Tags containing: "AI"

Icons that represent podcasting innovation

3 podcast innovations that build audience and revenue

May 15, 2025 Esther Kezia Thorpe – Independent Media Reporter

The hype cycle is over for Podcasting. Now that reality has set in, it is exciting to see examples of publishers pushing the podcasting envelope – experimenting to drive innovation in the maturing market. Whether it’s using AI to expand to new audiences, or smart show bundling, there’s a lot of inspiration for those with their own podcasts or looking to launch.

Here are some impressive experiments and innovations in the podcast space:

AI translations

UK news publisher The Telegraph has been producing a podcast called Ukraine: The Latest daily since the start of the war. It has been downloaded over 100 million times since 2022, with episodes exploring military strategy, history, weaponry, economics, and more.

In February, to coincide with the third anniversary of the war in Ukraine, The Telegraph launched translated versions of the podcast in Ukrainian and Russian. This was made possible using an AI-powered voice cloning and translation model. This creates a “digital likeness” of the presenters, closely mimicking the rhythm and nuance of their voices.

“To be clear, this is AI helping to present our journalism, not produce it,” said Associate Editor and presenter Dominic Nicholls in an introductory video demonstrating the technology. The translations help reach those with restricted access to news on the war, as well as expats around the world.

The AI model was adapted by The Telegraph team in-house, before being refined by a native Ukrainian speaker fluent in Russian and English. The Telegraph emphasises that all episodes will be checked to ensure translation accuracy, as well as fine-tune speed and pacing.

Although there have been experiments with AI hosts and translation, this is the first example of a media company deploying it on this scale. For a daily podcast, especially one where the information need is so critical, this is a worthwhile investment that will help it reach the people it needs to.

High quality AI translations like this will be beyond the budgets of many publishers. But as the translational tools improve and become more accessible, using AI translation to reach new audiences is worth considering. Editorial oversight, however, is vital to maintain trust and quality.

Bundled podcast subscriptions

Publishers have been experimenting with paid podcasts for some time now, as paywalling technology has improved. From The Economist putting almost all its podcasts behind a paywall, to The Local offering premium versions of its free weekly podcast for members, there are many variations of paid audio strategies.

One that stood out was the DMG Media’s launch of The Crime Desk. The publisher had seen success with true crime podcasts like The Trial of Lucy Letby. Now, it has brought all podcasts under ‘The Trial’ brand into one subscription bundle.

The Crime Desk offers subscribers ad-free bonus episodes on global trial cases. It also includes access to the archive of more than 200 episodes covering everything from the Holly Willoughby kidnap plot to the Diddy trial. Subscribers will also get new series released in their entirety. However, free listeners will only be able to access one episode a week. The launch offer is £1.99 a month, or £19.99 annually.

“There will always be a free trial to air – we’ve got to have a shop window. It’s arguably a public service as well,” the Daily Mail’s head of podcasts Jamie East told Press Gazette. A soft launch phase “had seen subscriptions well into the thousands, and at a similar conversion rate to the podcast industry standard of 5%.”

Building a paid bundle around groups of podcast topics is viable for publishers that produce a wide range of podcasts or with strengths in specific subject areas. However, East noted that although they’ve had success elsewhere, that doesn’t necessarily mean a paywall is viable. “You can only really launch a subscription model around a hit. There’s no point otherwise,” he told Press Gazette. “It needs to be pretty bedded in before you can do it, or achieve such huge scale that it’s a no-brainer. We’ve not quite reached that with any of the other verticals.”

Reusing popular print content

One unusual podcast launched last year is Your History, from The Times. The newspaper has published daily obituaries for over a century, many of famous people. The team realized that there was an opportunity to highlight some of the Times’ best writing, which happens here, as well as capitalize on audience curiosity in historical figures.

The twice-weekly podcast brings out”‘remarkable tales of lives well lived,” from musicians to politicians, scientists, and sporting legends across episodes averaging 10-15 minutes. Anna Temkin, deputy obituaries editor, presents the podcast.

This is an excellent example of taking existing content and transforming it into another medium. The obituaries pages of newspapers contain a wealth of fascinating life stories, especially when someone well-known dies. By simply reading out the obituary – a low tech and low cost solution – The Times makes this content accessible and relevant to a new audience who aren’t necessarily newspaper subscribers.

Podcasting has room for innovation

Reader revenue is an important strand for each of these publications. The Telegraph and The Times both have hard paywalls, and use podcasts as a top-of-funnel strategy to introduce listeners to their journalism. In these cases, applying strategies that help widen listenership through translation or opening up paywalled content is key.

Although the Daily Mail has some paywalled content, the majority is accessible to read for free. This allows the podcasts to build up a large audience.In this case, The Daily Mail has created a paid bundle around popular shows to monetize a smaller but more dedicated fan base.

The extent to which other publishers can use these tactics will depend on where podcasts sit strategically. If they’re a “shop window” to showcase journalism, it is worth exploring options to leverage podcasts to expand audiences. However, podcasts also have great power as a retention tool superserving a publisher’s most loyal readers. With continued experimentation and innovation, podcasts offer the potential to grow audiences and support, or even build, direct revenue. That’s not hype; that’s just smart strategy.

A robot sits behind an unhappy jouralist to show the possibly negative impact of AI in journalism

Industry-Research

Americans skeptical about AI’s role in journalism

May 13, 2025 Rande Price, Research VP – DCN

As artificial intelligence gains traction in American newsrooms, most of the public has growing concerns about its long-term effects on journalism. While AI may promise efficiency and innovation, audiences fear the loss of editorial quality, factual accuracy, and jobs.

Michael Lipka’s report for the Pew Research Center reveals that half of U.S. adults (50%) believe AI will negatively impact the news people receive over the next 20 years, while only 10% foresee a positive effect. The rest see a mixed impact (23%) or aren’t sure (16%).

Concerns for journalists’ jobs

One of the starkest findings from Pew’s nationally representative survey is the projected toll on employment in journalism. Nearly six in 10 Americans (59%) believe that AI will reduce journalism jobs over the next two decades. Only 5% expect AI to create more roles, which underscores widespread anxiety about automation displacing skilled labor in newsrooms.

This echoes themes from other industry-focused studies. The 2024 European Broadcasting Union (EBU) News Report reinforces the importance of human oversight in AI generative content. While acknowledging that AI technologies can enhance newsroom efficiency and content creation, the report stresses that human editorial judgment remains essential to ensure accuracy and uphold accountability. It also highlights transparency as a critical factor in maintaining public trust, emphasizing that the integration of AI in journalism must not come at the expense of ethical standards and journalistic integrity.

Performance gaps and trust deficits

The public also doubts AI’s current ability to match the standards employed by human journalists. In Pew’s survey, 41% of Americans say AI would do a worse job than professional journalists at writing a news story, compared with only 19% who believe it would do better. Another 20% say the quality would be about the same.

Pew research chart showing American Concern about AI and information/ journalism

These concerns are not just hypothetical. Developers of leading generative AI systems—such as OpenAI’s ChatGPT and Google’s Gemini publicly acknowledge the ongoing issue of “hallucinations,” or confident-sounding but factually incorrect statements. Two-thirds of respondents (66%) in the Pew study say they are extremely or very concerned about people getting inaccurate information from AI-generated content, with another 26% somewhat concerned.

The Reuters Institute’s Journalism, Media and Technology Trends and Predictions 2025 report supports this sentiment. While 80% of news executives report testing generative AI, most remain cautious. Only a minority (around 20%) use AI to produce original stories. Instead, common applications include headline optimization, translation, transcription, and tagging—low-risk tasks that minimize the threat of spreading misinformation or editorial errors.

Bipartisan concern despite a divided media environment

One surprising aspect of the Pew findings is the bipartisan nature of AI skepticism. Despite sharply differing views on mainstream media trust, Democrats and Republicans largely agree about AI’s potential risks in journalism. For instance, 68% of Democrats and 67% of Republicans say they are extremely or very concerned about AI-generated misinformation. And when it comes to AI’s long-term impact on news, the gap is narrow: 54% of Republicans compared to 49% of Democrats expect a negative outcome. In an era of political polarization, this rare alignment suggests that concern over AI’s role in news transcends ideological divides.

Educational disparities in AI perceptions

Pew’s data also reveals differences in perceptions based on education level. Americans with at least some college education are more likely to negatively view AI’s impact on journalism. For example, 56% of college graduates and 54% of those with some college experience expect harm to news quality versus 44% of those with a high school diploma or less.

Those more familiar with the nuances of journalism may better understand the stakes around issues like source vetting, bias detection, and ethical storytelling. For news organizations, these findings highlight the need for greater public education about AI integration into the editorial process and how humans must oversee the process.

Incorporating innovation and integrity

As AI grows more capable, its role in media will continue to expand; however, its success will depend on maintaining audience trust.

Strategies to consider include:

Transparent disclosure of AI use in news production (e.g., “AI-assisted reporting” tags).

Human-in-the-loop editorial models that pair AI speed with journalistic judgment.

Clear internal guidelines around the ethical deployment of generative tools.

Investments in staff training to help journalists leverage AI while maintaining standards.

As artificial intelligence becomes more deeply embedded in society, its impact on journalism is just one example of broader public concerns about automation, trust, and accountability. Skepticism surrounding AI in newsrooms reflects unease about how these technologies might reshape essential human-centered fields. The challenge lies in balancing innovation with ethical responsibility, to ensure that progress enhances – and does not undermine – the media institutions the public relies upon.

Even Google search knows the impact of AI Overviews on CTRs

Industry-Research

Google’s AI overviews linked to lower publisher clicks

May 6, 2025 Rande Price, Research VP – DCN

When you search on Google today, you’re likely to see a boxed summary at the top of the results page. This feature, called an AI Overview, aims to answer your question instantly by summarizing information pulled from multiple websites. According to Google CEO Sundar Pichai, AI Overviews benefits publishers. “If you put content and links within AI Overviews, they get higher clickthrough rates than if you put it outside,” he claims. However, publishers and website owners are skeptical of this perspective. Research suggests that this skepticism is not unfounded.

In a new report, AI Overviews Reduce Clicks by 34.5%, researchers Ryan Law and Xibeijia Guan analyze how AI Overviews impact user behavior in Google Search. Their findings suggest that these AI-generated summaries are not increasing clicks to websites but decreasing them.

Research framework

Law and Guan examine 300,000 search keywords using data from the Ahrefs Keywords Explorer database:

150,000 keywords trigger AI Overviews in Google Search.
150,000 keywords are similar in topic and intent but do not trigger AI Overviews.

They focus specifically on informational queries—questions like “how to start composting” or “symptoms of low iron”—since 99.2% of keywords that trigger AI Overviews fall into this category.

Using aggregated Google Search Console (GSC) data, the researchers calculate the average desktop clickthrough rate (CTR) for the top-ranking result on each keyword. They compare results from March 2024 (before AI Overviews roll out in the U.S.) to March 2025 (after rollout) to evaluate the impact.

Impact of AI Overviews: less clicks

AI Overviews: Impact of AI overviews in search results on click through rates (CTR)

In March 2024, the average CTR for the #1 result on AI Overview keywords is 7.3%.
In March 2025, that number falls to just 2.6%.
This represents a 34.5% decrease in clicks when an AI Overview appears at the top of the search results.

In plain terms, websites that once received steady traffic from top search rankings now see far fewer visits when AI Overviews appear.

Why are search clicks dropping?

AI Overviews operate similarly to Google’s Featured Snippets. The search results attempt to answer the user’s question directly on the results page, eliminating the need to click through to any website.

While Google includes source links in these summaries, it often shares them across multiple sites. No single publisher is likely to capture a significant share of the clicks. The result is a growing number of zero-click searches, where users get what they need without ever leaving Google.

Adding to the issue, Google’s Search Console doesn’t separate AI Overview clicks from standard organic traffic. Publishers can’t easily see whether changes in performance are related to AI Overviews or other factors. This lack of transparency makes it even harder for site owners to understand and respond to traffic declines.

Industry experts see the same trend

The study’s findings aren’t isolated. SEO professionals across the industry report similar patterns. Lily Ray of Amsive Digital warns that AI-dominated search results could “decimate” organic performance. Rand Fishkin of SparkToro has also voiced concern over Google’s increasing efforts to keep users within its ecosystem rather than sending them out to external websites. These observations align with the research: AI Overviews appear to reduce visibility and engagement for content publishers.

Looking ahead

The study by Law and Guan represents the most detailed empirical look so far at the impact of AI Overviews. It challenges Google’s claims and raises important questions about the future of search visibility for publishers.

Despite Google’s assurances, the current data shows that AI Overviews are pulling attention away from websites, not driving it toward them. This shift signals a need to rethink how we approach content and visibility in a rapidly evolving search landscape.

-hands working together to align cogs to represent publishers negotiating AI licensing together-

Policy | Revenue | Supporter Spotlight

When it comes to AI negotiations, publishers are stronger together

April 24, 2025 Paul Bannister, Chief Strategy Officer – Raptive

By now, we are all painfully familiar with the way AI systems are reshaping how audiences discover and consume information—often at publishers’ expense. These powerful technologies reuse publishers’ content, usually without permission or fair compensation, placing growing pressure on publisher revenue and content control. Premium content creators of all sizes face identical risks as AI companies increasingly set the rules.

However, media companies are far from powerless. By taking collective action, the media industry can assert control over how our content is used and ensure our voices are central to shaping AI policies. Several practical pathways exist, including regulatory advocacy, strategic litigation, licensing agreements, and technological measures. The key is that we must work together.

Regulation/policy: defining the rules for AI

Enforceable policy regulations represent a clear line of defense against unauthorized use of content. Currently, ambiguity around “fair use” allows AI companies significant leeway. OpenAI’s CEO, Sam Altman, recently acknowledged this plainly, admitting that restrictions on AI scraping copyrighted material would threaten his company’s existence.

Altman’s candid admission underscores exactly why publishers must engage policymakers immediately. President Donald Trump’s recent executive order, “Removing Barriers to American Leadership in Artificial Intelligence,” explicitly seeks to minimize regulations that might hinder AI companies from pursuing their current path. OpenAI and Google have seized this opportunity to advocate aggressively for fewer copyright restrictions, claiming tight regulations threaten American AI dominance in a geopolitical race with China. Help will not come at the federal level anytime soon.

Several state legislatures are actively addressing AI’s impact on copyright, notably California’s AI Copyright Transparency Act (AB 412) and New York’s Artificial Intelligence Training Data Transparency Act (S6955), both of which mandate transparency from AI developers about copyrighted materials used in training models. These initiatives indicate state-level momentum and promise to set precedents that other states will follow. That said, the most immediate forum for action is likely in the courts.

Courts: establishing clear legal precedent

Legal action is the most promising line of defense and has already proven effective. Recent cases, notably Thomson Reuters v. ROSS Intelligence, represent critical opportunities to establish binding precedents around copyright and AI that can level the playing field.

In February 2025, the U.S. District Court for Delaware ruled decisively in favor of Thomson Reuters, determining that ROSS’s unauthorized use of copyrighted content to train its competing AI product was not protected by fair use. This is a big win for every publisher because it clarifies what has historically been a vague and uncertain doctrine. Making it stick will require a broader chorus of legal wins, but it’s a start.

Recognizing these stakes, publishers are increasingly acting together in the courts. One example is a joint lawsuit from 14 major media organizations—including Condé Nast, Forbes, and The Atlantic—against AI startup Cohere. Similarly, litigation initiated by The New York Times against OpenAI and Microsoft has been consolidated with cases from the Daily News and the Center for Investigative Reporting, forming the beginnings of a unified front of defense.

The outcomes of these collective efforts matter profoundly. While individual settlements might resolve immediate conflicts, only definitive court rulings can deliver lasting protections. Publishers at every scale share a vital common interest in supporting cases that reinforce strong, enforceable copyright standards for everyone. Everyone.

AI licensing negotiations: balancing opportunity and equity

Licensing agreements offer publishers another critical tool to monetize their content and control AI usage. These deals can deliver revenue and clearly define permissible AI applications, and we’ve seen a string of them recently. Yet licensing strategies carry risks: agreements negotiated by major publishers could inadvertently create a market divided between haves and have-nots. It’s also unclear if any of these deals will have long-term value, as the damage done to publishers will likely be much higher than any small payments from these deals.

Smaller publishers risk marginalization if AI licensing standards and terms are set exclusively by larger publishers. Collective approaches that define fair, equitable standards can help ensure licensing agreements work for the entire publishing ecosystem rather than fragmenting it.

Technological barriers: limitations of blocking AI crawlers

Technological measures, such as blocking AI crawlers from publisher sites, are another avenue. It’s worth pursuing, but we should not look at this as a long-term strategy. AI companies regularly evolve their technologies, circumventing technical barriers almost as quickly as they emerge.

While publishers can (and should) employ these measures strategically, lasting protection depends more heavily on clear regulatory policies, decisive court precedents, and equitable licensing agreements.

Making collaboration count

General calls for industry collaboration frequently fall short, offering little beyond vague ideals. Yet the AI challenge distinctly highlights how all publishers, regardless of size, share identical interests. Whether an independent blogger, a small-town newspaper, or a global publisher, AI-driven content reuse affects everyone similarly. AI does not care how big you are.

We’ve already observed direct negative impacts on publisher traffic from AI-powered overview summaries in search results. These early signs are merely the beginning. The entire digital landscape—search behaviors, traffic patterns, and monetization structures—is changing fundamentally with AI, and fast.

Publishers need support to run a sustainable business. This has compelled Raptive to advocate on the AI issue precisely because we recognize it is existential to the viability of independent publishing–and the power of strength in numbers. We have invited publishers with whom we work to sign a new agreement that lets us represent their interests in conversations with tech platforms around AI negotiations.

All premium content creators—those supplying the original, authentic content powering the internet—share a truly common interest. Now is the moment to advocate for it; we’ll be stronger if we do it together.

Robot head and AI doodles to suggest the use of AI in the media business

Featured Post | Industry-Research | Innovation

Four AI trends and why they matter to media businesses

April 17, 2025 Damian Radcliffe, Carolyn S. Chambers Professor in Journalism – University of Oregon

It’s no surprise that investment in AI tools and platforms is a major priority for media companies. Recent research from the Reuters Institute found that investing in platforms such as OpenAI and Perplexity is the leading priority for industry leaders in the year ahead. Meanwhile, WAN-IFRA’s annual World Press Trends study highlighted this as an area of both improving relationships and continued investment.

These moves are driven by a combination of factors such as fear of missing out and falling behind as Generative AI continues to evolve. Companies also want to have the ability to use these technologies for a range of benefits including efficiencies and the development of new products.

The opening months of 2025 have witnessed the continued integration of AI into workflows and further developments that promise to yield a range of benefits for content creators.

Here are four noteworthy AI trends and how media executives should be thinking about the emerging opportunities they present.

1. Conversational AI enhances connections

Both audio and text-based conversational AI are gaining traction. According to the Reuters Institute, harnessing AI to turn text into audio is the top audience-facing AI application for media leaders in the year ahead. These moves are driven by “advances in voice technologies [that] have made it possible to transform text articles into audio (in multiple languages or tones).”

Moreover, as noted by ElevenLabs, an AI Audio research and deployment company that works with publishers such as Time, “the shift to AI-driven audio isn’t just about convenience — it’s about survival in a landscape where audiences increasingly prefer to listen rather than read.” (NB: their italics.) This trend is evidenced at outlets such as The Washington Post which saw daily audio listens double in the first six months of last year.

And as these AI tools get cheaper, more accessible, and sound increasingly more human, AI-powered consumer experiences will become more mainstream across the media landscape. That includes local – as well as national and international – media outlets.

Alongside these audio formats, AI-driven chatbots are also becoming more prominent.

Although there are legitimate concerns about the accuracy of news summaries provided by these tools, the way in which they access content from sites that have blocked their crawlers, coupled with a frequent inability to cite sources or provide referral traffic, these products are becoming more prevalent. This week, for example, saw The Straits Times in Singapore launch a chatbot that answers questions from readers on career-related topics, drawing on an archive of 5,000 stories published on this topic since 2020.

Sensing the opportunity, businesses like Tars offer a chatbot specifically designed for news organizations. Its functionality allows audiences to interact with news stories in a conversational format, ask follow-up questions, rate articles, and access related visual content.

Meanwhile, several major media providers have signed deals to provide content for AI chatbots owned by some of Silicon Valley’s biggest players. Late last year, Meta revealed it would use content from Reuters to answer user questions in its chatbot about the news and current events. More recently, AP inked a similar deal with Google, which will see news from the Associated Press featured in the tech company’s Gemini app.

Takeaway

As user needs and preferences continue to evolve, media companies must respond accordingly. Delivery of content via voice and chatbots, may become more mainstream, given the growing demand for more informal and conversational interactions with content. Catering for these audiences will become further engrained in media distribution strategies.

2. The return of content at scale

AI-assisted content creation is not a new phenomenon. However, it is creating an opportunity for some media outlets to turn back the clock to the era when scale was seen as king.

Patch, the local news provider that was acquired by AOL in 2009 (and offloaded in 2014) has used AI newsletters to expand Patch’s reach over the past few months from 1,100 U.S. communities to 30,000. As Axios explains, these newsletters feature five stories from Patch sites along with material aggregated from other online sources.

Despite its use of AI to scale, Patch purportedly has 85 full time newsroom employees. However, Nieman Lab reported in January how a company producing AI-generated newsletters in 47 states and 355 towns and cities across the U.S. appeared to be operated by a single person.

These examples demonstrate the ease with which AI can help curate content at scale.

Although these efforts can curate content to consolidate coverage, they don’t deliver original journalism. Moreover, it can be difficult to check the veracity and accuracy of content produced at this volume.

Questions around accuracy and the absence of fresh reporting were similarly leveled at the Italian conservative newspaper Il Foglio, which recently published a four-page edition produced entirely by AI. “The articles were structured, straightforward and clear, with no obvious grammatical errors,” the Guardian notes. “However, none of the articles published in the news pages directly quote any human beings.”

These examples may make some media leaders, and audiences, uncomfortable. Nevertheless, they can be viewed as an extension of some of the ways AI technologies are already being used.

AP has been using AI to produce stories based on earning reports for over a decade, dramatically increasing the number of stories it produces in this arena as a result. At the same time, Gannett publications in the Boston area have begun harnessing a generative AI tool called Espresso to draft articles from community announcements and press releases.

Similarly, Semafor has revealed how The New York Times is exploring using AI tools to assist with SEO, research, headline writing, content for social media and other purposes. This can speed up the production process, potentially creating time for employees to produce more in-depth and creative content, as well as increasing the volume of output.

Takeaway

The use of AI to automate routine tasks has long been cited as a benefit of these tools. Advocates argue AI will enable staff to focus on original and deeper work. However, there is a risk that these technologies will have the opposite effect, encouraging creators to publish more content, much of it low quality “AI slop.” The need for originality and distinctiveness will be the differentiator for most players in an AI-driven world. While some providers can use AI to scale their output, doing so while maintaining quality, distinctiveness and value isn’t always easy or an approach that will work for everyone.

3. Beyond efficiency: AI as a tool for accessibility

Discussions around AI often focus on efficiencies, the ability to streamline workflows, or harness these tools to create new products. This can certainly be true. Last month, political news outlet Politico launched their Policy Intelligence Assistant — a new AI-powered tool enabling Politico Pro subscribers to generate in-depth policy reports using the company’s proprietary reporting and analysis.

However, at the same time, AI can also be used to ensure that content is able to reach wider, more diverse, audiences.

Publishers are already using AI to help with translation. But the benefits can go well beyond that. Speech-to-Text tools can generate captions for live broadcasts, webinars, and events, making the information more accessible. Similarly, these technologies – augmented by human input – can aid with audio description, the creation of ALT text, and personalization. This can represent a business opportunity that expands reach and potentially fosters greater audience loyalty.

In Austria, the APA (Austria Press Agency) is developing an AI tool to generate alternative text for infographics. The goal is to help visually impaired users better access data-driven journalism.

Chitranshu Tewari, the Director of Product and Revenue at Newslaundry in India, argues that “AI-driven accessibility isn’t only better product design but also good business.” Reflecting on their own experiences, he comments that “our accessibility efforts didn’t just make our platform more inclusive — they also attracted new paying subscribers.”

Takeaway

AI can do more than help media companies tick compliance boxes. By making content more user-friendly there are opportunities to better serve all audiences, especially those that have historically been underserved or overlooked. AI can help to embed inclusive design principles, while at the same time making access to your products more equitable and valuable.

4. Trust and transparency in media’s AI age

As AI becomes more deeply integrated into content production and consumption, media leaders must continue to understand – and address – attitudes towards these technologies among consumers.

Research demonstrates that public sentiment towards AI in the production of content, such as journalism, varies widely. “On the whole, people are generally positive about journalists’ ability to use technology for professional purposes,” says the Center for News, Technology & Innovation. Nevertheless, attitudes are often shaped by users’ personal experiences and knowledge of these technologies.

This divergence in public opinion reaffirms the need for transparency about the usage of AI technologies. That can be particularly true in the creation of news content.

Takeaway

In an age of low levels of trust in mainstream media, disclosure and the presence of clear – publicly available – guidelines around how AI is being deployed, is important. Media companies should be upfront about when and how AI is involved in content creation, as well as the potential limitations inherent within these technologies. For example, do audiences understand how answers generated by your AI chatbot are produced? If they don’t, arguably they should.

Putting the AI pieces together

AI is already a transformative force that is reshaping the media industry. It is redefining workflows, as well as how content is produced, distributed, and consumed.

As we’ve seen, some of the trends in this space have only accelerated in the first part of 2025, although they are often underpinned by core principles which have always made good, strategic, sense. These changes touch on the core of what great content looks like: how those stories are made, where – and how – they are consumed, and what trust looks like in an increasingly AI-driven world.

Chief among these, media companies must continue to meet audiences where they are. In 2025, this is increasingly in conversational online environments. Whether it is via voice or chat, AI can be used to create experiences that feel more informal, responsive, and interactive.

At the same time, even though AI enables publishers to automate routine tasks, freeing up some staff time in the process, outlets should avoid the temptation to flood platforms with more material. In an era of abundance, content isn’t a numbers game. Originality and distinctiveness will determine which providers survive and thrive.

AI’s role in the origination of creative work also needs to be effectively communicated. Audiences want, and deserve, transparency, ethical clarity, and the knowledge that there is still human and editorial oversight of the content they consume.

And lastly, in doing all of this, it is incumbent on media players to integrate inclusive design into everything they do. This approach isn’t just important from an ethical or compliance standpoint, it can also be commercially beneficial, with AI potentially making this easier to do than ever before.

The strategic use of AI tools and technologies offers media companies considerable opportunities, but leaders also have to recognize that there are also inherent risks too. This includes resisting the urge to use AI simply to do more. Rather, the focus for folks in the C-Suite must be to do better: creating enhanced opportunities for engagement and doing so in a way that is transparent and where accuracy and quality remain paramount.

The next chapter of AI in the media business is being written now. It’s up to all of us to ensure it’s one worth consuming.

Closeup of helmeted female athlete's eyes to demonstrate a focus on niche streaming sports

Innovation | Video

How AI is changing the game for “niche” sports streaming

March 25, 2025 Christy Tanner – Founder, Coraly Partners & Chair, Swerve Sports

Sports and sports media outside of the major leagues often are labeled as “niche.” But that term is quickly becoming obsolete. Easy and inexpensive AI tools are changing the game. They create new sports media and marketing opportunities for free streaming and social-first athlete-creators, regardless of traditional audience bases and reach.

How is AI accelerating this transformation? It’s helping underrepresented sports and athlete-creators identify and capture new fans, super fans, and monetization opportunities. With smarter data analysis and faster content distribution, sports once considered “niche” have a chance to grow their media audiences and revenues in many ways.

How AI Is expanding the reach of sports

AI is widely used in sports media but it’s not just for the majors. AI presents opportunities for targeted streamers and independent creators. Here’s how leaders are using it to grow:

1. Identify and engage new fanbases

AI is helping sports organizations analyze viewership patterns, social media engagement, and fan demographics to uncover potential new audiences. By leveraging machine learning, teams and leagues can:

Identify super fans: Find those most engaged and willing to spend on tickets, merchandise, and streaming subscriptions.
Uncover new fan segments: AI can pinpoint audiences with similar behaviors and interests, even if they haven’t engaged with the sport yet.
Optimize monetization strategies: AI-driven insights help organizations determine the best ways to engage and convert fans through advertising, merchandise, and licensing opportunities.

2. Speed up content distribution

The way fans – particularly Gen Z fans – consume sports content has changed. Short-form videos, highlights, and real-time updates dominate engagement, and AI is making it easier to deliver this content faster than ever. AI-powered tools now handle:

Video ingestion and indexing: AI quickly processes and categorizes game footage for highlights.
Automated captioning and headlines: AI helps create more engaging, searchable content.
Smart clip generation: AI identifies the best in-game moments and instantly produces highlight reels.

This reduces production time and costs, allowing sports organizations to share media with fans faster and at scale.

3. Break language barriers and expanding globally

AI-powered translation tools are making sports media more accessible and global. Now, leagues and teams can automatically translate commentary, subtitles, and captions into multiple languages, opening doors to international markets and audiences.

More inclusive media: AI-driven translations provide accessibility for fans who speak different languages or have hearing impairments.
Stronger international engagement: With real-time translations, sports can reach new audiences without the need for costly localization efforts.

Athlete-creators: the new hybrid skill set

From NIL-driven revenue opportunities to the dominance of the Paul brothers, athlete-creators are increasingly leveraging AI. Are we looking at a future of sports in which the highest-performing athletes are not the best-known athlete-creators and vice versa? Yes, it may be challenging for some athletes without the resources or a team of assistants to fully realize their earning potential. However, AI may help level the playing field for athlete-creators. Here are some ways athlete-creators are using AI:

Content creation and editing: AI tools can simplify design and enable quick creation of engaging content without professional design expertise.
Social media engagement: AI analysis of social media trends and audience preferences can be used for targeted creation strategies.
Streamlining distribution: Automation of delivery can make it easier for athletes to focus on training and performance while staying engaged with fans and optimizing revenue opportunities.

Ascendant sports: the next stage of AI-driven growth potential

For an underrepresented sport looking for media expansion potential, new AI tools can help with assessing and answering some key questions:

Does it have a strong but underserved fanbase? If finding free, high-quality broadcasts is a challenge, it’s now much easier to explore serving a fanbase via free streaming options, from FAST to YouTube to other live social short-form distribution outlets.
Does it need better production and distribution? AI-enhanced production – from graphics to real-time statistical analysis – can help sports that have previously not been considered TV-friendly, making them more exciting to watch and easier to follow on digital platforms.
Are there marketers looking to align with its fanbase? Using AI-enabled analysis of data, it’s easier to identify cost-effective and targeted opportunities to connect with sports fans.

With the right application of AI and streaming strategies, ascendant sports can dramatically expand their audience and become stronger players in the sports content ecosystem.

The future: AI will define the next era of sports growth

The sports industry is at an inflection point. The traditional “big vs. small” sports hierarchy is being disrupted by technology, streaming, and AI-driven content strategies. Many sports, regardless of their historical followings, now have the opportunity to thrive and expand their reach.

A confused robot to show how AI search has trouble with news citations and attribution

Research

AI search has a news citation problem

March 24, 2025 Rande Price, Research VP – DCN

Artificial intelligence is transforming the way people search for and consume news. With nearly one in four Americans now using generative AI chatbots instead of traditional search engines, the way journalism reaches audiences is shifting dramatically. While AI-powered search promises speed and convenience, it presents serious challenges for news publishers, including loss of attribution (citations), declining referral traffic, and misinformation.

A new Tow Center for Digital Journalism study highlights these risks, revealing how AI-driven search tools often fail to credit, or to accurately credit, sources and bypass publisher controls, such as paywalls. As AI search rapidly evolves, media executives must understand its implications and develop strategies to protect journalism’s integrity and financial viability.

AI search engines struggle with accuracy and attribution

The Tow Center analysis studies eight AI-powered search tools: Perplexity, Google’s AI Overviews, Bing Chat, ChatGPT, Claude, Gemini, Meta AI, and Grok. The research concludes that over 60% of AI-generated responses contain incorrect or misleading information. Unlike traditional search engines that list multiple sources for verification, generative AI tools present single, authoritative-sounding answers—often with a false sense of confidence.

When AI search engines provide sources, they cite syndicated or republished versions rather than providing a citation for the original publisher. This practice diminishes the visibility of primary news organizations and deprives them of direct traffic. Even more troubling, some platforms, such as Grok and Gemini, regularly generate broken or fabricated URLs, misleading users and reducing referral traffic to legitimate news sites.

AI platforms ignore publisher controls

Beyond attribution issues, many AI search tools fail to respect industry norms designed to protect publishers’ content. The study finds that AI platforms routinely retrieve content from sites even when publishers explicitly block them using robots.txt, a standard tool for controlling web crawling. This disregard for publisher restrictions raises ethical concerns and undermines publishers’ ability to manage the use of their content.

The Tow Center analysis also identifies inconsistencies in using publishers’ content with licensing agreements. Some news organizations with formal partnerships with AI companies still experience misattribution or see their content surface in ways that do not drive traffic back to their platforms. These findings suggest that agreements alone are insufficient to ensure proper credit or compensation for news publishers.

Revenue and engagement threat

For media companies, the rise of AI search directly threatens digital traffic, audience engagement, and revenue generation. Traditional search engines drive referral traffic to news sites, supporting subscription models, advertising revenue, and brand visibility. If AI search tools continue to bypass proper citation and link attribution, they could significantly weaken publishers’ brand recognition, authority, and ability to monetize their content.

Additionally, AI-driven search changes user behavior by reducing the need to visit source websites. Instead of clicking through to read full articles, users increasingly rely on AI-generated summaries, limiting publishers’ opportunities to engage audiences directly. This shift challenges established business models and forces publishers to rethink how they distribute and monetize content in an AI-driven search environment.

Responding to AI search disruption

Despite these challenges, there are opportunities to influence how AI search evolves. Since AI search models rely on high-quality journalism to function effectively, publishers have the leverage to demand better attribution, transparency, and enforceable policies. This study suggests four key strategies for publishers:

Advocate for stronger AI regulations: Industry groups and publishers can push for more transparent policies that require AI search engines to cite and link to sources properly.
Negotiate licensing agreements: While existing agreements show inconsistencies, publishers can strengthen negotiations to ensure AI companies provide meaningful attribution and compensation.
Develop AI-optimized content strategies: As publishers adapted to search engine optimization (SEO), they must now consider how AI models surface content and explore ways to maximize visibility.
Educate readers on AI search limitations: Publishers can inform audiences about the risks of AI-generated misinformation and encourage direct engagement with trusted news sources.

The Tow Center study concludes that media companies must remain proactive and ensure that AI search platforms operate with fairness, transparency, and respect for original reporting. The long-term health of the news industry depends on ensuring that AI-driven discovery tools do not replace or undermine the very sources that power them. By pushing for industry-wide standards and holding AI companies accountable, publishers can work toward a digital ecosystem where AI search enhances, rather than diminishes, quality journalism.

A golden light bulb leaning against a copyright symbol to show how copyright protection supports AI innovation

Copyright and AI: a win win

March 20, 2025 Chris Pedigo, SVP Government Affairs – DCN

In terms of public policy debates, Artificial Intelligence continues to be the belle of the ball with nearly every major government courting the industry to locate their investments and jobs within their jurisdictions. Europe, China, Korea, and the U.S. (among others) have laid out competing tax and government spending plans to entice and encourage AI companies. Against this backdrop of AI frenzy, President Donald Trump, via the Office of Science Technology and Policy, has solicited input on the formation of an “AI Action Plan” in order to “define the priority policy actions needed to sustain and enhance America’s AI dominance.”

Unsurprisingly and unabashedly, tech companies advocate that the U.S. government allow their content-generating AI models to train on copyrighted material without consent or compensation. However, as DCN noted in our comments regarding the action plan, a key component to achieving the stated goal of enhancing America’s AI dominance – and the broader success of American businesses – is the robust protection and enforcement of U.S. intellectual property law including the Copyright Act.

Copyright protection makes legal, and financial, sense

The longstanding legal rights for copyright holders are derived from the U.S. Constitution (Article I, section 8, clause 8), which affords them the opportunity to monetize the results of their hard work and investment in a variety of ways and incentivizes them to reinvest in the creation of additional content and new innovative delivery mechanisms to potential consumers. As a result of these longstanding rights, American content creators, including news organizations and other publishers, are able to contribute significantly to U.S. economic growth, including through employment, exports and important trade surplus, and digital services and goods.

According to a recent study, copyright-based industries accounted for 12.31% of the U.S. economy and 63.13% of the U.S. digital economy. From 2020 to 2023, these industries outpaced U.S. economic growth almost threefold. In the digital sector alone, copyright-based industries employ 56.6% of all employees in the digital sector. The annual compensation paid to core copyright workers is approximately 50% higher than the average U.S. annual wage. As for the global impact, the sales of select U.S. copyrighted products in overseas markets amounted to $272.6 billion, which exceeded the sales of other IP industries including pharmaceuticals, agriculture, and aerospace.

Copyright, competition and a fair market

Unfortunately, the manner in which many AI developers have exploited original content without consent or compensation – to build and operationalize their commercial products – has unjustifiably violated the rights of copyright holders. It has upended the existing balance which has historically sustained and promoted innovation.

AI developers use copyright protected content not only to “teach” their models to predict and mimic language skills, but also as a means to create compelling outputs which have the compounding harm of substituting for the original works on which the models were trained. This activity unfairly competes with those who invested in the creation of the original material and undermines their ability to seek a fair economic return. In fact, U.S. Senior District Judge Beryl Howell noted earlier this week in a copyright case attempting to argue fair use that the publisher’s content is “so valuable they put a copyright on it.” Exactly.

By “reaping that which they do not sow” AI companies cause harm to creators, publishers and the ecosystem as a whole. It is important that this form of destructive misappropriation be deterred, whether by copyright law or other appropriate means. In the U.S, there are 39 related lawsuits and counting. The outcome of these suits will provide much-needed clarity regarding the application of existing copyright law, including the fact-specific defense of fair use, to the infringement of the rights of copyright holders to develop generative AI technology.

However, one U.S. District Court recently confirmed that licensing is required for the use of copyrighted content to train an AI system. In Thomson Reuters Enter. Ctr. GmBH v. Ross Intel. Inc., the court, applying clear and recent precedent from the U.S. Supreme Court, held that the defendant’s unauthorized use of the plaintiff’s works to train the defendant’s AI system was direct infringement and did not constitute fair use. The Court reaffirmed that the impact of the use on existing and potential markets is the single most important element of a fair use analysis, and that there was clearly a potential market to use the materials at issue in the case to train AI.

Innovation flourishes within the copyright framework

Lest the VC crowd be dismayed, a licensing framework is emerging as many deals have been struck by publishers, record labels, motion picture industries, and others. OpenAI, Google, and Perplexity have all made efforts to pay for the right to use protected content to power their models and tools. This is a clear acknowledgment that this model is not only necessary, but eminently feasible.

While publishers’ rights are coming into clearer focus in the U.S., AI companies are beginning to feel a shared pain as evidenced recently by DeepSeek’s R1 model. OpenAI accused the company of IP theft, claiming that DeepSeek may have used OpenAI’s IP and violated its terms of service to develop its AI model.

“We know PRC (China) based companies – and others – are constantly trying to distill the models of leading US AI companies,” OpenAI said in a statement to Bloomberg. “As the leading builder of AI, we engage in countermeasures to protect our IP, including a careful process for which frontier capabilities to include in released models, and believe as we go forward that it is critically important that we are working closely with the US government to best protect the most capable models from efforts by adversaries and competitors to take US technology.”

A rising tide can lift all boats. Only maintaining existing copyright protections will lead to a robust, free market where creators are incentivized to make high quality works and AI companies are incentivized to license them. Importantly, in this robust market, AI companies would continue to have access to quality content which is critical for training and outputs. The American values of IP protection have been a cornerstone in our country’s innovative spirit and competitive edge over foreign adversaries. Protecting IP is a matter of preserving the core principles that distinguish American businesses in the global market. For the history of the U.S., copyright and innovation have gone hand in hand and there is no reason to deviate from that successful combination as we build the next chapter.

Read DCN’s Comments on the AI Action Plan, which were filed with the Office of Science and Technology Policy on March 15, 2025

human hands at a computer with lightening bolts to indicate Journalism and Journalists use of AI

Industry-Research | Innovation | Research

Journalists confront the reality of media use of AI

March 17, 2025 Rande Price, Research VP – DCN

The rapid adoption of Generative AI (Gen AI) in newsrooms sparks important discussions among journalists and media professionals, especially about transparency and trust. Across the industry, publishers vary in how they communicate their AI strategies to their workforce. Reports suggest that some journalists seek more transparency around management’s AI implementation efforts and agreements with AI companies. This lack of clarity also applies to content, as some publishers explore AI-generated articles without consistently informing staff or readers.

A lack of transparency around AI fuels distrust

Mike Ananny and Jake Karr examine how news media unions are trying to manage and stabilize the use of Generative AI. Their analysis, How Media Unions Stabilize Technological Hype, draws from a review of industry reports, expert interviews, and case studies of newsrooms integrating generative AI. The methodology emphasizes qualitative insights to understand AI’s impact on editorial processes, ethics, and audience trust.

According to the authors’ analysis, many employees only learn about AI licensing deals through sudden announcements, often without prior consultation. Some must rely on external reporting to understand their company’s AI initiatives. Union representatives consistently face resistance when requesting information, reinforcing a broader mistrust of employer intentions.

However, solutions are emerging. Some unions are pushing for contractual guarantees to ensure greater transparency. The Associated Press and certain Gannett-owned publications propose contract language requiring 90 days’ notice before implementing new AI-related newsroom technology. Similarly, The Onion and Wirecutter unions successfully bargain for advance notice and transparency requirements regarding AI procurement. These efforts signal a path to restoring trust through openness and accountability.

Journalists defend creativity and quality

News professionals ensure accuracy, provide context, and uphold ethical standards that AI alone cannot fulfill. Ananny and Karr conclude that AI’s so-called “creativity” is a remix of existing human work, lacking the depth, insight, and contextual awareness that define quality journalism. No matter how advanced AI becomes, skilled journalists must verify facts, interpret events, and shape narratives with integrity.

Recognizing this, some media organizations are implementing safeguards. The Associated Press is committing to using Gen AI only with direct human oversight to maintain compliance with journalistic standards. Another example includes editorial employees reviewing AI content before publication at The Onion, The A.V. Club, Deadspin, and The Takeout. And the MinnPost treats AI-generated material as a source requiring human editing and fact-checking.

But beyond oversight, journalists are pushing for the right to decide whether or not to use AI. Many unions argue that workers, as experts in their field, should determine the appropriate role of AI in journalism. The CNET Media Workers Union demands the right to opt out of using AI if it fails to meet publishing standards. The Atlantic Union similarly insists that journalists may use AI within ethical guidelines, but no one should force them to do so.

These demands reflect a broader principle: journalism is, at its core, a human-driven endeavor. AI may assist but cannot replace the judgment, creativity, and accountability that define quality reporting. The analysis concludes that to integrate AI responsibly, newsrooms must prioritize transparency, trust, and human journalists’ role in safeguarding the profession’s integrity.

Ethical and legal ramifications of AI in journalism

Beyond transparency and journalistic integrity, the rise of Gen AI raises significant ethical and legal questions. One of the most pressing concerns is intellectual property: Who owns the content produced by AI models trained on vast amounts of copyrighted material? Many publishers argue that AI-generated work lacks originality and merely regurgitates existing human-created content. This also raises potential plagiarism and copyright infringement issues. In response, some media companies are taking legal action.

Additionally, there is concern about AI’s ability to spread misinformation. Unlike human journalists, AI lacks the critical thinking skills to discern fact from fiction. Without rigorous oversight, AI-generated content can amplify biases, fabricate sources, and misinterpret data, posing a direct threat to public trust in news media.

Regulatory bodies are beginning to take notice. Governments worldwide are considering policies to ensure AI transparency and ethical implementation in journalism. For example, the European Union’s AI Act includes provisions requiring companies to disclose AI-generated content and implement safeguards against misinformation. The Federal Trade Commission warns companies against deceptive AI practices, signaling potential regulatory intervention in the media industry.

Balancing innovation and integrity for journalism and AI

Despite the challenges, AI’s presence in newsrooms is likely to grow. Some publishers are taking a proactive approach by developing AI policies that prioritize ethical considerations. Reuters, for example, offers internal guidelines to ensure journalists use AI tools responsibly and transparently. The BBC is similarly on board to maintain human oversight over AI-generated content and clearly labeling AI-assisted reporting.

Ultimately, the future of AI in journalism will depend on striking the right balance between technological innovation and journalistic integrity. The authors concur that if publishers prioritize transparency, enforce accountability, and uphold journalists’ fundamental role, AI can be a valuable tool rather than a disruptive force.

A robot signing a check to represent AI content licensing

AI content licensing lessons from Factiva and TIME

March 6, 2025 Jessica Patterson – Independent Media Reporter

Content licensing has long been an important revenue stream for digital media companies. For decades, it allowed publishers to monetize their content by granting rights for others to republish or repurpose their material, evolving from licensing to aggregators, databases, social platforms, to streaming video services. Now, content licensing faces another evolution: artificial intelligence (AI).

Digital media publishers are finding themselves in a unique position in that they possess decades worth of quality content AI companies crave. “Over the next few years, content creators and AI companies will deepen their relationships,” predicts Yulia Petrossian Boyle, founder and principal of YPB Global LLC and FIPP chair. “However, as AI players try to secure more original content, those relationships will need to transition from one-off deals to well-structured, ethical partnerships with strict IP protection and meaningful ongoing revenue for publishers.”

TIME’s COO Mark Howard believes that publishers currently have three ways they can approach the AI dilemma: “You can do nothing. That’s just not something we would consider, to sit on the sidelines and just let everybody else figure it out. The other two options are to litigate and negotiate. Litigation is a very, very large commitment… So, that leaves negotiation.”

For some media companies, AI licensing agreements offer an alluring mix of copyright protection and monetization opportunities as DCN contributor Damian Radcliffe points out. And, as they negotiate these deals, publishers are discovering they must balance the potential for monetization with the need to protect intellectual property rights, navigate complex legal challenges, and ensure responsible AI usage.

Fair value in AI content licensing

According to a recent INMA report, executives considering licensing deals need to understand the value of their content in an AI-driven market. Then they have to negotiate attribution and compensation models that align with business goals. The report recommends collaborating with industry peers to create standardized agreements. It emphasizes the importance of advocating for responsible AI practices, including transparency in data usage.

An illustration depicting the state of publishing and media AI content licensing — Image credit: Ezra Eeman, Strategy & Innovation Director – NPO

The report also highlights emerging licensing models, which include direct licensing, value-in-kind partnerships, training fees, bundled partnerships, and per-use compensation. Boyle notes promising approaches, like “data-as-currency” deals, where AI companies offer analytics in exchange for access to their platforms and services (in some cases in addition to some smaller flat fees).

“Revenue-sharing is on the rise, where publishers earn a portion of subscription revenue or performance-based compensation (based on lead-gen, or engagement analytics),” she says. “For example, Perplexity AI’s Publishing Program launched in July 2024 offers revenue share based on the number of a publisher’s web pages cited in AI-generated responses to user queries. Those in the program earn a variable percentage of ad revenue generated per cited page.”

Boyle says that, while compensation models are improving, she worries that AI companies do not adequately compensate for content that has higher production costs, such as investigative journalism. She points to pushback from publishers like Forbes, who rejected the Perplexity proposal.

Negotiating with AI companies on behalf of her consultancy, Boyle has observed that offers by some AI companies for training datasets are insufficient. “Since agreements are not indefinite, it is unclear to me how publishers will be compensated in future when AI companies may no longer need training data for their data sets.”

In her opinion, current compensation models between major AI companies and publishers do not adequately reflect the significant investments that publishers make in creating original content. She believes compared to the substantial amounts AI companies invest in technology, such as chips, their expenditure on content seems disproportionately low. This disparity highlights a need for a more balanced financial recognition of the value that original content creators bring to these partnerships, she says.

However, striking these deals isn’t simple. Howard notes that each one is different, each has different monetization models and philosophies on revenue sharing.

“Some of them are flat fee for training, some of them are variable based on user adoption of their own products, and some of them are based on future ad models that haven’t even launched yet,” Howard says. “Many of them have some form of value-in-kind around technology or technology resources, which makes me very excited. I think that that may end up being where most of the value is derived in the long term.”

TIME for an active role in AI negotiations

Over the last year, TIME has struck deals with six AI companies, including OpenAI, Perplexity and ProRata.ai among others. The 101-year-old global media brand also launched TIME AI in December, an innovative platform developed in collaboration with Scale AI, that tailors content to individual readers.

A few of TIME’s AI partnerships are infrastructure-based, like Fox Verify, which uses their blockchain-based technology to verify all of the content TIME publishes in the CMS. This provides them with a ledger of all of their intellectual property going forward. After that, according to Howard, they worked with Tollbit and Scalepost to track and monitor all of the AI bots on TIME’s site any given day and see what they’re doing.

Access to technology is a key benefit of TIME’s AI partnerships for Howard. “We’re partners of theirs. I have direct access to their CTO and their senior leadership team. We get to hear what… they’re thinking about the market, that’s a really valuable conversation for us to have.”

“We brought money in as a result of these deals,” he says. “I’m happy about what we brought in. Some of it is fixed, a lot of it is variable and a lot of it is access to product resources and technology.”

Factiva puts trust first in its AI licensing

Dow Jones launched Factiva Smart Summary in November, a groundbreaking feature in its business intelligence platform engineered with Google’s Gemini models on Google Cloud. Smart Summary leverages generative AI technology to create concise summaries for Factiva users that are fully transparent and traceable, utilizing licensed content from each of their publishing partners.

To do so, Factiva approached every one of its nearly 4,000 sources in 160 countries with licensing agreements. “We did this because we are a publisher first and arbiter for publishers… We won’t ask any of our publishing partners to do anything that we’re not prepared to do ourselves,” explains Traci Mabrey, general manager of Factiva. “As such, we have elected and will continue to elect, to reach out to publishing entities and request additional licensing permissions and actual rights for generative AI use.” Today, its marketplace includes nearly 5,000 partners.

Dow Jones emphasizes the importance of respecting and compensating intellectual property and content creation. Mabrey outlines four key criteria guiding their AI partnerships: trust, transparency, segmentation, and compliance.

“We believe that trust is imperative. We believe there needs to be transparency in terms of content being created, used, surfaced and attributed,” Mabrey says. “There also needs to be relative segmentation in terms of use cases across different solutions. And there needs to be compliance and governance to adherence to the first three, of trust, transparency and segmentation.”

Deal points when licensing content for AI training

There’s no one-size-fits-all model for licensing deals, and the best approach depends on a publisher’s specific goals, content, and resources. Some determine how easily an LLM can integrate into their existing systems and CMS. Some choose LLMs based on those they already deal with.

But, data privacy and security are central concerns in these agreements. Vadim Supitskiy, chief digital and information officer at Forbes, told Digiday that ensuring interactions with AI products remain safe and protected is a key priority.

Mabrey echoes this sentiment, emphasizing that privacy and security are integral components to negotiations with AI partners. “As we’re looking at responsible delivery of AI, responsible usage of content and privacy and security in terms of technical infrastructure, that is our leading indicator.”

Publishers must have review rights over AI-generated outputs, ability to see proof of usage logs, and be able to enforce brand guidelines, according to Boyle. “All those things have to be clearly defined in the licensing agreements. Tracking metrics of engagement, attribution, and demographic insights is also important for publishers to receive, to be able to see how valuable their licensed content is,” she says.

Essential safeguards in the agreements themselves ought to include strong, sophisticated clauses to protect publishers’ IP, says Boyle, “including mechanisms to prevent unauthorized reproduction, clear ownership definitions, restrictions on data usage, well defined termination provisions, attribution and fair compensation.”

Howard emphasizes that no two content licensing deals with AI companies are the same, and each comes with significant legal and technical hurdles. “First, there’s the legal aspect and every company needs to come up with their own legal terms and what is acceptable to them and what is not. What do they have the rights to? What do they not have the rights to?” he says.

“Once you’ve determined all of that, you need a technology solution to be able to deliver the content to them… All of the delivery mechanisms are quite different and require some form of customization.”

These complexities point to why AI companies have slowed the pace of new licensing agreements after an initial rush. Negotiating unique terms and building tailored tech solutions for each partner has proven difficult to scale, Howard notes.

Where AI licensing is headed

AI is reshaping how content is distributed, discovered, and monetized. For media companies, the choice is clear: engage in legal battles or proactively negotiate terms that ensure fair compensation. The market is rapidly evolving with new players, technologies and partnership models.

For companies currently negotiating content licensing deals with AI, Howard says to move forward. He points out that, while there are benchmarks based on what other companies have secured, the initial rush of deals has likely passed. He doesn’t expect future deals to improve; in fact, he thinks they’ll probably get worse.

Mabrey believes that the industry has reached a unique inflection point, where generative AI gives it the chance to assert that content is intellectual property and requires compensation. “We, as a media community around the world, should be coming together to assure that all of us are asserting our rights in the same manner.”

In light of these shifts, there’s a clear message for media executives: the future of content licensing is in their hands. Instead of letting the industry define them, publishers can shape the future of the industry by hammering out a windfall through litigation and the courts, negotiating partnerships, and advocating for fair treatment.

Innovation

5 Ways media companies can boost audience trust when using AI

March 4, 2025 Kevin Rehberg, Vice President, Client Development – Alliance for Audited Media

Artificial intelligence is rapidly transforming the way media companies operate. From automating article summaries to addressing editorial efficiencies, the use of AI has helped media companies save time and streamline operations. While AI offers substantial benefits, recent studies have revealed a trust gap between media companies and their audiences around AI use:

85% of news consumers expect clear disclosure when AI is involved in news production
82% of Americans care about the ethics of AI
32% of survey participants stated AI-generated content often lacks proper citations

Since trust is the cornerstone of media, AI implementation introduces new challenges. Missteps can result in loss of reader trust, damage to brand reputation and potential legal and regulatory challenges.

As AAM developed its new Ethical AI Certification program, we researched how media companies are implementing AI and studied industry-recommended best practices for increasing transparency and disclosing AI use. This research resulted in the development of several guidelines for media companies to increase transparency and maintain reader trust when integrating AI solutions into their operations.

1. Clear and consistent AI labeling

AI-generated or assisted content should be visibly labeled and disclosed. Labels should be placed prominently with an article or video rather than buried in fine print.

Here are two examples of how media companies are disclosing AI use:

The Associated Press created standards around generative AI. While the tools may change, the core values remain – journalists are accountable for the accuracy and fairness of the information they share.
USA Today adds disclosures to indicate when AI is used to write its “Key Points” at the top of selected articles. It also discloses that a journalist reviewed the AI-generated content before publication and includes a link its ethical conduct policy.

2. Create and publicize AI policies to build trust

Media companies should publish a clear AI policy outlining:

How and when AI is used
The company’s privacy policy when involving AI use
Editorial guidelines for AI-generated content
How the company will handle ethical issues including bias mitigation and misinformation prevention

Policies should be easily accessible on company websites and updated regularly. Media companies also should ensure that they have licensing agreements in place to use the information and data provided by their AI solutions in published content.

3. Human oversight and accountability

Human oversight of AI-generated content is also essential to include when implementing AI, especially in editorial. Assign clear roles and responsibilities for AI oversight within newsrooms and establish an internal AI ethics committee to assess AI applications, guide policy development and ensure ongoing compliance with ethical standards.

4. Ongoing education

Since AI best practices and regulations are constantly evolving, it’s important for media companies to provide ongoing training for staff on AI technology, ethics and best practices. Hosting regular training workshops and updating employees on policy changes helps companies stay ahead of evolving AI trends while ensuring responsible and ethical AI usage.

5. Regular audits and risk assessments

Media companies should conduct regular assessments to manage AI risks including assessing the accuracy of AI-generated content, the effectiveness of company transparency measures and potential challenges including bias and inaccuracy in AI-generated content.

Independent oversight and third-party audits can help identify risk areas and offer suggestions for improvement to improve transparency and efficiency.

As AI continues to evolve, transparency remains essential to preserving trust between media companies and audiences. By implementing these industry best practices and guidelines, media companies can take the lead in setting a higher industry standard, maintaining audience trust and ensuring ethical AI implementation within their operations.

AI and human playing chess to represent AI licensing negotations

The AI reckoning for publishers and platforms

February 27, 2025 Jason Kint, CEO – DCN

The publishing industry has been of two minds on AI’s rapid advancements – optimistic and cautious – sometimes within the same company walls. Business development teams explore much-needed new revenue opportunities while legal teams work to protect their art and existing rights. However, two major legal developments, the Thomson Reuters v. Ross Intelligence ruling and shocking new revelations in Kadrey v. Meta, expose the fault lines in AI’s unchecked expansion and set the stage for publishers to negotiate fair value for their investments.

One case confirms that publishers have a right to license their content for AI training and that tech advocates’ tortured analysis of fair use doesn’t throw out rights engrained in the U.S. Constitution or require publishers to opt-in to attain them. The other case suggests that Meta may have knowingly pirated books in its high-stakes race to keep up with OpenAI and that Meta’s notorious growth-at-all-cost playbook is more exposed than ever.

AI companies can no longer operate in a legal gray zone, scraping content as if laws don’t apply to them. Courts, lawmakers, researchers and the public are taking notice. For publishers, the priority is clear: AI must respect copyright from the beginning including for training purposes, and the media industry must ensure it plays an active role in shaping AI’s future rather than being exploited by it.

Thomson Reuters v. Ross: A win for AI licensing, a loss for those who intentionally avoid it

In a landmark decision, a federal judge ruled this month in favor of Thomson Reuters against Ross Intelligence, a startup that trained its AI model without rights or permission using the Reuters’ Westlaw legal database.

Judge Stephanos Bibas’ ruling in the Delaware district court is notable because he explicitly recognized the emerging market for licensing AI training data. This undercuts the argument that AI developers can freely use copyrighted works under “fair use” factors. And, consistent with DCN’s policy team, it also highlights the significant importance of the fourth factor of fair use, which publishers have been demonstrating with the signing of each new licensing deal.

For publishers, this is a crucial precedent for two reasons:

AI training is not automatically fair use. Content owners have the right to be paid when their work is being used to train AI.

A market for AI licensing is forming – this is the fourth factor. Publishers should define and monetize it before platforms dictate the terms.

This decision marks a turning point, ensuring that AI development doesn’t come at the expense of the people and companies producing high-quality content. Sam Altman of OpenAI, and other leadership across the powerful AI industry, have attempted to invent a “right to learn” for their machines. That’s an absurd argument on its face but regularly repeated in high-profile interviews, as if the technocrats might will it into reality.

Kadrey v. Meta: Pirated Books, torrenting, and a familiar playbook

While the Reuters ruling validates AI licensing, Kadrey v. Meta reveals how some AI developers have worked to avoid it.

Recently unsealed court documents suggest that Meta employees knowingly pirated books to train LLaMA AI models used as their first commercial version (LLaMA2). Significantly, their fair use analysis shifted from “research” to making bank – a lot of it.

Evidence revealed that demonstrates this knowing strategic shift:

Meta employees downloaded pirated book datasets from a massive, pirated dataset, LibGen, with employees even using torrenting technology to pull it down.

They may have “seeded” and distributed this pirated content to others. That’s a potential violation of criminal code that their own employees shared this, “What is the probability of getting arrested for using torrents in the USA?”.

Meta worried that licensing even one book would weaken its fair use argument, so it didn’t license any at all.

Some employees explicitly avoided normal approval processes to keep leadership from having to formally sign off.

Some documents suggest Mark Zuckerberg himself may have been aware of these tactics with documents referencing escalations to “MZ.”

Meta appears to have stopped using this material ahead of LLaMA3, possibly signaling awareness that their actions were legally indefensible.

Making matters worse, Meta’s case is being overseen by Judge Vincent Chhabria in the Northern District of California. This is the same judge who sanctioned Facebook’s lawyers in its massive privacy settlement that led to record-breaking settlements approaching $6 billion with the FTC, SEC and private plaintiffs. In that case, Facebook was accused of stalling, misleading regulators, and withholding evidence related to its user data practices. In other words, Judge Chhabria knows Meta’s playbook: delay, deny, deflect.

Now, Meta faces a crime-fraud doctrine claim. This means that some currently sealed legal advice could be unsealed if it was in furtherance of a crime. If proven, this would not be a simple copyright dispute; it could potentially lead to criminal liability and further regulatory scrutiny. The Court is ordering Meta to unseal more documents this week.

Move fast, break things… again: Meta’s AI strategy mirrors its past scandals

The Kadrey case’s revelations closely resemble Meta’s past data controversies, particularly those that were all put into the basket of Cambridge Analytica. The many ongoing details of the cover up of the scandal are still emerging today. Unfortunately, they were mostly overlooked by the tech press corp who have not been tuned in to these issues for far too long.

For years, Facebook pursued a strategy of aggressive data harvesting to accelerate its growth in mobile where it had risk of being supplanted by new platforms. The company:

Scraped vast amounts of publisher and user data without clear consent.

Shared this data widely with developers in exchange for reciprocal access to their user data – fueling Facebook’s mobile market share grab.

Ultimately settled with regulators for billions after repeated privacy violations.

Now, in Kadrey v. Meta, history appears to be repeating itself. Internal documents show that Meta feared OpenAI and needed to accelerate its AI development. Thus, Meta felt pressured to take outsized risks. Meta’s approach to AI training follows a similar pattern:

Acquire the best data – legally or not.

Use it to gain an edge over AI competitors.

Deal with legal and regulatory fallout later, if necessary.

Recently unsealed documents even expose a documented mitigation strategy.

Remove data clearly marked as pirated (but only if it’s in the filename despite letting the coders strip out copyright info in the actual content)

Don’t let anyone know what data sets they’re using (including illegal datasets)

Do whatever possible to suppress prompts that spit out IP violations

Key takeaways for publishers and media companies

The Thomson Reuters and Kadrey cases demonstrate both the risks and the opportunities for publishers in the AI era. Courts are starting to push back on AI’s unlicensed use of copyrighted content. But it’s up to the publishing industry to define what comes next.

Here are the big issues we must address:

AI models need high-quality data. And publishers must ensure they’re compensated for it. The Reuters ruling proves that a growing licensing market for AI exists.

Litigation is working. The unsealed evidence in the Kadrey case suggests that even AI giants like Meta know they’ve crossed legal lines. Facebook isn’t dumb, evidence from other peer companies may be even more damaging. The plural press needs to be shining the light on these wrongs as national security isn’t an excuse for AI companies to break copyright law.

Publishers must be proactive in shaping AI policy. Big Tech will push its own narrative. Meta and Google pay front groups like Chamber of Progress to stretch the meaning of fair use both in the U.S. and across the pond. Media companies must work together to establish AI licensing frameworks and legal protections and reinforce existing copyright law.

Regulatory scrutiny on AI will intensify. If Meta is found to have used pirated data, it will accelerate AI regulations. This will not likely be confined to copyright but could extend across tech policy as it did in 2018, when one scandal exposed larger problems leading to Facebook being dragged before parliaments around the globe.

The future of AI depends on trust, ethics and media leadership

The past year has shown that AI is both a disruptor and an opportunity. The Reuters ruling confirmed publishers can and should demand licensing deals. The Meta revelations prove why that’s so necessary.

AI is reshaping media, but it must be built ethically. The publishing industry has both the legal and ethical high ground. And media companies must use it to define the next phase of AI’s evolution. The future of AI isn’t just about innovation. It’s about who controls the data and the IP – and whether the people who create it are respected or exploited.