Podcasts are transforming how Americans consume news, offering on-demand access to trusted voices and in-depth analysis. As traditional news formats evolve, podcasts have become a critical medium for audiences seeking timely, engaging, and diverse perspectives.
Second only to comedy, news podcasts are a dominant podcasting genre. A new report from Sounds Profitable, in partnership with Signal Hill Insights, finds that 31% of podcast listeners consumed news content in the past month. The findings underscore a significant shift in how Americans engage with news, moving away from traditional TV broadcasts and toward more personalized, on-demand listening experiences.
News podcast consumer demographics
The average age of a news podcast consumer is 47, closely mirroring the overall U.S. adult population. This starkly contrasts television news audiences, where the average age skews significantly older—70 for MSNBC, 69 for Fox News, and 67 for CNN. This demographic shift highlights how younger audiences gravitate toward podcasts as a preferred medium for staying informed. Balancing short-form daily news updates with longer-form analytical discussions allows podcast listeners to integrate news consumption seamlessly into their routines.
The social influence factor
One of the study’s more interesting findings is the role of social influence in driving news podcast discovery and engagement. News podcast listeners exhibit significantly higher levels of social sharing and recommendations compared to their non-news counterparts:
73% receive podcast recommendations from friends and family, compared to 51% of non-news listeners.
73% actively recommend podcasts to others, versus 49% of non-news listeners.
83% say they are likely to listen to a podcast recommended by someone they know.
This word-of-mouth dynamic plays a crucial role in podcast adoption, highlighting the importance of personal connections in shaping media consumption habits. While platforms and algorithms contribute to discovery, personal recommendations remain the most powerful driver of engagement.
Additionally, news podcast listeners are more likely to consume content with others. Unlike other podcast genres that often cater to solo listening, news podcasts frequently become a shared experience. Group listening fosters discussions and deeper engagement with the content, whether in the car during a commute or as part of a morning routine. The study reveals that 88% of news podcast consumers who listen with others cite “listening while traveling” as a major benefit, compared to 66% of podcast listeners.
Advertising challenge and opportunity for news podcasts
Despite their high engagement levels, news podcast listeners are not immune to advertising fatigue. The study reveals that:
21% have stopped listening to a podcast due to excessive ads.
14% cite repetitive content as a reason for abandoning shows.
This finding challenges the assumption that strong host-listener relationships can completely counteract fatigue. Even among engaged audiences, there is a threshold for how much advertising they are willing to tolerate.
However, the research also uncovers a compelling opportunity for brands. News podcast listeners are more receptive to brand-sponsored content than the general podcast audience:
61% say they are likely to listen to a brand-sponsored podcast.
46% indicate that a company’s involvement makes them more likely to try a new podcast than 34% of non-news listeners.
Brands can forge meaningful connections with news podcast audiences by positioning themselves as content partners rather than just advertisers. By integrating seamlessly into the content, brands can enhance rather than disrupt the listener experience.
Podcasts and the future of news consumption
The traditional model of news consumption—gathering around the television at a fixed time—has largely faded. Instead, audiences curate their news experiences through digital and on-demand platforms. While social media and news websites play an important role in this transition, podcasts offer a unique advantage: deeper engagement and trust.
Unlike passive scrolling through headlines, listening to a news podcast requires intentional engagement. The hosts of these podcasts often become trusted voices, forming strong bonds with their audience. This level of trust is a significant draw, positioning news podcasts as a vital part of modern news consumption. However, the challenge lies in maintaining audience engagement without alienating listeners through excessive advertising.
The findings from this report offer a compelling look at the evolving media landscape. News podcasts attract a younger and more engaged audience and reshape how people discover, consume, and share news. The influence of social recommendations and the potential for shared listening experiences emphasize the unique role of news podcasts in today’s information ecosystem. Additionally, the nuanced relationship between advertising and engagement further solidifies their distinct position.
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.
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.”
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.
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:
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.
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.
Journalism support organizations face scrutiny regarding efficiency, strategic direction, and overall impact. Critics argue that these organizations function as bureaucratic intermediaries, consuming philanthropic resources without adequately addressing the needs of local news outlets. At the same time, many journalists and news organizations find these support structures essential to their survival, particularly as they navigate an increasingly complex media landscape. This tension raises an important question: How can support organizations evolve to better serve the local news ecosystem?
Field-level agenda for journalism support organizations
One of the report’s findings is the lack of a shared framework to measure success in the local news sector. Many support organizations operate with distinct, sometimes overlapping missions, making it difficult to assess their collective impact. The report proposes a “field-level agenda” as a solution—an overarching strategy that brings together diverse players to set priorities, establish success metrics, and enhance collaboration.
Support for this concept is widespread. Many industry leaders echoed the idea that a structured, collaborative framework is needed to ensure that support organizations advance the field. David Grant of Blue Engine Collaborative notes that the industry struggles to effectively define its audience and measure impact. Similarly, Mary Walter-Brown of News Revenue Hub emphasized that support organizations should be held accountable for how well they help news organizations grow.
Roles and responsibilities of the news support ecosystem
There is broad agreement on the need for better organization within the field. However, stakeholders are still grappling with who should lead this transformation. Many argue that philanthropic organizations should establish more explicit expectations for accountability and collaboration.
Damon Kiesow, Knight Chair in Journalism Innovation at the University of Missouri, suggests that funders could accelerate progress by requiring grantees to adhere to standardized impact metrics. Tristan Loper of the Lenfest Institute points to recent grantmaking initiatives emphasizing partnerships rather than competition as a potential model for fostering greater alignment within the sector.
Others question the long-term sustainability of specific support organizations. Instead, they propose that funders adopt a more strategic approach to determining which initiatives should last decades and which should be time-limited interventions. This perspective underscores the importance of developing a clear vision for the role of support organizations within the broader journalism landscape.
A collaborative approach to news media support
The report highlights that while no single experience defines all support organizations, there is a shared desire for greater collaboration. The report was given to participates for review and some leaders noted that the report’s initial tone seemed overly defensive. They felt it reinforced criticisms rather than highlighting these organizations’ indispensable work. However, this feedback reinforces the issue’s complexity. Support organizations must navigate a fine line between responding to critiques and advocating for their essential role in the ecosystem.
Stefanie Murray of the Center for Cooperative Media challenged the notion that support organizations lack accountability. She pointed out that many already adhere to rigorous funding requirements. This debate underscores the need for a nuanced discussion about defining and measuring success in ways that reflect the realities of different organizations.
Next steps for journalism support organizations
While this report provides valuable insights, it raises questions that merit further exploration. For instance, how can the support field evolve, and what lessons can we learn from other industries? What balance should exist between funding direct journalism (news organizations) and intermediary organizations providing infrastructure and support?
Several leaders who participated in the report have proposed expanding the taxonomy of support organizations to include groups that act as bridges between journalists and community organizations. Others call for a similar taxonomy for newsmakers and funders, which could help clarify how different entities fit within the broader ecosystem.
The challenges facing journalism support organizations are complex. However, the Democracy Fund’s research states the need for reform is clear. Establishing a field-level agenda could bring greater coherence, accountability, and impact to the sector. However, achieving this vision will require sustained collaboration among all stakeholders, support organizations, funders, and local news leaders.
As the industry evolves, support organizations must adapt to ensure they remain valuable partners to local newsrooms. By embracing a more strategic, data-driven approach to measuring success and fostering collaboration, the field can move toward a more sustainable and effective future for local journalism.
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 sharedthis, “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.
Understanding the difference between having an audience and building a community isn’t just semantics—it’s a strategic necessity. With social referral traffic declining, third-party cookies being (semi) deprecated, and generative AI reshaping search, media organizations must reclaim their communities from third-party platforms. By fostering deeper engagement and stronger loyalty, they can create sustainable revenue streams and drive long-term growth.
Arc XP recently hosted a webinar featuring Mark Zohar, President and CEO of Viafoura, to explore how publishers can cultivate thriving communities. Below, we break down the key insights, strategies, and real-world examples that highlight why community-building is essential for long-term success.
Why community matters more than ever
Traditional approaches to audience acquisition, like relying on social media platforms for referral traffic, are no longer reliable. Social networks like Facebook and X are sending less traffic to publishers, and the rise of alternative content platforms like Substack and podcasts has further fragmented media consumption. Meanwhile, changes in Google’s search algorithms, which prioritizes user-generated content and community engagement, are shifting how audiences discover information.
Anthony DeRosa, former Head of Content and Product at ON_Discourse, expresses the urgency of this shift when he said, “Media companies should own their audiences. They’ve allowed tech companies to steal their content and monetize it by providing a platform for readers to discuss it. How absurd is that?”
The solution? Own your audience. Create spaces where audiences don’t just consume content—they engage with it, discuss it, and contribute to the discourse. As Mark Zohar put it, “An audience listens, while a community interacts, shares, and grows together.”
The benefits of community-building
An effective community strategy provides tangible benefits, including:
Higher Engagement & Retention – Community members spend 5.3x more time on-site and visit more frequently than anonymous users.
Increased Conversions – A strong community drives higher registration and subscription rates, with members being 31% more likely to pay for a subscription.
Reduced Churn – Engaged community members are 2.5x less likely to unsubscribe compared to passive readers.
Better First-Party Data – Communities provide valuable user insights, helping media organizations develop targeted advertising and personalized campaigns.
Stronger SEO – Google now prioritizes user-generated content, meaning active community engagement can significantly boost search rankings.
The Financial Times’ Next Gen News: Understanding the audiences of 2030 study found that younger, digitally native audiences are particularly drawn to participatory experiences. Many skip over full articles and head straight to the comments section to gauge the conversation. For them, community interaction isn’t just a feature, it’s the primary draw.
Building a community: the TRIBE framework
To successfully transition from an audience to a community, Zohar introduced the TRIBE framework, originally developed by Greg Isenberg, CEO of LateCheckout and former TikTok and Reddit Advisor. This framework serves as a guide for media organizations to evaluate how they are fostering community within their brand. TRIBE stands for:
Togetherness – Are we creating spaces where users can engage directly with our content and each other?
Rituals – What habits or recurring experiences keep our users coming back, such as weekly Q&As or interactive polls?
Identity – How are we fostering a sense of belonging through shared interests and values?
Belonging – Are we giving users a reason to feel invested in our community’s success?
Engagement – What opportunities are we providing for active participation, from commenting to user-generated content?
Leveraging the creator economy
A thriving community attracts creators, influencers, and contributors who can help expand reach and enrich discussions. To tap into this potential, media brands should actively collaborate with content creators, bringing fresh perspectives and loyal audiences into their ecosystems. This can be achieved through partnerships on platforms like TikTok and Instagram, as well as influencer collaborations within their own channels. By offering monetization opportunities and fostering engagement-driven spaces, media brands can encourage influencers to participate directly on their platforms rather than relying solely on external networks.
Examples of media brands successfully leveraging the creator economy include:
Yahoo for Creators – A platform that offers writers a community to share expertise and connect with engaged readers.
Forbes Contributor Network – A model where industry experts contribute content while benefiting from Forbes’ audience reach.
Community-building is a strategic priority
Community-building isn’t just about engagement. It is a direct driver of business growth. Organizations that invest in fostering vibrant communities see measurable benefits across key revenue and operational metrics:
Higher Revenue Per User – Community members generate 5x more revenue than general audiences.
Registration Growth – Implementing community features can double registration rates by offering a compelling value exchange.
Sustained Engagement – For some early adopters, community interactions now drive over 30% of total site registrations.
A well-designed community strategy transforms media brands from content distributors into engagement hubs, where audiences aren’t just passive consumers but active participants contributing to the brand’s success.
Foundations for a successful community strategy
For media brands looking to build a thriving community, success depends on three core pillars:
Intention – Community-building must be treated as a business strategy, not an afterthought. Define clear goals, KPIs, and secure executive buy-in to ensure long-term commitment.
Cultivation – A strong community is built on trust and inclusivity. Active moderation, clear user guidelines, and engagement incentives create a safe space where discussions flourish.
Operationalization – A community can’t sustain itself without consistent efforts. Media organizations must develop editorial playbooks, monetization models, and regular engagement cadences to ensure continued growth and participation.
The path forward: own your audience
Media companies can no longer afford to rely on third-party platforms to engage their audiences. Instead, they must take control by fostering direct relationships through community-driven experiences.
The future of media isn’t just about publishing content. It is about facilitating conversations, connections, and shared experiences. By embracing community-building as a core strategy, publishers can create deeper loyalty, drive sustainable revenue, and future-proof their businesses in an era of increasing digital fragmentation.
Artificial intelligence is rapidly transforming the way people access and consume news. With AI assistants increasingly serving as intermediaries between audiences and trusted news sources, it is essential to understand how accurately and reliably they present information. Unfortunately, according to recent research from the BBC, AI does not accurately deliver news.
In new research, the BBC is evaluating how well leading AI assistants—ChatGPT, Microsoft’s Copilot, Google’s Gemini, and Perplexity—deliver news-related answers. By granting these AI models access to its website, the BBC sought to assess its ability to effectively reference and represent its journalism.
This study examined the quality of AI-generated responses using 100 news-related questions, with BBC journalists evaluating them based on seven key criteria, including accuracy, attribution, and impartiality. The reviewers then determined whether the responses contain minor, significant, or no issues across these areas.
Significant errors in AI news
The results show that over half (51%) of AI-generated responses contain significant issues, while 91% exhibited some inaccuracy, bias, or misrepresentation. Specific issues include factual errors, misattribution of sources, and missing or misleading context. When evaluating how these AI assistants represented BBC content, the study finds that Gemini (34%), Copilot (27%), Perplexity (17%), and ChatGPT (15%) produce responses with errors in their use of BBC sources.
Accuracy and misinformation
AI-generated responses frequently report factual inaccuracies, even when citing BBC sources:
Gemini incorrectly states that the NHS discourages vaping as a smoking cessation method, despite BBC coverage explicitly confirming that the NHS supports vaping for smokers that want to quit.
Copilot misrepresents the case of rape survivor Gisèle Pelicot, falsely claiming that blackouts and memory loss led her to uncover the crimes against her.
Multiple assistants incorrectly report figures, such as significantly underestimating the number of UK prisoners released and misattributing Chrome’s market share statistics.
ChatGPT erroneously reports that Ismail Haniyeh, assassinated in July, is still an active Hamas leader.
Attribution and sourcing errors
AI assistants frequently misattribute or incorrectly source information. Some rely on older articles, leading to misleading conclusions. In several instances, assistants claim to summarize BBC reporting but include details that did not exist in the BBC articles.
Impartiality and editorialization
In addition to prevalent factual errors, AI assistants struggle with maintaining any semblance of journalistic impartiality. The study flags multiple instances where opinions are presented as facts, sometimes falsely attributing to the BBC as the source. For example, Perplexity characterized Iran’s actions in the Middle East conflict as “restrained” and described Israel’s response as “aggressive,” despite no such characterization appearing in the BBC article.
AI errors in news is a risk to public trust
These findings highlight serious risks in AI-generated news summaries. Misinformation can erode public trust in news media, whether due to factual errors, misleading context, or editorialized conclusions. Distortion of BBC’s content can have significant consequences. If these risks continue, audiences may question the credibility of BBC’s reporting.
AI assistants are set to play an increasing role in how people access news and because they do not generate meaningful traffic to media websites, it appears that the majority of people using them are not exploring further to determine the accuracy of the purported news AI delivers. This, it is critical that AI agents or chatbots endeavor to uphold the information ecosystem’s rigorous and trusted editorial standards.
Ultimately, AI developers are responsible for ensuring their products align with fundamental journalistic principles, including accuracy, impartiality, and reliable sourcing. The BBC warns that if these challenges go unaddressed, AI risks undermining the news organizations it depends on for credible information. As AI continues to evolve, the BBC emphasizes the need for the media industry to champion responsible AI integration to safeguard audiences and preserve journalism’s integrity.
Subscriptions remain a vital revenue stream for most media companies, but the landscape is rapidly shifting. In response, publisher strategies also need to adapt and evolve.
The days of easy subscriber growth are over. To drive subscription growth, media companies must double-down on addressing core challenges such as churn, consumer fatigue, declining social referrals, and opportunities afforded by AI to sharpen their engagement strategies.
This will mean focusing on retention and maximizing lifetime value. Media organizations will also need to refine paywall strategies and offer flexible, engaging, experiences to ensure audiences keep coming back – and, ideally, keep paying for your content.
To better understand these trends, I reached out to four leading industry experts: Kevin Anderson, Peter Houston, Greg Piechota, and Madeleine White, and examined the latest insights from WAN-IFRA and the Reuters Institute for the Study of Journalism.
Here’s what you need to know.
Trend 1: Retention is king
“Publishers long ago converted the low-hanging fruit of their most engaged audiences to subscribers,” notes Kevin Anderson, Director Consulting Services at Pugpig. This is one reason why, as the latest Digital News report revealed, subscription growth has largely flattened.
Moreover, in an era of news avoidance and on-going declines in social media referrals, “the flow into the top of the conversion funnels is drying up,” Anderson adds. “Growth is getting harder to find.”
As a result, a focus on retention will a key priority for publishers in 2025. Afterall, as Greg Piechota, Researcher-In-Residence at the International News Media Association (INMA), reminds us, “you make more money with higher retention than with higher price.”
An emphasis on reducing churn and developing long-term customer relationships can be seen across the subscription economy. Recurly’s 2025 State of Subscriptions report found that return acquisitions account for 20% of new subscribers, underlining the value of retaining your audience.
Tactics to successfully do this include payment flexibility (e.g. weekly, monthly and annual plans), and the ability for users to pause a subscription, rather than cancel it.
Local newspapers like the Bangor Daily News in Maine, enable you to pause your print subscription when going on vacation. The New York Times offers something similar. Applying this principle to digital products may reduce cancellations and keep more consumers engaged long-term.
This matters because, as The Daily Beast discovered, subscribers are worth 18 times more than unknown users. And that figure grows to 169% when revenue from first-party data and advertising is taken into account across channels such as newsletters and apps.
Retention strategies therefore need to encompass your whole product stack. Newsletters, apps, podcasts and push notifications aren’t just pathways to conversion. They are a means to drive revenue and deepen audience loyalty across multiple touchpoints.
Trend 2: Harness AI to become truly audience-first
Media companies have talked about being “audience-first” for years, says Madeleine White. But a lot of this potential is unfulfilled, she contends. White, VP Marketing at Poool, and Editor In Chief and co-founder of The Audiencers, believes advancement in AI offers a means to finally deliver on this promise.
AI allows us to segment readers based on interests, engagement levels, and traffic sources. This means that media companies can move away from generic offerings to more personalized experiences that support subscription growth.
White points to TIME’s Person of the Year experience as a case in point. Through the use of Generative AI, audiences could consume the cover story through a range of formats. This included an audio version, a concise summary, an in-depth analysis, and the ability chat with an AI assistant about the winner, President Donald Trump.
“Instead of simply kind of creating this single form, the article becomes shapeless,” White says. “It can be transformed and controlled by each reader, which is basically what audience first, is all about.”
Through the use of Generative AI, audiences could consume the cover story through a range of formats.
Trend 3: AI-powered paywalls become commonplace
Dynamic AI-driven paywalls are nothing new. But they are growing in adoption and sophistication. And this evolution offers subscription growth.
As INMA’s Piechota explains, “publishers are using data and AI to tailor paywalls more precisely. This boosts conversion by predicting both each user’s and each article’s propensity to subscribe.”
Hearst USA is one such publisher adopting this more sophisticated approach. They worked with Mather Economics to create a machine learning model that uses 75 different variables to trigger actions designed to mitigate churn and engender long-term customer loyalty.
“The biggest challenges lie around putting this into practice,” White contends. Many “publishers are kind of trying to jump the gun and go straight to a very machine learned AI based model,” she says. She recommends a more incremental approach. Articles that provide unique value should sit behind a paywall, White suggests. More “commodity content” can be open to all, in order to get as much advertising revenue as possible.
Argentina’s Clarín, the Spanish-language newspaper with the largest number of digital subscribers in the world, is already adopting this approach. As outlined by Spanish journalist and consultant Ismael Nafría, hindering access to what Clarin calls “decisive articles” is essential to persuading audiences to subscribe. The publication seeks to publish 10 to 12 of these kinds of articles per day.
Trend 4: Bundling 2.0
I wrote about bundling strategies back in May 2023. Since then, a growing number of publishers have sought to innovate and expand their efforts in this space to fuel subscription growth. Piechota observes how companies aren’t just bundling their own products. They’re “increasingly partnering with other publishers, even competitors, to engage broader audiences.”
One such business, The New York Times, “is obviously the Queen of the bundle,” says Peter Houston, co-founder of Media Voices and the author of The Magazine Diaries.
The Gray Lady recently announced it has more than 11.4 million total subscribers. However, that hasn’t stopped it looking for subscription-rooted partnerships, at home and abroad.
Meanwhile, both Anderson and Piechota point to the success of the Norwegian publisher Amedia as a leader in this space. “Amedia is a super bundler,” says Piechota, “selling readers access to more than 100 brands with one price and app.” He notes that 75% of digital subscribers at Amedia upgraded to such a bundle; compared to 50% at the Times.
Trend 5: An emphasis on pricing and value
Media companies are increasingly vying for our time, as well as our wallets. “If Netflix puts its prices up, do you cancel Netflix, which you watch for hours every week, or the hobbyist magazine which you love but only read once a month?” asks Houston. Against this backdrop, the perceived value of your offer will define a consumer’s propensity to subscribe or keep a subscription.
The breadth and depth of content you offer is part of this equation. However, specialist content, which allows you to dig deeper, can also be a major draw. As Houston explains, “super-niche coverage will also become attractive to consumers who want less distraction and more of what they really care about.”
Tortoise Media’s Daily Sensemaker podcast Is a case in point. It hits multiple consumer needs via a daily 10-minute show exploring a single topic, designed “to make sense of the world.”
“Value adds” can also be part of this mix. Membership models have long leaned into this, with a mix of exclusives, events and discounts. Last week the podcast The Rest Is Politics US announced that founding members would be able to join recordings of new episodes live on YouTube. Everyone else gets to see (or hear) the show a day later.
Print might also be part of the equation. In October, The Atlantic revealed it would return to monthly editions of its print publication due to subscription growth and a return to profitability. The title had been published 10 times a year for 22 years running.
And after a four-year hiatus, Saveur magazine, a 30-year-old gourmet, food, wine, and travel publication, resumed print editions last spring. “We see our print product as the couture of our brand,” Editor in Chief and CEO Kat Craddocktold The Publisher Podcast. “It’s for the superfans.”
In short, subscribers want to feel they are getting their money’s worth, both in terms of content and experience. Delivering on both of these fronts is the sweet spot publishers will increasingly need to hit to drive subscription growth.
Assembling strategic pieces for subscription growth
The subscription landscape is beginning to undergo a major transformation, driven by the need to innovate, and the ability to harness AI and audience data to create more tailored and media-rich offerings. These factors combine to create opportunities for subscription growth.
INMA’s Greg Piechota highlights the key takeaway. “The common thread,” he says, “is a blend of differentiated journalism and engagement-driving products.” And this must be underpinned by “mastery in data analytics, and a willingness to experiment.”
Success in this arena is vital for the financial health of most media companies. A survey of 326 media leaders in 51 countries, as the Reuters Institute’s annual predictions report, found that 77% of respondents said subscriptions were “likely to be important or very important” for their company in 2025.
To succeed publishers must move “beyond long and discounted trials, and targeted price increases at renewal,” Piechota contends. Moreover, as Pugpig’s Anderson points out, although many publishers have been trying to increase the average revenue per user (often through premium bundles), that’s not an option that’s open to everyone.
As a result, in the coming year, expect to see a refinement of subscription tactics, with an emphasis on retention, personalization, and flexibility. These principles will cut across price structures, bundling strategies and wider engagement strategies.
“The bottom line for subscriptions is that people don’t want to waste money or time on them,” argues Media Voices’ Houston. “So many people have a bloated subscription stack and the reckoning is coming.”
With many outlets continuing to see a decline in monies from advertising and print, an emphasis on reader revenue will remain a strategic priority.
As Poool’s White emphasizes, that means it’s more important than ever to deploy user-focused, audience-first approaches. These models value loyalty and long-term relationships more than short-term conversions.
Continued subscription growth is possible for media companies that understand and incorporate these factors. By evolving their subscription growth strategies, they will be most likely to prosper in the year ahead and beyond.
/ An inside look at the business of digital content
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