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Americans see growing risk they’ll get turned down for loans

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A growing share of US consumers say they’re not seeking loans because they expect to be refused amid tight credit conditions, according to data from the Federal Reserve Bank of New York. 

The share of discouraged borrowers, defined as respondents who said they needed credit but didn’t apply because they didn’t expect to get approved, climbed to 8.5% in the New York Fed’s latest Survey of Consumer Expectations. That’s the highest level since the study began in 2013.

The perceived likelihood of being rejected increased across different forms of credit, from cards to secured loans to buy homes and cars. Roughly one-third of auto loan applicants expected to get turned down, the highest share since the start of the series, while nearly half of all respondents in the February survey said it’ll be harder to get credit in a year’s time.

The data adds to a picture of increasingly fragile household finances for many Americans, as a cooling job market slows wage gains while high borrowing costs are making bills harder to pay. Delinquency rates remain low by pre-pandemic standards but they’ve been edging higher in most categories, and lenders are turning cautious.  

More than four in 10 US homeowners who sought to refinance their mortgages had their applications rejected, according to the February survey, quadruple the share in October 2023. 

With mortgage lending rates still much higher than a couple of years ago, many people seeking a refi are likely trying to tap equity accumulated during the recent housing boom in order to meet other debt costs or expenses, rather than to reduce their monthly payments. Inability to do so could put some under pressure to sell their homes. 

Meanwhile, the share of consumers in the New York Fed survey who said they could come up with $2,000 in the event of an unexpected need declined to 63%, a new series low.

This story was originally featured on Fortune.com



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How news organizations should overhaul their operations as the gen AI threatens their livelihoods

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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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Meta becomes final Magnificent 7 stock to turn negative in 2025

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Meta Platforms Inc. tumbled into negative territory Tuesday, becoming the last of the so-called Magnificent Seven stocks to turn lower this year.

The Facebook parent fell more than 4%, extending a recent selloff. Its decline is especially notable as it comes in the wake of a historic rally that saw shares gain for an unprecedented 20 straight sessions. At its peak, the stock climbed nearly 26% in 2025, but it has since erased all those gains. 

Meta has lost a certain amount of flexibility given their investments into artificial intelligence, according to KeyBanc Capital Markets analyst Justin Patterson, who cut his price target on the stock to $710 from $750, citing “greater macro uncertainty.” 

“The challenge we see today is that the AI cycle is increasing fixed costs” at Meta, “which limits the ability to reduce expenses in a downturn,” Patterson wrote in a note, which also said Google parent Alphabet Inc., another Magnificent Seven company, faces similar headwinds.

Tech has come under broad-based pressure this year as the economic outlook has been roiled by the Trump administration’s policies on tariffs and questions about the direction of the AI trade. The Magnificent Seven stocks — Apple Inc., Microsoft Corp., Nvidia Corp., Amazon.com Inc., Tesla Inc., Alphabet and Meta — are seen as particular beneficiaries of AI.

The Bloomberg Magnificent 7 Total Return Index is down 16% this year, and more than 20% off its December peak. Among notable decliners, Tesla is down 44% this year, while Alphabet is down 17%, and both Apple and Nvidia are off 14%. The index is lower by over 2% on Tuesday.

Meanwhile, the broader Nasdaq 100 Index is down 7.3% so far this year, recently falling into a correction. The tech-heavy index is currently more than 12% below its own peak.

Big tech’s two-year outperformance has made it a favored place for investors to take profits amid the uncertainty. 

This story was originally featured on Fortune.com



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Exclusive: Superlawyer David Boies expected to hit Boeing with wrongful death suit spurred by suicide of whistleblower John Barnett

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