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pop art signs saying do not scrape
Perspectives | Supporter Spotlight

Why ‘do not scrape’ policies matter more than you think

It’s easy to feel like publishers have little recourse to the fast-track of AI adoption. Big Tech keeps updating its offerings, cribbing more content, clobbering website traffic, and battling for legal traction. This is a wake-up call.  

On the other side, consumers keep changing their consumption habits. They increasingly turn to AI for search. They accept the answers provided by AI Overviews (even if they know about AI hallucinations), and reduce their click-throughs to publishers.

Suffice to say, we’re in the messy middle. Media companies and independent publishers have been left to fend for themselves.

But it doesn’t have to be this way. Publishers are better off working together to set standards for what they want when it comes to AI, from negotiations to government legislation, rather than piecemeal settlements and implementation that might not always go our way. One way we can proactively do just that: Consider setting an industry standard for do-not-scrape policies.

This isn’t the time to sit back while AI companies help themselves to the internet’s creative work. At Raptive, we work with publishers to find ways to help them better protect their content and prepare for what’s next.

Do not scrape

Blocking AI crawlers from sites isn’t new. Publishers have been doing so for years. As Axios reported in 2023, per Originality.AI data, some 20% of the top 1000 websites have been blocking crawlers from their sites. Back in August of that year, after OpenAI announced its GPTBot crawler, major news publishers like The New York Times, Reuters and CNN, among others, made public that they would block the crawler from their sites.

The publishers aren’t alone in pushing back against the scraping. Last week, Cloudflare rolled out a new a permission-based system for AI crawlers, becoming the first internet infrastructure provider to block AI crawlers by default.

Reddit, meanwhile, has been waging its own battle against content scraping. Last June, for example, the company updated its robot exclusion protocol to have firmer restrictions against the third-party companies that crawl the company’s content; this followed the shift to charge for API access, enabling the company to monetize its vast user-generated content and control how AI companies use its data for model training. Earlier this month, Reddit filed suit against AI company Anthropic with claims that the company had “obtained access or tried to obtain access to Reddit data more than 100,000 times,” reported The New York Times.

Until recently, crawlers’ main function seemed to benefit publishers and platforms alike: traffic. Sites allowed their content to be crawled, and in doing so, they would get the monetizable eyeballs they relied upon. Robot.txt code allowed publishers and platforms to make clear who could scrape their content and what they could do with it.

That gets a lot more complicated when AI companies are the ones doing the scraping. They take content without permission and use it to benefit their companies without compensating the publishers, platforms, or creators. Even with do-not-scrape policies in place, AI companies have been reportedly trying to get around those policies to continue scraping the content and using it for their benefit.

And while Google does allow publishers to opt out of AI training and Gemini app inclusion, avoiding AI Overviews and the newer AI Mode requires opting out of all Google Search indexing—a rigid all-or-nothing approach, despite Google considering but ultimately rejecting more flexible, a la carte controls that we believe publishers deserve. 

If AI companies are crawling content, serving AI Overviews and link-less search replies that cut traffic to websites without crediting or compensating the originators of that content for said content, it’s only a matter of time until the very companies AI is scraping no longer exist. Figuring out a model for compensation for publishers, platforms and creators isn’t tomorrow’s issue–it’s today’s survival.

The future of the internet

Media companies can’t ignore tanking traffic and stolen content and hope these issues will solve themselves. Publishers need to be proactive and meet the moment. They must not only protect their content but also their brands, livelihoods, and the future of the internet. They need to be prepared for the potential legal battles that they will need to fight to ensure that their content isn’t endlessly scraped. We’re urging others to meet this moment head-on.

The future of the web shouldn’t just reward a few players who happened to move fast on AI adoption. Publishers need to remember the lessons of the print-to-digital shift: This is another seismic turning point, and getting left behind again isn’t an option. 

This text is provided for human readers at dcn.org only. Any automated reuse for AI model training is explicitly prohibited.

Concept art showing areas of AI governance
Perspectives | Supporter Spotlight

From policy to practice: Responsible media AI implementation

As artificial intelligence becomes more embedded in editorial and business processes, media companies face increased pressure to ensure AI is implemented responsibly. This requires companies to develop a plan for AI use that covers several areas including bias mitigation, risk management, legal compliance and long-term governance.

In my last article, I shared real-world examples of how media companies are implementing ethical AI best practices for transparency and disclosures, bias and ongoing staff education. Here we go deeper into the steps media companies are taking to reduce risk, protect privacy and maintain editorial oversight while integrating AI tools into their processes. Together, these form the eight pillars of ethical AI.

Ethical guidelines and standards

Establishing clear policies that define how AI is used across editorial, marketing and operational teams is essential to increasing transparency and building trust with audiences. Already, some media leaders have not only created policies around AI usage but share them publicly – which offer some great examples to other organizations grappling with AI governance.

The New York Times outlines its AI policies as part of its ethical journalism handbook, which was developed for its editorial and opinion teams and is available to the public. The guidelines state the importance of human oversight and adhering to established standards for journalism and editing.

The Financial Times also made its AI governance publicly available by sharing its principles in articles that outline specific tools staff are integrating into their workflows. It also discusses its investment in skill development and how it has transformed into a company committed to AI fluency and innovation.

Media companies need to develop formal AI ethics guidelines that help guide staff and increase transparency with the public. However, it’s equally important to regularly evaluate these guidelines as technology evolves.

Rights and permissions

As part of their governance strategy, companies must also take steps to ensure that any content produced through AI does not infringe on intellectual property rights or violate content licensing agreements. This means securing applicable rights and permissions to use the information generated by the AI tools and creating internal processes to ensure that AI outputs do not use third-party content without permission.

The New York Times encourages staff to use AI to create content including quizzes, quote cards and FAQs. However, its guidelines state that copyrighted material should not be input into AI tools, which prevents potential misuse of third-party content in AI training.

The Guardian outlines its commitment to protecting content creators’ rights when selecting third-party AI tools by stating it will only use tools that have addressed permission, transparency and fair reward for content usage.

These practices can reduce risk and reinforce a publisher’s commitment to responsible content development.

Accountability and human oversight

Even sophisticated AI systems can produce biased, inaccurate or misleading output. To safeguard against this, media companies should take a “human-in-the-loop” approach and assign qualified individuals to oversee AI tools at every stage of use.

Bay City News, a San Francisco Bay area nonprofit news organization, maintains audience transparency by publicly sharing how the team uses AI including in-depth context about the process behind each project. When it created its award-winning election results hub using AI, human oversight including fact-checking was a vital part of the project’s success.

While BBC prohibits the use of AI to directly create news content, AI use in other areas such as research must be actively monitored and the outcomes assessed by an editor.

Wired also does not create content directly from AI, but the company states that if AI is used to suggest headlines or social media posts, an editor needs to approve the final choice for accuracy.

Privacy and data protection

As readers grow more concerned about how their personal data is collected and used, publishers must take steps to ensure that AI tools are deployed in ways that maintain legal compliance. AI governance must include the development of transparent data collection policies and adhering to privacy regulations such as GDPR and CCPA.

Graham Media Group, a Detroit-based media company, prioritizes reader privacy and security and shares its compliance with data privacy laws on its disclosure page and in its privacy policy. The company also uses an in-house AI tool to help employees streamline their workflows without relying on free AI tools or unsecured platforms.

BBC states in its responsible AI policy that if staff intend to include personal data in an AI tool, a data protection impact assessment must be completed prior to use.

Risk management and adaptation

Using AI introduces a range of potential risks such as bias and fairness that must be actively managed. Effective AI governance requires continuous monitoring and a proactive approach to identifying and addressing these risks.

BBC created its AI Risk Advisory Group that includes subject matter experts from legal, data protection, commercial and business affairs and editorial departments. The group provides detailed advice on potential risks of using AI in both the newsroom and across the company.

As AI technologies evolve, so must the ethical frameworks that support their use. By integrating ethical AI principles into daily operations, media organizations can protect their brands, maintain audience trust and demonstrate their value to advertisers and partners who seek reliable, trusted media environments.

A melting clock to show that time is up for platforms / big tech's anticompetitive behavior
Featured Post | Policy

Time’s up for platform privilege

Just as a leopard doesn’t change its spots, Google and Meta haven’t changed their ways. Despite mounting legal threats and public backlash, both big tech platforms continue to behave as if rules don’t apply to them.

New evidence has emerged to underscore that Google’s original unofficial motto of “Don’t be evil” was never really their true North Star. Instead, it is a smokescreen for big tech’s naked ambitions. Meta’s early motto—“Move fast and break things”—may have been more honest, but the honesty makes it even more damning. As it turns out, the broken things weren’t just outdated norms or sluggish competitors. They were the foundations of fair competition, user privacy, democratic discourse, and now, copyright law. The damage isn’t merely collateral; it is strategic.

Big tech’s anticompetitive behavior enters its AI era

Now we’re seeing a similar pattern unfold with generative AI. In Kadrey v. Meta, evidence unsealed early this year suggests Meta execs, including Mark Zuckerberg, chose to pirate copyrighted content to train its LLaMA AI model. It was revealed that Meta initially explored licensing but opted instead to download pirated content via BitTorrent from LibGen under the reasoning that doing things the legal way would take too much time.

Worse, the company allegedly stripped copyright management info from the files to cover its tracks. Clearly, they’re following the motto of moving fast and breaking things. This time around, they seem intent on breaking copyright law. Given Meta’s long track record, I’m not sure what is most surprising: the planning of such a sophisticated heist or the ham-handed cover up. Either way, they graciously documented it all in email.

Meanwhile, over in Mountain View, Google has once again leveraged its search dominance to take traffic and revenue from publishers. In May, Google launched AI Mode, which scrapes and summarizes publishers’ original content to give users the answer without needing to click through thereby extracting out all of the incentives for the publisher.

In a bit of stunning bravado, Google rolled out AI Mode just 48 hours before closing arguments in the remedies phase of the Google Search trial, where the evidence clearly shows that Google abused its market power in search to maintain its significant advantages in crawling, clicks and query data which are paramount to the AI era. Google claims publishers can opt out. However, they can only do so by removing themselves from search entirely–which is no choice at all when it involves a company with more than 95% of the mobile queries. Google’s unauthorized use of copyrighted content to create a substitutive product has, to no one’s surprise, led to a massive downturn in traffic to publisher sites. Simultaneously, Google announced that Gemini will soon be on by default for consumers, collecting data about their activities. This is an oft-used strategy by Google. They tune the defaults to maximum data collection, knowing full well that consumers won’t know or take the time to shut them off.

The courts push back

However, despite big tech’s brazen and predictable pattern of brutish behavior, the legal system may be starting to catch up with the platforms’ anticompetitive tactics. Google has been found guilty of violating antitrust law in both the search and ad tech markets. And at least in the search case, the Court has been very focused on ensuring AI is a competitive marketplace rather than the fruit of more Google abuses. In addition, we’re starting to get additional clarity on how copyright law applies in this new digital age of AI.

In Thomson Reuters v. Ross Intelligence, U.S. District Court Judge Stephanos Bibas ruled that Ross infringed copyright by using Westlaw’s headnotes to train an AI competitor, despite Ross’ claims of fair use. Initially, Ross reached out to Thomson Reuters to license the content but ultimately opted to acquire the Westlaw content from a third party, LegalEase (which sounds eerily similar to Kadrey v Meta).

Judge Bibas rejected all of Ross’ defenses, stating that innocent infringement, copyright misuse, merger defense, scenes à faire defense, and fair use did not apply. On fair use, Judge Bibas eloquently analyzed the four established factors: the use’s purpose and character; the copyrighted work’s nature; how much of the work was used and how substantial a part it was relative to the copyrighted work’s whole; and how Ross’s use affected the copyrighted work’s value or potential market.

On the fourth factor, Judge Bibas found that Ross “meant to compete with Westlaw by developing a market substitute.” He wrote that this factor is “undoubtedly the single most important element of fair use.” That seems like an important ruling in light of the way Google’s AI Mode trains on and serves as a substitute for publisher’s original content.

In April, U.S. District Court Judge Sidney Stein rejected OpenAI and Microsoft’s motion to dismiss, thereby allowing all of the copyright and trademark dilution clams from The New York Times’ suit to proceed. While the bar is admittedly lower for a motion to dismiss, Judge Stein noted “that plaintiffs have plausibly alleged the existence of third-party end-user infringement and that defendants knew or had reason to know of that infringement.”

Then, in May, the U.S. Copyright Office released a report on AI training and fair use. It concluded that using massive troves of copyrighted content to generate commercial AI outputs likely fails fair use, especially when done through illegal means. The report also notes that “effective licensing options can ensure that innovation continues to advance without undermining intellectual property rights.” The Copyright Office rightly recognized that creative works are not mere “data” to be harvested, but expressions of human authorship protected by the Constitution and enshrined in U.S. copyright law.

From slogans to standards

So, what does this mean? For one, courts are rejecting the Silicon Valley myth that fair use lets AI companies take whatever they want. Licensing isn’t just viable, it’s required. Congress should pay attention.

Although there will inevitably be bumps along the road as fair use analysis is unique to each case, these rulings act as a compass to where things are headed. They send important signals to big tech companies with a history of anticompetitive behavior: don’t be evil or you may be held liable. The old playbook—take first, ask questions never—isn’t going to work in this new AI era. It’s time for a better North Star: accountability, transparency, and fair competition.

Perspectives | Supporter Spotlight

Navigating AI: shaping–not taking–media’s future

In an era of shifting media consumption habits and the proliferation of GenAI, trusted journalism has never been more vital. Yet it’s being treated as a commodity rather than a premium, one that is being sourced to power AI models, often without compensation or credit. Some see GenAI as a threat to intellectual property and others as an opportunity, but in reality, it is both. Since the birth of the free web, the media industry has proven it can make headway in innovating and monetizing through disruption. 

GenAI is once again rewriting the rules–not only for content creation but also for consumption and distribution. GenAI tools can summarize and create content at lightning speed in easily digestible formats without having to shift to other platforms, attributes that are turning into expectations from audiences. Although this promise is exciting, it creates real risks for publishers. We face a complex reality: Content, in all its forms, has never been more accessible, but trusted content has also never been more vulnerable and simultaneously essential to creating quality outputs using GenAI. 

Why publishers must act on AI

GenAI is already reshaping the world of content, journalism and intellectual property and will only continue to do so. Undoubtedly, publishers around the globe would be wise to harness the opportunities. 

However, we’re seeing AI models distributing and using publisher content without permission, attribution or compensation, potentially creating a world where reliable information could become a scarcity. This moment demands clarity, leadership and unity. The industry needs to ensure that during this period of disruption, publishers are in the driver’s seat to create a model where platforms work with publishers for mutual benefit. 

From automatically generated summaries to chatbot-driven insights and intelligence, GenAI has already altered the way we live, work and consume news. This transformation has led to unauthorized use of our intellectual property. AI models don’t develop themselves. They rely on data, and that data includes journalism, which is often created at great risk and investment. When GenAI produces outputs that are not appropriately attributed and created in an environment that ignores source compensation, credible information is in jeopardy. 

Publishers begin to take a stand on AI

The publishing industry has already taken steps towards meaningful advocacy. In the UK, the “Make it Fair” campaign is pushing for fair compensation for publishers and stronger legal protections for content. This campaign has united leaders in journalism and sent a clear message to lawmakers and tech companies: Use journalism responsibly and compensate it fairly.  

In the US, the News/Media Alliance launched the “Support Responsible AI” campaign highlighting the alarming reality of AI systems using publisher content without fair compensation and credit. This call to action is clear: Build a framework for ethical AI practices. 

Both of these efforts show that there is strength in numbers and must inspire further action. They’ve sparked awareness, but we’ve yet to see measurable change. Policy takes time and differs across regions. Publishers must act now, to come together to create standard processes for platforms to adhere to; opening up the industry for business only if certain parameters are followed. 

The path forward: outreach & licensing agreements

To preserve the value of our content, media leaders must move swiftly to establish licensing frameworks that protect our journalism while also enabling and embracing responsible innovation. Licensing isn’t about saying no to AI; it’s about creating a healthy symbiotic relationship between publishers and platforms. 

A strong licensing agreement should be clear, fair and transparent. It must define the scale of content usage, set transparent boundaries for how AI systems can engage with intellectual property, outline where and how content will appear and be compensatory to publishers and content creators. Publishers must also be involved in every stage of this process. This requires open conversations where AI developers acknowledge the value of publishers and the  content which is indispensable to a successful AI ecosystem.

If collaboration and compensation are mutual priorities, AI becomes an opportunity. Done right, it allows publishers to reach new audiences and explore new formats while maintaining control over their content, bringing a new realm of GenAI forward.

Protecting the media industry through collaboration 

No single publisher can do this alone. The challenges GenAI presents are an industry-wide issue and solving it requires an industry-wide response. While policy development takes time and currently lacks unified alignment, tech companies are moving quickly and setting their own parameters. Needless to say their priorities often fall short of safeguarding the future of journalism. Shared standards and frameworks are needed. Most importantly, publishers must speak with a united voice. When publishers come together, they’re not just defending content, but exemplifying the values their journalism stands for.  

It is already clear what’s possible when publishers organize. The campaigns mentioned earlier are just the beginning. Collaboration helps us navigate the ethical gray areas like, “How do we preserve the authenticity of human voices in an age of machine-generated content?” “What’s the line between inspiration and plagiarism when AI synthesizes content from millions of sources?” These are questions publishers must answer together. 

The future of journalism and AI

With the level of disruption from GenAI in just two years, it’s clear that industries everywhere will continue adapting and transforming to take advantage of the opportunities the technology is already delivering. There is enormous potential for these tools to support journalists and expand the reach of trusted journalism. However, these tools must be used responsibly and work in harmony with people. Human judgement, context and ethics are not replaceable. AI can mimic tone, but it cannot understand nuance. 

The role of the journalist as a trusted and accountable storyteller remains indispensable. As we move forward, our role as publishers will be more important than ever. We must ensure our content fuels innovation with consent. We must advocate for transparency, compensation and respect for the reliable information our journalists go to great lengths to provide.

The entire publishing community has a shared interest in ensuring GenAI serves our industry, not the other way around. This is a defining moment for publishing–providing publishers with an opportunity to shape the future with purpose. Let’s lead with integrity, together. 

Featured Post | Industry-Research | Perspectives

Don’t rush to replace your science reporters with AI just yet

The promise of AI’s efficiency and speed in sifting through vast amounts of data and producing content has led many to speculate that it can replace parts of the journalistic process. (This despite a lukewarm reception from news consumers and ambivalence among journalists themselves.) However, Axios’ recent move to ask employees to argue why a human should do a job that AI might be able to do seems to signal a false binary. In this either-or situation, we’d replace expertise with expedience, possibly at the risk of accuracy – which could have grave consequences.

Axios is far from alone in experimenting with AI to augment journalism. However, as is the case in many industries, there is a rush to employ AI in a way that supplants rather than supplements. And that, in turn, could create a gap in the entry level employee pipeline. In the case of journalism, this may mean that the established process of developing expertise breaks down. It is certainly a challenge to build deep subject domain knowledge. But it is possible that this lengthy process could be restructured to be both more supportive and efficient.

A recent study, Generalization bias in large language model summarization of scientific research published in Royal Society Open Science, highlights a critical issue: media coverage of scientific research often fails to capture the nuance and complexity inherent in the scientific process. However, the research finds that AI also fails at the task of summarizing scientific papers and abstracts accurately. As more and more people – journalists, medical professionals, scientists, educators and students – rely on AI-generated summaries and content, the results of this study beg the question as to whether we’re integrating AI to effectively support work processes.

Media coverage of scientific research has issues

As the public’s primary source of information on the latest scientific discoveries and advancements, the media holds the power to shape public understanding, perception and opinion. Accurately presenting findings from scientific and medical studies is paramount. As this study and others show, AI agents tested weren’t able to do the job. This seemingly automatable task would appear to be not-so-automatable after all.

Reporting on scientific research isn’t simple, as we’ve seen in the past. The temptation to prioritize sensationalism and oversimplification over nuanced reporting looms large, in part because of media business models driven by engagement metrics and fast news cycles. Scientific work does not produce definitive outcomes (inferences) so much as it accumulates evidence to suggest a truth. Even in the case of studying phenomena that are reasonably and reliably measured, such as in physics, the theories that are the outcome of science are, in fact, theories! For example, if a study says a certain treatment was found to reduce incidences of a disease in a very specific group of people during a 10-week trial, the exciting news might seem to be that a given disease is now curable. Sadly, it isn’t. This would be a misinterpretation of the study results.  

Moreover, science itself is rarely straightforward. Study results can be contradictory, because of subtle, or not so subtle, differences in hypotheses, cohorts, measurements and methodology. Particularly because journalists often work on tight deadlines, statistics may be misinterpreted or decontextualized thus potentially misleading the public.

This new research found that, when summarizing scientific texts, chatbots may omit details that limit the scope of research conclusions, leading to generalizations of results broader than warranted by the original study. (Mind you, they compared the chatbot-derived summaries to those published in The New England Journal of Medicine Journal Watch, and did not check other types of summaries written by individuals with lesser domain expertise.) Press releases written to “hype” new research, may succumb to overgeneralization, as well. And, like all research, there are likely other teams putting AI tools to similar tests. So, even as I anchor my point to the article published in RSOS, I remain keenly aware that new research that reinforces, expands, or contradicts these findings may be published imminently.

What’s at stake when media fails to get science right

It is critical not only that we best employ emerging tools such as AI to support the production of quality journalism, but also that we continue to support and foster the development of science reporters. There’s much at stake if we fail to get scientific coverage right.

At its worst, the media’s coverage of scientific research can contribute to misinformation and the erosion of public trust in science.

However, at its best, science-based journalism has the power to inform the public and help set the agenda in two ways: First, by circulating what the scientific community considers the most important discoveries and applications. And secondly, by generating public interest in specific ideas, discoveries, or technologies that have previously been rejected or overlooked, as noted by Maxwell E. McCombs and Donald L. Shaw over 50 years ago.

Science communication acts as a bridge, shaping perceptions and priorities on both sides. As new breakthroughs emerge, the media plays a crucial role in determining which ones capture the public’s attention. This symbiotic relationship between science and society is an important dynamic for media professionals to understand. Journalists must embrace the nuance and complexity of scientific research to effectively inform and educate the public, even as AI threatens to displace journalists and media organizations move beyond delegation towards automation.

Using the tools properly to create the best science journalism

If science reporting is indeed in crisis, reliance on AI will not improve the situation. Instead, we should give novice journalists the opportunity to summarize abstracts and papers, thereby learning to translate scientific jargon into more accessible language without losing context and nuance. Perhaps, this could be achieved through collaboration with a “dumb” AI chatbot and a human mentor, fostering a learning environment that emphasizes the importance of nuanced understanding.

If the future of white collar work is to rely more heavily on experts and cutting entry level positions, as suggested by both Anthropic’s CEO and Axios’ chief executive, then the future does indeed look grim. Experts aren’t hatched fully formed. Expertise is cultivated through experience and engagement with complex tasks. It starts, however, by slogging through the boring, automatable tasks now passed on to AI. As a doctoral student, I would have been tempted to use AI to summarize academic papers, find relevant quotes. But I wouldn’t be the expert I am today if I had taken that shortcut. A person becomes an expert. No one is born an expert, and neither is an AI assistant.

I’m not arguing against the use of AI. It’s a brilliant tool with valuable applications. However, as with most major shifts – and scientific discovery and reporting itself – it is often necessary to invest time and effort to get things right. Media leaders may want to take a step back, evaluate the hype and explore ways to integrate AI that supports the delicate task of reporting on science in a nuanced, thoughtful way. Perhaps then, we can ensure that reporting on science informs rather than misleads, ultimately fostering a more scientifically literate society.

Concept art to suggest leaders considering AI risk
Industry-Research | Policy

What media leaders need to know about AI risk and regulation

Organizations and individuals around the world are becoming increasingly reliant upon AI. However, adoption of AI methods by organizations is outpacing risk mitigation, and consumers are increasingly apprehensive about its use. While geographic regions have different approaches to regulating AI use, research indicates that increased data security measures and regulation positively influence customer confidence.

User concerns grow globally

Recent research found low trust in AL combined with high support for increased regulation. Trust, Attitudes, and Use of Artificial Intelligence: A Global Study 2025, led by the University of Melbourne in collaboration with KPMG, surveyed over 48,000 people across 47 countries. The study found that most people report using AI regularly. And even more support AI regulations:

  • 66% of people use AI regularly, and 83% believe AI will bring significant benefits.
  • Despite this, only 46% of people globally report trust in AI systems.
  • 70% of respondents support national and international AI regulation.

As international consumers are increasingly relying on AI, they are also expressing increased trepidation over issues of trust and transparency. Tech engagement, while strong across markets, is especially robust in emerging markets such as India, Brazil, and China, according to Kantar Media’s Global Digital Media and Tech Trends Report. The report analyzed data derived from 80,000 respondents in 37 countries. 86% of media users from India answered yes to “I try to keep up with developments in technology” as did 76% of those in China and 73% of those in Brazil – compared to 62% of U.S. respondents.  A large majority of media users in India (78%) agreed with the statement: “Artificial intelligence has had a significant impact on my daily life.” More than half of Brazilians agreed (52%), compared to less than half (46%) of U.S. respondents.

According to the Adobe 2025 Digital Trends Report, key issues around AI adoption include trust and the use of AI include balance, transparency, and data security.  The balance between innovation and trust is an ongoing challenge: both privacy concerns and governance complexities remain significant hurdles. Of the 8,301 consumers surveyed by Adobe, close to half (45%) claim to prioritize visibility and control over their data, while a third (33%) say they demand clarity on how AI is used to generate recommendations. As organizations move beyond pilot programs to scale AI initiatives, they must focus on clear disclosure and ethical AI practices to maintain credibility.

Trust erosion and AI adoption

A recent study by Thales reveals that consumers’ trust in organizations to use their data responsibly in the age of AI is rapidly waning. However, increased regulation alleviated some of the distrust.

  • In 2024, 47% of consumers questioned whether companies used AI responsibly. By 2025, this concern rose to 57% – a marked leap in just one year.
  • The 2025 global trust index found global trust rates stagnating or declining, with no sector achieving more than 50% “high trust” ratings. However, industries (such as banking and healthcare) and geographies with the most regulations had higher trust rates.
  • Trust in news media hit a new low, with news organizations trusted by only 3% of consumers in 2025, a decline from 6% in 2024. Some of this drop was attributed to slackening oversight (particularly from social platforms).
  • In contrast, Government services saw improvement, increasing from 37% trust in 2024 to 42% in 2025, an improvement possibly driven by enhanced regulatory frameworks like the EU’s Digital Operational Resilience Act (DORA).
Few organizations have a policy around AI usage, which will affect consumer trust

These studies indicate increasing awareness on the part of consumers about the risks inherent in AI technology. This underscores the need for media executives to demonstrate strong governance and proactive leadership.

AI data risk found to be almost universal

Consumer trepidation is far from unfounded. The 2025 State of Data Security Report by Varonis quantifies the data risk entailed by AI usage based on data obtained from 1,000 companies. Findings confirm that AI adoption is leaping ahead of risk mitigation. Among the findings: 99% of organizations have had sensitive data exposed to AI tools.

The report also indicates 88% of organizations evaluated had old but still enabled user accounts, which are potential entry points for attackers. The report also reveals that 90% of the organizations studied have exposed sensitive cloud data, and 98% were found to have employees using unsanctioned apps, including shadow AI. The study underscores an urgent need for stronger data governance frameworks in the age of AI.

As previously reported by DCN, a plan that includes transparency, balance, and education can offset some AI concerns. However, the conundrum remains that while most users claim to want transparency around the use of AI, revealing such origins can undermine trust. In addition to the very real risks of data exposure, AI use by organizations risks turning off consumers who perceive a lack of human connection and oversight. Media Pulse points out that human creator content, even when flawed, feels authentic and drives stronger engagement. Thus, AI tools must always be orchestrated with human creators to maintain community trust.

Global governments differ on AI regulation

The impact of AI adoption and data security concerns will likely spur increased regulation in many locales, so companies will be wise to get ahead of future requirements. The EU Artificial Intelligence Act (AI Act) – the world’s first comprehensive AI regulation – officially went into force in August of 2024. Designed to ensure safe, ethical, and transparent AI development and deployment across the European Union, it requires AI content and deepfakes to be clearly labeled by 2026. Failure to disclose can lead to legal penalties.

Other countries are likely to follow suit as the impact of AI-related data risks become increasingly apparent. Brazil and Peru are currently working on AI governance frameworks based on the EU model. Canada has established the Artificial Intelligence and Data Act (AIDA), which focuses on transparency, accountability, and risk management for AI systems. China’s strict AI regulations include content moderation laws and licensing requirements for AI models. Meanwhile, India is developing a techno-legal approach to AI regulation. The United Kingdom leans towards a more pro-innovation approach, relying on existing regulators rather than creating new AI-specific laws. Australia boasts a comprehensive “AI assurance framework” at federal, state and territory levels.

According to Pew Research, the public is more concerned about not enough government regulation of AI than too much

Meanwhile, AI governance in the U.S. has been fragmented so far, with state-level regulations and sector-specific guidelines. Until 2025, a more nationwide approach to shaping AI governance seemed likely, but recent executive orders have been aimed at repealing AI regulations. As of this writing, The House of Representatives has passed the Budget Reconciliation Bill, which includes a 10-year moratorium on state and local laws regulating the use of AI technologies. This repeal of AI regulations, however, may be the opposite of what the majority of the public wants: More than half of U.S. adults (58%) say they are concerned that government regulation won’t go far enough in managing AI risks. Only 21% fear it will go too far, according to a recent Pew Research poll.

Different attitudes towards AI risks mean that a policy acceptable in one region might not be elsewhere. Media companies operating internationally may have to tailor AI-driven strategies to align with local regulatory expectations. The Global AI Regulation Tracker offers an interactive real-time comparison of how various countries are responding to the explosion of AI use with regulations, laws and policies

Given the research linking increased AI governance with customer confidence, being proactive about AI policy is wise from a customer service perspective. Whether or not their region requires it, media leaders will want to establish clear guidelines for AI use within their organizations to ensure practices that align with user expectations. Regulations aren’t just about compliance; they are about setting standards that align with public trust. Media leaders who responsibly integrate AI can gain a strategic advantage, with ethical AI use as a key differentiator among market competitors.

AI impacts journalistic gatekeeping and news distribution
Industry-Research

How AI reshapes editorial authority in journalism

Artificial Intelligence (AI) is changing how news organizations produce and distribute content. Newsrooms use AI to generate headlines, curate stories, automate writing, and optimize content for digital platforms. Rather than simply supporting journalists, AI is actively reshaping editorial workflows, impacting how newsrooms select, edit, and present information. In short, AI is reshaping the newsroom process and the news itself, which has broad impacts on the business of media and audience perception.

Shifting controls

As AI takes on more editorial roles, it shifts control from individual journalists to automated systems optimized for engagement and scalability. Felix M. Simon’s research on how the use of artificial intelligence shapes the way news gets produced and distributed explores this shift. Instead of relying on human judgment, experience, or what’s best for the public, news decisions are now more focused on numbers, data, and technology. This change is part of a bigger trend of running things in a more controlled, predictable, and measurable manner.

Editors are using tools like algorithms and performance stats to decide what news to publish rather than just their instincts or values. This raises concerns that news is becoming too uniform, that editors have less freedom, and that journalism may not serve democracy as well as it used to.

Simon explains this type of change using the “gatekeeping theory.” The theory views news production as a series of choices, like gates, where people decide what becomes news. In the past, journalists mostly made these choices using their judgment. Digital tools and social media platforms now play a significant role in shaping those decisions.

Reshaping journalism

Simon’s study analyzes AI’s role in reconfiguring journalism processes. Based on 143 interviews across 34 news organizations in the U.S., U.K., and Germany, he identifies how and where AI integrates into news workflows. He structures his research around these central questions:

How does AI shape the gatekeeping process of the news in terms of production and distribution?
  • Where do news organizations use AI in editorial processes?
  • What effects result from its adoption in news production and distribution?

Simon’s questions guide his investigation into how AI reshapes gatekeeping and journalism’s role in the public sphere, revealing three key patterns. First, AI boosts efficiency by automating routine, time-consuming tasks such as transcription, translation, and content formatting. Second, it enhances effectiveness by enabling previously impractical tasks, like large-scale data analysis and personalized content delivery. Third, AI supports greater optimization, particularly in refining distribution strategies and managing dynamic paywall systems, ultimately transforming how news reaches and engages audiences.

AI reconfigures gatekeeping

In many cases, AI systems influence news judgment not by replacing human editors but by steering their attention through data-driven cues. These cues include story performance metrics, SEO recommendations, or algorithmic predictions of audience interest. These subtle nudges shape editorial agendas and shift newsroom priorities over time.

Additionally, AI’s influence extends into ethical domains, where concerns emerge about bias in training data, transparency in automated decisions, and the implications of delegating journalistic choices to opaque systems. These challenges push newsrooms to assess the values embedded in AI tools and develop policies for their responsible use.

Further, researchers and practitioners continue to explore how AI alters productivity, quality, and editorial judgment. Simon’s findings show that AI is not revolutionary but evolutionary and deeply embedded in journalism’s digital transformation.

  1. AI retools news production and distribution without changing journalism’s core goals.
  2. Its impact is often subtle and only becomes clear over time or at scale.
  3. AI is a mix of technologies used across the entire news process, not just in editorial work.
  4. It continues a broader trend of automation and digital transformation in journalism.
  5. While its effects are real, they’re still hard to measure or quantify fully.

Notably, Simon’s study shows how AI transforms journalistic gatekeeping and connects AI’s rise to a broader trend of automation and efficiency in journalism. While effects vary and remain difficult to measure, AI is shifting decision-making power and newsroom practices and media leaders need to be mindful of these shifts and their implications.

AI ethics concept to suggest trust in AI (or not)
Perspectives | Supporter Spotlight

Ethical AI in action: strategies to build audience trust

As artificial intelligence becomes more integrated into both newsroom workflows and business operations, media companies are under increasing pressure to be transparent about how these tools are being used. Recent research shows that most audiences expect clear disclosure when AI plays a role in media production. To maintain trust and credibility, publishers must follow ethical best practices for implementing AI and communicate openly about its use across their organizations.

Here are examples of how several media companies are integrating ethical AI best practices into their operations to build trust and accountability with audiences and advertisers.

Transparency and disclosures

As readers become more aware—and skeptical—of AI’s role in content production, they expect transparency about where and how AI is involved. Developing a disclosure strategy helps audiences better understand the context around AI’s role in producing content.

The Associated Press publicly shares its AI standards and policies and discloses its intent to use AI to improve the quality of its work, while adhering to strict standards for accuracy. The policy also highlights the importance of human oversight in all aspects of content production.

USA Today adds disclosures when AI is used to write summaries or “Key Points” at the top of select articles. The disclosures state that the content was AI-generated and reviewed by a journalist before publishing. USA Today also includes a link to the publication’s ethical conduct policy with the disclosure.

Bias, balance, and fairness

AI models train on a variety of online content. If biases exist in these sources, these biases could be included in AI-generated output. Media companies are creating ways to identify and minimize bias and making these processes public.

The Guardian developed a statement to share how it’s minimizing bias including a working group to discuss ways to prevent bias and develop company-wide policies. The statement also explains how it integrates human oversight into its AI processes.

National Public Radio (NPR) added a special section on AI to its ethics handbook to encourage journalists to identify and counter bias inherent in these tools. The section also emphasizes the importance of providing transparency about how AI is used and discussing questionable output with editors.

Training and education

As AI technologies and best practices evolve, regular training ensures teams stay informed and continue to uphold ethical standards.

Radio-Canada launched a comprehensive AI literacy program to provide a foundation for its staff’s implementation and understanding of AI. The program includes a foundational training session to provide an overview of AI concepts and ethical considerations as well as follow-up workshops focused on practical applications. The company also developed a cross-functional group that includes developers, analysts and journalists to meet bi-weekly to discuss AI developments and share ongoing projects. Since its inception, more than 100 staff members have participated in the training program.

The New York Times implemented a training program that outlines how AI should be used and includes editorial guidelines, use cases and sample prompts. The company also developed an internal team to explore how AI can be applied to newsroom processes.

AI ethics and the broader media ecosystem

Marketers and advertisers are also placing greater focus on ethical standards. The Institute for Advertising Ethics recently released its AI Ethics Toolkit, designed to help buyers navigate the responsible use of AI.

Media organizations can’t slow their pace of innovation. They need to experiment with and implement technologies while ensuring that they keep audience trust at the forefront. The growing focus on ethics across the broader ecosystem signals that publishers who adopt transparent, responsible AI practices will not only build audience trust but also strengthen their position with advertisers seeking accountable media partners.

Icons that represent podcasting innovation
Featured Post | Innovation | Revenue

3 podcast innovations that build audience and revenue

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 

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

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

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. 

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.

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