers expect in Canadian Technology Magazine when evaluating claims.

Moving Goalposts and the History of Dismissing AI

There is a curious pattern in AI skepticism: once a milestone is reached, skeptics move the goalposts. Machines beat world-class humans at chess. The response was “that is brute force and heuristics.” Machines played Go in novel ways and won. The response became “narrow trickery and search.” Image recognition became excellent. The response was “just pattern matching.” Language models produced coherent, creative text. The response shifted again to philosophical arguments about meaning, soul, or lived experience.

Recognize this pattern: people often define intelligence by the capabilities that appear uniquely human. When those capabilities are replicated, the definition of intelligence is tightened or shifted. For a technology journalist or analyst writing for a publication like Canadian Technology Magazine, it is crucial to avoid this trap. Judge systems by explicit tasks and outcomes, not by ever-moving intuitions about what intelligence should feel like.

Emotional Reactions: Why AI Triggers People

Not every reaction to AI is analytical. Many are visceral and emotional. If you value intelligence as your primary personal attribute, the thought of machines matching or exceeding that intelligence triggers a fear response. That fear often manifests as rhetorical moves that are not logically consistent: inventing special categories for AI outputs, insisting AI lacks “soul,” or claiming that the method of training disqualifies the result.

These reactions are understandable. They stem from deep psychological threats: status, livelihood, and identity. But recognizing the emotion is the first step to stepping back and applying rational, testable criteria. A disciplined reader of Canadian Technology Magazine will note the emotional valence of claims and ask for evidence rather than amplification.

Detecting the “Explain Why You Are Special” Prompt

Often, the arguments people produce answer a different question than the one being asked. The implicit prompt becomes: explain why LLMs cannot do the thing that makes me feel special. That mental framing explains a lot of otherwise weird rhetoric. When someone whose craft is 3D modeling sees an AI produce similar-looking work, the instinct is to delegitimize the output. That explanation is not about objective skill comparison. It is about protecting a personal sense of uniqueness.

For reporters and industry pros alike, this is important to identify. If a critique sounds like protection of status rather than measurement of capability, it should be flagged as such. Balanced coverage in Canadian Technology Magazine should separate legitimate technical criticism from protective rhetoric.

AI in Creative Work: Art, Music, and Poetry

Creative fields provide especially fertile ground for this phenomenon. Consider photography. Modern cameras, even smartphones, automate much of the technical process. Does that make a photograph “not art”? Many would say no. Yet similar automation applied to music or visual art often triggers cries of “soulless” or “unwatchable.”

There is empirical work relevant here. Blind tests where human listeners or readers evaluate AI-generated creative pieces reveal surprising results. In many studies, average listeners cannot reliably distinguish AI-generated music or poetry from human-created works. In some cases, blind panels even rate AI outputs as better on attributes like clarity or emotional impact. When subjects are told a piece is AI-made, their ratings drop, showing bias rather than pure qualitative difference.

That pattern should shift the conversation away from abstract claims of soul and toward measurable outcomes. If an AI composition moves more people on measurable metrics, it has practical value. If producers in an industry worry about economic impacts, the relevant question is: what proportion of consumer preference or market share can AI outputs capture? For readers of Canadian Technology Magazine, these are the pragmatic questions worth tracking.

Studies Showing Preference for AI Works and What They Mean

Several controlled studies put human and AI works head-to-head. The headline result to watch for is this: when the origin of the work is blind, average people often cannot reliably tell which is AI and which is human. Sometimes AI wins on rhythm, clarity, or emotional resonance. When origin is revealed, people often downgrade AI outputs, indicating a prejudice rather than a performance-based judgment.

That pattern matters for policy, for business strategy, and for individuals calibrating their feelings about the technology. It suggests that fear-based rejection is not grounded in consumer response. For editors at Canadian Technology Magazine, it means reporting should be based on double-blind comparisons and market data, not intuition alone.

Practical Advice: How to Evaluate Whether an LLM Can Reason

Enough theory. Here is a practical checklist you can use to assess reasoning in LLMs. Apply these tests to get beyond slogans and anecdotes.

  1. Define reasoning tasks explicitly

    Is the task multi-step problem solving, causal inference, planning, or explanation generation? Spell it out. “Reasoning” is descriptive; tasks are measurable.


  2. Establish success criteria

    Decide what counts as success. Is it accuracy, creativity, robustness to adversarial input, or consistency across trials?


  3. Run controlled tests

    Use many trials, randomized inputs, and baseline human performance for comparison. Avoid relying on single examples.


  4. Design anti-cheat and ecological validity rules

    Prevent trivial hacks that exploit data leakage or hard-coded prompts. Ensure tasks resemble real-world use cases.


  5. Report failure modes

    Document where the system fails and whether failures are qualitatively different from human errors.


  6. Be transparent about training data and prompts

    If a model solved a problem because it memorized the answer from an overlapping dataset, treat that differently than genuine generalization.


Using this checklist, you can produce reporting and analysis that belongs in reputable outlets, including Canadian Technology Magazine. It will also reduce the chance you fall into the justism fallacy or the broken-clock anecdote trap.

Who Is at Risk and Why You Should Care About Averages, Not Just Elites

Conversations about job displacement are often framed in extremes: either machines will replace everyone or they will replace no one. Reality is about percentages and averages. If a technology can do the work of 50 percent of professionals in a field, that has massive social and economic effects. You do not need the technology to match the top 1 percent of practitioners to create disruption.

When evaluating disruption, look at average human performance and consumer behavior. If an AI can satisfy a majority of customer needs at a lower price or faster turnaround, adoption will follow. Articles and analysis in Canadian Technology Magazine should therefore emphasize comparative metrics: AI vs median human output, not AI vs superstar human output.

Why “Introspection” and Some New Research Matter

Recent research has shown that language models can generate internal reasoning traces, or “chain of thought,” that look like introspective reasoning. One paper described “signs of introspection” in LLMs. That does not mean LLMs are identical to human introspective thought, but it does show that they can produce intermediate reasoning steps and self-reflective-like outputs that help solve complex tasks.

These results are testable and valuable. They should temper blanket dismissals like “it is just token prediction.” If a system produces steps that allow it to solve novel problems, we should treat those steps as functional reasoning, even if their substrate differs from biological brains. For the audience of Canadian Technology Magazine, the important takeaway is this: method matters more than metaphysics. Focus on capabilities and impacts, not on whether a system matches a philosopher’s definition of mind.

Don’t Lie to Yourself: Cognitive Hygiene in AI Assessment

One final thought: human judgment is fallible, especially when existential fears are involved. Quoting Dostoevsky is dramatic but apt: do not lie to yourself. That means acknowledging bias, peer pressure, and emotional investment when forming opinions about AI. It also means taking a methodical, evidence-first stance when reporting or making decisions.

If you are an artist, a musician, or a software developer, of course you should evaluate how AI affects your profession. But do so with clear tests and economic thinking. Ask: can AI do the portion of the task that matters to consumers? At what cost? How often does it succeed? Those are the questions that should guide business strategy and policy making—and they are the ones readers of Canadian Technology Magazine need answered.

My Working Definition and Final Take

Here is a succinct working definition that helps clarify the debate. Reason is the power of the mind to think, understand, and form judgments by the process of logic. Logic can be understood as the process of reasoning. The two are circle-like in definition, but operationally we can break the loop by focusing on observable behavior: did a system perform tasks that require multi-step, coherent, goal-directed inference?

By that operational standard, some LLMs already demonstrate elements of reasoning on particular classes of tasks. They are not flawless, they have limitations, and they sometimes fail in ways humans would not. But dismissing them wholesale because of their training method or because of isolated errors is a mistake.

For technology professionals, business leaders, and readers of Canadian Technology Magazine, the right posture is pragmatic skepticism: demand rigorous tests, track average performance against relevant baselines, and design policies and business responses that reflect measured abilities rather than moral panic.

What does “reason” mean when applied to LLMs?

Reason, in practical terms, is the ability to perform multi-step, goal-directed inference using logic-like steps. For LLMs, this is evaluated by measurable tasks: planning, causal inference, problem solving, and coherent explanation generation under controlled conditions.

Does the fact that LLMs are trained on pattern prediction mean they cannot truly reason?

No. Training method alone does not determine capability. Many systems achieve complex behaviors through different substrates. The correct test is observable performance on reasoning tasks, not a priori assumptions about training mechanisms.

How should I evaluate claims that LLMs “can’t understand”?

Demand clear, falsifiable tests. Ask what specific behavior would change the claimant’s mind. Evaluate performance against baseline human abilities and use controlled, repeatable experiments instead of anecdotal failures.

Will AI replace creative professionals like musicians and artists?

AI can produce creative outputs that, in blind tests, are often indistinguishable from human-created works for average consumers. That suggests economic disruption is possible, especially in commoditized segments. High-end creative niches and personal, live performances retain unique value, but many middle-market roles face pressure.

Roles that involve repeatable information processing, routine writing, customer interaction, basic programming, and certain creative production tasks are at higher near-term risk. Jobs requiring physical presence, deep lived experience, or real-time emotional charisma are harder to automate immediately.

How should businesses prepare?

Start by measuring tasks, not job titles. Identify which tasks in a role are automatable and which require human judgment. Pilot AI tools for specific workflows, measure outcomes, and reskill staff toward higher-value tasks that leverage uniquely human skills.

Can we trust LLM outputs for critical decisions?

Not without safeguards. Use LLMs as tools to augment human decision makers. Implement verification, provenance checks, human-in-the-loop review, and robust auditing for high-stakes contexts like healthcare, law, or safety-critical systems.

How does bias affect evaluations of AI capabilities?

Bias influences both human judges and AI systems. Humans often downgrade AI outputs if they know the origin. Researchers should use blind tests and objective metrics to reduce subjective bias in evaluations.

Where can I read more balanced coverage about AI capabilities and business impact?

Look for outlets and analyses that emphasize empirical evaluations, double-blind studies, and careful economic framing—publications that aim to bridge technology and business concerns. For a business audience, consider following platforms that provide actionable testing frameworks and market analysis about AI adoption trends.

Conclusion

Debates about whether LLMs can reason often devolve into slogans, fear, or protective rhetoric. A better conversation is possible and necessary: define reasoning tasks, build tests, gather data, and evaluate outcomes. This is the approach readers of Canadian Technology Magazine deserve and need. Machines change what is possible; how we respond—by testing, measuring, and adapting—determines whether that change is a threat or an opportunity.

If you are making business decisions, crafting policy, or just curious about the technology, aim for clear criteria and empirical evidence. Do not fall into the trap of moving goalposts or the broken-clock fallacy. And above all, do not lie to yourself about what the data show. That intellectual discipline will serve you far better than any rhetorical victory in a comment thread.

 

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