On this episode of, our hosts unpack the news from Davos, where Trump and major AI companies shared the stage at the World Economic Forum. This week, WIRED's Brian Barrett and Leah Feiger are joining the show as the new cohosts, alongside Zoë Schiffer. And our attention has been drawn to the drama going down in the quaint little town of Davos. Zoë tells us how at the World Economic Forum's event, major AI players like Anthropic have been the protagonists--sharing the spotlight with President Donald Trump, who insists on invading Greenland. Brian has been looking at how ICE activity is developing, and Leah is forcing us to think about this year's midterms because tech giants are already pouring millions into it. Plus, we dive into why OpenAI's decision to roll out ads in ChatGPT was a long time coming. Ads Are Coming to ChatGPT. Write to us at uncannyvalley@wired.com . You can always listen to this week's podcast through the audio player on this page, but if you want to subscribe for free to get every episode, here's how: If you're on an iPhone or iPad, open the app called Podcasts, or just tap this link . Today, we're starting a bit of a new chapter here on the show, and I want to introduce you to my brand new cohost, Brian Barrett, our executive editor here at WIRED, and Leah Feiger, our senior politics editor. So thrilled to be here. So longtime listeners know the show has taken on a bunch of different formats since it launched. We had the Gadget Lab days, the roundtable, news episodes. We really created this podcast because we want to bring you the best stories and the best takes about what's happening in tech and politics. That's all going to stay the same, but this time we're going to go even deeper. What trends you should be watching for, the news that's already happened or about to break, and how we are thinking about all of it.

TikTok Is Now Collecting Even More Data About Its Users. According to its new privacy policy, TikTok now collects more data on its users, including their precise location, after majority ownership officially switched to a group based in the US. When TikTok users in the US opened the app today, they were greeted with a pop-up asking them to agree to the social media platform's new terms of service and privacy policy before they could resume scrolling. These changes are part of TikTok's transition to new ownership. In order to continue operating in the US, TikTok was compelled by the US government to transition from Chinese control to a new, American-majority corporate entity.

US Customs and Border Protection is paying General Dynamics to create prototype "quantum sensors," to be used with an AI database to detect fentanyl and other narcotics. United States Customs and Border Protection is paying General Dynamics to create a prototype of "quantum sensors" alongside a "database with artificial intelligence " designed "to detect illicit objects and substances (such as fentanyl) in vehicles, containers, and other devices," according to a contract justification published in a federal register last week. "This database and sensor project will integrate advanced quantum and classical sensing technologies with Artificial Intelligence and ultimately deploy proven concepts and end products anywhere in the CBP environment," the justification document reads. "Under this requirement, CBP will take additional steps to enhance its ability to detect, and thus, significantly reduce the harms of illicit contraband entering the United States of America, thus bolstering national security." The document redacts the name of the company developing the prototype; however, contract details included in the federal register entry reveal that the justification is for a $2.4 million General Dynamics contract that has been public since December 2025.

The Math on AI Agents Doesn't Add Up A research paper suggests AI agents are mathematically doomed to fail. The big AI companies promised us that 2025 would be "the year of the AI agents." It turned out to be the year of AI agents, and kicking the can for that transformational moment to 2026 or maybe later. But what if the answer to the question "When will our lives be fully automated by generative AI robots that perform our tasks for us and basically run the world?" is, like that New Yorker cartoon, "How about never?" That was basically the message of a paper published without much fanfare some months ago, smack in the middle of the overhyped year of "agentic AI." Entitled " Hallucination Stations: On Some Basic Limitations of Transformer-Based Language Models," it purports to mathematically show that "LLMs are incapable of carrying out computational and agentic tasks beyond a certain complexity."

Nvidia CEO Jensen Huang said that a boom in artificial intelligence infrastructure will power the "largest infrastructure buildout in human history."

Plus: how wastewater tracking could help curb measles' rise in the US. Can ChatGPT Health do better? For the past two decades, there's been a clear first step for anyone who starts experiencing new medical symptoms: Look them up online. The practice was so common that it gained the pejorative moniker "Dr. But times are changing, and many medical-information seekers are now using LLMs. According to OpenAI, 230 million people ask ChatGPT health-related queries each week.

Freecash will actually pay money out to users but not for watching videos. This misleading marketing coincides with the app's rising popularity. I first encountered the Freecash app after clicking on a sponsored TikTok video with dubious claims. The advertisement didn't promote this app by name, rather it showed a young woman expressing her excitement about seemingly getting hired by TikTok at $35 an hour to watch videos on her "For You" page. When I tapped the link to "order now," it sent me to a website with TikTok and Freecash logos, featuring a download link for the Freecash app.

This "dream wish list for criminals" includes millions of Gmail, Facebook, banking logins, and more. The researcher who discovered it suspects they were collected using infostealing malware. A database containing 149 million account usernames and passwords--including 48 million for Gmail, 17 million for Facebook, and 420,000 for the cryptocurrency platform Binance --has been removed after a researcher reported the exposure to the hosting provider. The longtime security analyst who discovered the database, Jeremiah Fowler, could not find indications of who owned or operated it, so he worked to notify the host, which took down the trove because it violated a terms of service agreement. In addition to email and social media logins for a number of platforms, Fowler also observed credentials for government systems from multiple countries as well as consumer banking and credit card logins and media streaming platforms.

The first generation of LLMs were largely trained on human-generated data. However, the success of LLMs and their increased adoption has had an unexpected consequence of AI-generated content appearing in places where there was previously none. Thus machine learning practitioners should be aware that there is an increased chance that their training data is contaminated by LLM-generated content. Previous work has looked into the value of synthetic (i.e., AI-generated) data, and showed that while naively adding this data to the training mix may lead to model collapse, being more diligent about which data is added, the amount of curation it undergoes, and the specifics of the training process may mitigate that risk, or reverse it, leading to improved performance. These works almost uniquely focus on the LLM setting, trying to improve state of the art performance on a set of benchmarks. In contrast, in this work we take a traditional learning theory view on this problem. We begin by formalizing the setting and developing a framework that captures the invariants of having natural training data contaminated by synthetic additions. Specifically, we see three salient points: Groundtruth. There exists a (potentially small) set of natural data, coming from the true data generation distribution.

We generalize the attention mechanism by viewing it through the lens of Entropic Optimal Transport, revealing that standard attention corresponds to a transport problem regularized by an implicit uniform prior. We introduce Generalized Optimal transport Attention with Trainable priors (GOAT), a new attention mechanism that replaces this naive assumption with a learnable, continuous prior. This prior maintains full compatibility with optimized kernels such as FlashAttention. GOAT also provides an EOT-based explanation of attention sinks and materializes a solution for them, avoiding the representational trade-offs of standard attention. Finally, by absorbing spatial information into the core attention computation, GOAT learns an extrapolatable prior that combines the flexibility of learned positional embeddings with the length generalization of fixed encodings.