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Hello and welcome to Eye on AI. In this edition\u2026The news media grapples with AI; Trump orders U.S. AI Safety efforts to refocus on combating \u2018ideological bias\u2019; distributed training is gaining increasing traction; increasingly powerful AI could tip the scales toward totalitarianism. 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\u2014potentially letting organizations reach new audiences<\/a> in places they haven’t traditionally served, though most emphasized keeping humans in the loop to monitor accuracy.<\/p>\n\n\n\n One editor described using AI to automatically produce short articles from press releases, freeing journalists for more original reporting, while maintaining human editors for quality control. Journalists are also using AI to summarize documents and analyze large datasets\u2014like government document dumps and satellite imagery\u2014enabling investigative journalism that would be difficult without these tools. These are good use cases, but they result in modest impact\u2014mostly around making existing workflows more efficient.<\/p>\n\n\n\n There was active debate among the newsroom leaders and techies present about whether news organizations should take a bottom-up approach\u2014putting generative AI tools in the hands of every journalist and editor, allowing these folks to run their own data analysis or \u201cvibe code\u201d<\/a> AI-powered widgets to help them in their jobs, or whether efforts should be top-down, with the management prioritizing projects. 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\u2014like stories about the Olympics<\/a> or climate change<\/a>\u2014but they have tended to label these as \u201cexperiments\u201d 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. They worry that gen AI-produced hallucinations will undercut trust in the accuracy of their journalism. Their brands and their businesses ultimately depend on that trust.<\/p>\n\n\n\n This caution, while understandable, is itself a colossal risk. If news organizations themselves aren\u2019t 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\u2019s ChatGPT, and Google\u2019s Gemini and the \u201cAI Overviews\u201d Google now provides in response to many searches, and many others. Several news executives at the conference said \u201cdisintermediation\u201d\u2014the loss of a direct connection with their audience\u2014was their biggest fear.\u00a0 So far, news organizations have responded to this potentially existential threat through a mix of legal pushback\u2014the New York Times<\/em> has sued OpenAI for copyright violations, while Dow Jones and the New York Post<\/em> have sued Perplexity<\/a>\u2014and partnerships. Those partnerships have involved multiyear, seven-figure licensing deals for news content. (Fortune <\/em>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 \u201cstolen\u201d 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<\/a> CEO Mark Zuckerberg decided, after the 2016 U.S. presidential election, to de-emphasize news in people\u2019s feeds.<\/p>\n\n\n\n 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.”<\/p>\n\n\n\n He and another media industry advisor urged news organizations to get away from structuring their approach to news around \u201carticles.” Instead, they encouraged the news execs to 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\u2014podcasts, short-form video, bullet-point summaries, or yes, a traditional news article\u2014to 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.<\/p>\n\n\n\n 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\u2014perhaps incubated by the news organizations themselves\u2014to pioneer new business models for the AI age.<\/p>\n\n\n\n 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”\u2014if leaders are bold enough to reimagine what news can be in the AI era.<\/p>\n\n\n\n With that, here\u2019s more AI news. <\/p>\n\n\n\n
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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\u2014informing the public and helping to hold power to account. And, there are some similarities between how news executives are\u2014and critically, are not\u2014addressing 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 \u201cAI & News: Charting the Course,\u201d that was hosted at Reuters\u2019 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\u2019t tell you who exactly said what, but I can relay what was said.<\/p>\n\n\n\nTools for journalists and editors<\/strong><\/h2>\n\n\n\n
Bottom-up or top-down?<\/h2>\n\n\n\n
The bottom-up approach has merits\u2014it 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\u2019t 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\u2019t ensure that solutions generate the best return on investment\u2014a key consideration as AI models can quickly get expensive. Many called for a balanced approach, though there was no consensus on how to achieve it. From conversations I\u2019ve had with execs in other sectors, this dilemma is familiar across industries.<\/p>\n\n\n\nCaution about jeopardizing trust<\/strong><\/h2>\n\n\n\n
Those who hesitate will be lost?<\/strong><\/h2>\n\n\n\n
They have cause to be worried. Many news organizations (including Fortune) <\/em>are at least partly dependent on Google search to bring in audiences. A recent study by Tollbit<\/a>\u2014which sells software that helps protect websites from web crawlers\u2014found 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<\/a>, which is also offering to help protect news publishers from web scraping, found that<\/a> OpenAI scraped a news site 250 times for every one referral page view it sent that site.<\/p>\n\n\n\nAn AI-powered Ferrari yoked to a horse cart<\/h2>\n\n\n\n