China Is Using AI to Build Media. America Is Using It to Cut Costs.

Estimated read time: 7 minutes

China Is Using AI to Build Media. America Is Using It to Cut Costs.

The American media business has spent much of the AI era asking a defensive question: What jobs will this technology replace?

China appears to be asking a different one: What industries can this technology help expand?

That difference may help explain one of the more striking contrasts emerging in global media. According to the figures cited in the material above, China’s media industry is showing evidence of growth tied to AI adoption, while America’s broadcast sector has suffered deep job losses and wage pressure. The divide is not simply about who has better software or more powerful chips. It is about whether AI is being treated as a coordinated growth strategy or as a disruptive force left to crash through the business on its own.

For local media salespeople, ad agency professionals and station operators, that is not an abstract geopolitical argument. It is a practical business lesson.

If AI is introduced only as a way to do more with fewer people, it becomes a fear story. If it is introduced as a way to create better products, improve workflow, extend reach and build new revenue, it becomes a growth story.

Right now, much of American local media is still caught between the two.

The data points in the story are hard to ignore. U.S. broadcasting reportedly lost 36.2% of its jobs and 19.5% of its real wages between May 2022 and May 2024. Meanwhile, research cited in the article found a statistically significant positive relationship between generative AI adoption and media industry growth in China. China’s 15th Five-Year Plan reportedly mentions AI more than 50 times and aims for 90% AI adoption across industries by 2030. More than 600 million Chinese consumers were said to be using generative AI as of December 2025.

Whether one accepts every figure at face value, the strategic contrast is still unmistakable.

China appears to have integrated AI into a broad modernization effort. The United States, by contrast, has largely allowed AI to arrive as a market event — heavily funded, widely discussed, often feared and unevenly deployed.

That matters because local media does not live in theory. It lives in daily operations.

A radio station needs copy, production, promotions, social content and sales materials. A local TV newsroom needs scripts, clips, transcripts, weather graphics, video versions, digital summaries and social extensions. A cable operation needs localized messaging, audience targeting and efficient creative production. A newspaper or magazine needs article packaging, newsletters, sales collateral and audience insights. An outdoor company needs sharper creative testing, market intelligence and category prospecting. A digital publisher or agency needs faster campaign set-up, better content workflows, stronger reporting and more relevant messaging.

In each case, AI can either remove friction or deepen anxiety.

The difference comes down to management.

The article’s baseball metaphor is a useful one. America’s AI strategy is often described as swinging for the fences — betting heavily on frontier systems, artificial general intelligence and massive breakthroughs. China, in contrast, is portrayed as playing “Moneyball,” using AI for practical, compounding gains across daily life and industry.

Local media would be wise to study the second model.

Most local media companies do not need a moonshot. They need a better morning show prep process. A faster sales research tool. Cleaner ad copy generation. Smarter headline testing. Better local business prospecting. Easier translation and localization. Automated clipping. Quicker production workflows. Better use of transcripts. Sharper audience segmentation. More efficient repurposing of content across platforms.

In other words, they need singles and doubles.

That may sound less glamorous than grand declarations about the future of intelligence. It is also much more useful to a station manager in Scranton, a broadcaster in Phoenix, a radio cluster in Des Moines or a local publisher in Charlotte.

For television, the implications are especially important. Local TV remains one of the strongest vehicles for trust and local presence. But it is also labor-intensive. AI can help stations scale local value without hollowing out the product — automating transcripts, summaries, captioning, clipping, translation, sponsorship integration, archive mining and personalized digital extensions. If used thoughtfully, it can allow a local television operation to produce more useful content for more screens. If used carelessly, it can turn a trusted station into a thinner, more generic version of itself.

Radio faces a similar choice. AI can help with spec spots, script drafts, client research, category analysis, promo writing, podcast summaries and digital companion content. That can make a small team more capable. But radio’s edge is still human: personality, companionship, local knowledge, humor, urgency and credibility. If AI strips those away in pursuit of efficiency, the station saves time but weakens the very asset it sells.

Cable and connected TV businesses can use AI to improve targeting, version creative by geography, and help advertisers produce more tailored messages. But those gains only matter if the seller is also helping the advertiser think strategically. More personalized inventory with more generic creative is not progress. It is just faster mediocrity.

Print and local publishing may have a surprising advantage in this environment. Newspapers, magazines and local digital publishers already operate in a world of content packaging and information flow. AI can help them extract more value from what they produce: article summaries, newsletter customization, archive resurfacing, advertiser insight reports, SEO and answer-engine optimization, local business directories, and stronger sales presentations built around audience behavior. Used properly, AI can make a newsroom or publishing operation more extendable without compromising its editorial core.

Outdoor may seem less directly exposed, but even billboard companies can benefit from AI through smarter prospecting, category analysis, creative testing, route and audience insights, proposal automation and better use of local market data. The seller who walks into a meeting with smarter intelligence about likely spenders, competitive patterns and customer behavior becomes more valuable, no matter what medium is being sold.

Digital agencies, meanwhile, sit at the center of the storm. They already know clients expect more output, faster turnaround and more measurement. AI helps deliver that. But it also raises the risk of sameness. If every agency is generating campaigns, social posts and display concepts from the same tools, then differentiation shifts back to strategy, local knowledge and judgment. That is good news for serious agencies and bad news for agencies that were living on execution alone.

This is where the China-versus-America comparison becomes useful, not ideological.

Local media companies do not need China’s politics. They do need something closer to its operational seriousness.

The article notes that China’s media organizations adopted AI within a larger framework that defined the purpose, built infrastructure and connected the technology to practical outcomes. Many U.S. media companies have done the opposite. They have let employees discover AI on their own, let departments experiment in isolation, or let vendors define the agenda. That produces scattered gains but no coherent advantage.

For local media, the right question is not, “Should we use AI?”

That question has already been answered.

The better questions are:

What workflows should be automated?
What work should remain unmistakably human?
What products can AI help us build faster?
What new revenue can AI help unlock?
What client services can we now deliver at a higher level?
How do we retrain our people so AI extends them instead of replacing them?

That last question matters most.

The AI transition has already created enough workforce anxiety in media. If employees believe AI is simply management’s latest excuse to cut headcount, adoption will be cautious, resentful and politically fraught. If employees see it as a tool to remove drudgery, improve output and give them more time for higher-value work, adoption becomes more constructive.

Local media sellers should pay close attention to that distinction because it affects client value too.

Advertisers are not buying your back office. They are buying your ability to help them grow.

A local advertiser does not care that your team saved an hour writing copy. The advertiser cares that the message got sharper, the turnaround got faster, the proposal got smarter, the category insight got better and the campaign performed more effectively. AI only matters in the marketplace when it improves what the client experiences.

That is why the article’s final recommendation — essentially calling for a local-media version of a five-year AI plan — is exactly right.

Every local media company should have one.

Not a 60-page consulting deck. Not a collection of buzzwords. A real working plan.

It should specify:

• which workflows to automate first
• which teams need retraining
• which audience products to develop
• which advertiser tools to introduce
• which revenue opportunities AI could unlock
• which editorial, legal and ethical boundaries must remain firm

For smaller operators, that may begin with a one-year AI audit. Where is AI already being used? Where is it saving time? Where is it creating risk? Where does human judgment remain essential? Which tasks are repetitive enough to automate? Which are too central to trust, creativity or client relationships to hand over casually?

This is where the future of local media will be decided.

Not in a lab.
Not in Washington.
Not in Beijing.

In the day-to-day decisions inside stations, agencies, publishers and sales departments.

China’s example suggests that AI can be a growth engine when leadership gives it direction. America’s media experience so far suggests that AI becomes a wrecking ball when strategy arrives after the disruption.

For local media sellers and agency professionals, the lesson is not to fear the technology or worship it. It is to organize around it before it organizes the business for you.

The local media companies that treat AI as a leadership challenge will build stronger products, smarter operations and more useful client services.

The ones that treat it as a loose collection of tools may save some money.

But they may also discover too late that efficiency, by itself, is not a strategy.