journalism

The AI backlash is real. Newsrooms are caught in the middle.

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A New York Times/Siena poll released this week found that 35 percent of Americans think artificial intelligence is “mostly bad.” Only 16 percent think it’s “mostly good.”

Sit with that for a second. The technology that every media company is rushing to implement — the thing your leadership is mandating, your vendors are pitching and your competitors are announcing — is viewed unfavorably by more than a third of the country.

When the Hard Fork hosts put that number to Google CEO Sundar Pichai this week, he didn’t spin it. He said people’s anxiety is legitimate, that humans aren’t built to process change at this pace, and that the industry has more work to do.

He’s right. But he’s also the CEO of a company that sells AI products. His problem is winning back public trust. Your problem, if you run a newsroom, is more complicated than that.

News organizations are caught in a specific bind that Pichai doesn’t have to navigate.

You are simultaneously trying to cover AI fairly — to report on job displacement, on bias, on regulatory battles, on the legitimate concerns that 35 percent of your audience holds — while also implementing the technology inside your own walls. You are asking your staff to use tools that many of them distrust, to produce work for an audience that’s increasingly skeptical of it, and to do it without losing the credibility that makes your journalism worth anything in the first place.

That is a tension most AI coverage glosses over. The trade publications write about the tools. The think pieces write about the ethics. Almost nobody writes about what it actually feels like to be a news director in 2026 trying to thread that needle every day.

I’ve been in those rooms. Here’s what I’ve seen work and what hasn’t.

The newsrooms that are getting this wrong treat AI implementation as a technology project. They bring in a vendor, run a pilot, announce the tool in a staff meeting and wonder why adoption is slow and morale is worse. They skip the conversation that actually needs to happen first: what are we afraid of, and what are we trying to protect?

The newsrooms getting it right treat it as a journalism question. What does this tool let us do that serves our audience better? What does it not touch — and why? Where is the line between AI-assisted work and AI-generated work, and how do we explain that line to our audience clearly?

The second approach takes longer up front. It involves harder conversations. But it produces something the first approach never does: staff who are invested in the outcome rather than waiting to be replaced by it.

Pichai used an example in the interview that stuck with me. He described asking an AI agent to look ahead at his calendar and color-code meetings by category. It took seconds. It probably would have taken him or an assistant 20 minutes to do manually.

That is the version of AI that most newsroom staff can accept — a tool that handles the administrative noise so they can do more actual journalism. The version they can’t accept, and won’t, is one that feels like it’s being used to justify cutting the people doing the journalism.

The distinction isn’t about the technology. It’s about the intent, and whether leadership communicates it clearly.

The 35 percent number isn’t going to fix itself. Public trust in AI will shift based on whether the technology demonstrably improves people’s lives — and whether institutions like news organizations handle it in ways their audiences can respect.

That means being transparent about where and how you’re using it. It means not hiding AI-assisted work inside a byline that implies otherwise. It means giving your staff a voice in how it gets implemented, not just a training session after the decision is already made.

Pichai said anxiety is healthy; in a democracy, you want citizens engaged and making their preferences known. That’s true. It’s also true inside your newsroom. The journalists who are worried about what AI means for their jobs — they’re not wrong to be worried. The news directors who listen to that and respond thoughtfully will build something durable. The ones who don’t will have a different set of problems in about 18 months.

Earned, Not Assumed: Why AI Augmentation Depends on Who Gets Access to Training

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The global labor market is undergoing a structural transformation driven in significant part by artificial intelligence. The World Economic Forum Future of Jobs Report 2025 projects that 92 million roles may be displaced globally by 2030, while 170 million new roles could emerge. However, AI’s impact is not best understood as simply job creation or destruction. From a task-based labor economics perspective, technological change operates at the level of discrete tasks rather than entire occupations. AI decomposes jobs into component activities, automating those that are routine, codifiable, and data-intensive, while recomposing remaining work around judgment, oversight, and complex problem-solving. In this sense, AI is less about eliminating work than about reorganizing how value is created within occupations—reshaping skill premiums and redefining the boundaries between human and machine contributions.

Similarly, McKinsey & Company stresses that AI is reshaping organizational design and career paths, moving beyond marginal productivity gains. They highlight the concept of “reconfiguring work,” where organizations build “minimum viable organizations”—a lean, technology-amplified operating model designed around AI-native workflows. In this model, AI handles an increasing share of structured and repeatable tasks, while human work shifts toward oversight, judgment-intensive functions, and areas requiring contextual interpretation.

Broadcast and digital journalism, a field I have navigated for over three decades, serves as an illustrative microcosm for these shifts. It demonstrates both the rapid creation of technical roles and the friction of workforce adaptation. A separate analysis from Microsoft Research examining occupational exposure to generative AI suggests that journalism-related roles involve tasks highly susceptible to automation, particularly drafting, summarization, and transcription. Rather than forecasting wholesale job elimination, the study highlights task-level vulnerability, indicating that portions of newsroom workflows may be restructured as AI systems assume responsibility for routine content production.

Emerging AI Roles in Journalism

Recent studies show that over 65% of U.S. newsrooms have integrated AI, experimenting with or deploying AI tools in at least one workflow, driving the creation of highly specific roles. Digital platforms leverage algorithms for personalization and fact-checking, creating a critical need for AI Ethics Editors to ensure integrity. Broadcasters utilizing AI for transcription and translation are hiring Automated Content Managers to handle these dynamic workflows. Meanwhile, the ability to scan massive datasets for market trends has elevated the Data Journalist from a niche specialty to a core newsroom necessity.

Evolving Demands for AI Skills

Journalists now require technical fluency—prompt engineering, data literacy, and understanding LLM constraints—alongside traditional skills. As AI automates copy generation, uniquely human skills command a premium. Empathy in interviewing, high-level investigative intuition, and ethical decision-making are becoming more vital than ever.

Research by Northwestern professor Nick Diakopoulos highlights this shift. While editorial job postings declined significantly post-ChatGPT (from 28,566 to 18,156), listings requiring AI skills tripled. While the timing coincides with the emergence of generative AI, the contraction in editorial roles likely reflects a confluence of technological, economic, and platform-market forces rather than direct substitution alone. Still, the growing listing of AI roles reveals four emerging roles: “AI-doers” (building tools), “AI-users” (applying tools), “AI-strategizers” (planning), and “AI-reporters” (covering AI). Crucially, demand also surged for human capabilities like ethics, critical thinking, and fact-checking—skills that directly complement AI’s weaknesses.

The Challenges of Upskilling and Reskilling

Despite the clear need for AI fluency, the media industry faces significant structural hurdles in reskilling a workforce already stretched thin by the 24-hour news cycle:

  • The Hidden Costs of Training: The financial burden extends beyond software licensing to implementation time, workflow redesign, and productivity disruption. Many newsrooms accumulate what can be described as “integration debt”—the deferred organizational costs that arise when AI systems are layered onto legacy processes without structural redesign. When tools are adopted faster than governance standards, editorial protocols, and workforce capabilities evolve, inefficiencies compound, requiring future investments in retraining, oversight, and workflow correction.
  • The New Digital Divide: A two-speed transformation is emerging. Large national networks possess the capital to build proprietary AI infrastructure and dedicated oversight teams, while under-resourced local newsrooms lack comparable investment capacity. This widening “AI divide” risks exacerbating existing inequalities in reporting depth, investigative capacity, and technological resilience across the industry.
  • Union Resistance: Automation anxiety is increasingly manifesting in labor negotiations. Organizations such as The NewsGuild and the Writers Guild of America have pushed for contractual guardrails governing AI deployment, seeking transparency, attribution protections, and limits on automation. While these efforts aim to protect workers, negotiations can slow implementation timelines as management and labor debate control, accountability, and long-term job security.
  • The Curriculum–Skill Gap: AI capabilities are evolving more rapidly than formal training programs. While academic institutions often emphasize ethical frameworks and media theory, employers increasingly seek operational competencies such as prompt design, model evaluation, and workflow integration. This misalignment leaves mid-career professionals navigating a fragmented retraining landscape without standardized pathways.

Conclusion

The same newsrooms introducing AI ethics editors and data journalists are struggling to train the veteran reporters and producers sitting beside them. The dominant pattern currently appears to be augmentation combined with role hybridization, but optimism must be earned, not assumed. The tripling of AI skill requirements signals opportunity, but journalists who fail to upskill risk being quietly filtered out of the hiring market. Augmentation is only a win for those with access to training.

AI integration represents not incremental optimization but an active restructuring of value creation. Newsrooms cannot rely solely on external hiring to fill emerging skill gaps; as McKinsey & Company notes, recruitment alone is neither cost-efficient nor strategically sustainable. Instead, organizations must invest in disciplined strategic workforce planning while cultivating superagency—an organizational condition in which journalists possess both the technical literacy and institutional authority to actively direct, interrogate, and refine AI systems within their domains. By strengthening human-in-the-loop capabilities such as ethical reasoning, investigative judgment, and contextual analysis, newsrooms can ensure that AI enhances journalistic rigor rather than eroding it.

References

Corden, Jez. “Microsoft reveals 40 jobs about to be destroyed by (and safe from) AI.” Windows Central. https://www.windowscentral.com/artificial-intelligence/microsoft-reveals-40-jobs-about-to-be-destroyed-by-and-safe-from-ai

Diakopoulos, Nick. “The Impact of Generative AI on Journalistic Labor.” Generative AI in the Newsroom. https://generative-ai-newsroom.com/the-impact-of-generative-ai-on-journalistic-labor-e87a6c333245

Fu, Angela. “As AI enters newsrooms, unions push for worker protections.” Poynter. https://www.poynter.org/business-work/2023/artificial-intelligence-writers-guild-unions-journalism-jobs/

McKinsey & Company. “The critical role of strategic workforce planning in the age of AI.” McKinsey People & Organizational Performance Practice, February 2025.

McKinsey & Company. “Generative AI and the future of work in America.” McKinsey Website. https://www.mckinsey.com/mgi/our-research/generative-ai-and-the-future-of-work-in-america

Research.com. “2026 AI, Automation, and the Future of Journalism Degree Careers.” Research.com. https://research.com/advice/ai-automation-and-the-future-of-journalism-degree-careers

World Economic Forum. “Future of Jobs Report 2025.” World Economic Forum Website. https://reports.weforum.org/docs/WEF_Future_of_Jobs_Report_2025.pdf

AI Ethics in Journalism: Beyond Human Baseline

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The “human baseline” approach posits that the ethical success of artificial intelligence is achieved when its decision-making mirrors or marginally improves upon that of a competent human.  In the classic “trolley problem,” this implies that if an AI can consistently choose the “lesser of two evils” with more precision than a panicked human, it has cleared the ethical bar.

However, as the media and journalism industry increasingly integrates generative AI and automated editorial systems, it is becoming clear that a “slightly better than human” standard is insufficient. In the context of information dissemination, a human-level baseline for AI is not a gold standard; it is a liability.

While comparing AI to the human baseline in moral dilemmas reveals the machine’s capacity for consistency, it fails to account for the unique accountability required in journalism.  

Because audiences in 2026 are caught in a “breaking verification” crisis where trust is the ultimate currency, an AI that is merely “slightly better” than a biased human is ethically insufficient. To be truly ethical, AI in media must move beyond mimicking human choice to provide a level of transparency and evidentiary rigor that transcends a journalist’s capability.

Our newsrooms are facing a speed-versus-verification dilemma.   The human baseline for a journalist is breaking the story vs. being 100% accurate.   AI’s logic is fundamentally different.   AI shifts control from individual journalists to automated systems optimized for engagement and scalability.   Therefore, an AI that performs ‘slightly better’ than a journalist at producing content quickly may be ethically inferior if its underlying logic lacks the transparency and evidentiary rigor that defines journalistic integrity.

Because so much information is published in many ways across many platforms, audiences are having a difficult time distinguishing fact from fiction. 

“‘Breaking verification’ will replace ‘breaking news’ in 2026, and trust will decide who survives,” according to Vinay Sarawagi, co-founder and CEO of The Media GCC.

Audiences need to see evidence and sources to back up what they see online, because seeing is no longer believing.   If AI only does as well as humans at spotting fakes, it’s not enough. To solve the trust crisis, the AI must be exponentially better at citing sources.

In 2005, Wallach and Allen argued that the principal goal of the discipline of artificial morality is to design artificial agents to act as if they are moral agents. They distinguish between operational morality, in which an AI simply follows pre-programmed human safety rules, and functional morality, in which a system can independently navigate moral dilemmas.  In journalism, an AI that merely mirrors an editor’s baseline choices is operating within a limited framework.   If the media is to serve the public’s best interests, a journalist AI must move toward a functional morality that transcends basic human instinct and provides the transparency and accountability the public expects.

From a strategic standpoint, “slightly better” is a recipe for disaster.   If AI-generated content results in a libel suit or negatively impacts a company’s stock price, the defense that AI is slightly more accurate than an average human is a losing argument.  As the media shifts into what is being termed the ‘Answer Economy’, the traditional value proposition of a newsroom is being disrupted. When AI models synthesize reports into a single summary, the value of a news organization is no longer just the ‘answer’ or the scoop itself, but the auditable trail of evidence that allows that answer to be verified (Seo Ai Club, 2026). If an AI only meets the human baseline for producing a plausible-sounding summary without providing this rigorous, machine-readable proof of its sources, it fails to meet the ethical demands of a 2026 audience.

Note: This is an essay originally written for a course on AI and business strategy at Johns Hopkins University.

References

Wallach, Wendell and Allen, Colin. “Artificial Morality: Top-down, Bottom-up, and Hybrid Approaches.” Ethics and Information Technology volume 7, no. issue 3 (September 2005): 149-155. https://link.springer.com/article/10.1007/s10676-006-0004-4.

Li, Haoran et al. “Artificial Intelligence and Journalistic Ethics: A Comparative Analysis.” Journal of Journalism and Media volume 6, no. issue 3 (August 2025): 105. https://www.mdpi.com/2673-5172/6/3/105.

Mee, S. et al. “Moral judgments of human vs. AI agents in moral dilemmas.” Scientific Reports volume 13, no. issue 1 (February 2023). https://pmc.ncbi.nlm.nih.gov/articles/PMC9951994/.

Simon, Felix. “How AI reshapes editorial authority in journalism.” Digital Content Next (June 2025). 

Reuters Institute. “How will AI reshape the news in 2026? Forecasts by 17 experts around the world.” Reuters Institute for the Study of Journalism (January 2025). 

Seo Ai Club. “The Answer Economy: A Comprehensive Analysis of Answer Engine Optimization Tracking Software and Strategic Market Leadership.” Seo Ai Club (January 2025). 

The Media Industry is not dead

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This New York Times article about how the media industry is losing its future is pretty doom and gloom, but I’d counter that media industry revenue continues to grow and hit all-time highs year after year. It’s certainly more competitive than ever, but I’d rather have an industry with a wealth of opportunities than one with only a few. And how amazing is it to be alive and working in an industry fueled by amazing technological change? Look how far we have come in such a short amount of time!

The article reminded me of Bob Iger’s book (paid link), his thoughts on disruption, and why many businesses have failed. He wrote, “Courage. The foundation of risk-taking is courage, and in ever-changing, disrupted businesses, risk-taking is essential, innovation is vital, and true innovation occurs only when people have courage. This is true of acquisitions, investments, and capital allocations, and it particularly applies to creative decisions. Fear of failure destroys creativity.”

We can’t be afraid of the future. Change may be disruptive to how things are, but how we adapt makes growth possible.