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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

Character vs Reputation: The True Measure of Success

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I recently listened to an episode of Freakonomics Radio titled “If You’re Not Cheating, You’re Not Trying,” featuring an interview with disgraced cyclist Floyd Landis. The conversation eventually turned to John Wooden’s famous maxim: “Be more concerned with your character than your reputation, because your character is what you really are, while your reputation is merely what others think you are.”

Landis, perhaps unsurprisingly for a man whose career was defined by a massive deception, rejected Wooden’s idealism. He argued that in the “real world,” reputation is the only thing that functions. It’s the currency that buys you the contract, the sponsorship, and the adoration. To Landis, character is just a consolation prize you cling to once your reputation has been torched.

I understand his cynicism. But I fundamentally disagree with it.

Landis views reputation and character as two separate assets you can trade, like stocks. But Wooden’s point was deeper: Reputation is merely the shadow cast by character. You can manipulate the shadow for a while – stand in the right light, distort the angle, make yourself look larger than you are – but eventually, the sun moves. The shadow always snaps back to the reality of the object casting it.

In my media career, I’ve seen this physics play out repeatedly. We live in an industry obsessed with the “shadow” – the ratings, the viral potential, the race to be first. I’m certainly not perfect; I’ve made mistakes in my career. But I’ve learned that the “reputation” of a news organization isn’t built on its speed; it’s built on its credibility. It’s built on the boring, invisible machinery of character: fact-checking, sourcing, and the refusal to cut corners when no one is watching.

A journalist can fake their way to a scoop once. They can build a reputation for being “first.” But if that reputation isn’t grounded in the character trait of accuracy, the fall is inevitable. When the correction comes – and it always does – the reputation doesn’t just dip; it evaporates.

Consider the case of Janet Cooke, a Washington Post writer whose heartbreaking profile of an 8-year-old heroin addict won a Pulitzer Prize. The unraveling of her reputation began, ironically, with a celebration of it.

Her former employer, the Toledo Blade, initially rushed to publish a tribute to their former staffer. But the tone shifted when editors compared the Associated Press biography—based on Cooke’s own resume—against their internal personnel files. While Cooke claimed to be a magna cum laude Vassar graduate with a master’s degree, the Blade’s records told the truth: she had only attended Vassar for a year and held a standard bachelor’s degree. Because the character didn’t match the reputation, the entire structure collapsed. Her prize-winning article, “Jimmy’s World,” was exposed as a complete lie, and the Pulitzer was returned.

I’m seeing a similar tension now as I study the business strategy and ethics of Artificial Intelligence. The temptation in the AI space is to let the “reputation” of the technology—the hype, the valuation, the promise of an AI future—outpace the “character” of the build (safety, bias, alignment).

Landis would argue that we should ride the hype wave because “that’s how the world treats you.” But history suggests that tech bubbles built on reputation without underlying substance always burst. The companies that last are the ones where the internal reality matches the external promise.

Warren Buffett famously said, “It takes 20 years to build a reputation and five minutes to ruin it.” Unlike Landis, Buffett doesn’t see reputation as a mask to wear; he sees it as a fragile byproduct of integrity.

Bob Iger, the retiring CEO of Disney, reinforces this in his memoir, The Ride of a Lifetime: “True authority and true leadership come from knowing who you are and not pretending to be anything else.” For Iger, character and decency are not merely “soft skills,” but strategic advantages that define a company’s success.

Floyd Landis believes he was punished for playing the game. I would argue he was punished for mistaking the shadow for the man. Ultimately, the spotlight always falls on a person’s character, not their reputation.

The ABC Solution

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I don’t know about you, but I have a hard time finding what to watch.   My nightly routine is usually starting with the cable guide to see what’s on, and then I’ll switch to Apple TV and nose around Netflix, Hulu or Prime or get distracted by something on YouTube.   If I can’t find anything, then I’ll do a Google – you know, what’s on TV tonight?  Or what can I binge watch?   And usually, I can narrow it down to a couple of choices, and if it’s a series, I’ll start binge-watching so I don’t need to do this whole process again at least for a few days.

If you are like me, you have a lot of haystacks to search to find the needle.

And that’s just TV.

Think about the 300 hours of video uploaded to YouTube every minute or the 5 billion videos watched on YouTube every day.

And then there are another 100 million hours of video watched daily on Facebook.

Not to forget, Twitter, Twitch, TikTok and the Gram.  Or how about LinkedIn.

So as a content creator or brand marketer, how is a consumer supposed to find your content in all these haystacks?

For this complex problem, I offer a relatively simple A-B-C solution.

With due respect to Alec Baldwin’s character in Glengarry Glen Ross, I’m not saying “Always Be Closing.”

I’m talking about “Attention, Brand Value, and Call to Action.”

A successful digital video is one that captures the audience’s attention, delivers brand value to that audience, and calls upon the viewer to act.    Action not in the form of a like, comment of share, but to say to themselves, “I learned something new and I need to try this product.”

To prove my theory, I offer three examples, but you can find many following the same basic formula.

Is the content memorable or shareworthy like Blendtek’s “Will It Blend” series?

Phinally! The Will It Blend? you’ve all been waiting phor! Watch Tom blend two phablets including the brand new iPhone 6 Plus and Samsung Galaxy Note 3. It’s…

Is the content something that the consumer associates with your brand’s value like RedBull’s live extreme sports coverage or action You Tube series like “Who Is JOB?

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Does the content inspire action on the part of the consumer like Krispy Kreme?   One of the many areas where Krispy Kreme excels is using influencers to market their product.   A year ago, when KK opened its first store in Ireland, Krispy Kreme Blanchardstown supplied the doughnuts that the TRY channel used to make an “Irish People Try Krispy Kreme Donuts For the First Time” video.   It’s their second most viewed video ever – just over 5 million.   And the Blanchardstown store is the most successful international store opening with 600,000 customers and 6.6 million donuts sold.

If you want to stand out in the crowd, begin by thinking about the ABC Solution.

Attention.

Brand Value.

Call to Action.

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