ChatGPT cited Grokipedia when repeating information that the Guardian has debunked. ChatGPT cited Grokipedia when repeating information that the Guardian has debunked. Guardian found OpenAI's platform cited Grokipedia on topics including Iran and Holocaust deniers The latest model of ChatGPT has begun to cite Elon Musk's Grokipedia as a source on a wide range of queries, including on Iranian conglomerates and Holocaust deniers, raising concerns about misinformation on the platform. In tests done by the Guardian, GPT-5.2 cited Grokipedia nine times in response to more than a dozen different questions. These included queries on political structures in Iran, such as salaries of the Basij paramilitary force and the ownership of the Mostazafan Foundation, and questions on the biography of Sir Richard Evans, a British historian and expert witness against Holocaust denier David Irving in his libel trial.

From seemingly AI-generated videos to repurposed old footage, TikTok, Instagram, and X did little to stop the onslaught of misleading posts in the wake of the US invasion of Venezuela. A crowd outside of Miami reacts to the news of the capture of Venezuelan President Nicolás Maduro on January 3, 2026. Within minutes of Donald Trump announcing in the early hours of Saturday morning that US troops had captured Venezuelan president Nicolás Maduro and his wife, Cilia Flores, disinformation about the operation flooded social media. Some people shared old videos across social platforms, falsely claiming that they showed the attacks on the Venezuelan capital Caracas. On TikTok, Instagram, and X, people shared AI-generated images and videos that claimed to show US Drug Enforcement Administration agents and various law enforcement personnel arresting Maduro.

Recent progress in natural language generation has raised dual-use concerns.

Children will be taught how to budget and how mortgages work as the government seeks to modernise the national curriculum in England's schools. They will also be taught how to spot fake news and disinformation, including AI-generated content, following the first review of what is taught in schools in over a decade. Education Secretary Bridget Phillipson said the government wanted to revitalise the curriculum but keep a firm foundation in basics like English, maths and reading. Head teachers said the review's recommendations were sensible but would require sufficient funding and teachers. The government commissioned a review of the national curriculum and assessments in England last year, in the hope of developing a cutting edge curriculum that would narrow attainment gaps between the most disadvantaged students and their classmates.

No, SNAP Benefits Aren't Mostly Used by Immigrants SNAP benefits are set to run out on Saturday. Far-right influencers and extremists are incorrectly claiming that immigrants are the main recipients of food stamps. A shopper carries a basket inside a grocery store in the Bronx borough of New York City on Oct. 24, 2025. As roughly 42 million Americans face the loss of food stamps this weekend, far-right influencers, extremists, and conspiracy theorists are using the crisis to push racist disinformation about who receives these benefits. As a result of the government shutdown, the Supplemental Nutrition Assistance Program (SNAP) will not be funded as of November 1, according to a message on the website of the US Department of Agriculture (USDA), which administers the program.

What it's like to be in the middle of a conspiracy theory (according to a conspiracy theory expert) Mike Rothschild has spent years studying the rise of QAnon and antivaccine conspiracism. After his house in Altadena, California, burned down, he found himself mired in similarly sticky webs of misinformation. On a gloomy Saturday morning this past May, a few months after entire blocks of Altadena, California, were destroyed by wildfires, several dozen survivors met at a local church to vent their built-up frustration, anger, blame, and anguish. As I sat there listening to one horror story after another, I almost felt sorry for the very polite consultants who were being paid to sit there, and who couldn't do a thing about what they were hearing. Hosted by a third-party arbiter at the behest of Los Angeles County, the gathering was a listening session in which survivors could "share their experiences with emergency alerts and evacuations" for a report on how the response to the Eaton Fire months earlier had succeeded and failed. It didn't take long to see just how much failure there had been. After a small fire started in the bone-dry brush of Pasadena's Eaton Canyon early in the evening of Tuesday, January 7, 2025, the raging Santa Ana winds blew its embers into nearby Altadena, the historically Black and middle-class town just to the north. By Wednesday morning, much of it was burning.

While Large Language Models (LLMs) have made agentic AI, chatbots, and other intelligent applications possible, they have also enabled the affordable creation of highly convincing AI-generated disinformation (Bontcheva et al., 2024), which poses a systemic risk to democratic stability and global security (VIGINUM, 2025; Bengio, 2025). Initially, AI-generated texts suffered from linguistic mistakes and thus were more easily detectable by humans. However, modern LLMs, particularly instruction-tuned models, have significantly improved in producing outputs which are indistinguishable from human-written text (Spitale et al., 2023; Heppell et al., 2024). These advances have resulted in their misuse in generating persuasive disinformation narratives, including political manipulation, health disinformation, conspiracy propagation, and Foreign Information Manipulation and Interference (FIMI) (Vykopal et al., 2024; Chen and Shu, 2024a; Barman et al., 2024; Chen and Shu, 2024b; Heppell et al., 2024; VIGINUM, 2025). While there is a growing body of research on the generation and detection of LLM-produced disinformation (Chen and Shu, 2024a; Lucas et al., 2023; Vykopal et al., 2024; Heppell et al., 2024), a critical aspect remains largely unstudied - namely, whether LLMs are capable of generating fluent and convincing personalised disinformation (i.e., disinformation narratives tailored to specific audiences) in multiple languages and at scale. The few prior studies on AIgenerated personalised disinformation are limited to English and address a very narrow set of personas (e.g., students, parents) (Zugecova et al., 2024). Crucially, prior work has not yet examined whether LLMs can adapt disinformation to country-specific linguistic and cultural contexts in multiple languages.

Musk posted nonstop at the beginning of the year about British politics until his focus was consumed by DOGE. Elon Musk loves responding to posts on X with heart emojis. He's sent dozens this year alone, often in response to people praising his cars or directly to his mother's posts . But this week, Musk sent a heart emoji to Tommy Robinson, the far-right Islamophobic activist from the United Kingdom. Though Musk largely ignored UK politics this year while working in the US government at his so-called Department of Government Efficiency (DOGE), he appears to be back across the pond, spending his money and using his platform to elevate far-right extremists.

Robby Starbuck speaks in an interview in New York in March. Robby Starbuck speaks in an interview in New York in March. Critics condemn Robby Starbuck, appointed in lawsuit settlement, for'peddling lies and pushing extremism' A prominent anti-DEI campaigner appointed by Meta in August as an adviser on AI bias has spent the weeks since his appointment spreading disinformation about shootings, transgender people, vaccines, crime, and protests. Robby Starbuck, 36, of Nashville, was appointed in August as an adviser by Meta - owner of Facebook, Instagram, WhatsApp, and other tech platforms - in an August lawsuit settlement. Since his appointment, Starbuck has baselessly claimed that individual shooters in the US were motivated by leftist ideology, described faith-based protest groups as communists, and without evidence tied Democratic lawmakers to murders.

Large language models (LLMs) are increasingly shaping how information is created and disseminated, from companies using them to craft persuasive advertisements, to election campaigns optimizing messaging to gain votes, to social media influencers boosting engagement. These settings are inherently competitive, with sellers, candidates, and influencers vying for audience approval, yet it remains poorly understood how competitive feedback loops influence LLM behavior. We show that optimizing LLMs for competitive success can inadvertently drive misalignment. Using simulated environments across these scenarios, we find that, 6.3% increase in sales is accompanied by a 14.0% rise in deceptive marketing; in elections, a 4.9% gain in vote share coincides with 22.3% more disinformation and 12.5% more populist rhetoric; and on social media, a 7.5% engagement boost comes with 188.6% more disinformation and a 16.3% increase in promotion of harmful behaviors. We call this phenomenon Moloch's Bargain for AI--competitive success achieved at the cost of alignment. These misaligned behaviors emerge even when models are explicitly instructed to remain truthful and grounded, revealing the fragility of current alignment safeguards. Our findings highlight how market-driven optimization pressures can systematically erode alignment, creating a race to the bottom, and suggest that safe deployment of AI systems will require stronger governance and carefully designed incentives to prevent competitive dynamics from undermining societal trust. There are clear economic and social incentives to optimize LLMs and AI agents for competitive markets: A company can increase its profits by generating more persuasive sales pitches, a candidate can capture a larger share of voters with sharper campaign messaging, and an influencer can boost engagement by producing more compelling social media content. In the presence of both the technology and the incentives, it is natural to expect adoption to move rapidly in this direction. In contrast, the incentives to ensure safety are far weaker. The costs of social hazards--such as deceptive product representation and disinformation on social media--are typically borne by the public rather than the organizations deploying these systems, who may be held accountable only when found legally liable. In this paper, we investigate the critical question: Can optimization for market success inadvertently produce misaligned LLMs? We experimentally show that misalignment consistently emerges from market competition across three different settings.