A Media Veteran Says the AI Debate Is Boring, and That’s the Point

John R. MacArthur, an editor and longtime media critic, has a blunt message for anyone glued to the latest artificial intelligence hype cycle: he’s bored.

In a sharply worded column published byLe Devoir, a major French-language newspaper in Quebec, MacArthur argues the problem isn’t that AI doesn’t matter. It’s that the public conversation around it has become a repetitive performance, big promises, big fears, and not enough hard reporting on who holds the power, who profits, and who pays the price.

His complaint lands at a moment when generative AI is everywhere, inside office software, customer service chatbots, classrooms, ad targeting, and newsrooms. The result, he suggests, is a nonstop stream of “AI” stories that often feel interchangeable, even when the stakes are real.

MacArthur’s target: the same recycled arguments, dressed up as breaking news

MacArthur isn’t dismissing AI as a fad. He’s attacking the way it’s discussed, an endless loop of buzzwords like “revolution,” “disruption,” and “the future,” with little clarity about what’s actually being deployed and where.

In his telling, boredom becomes a tool: a way to call out how often the debate substitutes speculation for verification. Instead of digging into industrial decisions, regulatory standards, legal liability, or workplace impacts, too much coverage defaults to sweeping predictions and familiar panic.

He also takes aim at the media machine itself. When AI becomes the automatic angle for every business story, education story, or culture story, it can flatten the reporting, turning complex questions into trend pieces and replacing investigation with hot takes.

Why a Quebec newspaper ran a column that pokes the AI “inevitability” narrative

Le Devoirpublishing the piece places it squarely in the opinion-and-ideas lane, not the product-launch lane. The column’s core argument is that AI has become a ritual topic: a new wave of op-eds, warnings, and corporate announcements, followed by another wave of commentary, without a matching increase in public understanding.

MacArthur’s broader point is about how consensus gets manufactured. In many workplaces and institutions, AI is framed as unavoidable, something you adopt because everyone else is adopting it. That framing can squeeze out basic governance questions: Who decided? Under what rules? With what oversight?

He also argues that “AI” has become a catch-all label. It can mean everything from decision-support software to generative models that spit out text, images, and code. Lumping wildly different tools into one magic term makes it easier to inflate claims, and harder for the public to pin down what’s real.

Follow the money, and the power, behind the AI boom

MacArthur’s underlying argument is simple: a topic can be hugely important and still be badly covered. He wants the focus to shift away from breathless debates about whether machines are “almost human” and toward the structures behind the technology, supply chains, business incentives, and the players who benefit from constant attention.

Companies selling AI systems have a clear interest in selling inevitability. If the public accepts that AI is the next industrial revolution no matter what, adoption gets easier, funding flows faster, and social resistance looks backward.

MacArthur pushes back on the idea that AI is some natural force. It’s a product of corporate strategy, investment choices, public policy, and regulation, or the lack of it.

Jobs, accountability, and the questions that keep getting shoved aside

One of the most concrete gaps, he argues, is labor. AI is routinely marketed as “productivity,” a word that sounds neutral until you ask who gets the gains and who absorbs the disruption. Productivity can mean reshuffled work, intensified monitoring, deskilling, layoffs, or all of the above.

Then there’s responsibility. When an AI system makes a harmful mistake, amplifies bias, violates privacy, or causes financial damage, who’s on the hook? In a debate dominated by awe or alarm, the legal and regulatory details often get treated as an afterthought.

But once these tools are deployed at scale, those details become the story: standards, audits, transparency, data access, and enforcement. MacArthur’s boredom is, in effect, an accusation that the public conversation keeps dodging the parts that matter most.

AI fatigue in 2026: too much noise, not enough clarity

MacArthur’s column also reads like a diagnosis of information overload. By 2026, generative AI isn’t a niche fascination, it’s a permanent feature of modern life, and the volume of AI-related content has exploded: corporate PR, analyst notes, government statements, think pieces, and endless “here’s what’s coming next” predictions.

The fatigue isn’t just about quantity. It’s about how hard it is for ordinary readers to sort truth from theater. Flashy demos collide with real-world limitations, and the public gets whiplash, AI is either a miracle or a menace, depending on the headline.

MacArthur also criticizes the way the debate gets forced into two camps, pro-innovation versus anti-innovation, because it makes for easy TV and easy columns. But it blocks the harder, more useful conversations about data quality, subcontracted labor, system audits, and the gap between marketing claims and measurable performance.

His implied challenge to journalists is straightforward: stop treating AI like destiny and start covering it like infrastructure. Who’s buying it? Who’s requiring it? Who becomes dependent on it? Who eats the costs, and who pockets the rewards?