Hello and welcome to Eye on AI. In this edition…The news media grapples with AI; Trump orders U.S. AI Safety efforts to refocus on combating ‘ideological bias’; distributed training is gaining increasing traction; increasingly powerful AI could tip the scales toward totalitarianism.
AI is potentially disruptive to many organizations’ business models. In few sectors, however, is the threat as seemingly existential as the news business. That happens to be the business I’m in, so I hope you will forgive a somewhat self-indulgent newsletter. But news ought to matter to all of us since a functioning free press performs an essential role in democracy—informing the public and helping to hold power to account. And, there are some similarities between how news executives are—and critically, are not—addressing the challenges and opportunities AI presents that business leaders in other sectors can learn from, too.
Last week, I spent a day at an Aspen Institute conference entitled “AI & News: Charting the Course,” that was hosted at Reuters’ headquarters in London. The conference was attended by top executives from a number of U.K. and European news organizations. It was held under Chatham House Rules so I can’t tell you who exactly said what, but I can relay what was said.
Tools for journalists and editors
News executives spoke about using AI primarily in internally-facing products to make their teams more efficient. AI is helping write search engine-optimized headlines and translate content—potentially letting organizations reach new audiences in places they haven’t traditionally served, though most emphasized keeping humans in the loop to monitor accuracy.
One editor described using AI to automatically produce short articles from press releases, freeing journalists for on-ground reporting, while maintaining human editors for quality control. Journalists are also using AI to summarize documents and analyze large datasets—like government document dumps and satellite imagery—enabling investigative journalism that would be difficult without these tools. These are good use cases, but they result in modest impact—mostly around making existing workflows more efficient.
Bottom-up or top-down?
There was active debate among the newsroom leaders and techies present about whether news organizations should take a bottom-up approach—putting generative AI tools in the hands of every journalist and editor, allowing these folks to run their own data analysis or “vibe code” AI-powered widgets to help them in their jobs, or whether efforts should be top-down, with the management prioritizing projects.
The bottom-up approach has merits—it democratizes access to AI, empowers frontline employees who often know the pain points and can often spot good use cases before high-level execs can, and frees limited AI developer talent to be spent only on projects that are bigger, more complex, and potentially more strategically important.
The downside of the bottom-up approach is that it can be chaotic, making it hard for the organization to ensure compliance with ethical and legal policies. It can create technical debt, with tools being built on the fly that can’t be easily maintained or updated. One editor worried about creating a two-tiered newsroom, with some editors embracing the new tech, and others falling behind. Bottom-up also doesn’t ensure that solutions generate the best return on investment—a key consideration as AI models can quickly get expensive. Many called for a balanced approach, though no one was sure how to achieve it. From conversations I’ve had with execs in other sectors, this dilemma is familiar across industries.
Caution about jeopardizing trust
News outfits are also being cautious about building audience-facing AI tools. Many have begun using AI to produce bullet-point summaries of articles that can help busy and increasingly impatient readers. Some have built AI chatbots that can answer questions about a particular, narrow subset of their coverage—like stories about the Olympics or climate change—but they have tended to label these as “experiments” in order to help flag to readers that the answers may not always be accurate. Few have gone further in terms of AI-generated content. This is for good reason—they worry that gen AI-produced hallucinations will undercut the trust in the accuracy of their journalism on which their brands and their businesses ultimately depend.
Those who hesitate will be lost?
This caution, while understandable, is itself a colossal risk. If news organizations themselves aren’t using AI to summarize the news and make it more interactive, technology companies are. People are increasingly turning to AI search engines and chatbots, including Perplexity, OpenAI’s ChatGPT, and Google’s Gemini and the “AI Overviews” Google now provides in response to many searches, and many others. Several news executives at the conference said “disintermediation”—the loss of a direct connection with their audience—was their biggest fear.
They have cause to be worried. Many news organizations (including Fortune) are at least partly dependent on Google search to bring in audiences. A recent study by Tollbit—which sells software that helps protect websites from web crawlers—found that clickthrough rates for Google AI Overviews were 91% lower than from a traditional Google Search. (Google has not yet used AI overviews for news queries, although many think it is only a matter of time.) Other studies of click through rates from chatbot conversations are equally abysmal. Cloudflare, which is also offering to help protect news publishers from web scraping, found that OpenAI scraped a news site 250 times for every one referral page view it sent that site.
So far, news organizations have responded to this potentially existential threat through a mix of legal pushback—the New York Times has sued OpenAI for copyright violations, while Dow Jones and the New York Post have sued Perplexity—and partnerships. Those partnerships have involved multiyear, seven-figure licensing deals for news content. (Fortune has a partnership with both Perplexity and ProRata.) Many of the execs at the conference said the licensing deals were a way to make revenue from content the tech companies had most likely already “stolen” anyway. They also saw the partnerships as a way to build relationships with the tech companies and tap their expertise to help them build AI products or train their staffs. None saw the relationships as particularly stable. They were all aware of the risk of becoming overly reliant on AI licensing revenue, having been burned previously when the media industry let Facebook become a major driver of traffic and ad revenue. Later, that money vanished practically overnight when Meta CEO Mark Zuckerberg decided, after the 2016 U.S. presidential election, to de-emphasize news in people’s feeds.
An AI-powered Ferrari yoked to a horse cart
Executives acknowledged needing to build direct audience relationships that can’t be disintermediated by AI companies, but few had clear strategies for doing so. One expert at the conference said bluntly that “the news industry is not taking AI seriously,” focusing on “incremental adaptation rather than structural transformation.” He likened current approaches to a three-step process that had “an AI-powered Ferrari” at both ends, but “a horse and cart in the middle.”
He and another media industry advisor urged news organizations to get away from organizing their approach to news around “articles,” and instead think about ways in which source material (public data, interview transcripts, documents obtained from sources, raw video footage, audio recordings, and archival news stories) could be turned into a variety of outputs—podcasts, short form video, bullet-point summaries, or yes, a traditional news article—to suit audience tastes on the fly by generative AI technology. They also urged news organizations to stop thinking of the production of news as a linear process, and begin thinking about it more as a circular loop, perhaps one in which there was no human in the middle.
One person at the conference said that news organizations needed to become less insular and look more closely at insights and lessons from other industries and how they were adapting to AI. Others said that it might require startups—perhaps incubated by the news organizations themselves—to pioneer new business models for the AI age.
The stakes couldn’t be higher. While AI poses existential challenges to traditional journalism, it also offers unprecedented opportunities to expand reach and potentially reconnect with audiences who have “turned off news”—if leaders are bold enough to reimagine what news can be in the AI era.
With that, here’s more AI news.
Jeremy Kahn
jeremy.kahn@fortune.com
@jeremyakahn
Correction: Last week’s Tuesday edition of Eye on AI misidentified the country where Trustpilot is headquartered. It is Denmark. Also, a news item in that edition misidentified the name of the Chinese startup behind the viral AI model Manus. The name of the startup is Butterfly Effect.
This story was originally featured on Fortune.com
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