A new wave of theft is rocking the luxury car industry--mixing high-tech with old-school chop-shop techniques to snag vehicles while they're in transport. When Sam Zahr first saw the gray Rolls-Royce Dawn convertible with orange interior and orange roof, he knew he'd found a perfect addition to his fleet. "It was very appealing to our clientele," he told me. As the director of operations at Dream Luxury Rental, he outfits customers in the Detroit area looking to ride in style to a wedding, a graduation, or any other event with high-end vehicles--Rolls-Royces, Lamborghinis, Bentleys, Mercedes G-Wagons, and more. But before he could rent out the Rolls, Zahr needed to get the car to Detroit from Miami, where he bought it from a used-car dealer. His team posted the convertible on Central Dispatch, an online marketplace that's popular among car dealers, manufacturers, and owners who want to arrange vehicle shipments. It's not too complicated, at least in theory: A typical listing includes the type of vehicle, zip codes of the origin and destination, dates for pickup and delivery, and the fee. Anyone with a Central Dispatch account can see the job, and an individual carrier or transport broker who wants it can call the number on the listing. Zahr's team got a call from a transport company that wanted the job. They agreed on the price and scheduled pickup for January 17, 2025.

Valuation methods of data and machine learning (ML) models are essential to the establishment of AI marketplaces.

Civitai--an online marketplace for buying and selling AI-generated content, backed by the venture capital firm Andreessen Horowitz--is letting users buy custom instruction files for generating celebrity deepfakes. Some of these files were specifically designed to make pornographic images banned by the site, a new analysis has found. The study, from researchers at Stanford and Indiana University, looked at people's requests for content on the site, called "bounties." The researchers found that between mid-2023 and the end of 2024, most bounties asked for animated content--but a significant portion were for deepfakes of real people, and 90% of these deepfake requests targeted women. Demand for electric vehicles and the batteries that power them has never been hotter. In 2025, EVs made up over a quarter of new vehicle sales globally, up from less than 5% in 2020.

New research details how Civitai lets users buy and sell tools to fine-tune deepfakes the company says are banned. Civitai--an online marketplace for buying and selling AI-generated content, backed by the venture capital firm Andreessen Horowitz--is letting users buy custom instruction files for generating celebrity deepfakes. Some of these files were specifically designed to make pornographic images banned by the site, a new analysis has found. The study, from researchers at Stanford and Indiana University, looked at people's requests for content on the site, called "bounties." The researchers found that between mid-2023 and the end of 2024, most bounties asked for animated content--but a significant portion were for deepfakes of real people, and 90% of these deepfake requests targeted women. The debate around deepfakes, as illustrated by the recent backlash to explicit images on the X-owned chatbot Grok, has revolved around what platforms should do to block such content.

Ubisoft, one of the world's largest games developers, says it's working to fix an apparent hack on popular online shooter Rainbow Six Siege. Servers for the tactical multiplayer game were taken offline on Saturday and Sunday after in-game currency thought to be worth millions of pounds was distributed to players. The company has since restored service, but suspended the game's marketplace until further notice and warned players they may face queues when trying to log on. In a statement on X, Ubisoft said it would continue to make investigations and corrections over the next two weeks. Rainbow Six Siege, commonly referred to as R6, has been a success story for Ubisoft, which is also behind the Assassin's Creed and Far Cry series.

As the scale of machine learning models increases, trends such as scaling laws anticipate consistent downstream improvements in predictive accuracy. However, these trends take the perspective of a single model-provider in isolation, while in reality providers often compete with each other for users. In this work, we demonstrate that competition can fundamentally alter the behavior of these scaling trends, even causing overall predictive accuracy across users to be non-monotonic or decreasing with scale. We define a model of competition for classification tasks, and use data representations as a lens for studying the impact of increases in scale. We find many settings where improving data representation quality (as measured by Bayes risk) decreases the overall predictive accuracy across users (i.e., social welfare) for a marketplace of competing model-providers. Our examples range from closed-form formulas in simple settings to simulations with pretrained representations on CIFAR-10. At a conceptual level, our work suggests that favorable scaling trends for individual model-providers need not translate to downstream improvements in social welfare in marketplaces with multiple model providers.

Algorithmic recommender systems such as Spotify and Netflix affect not only consumer behavior but also . Producers seek to create content that will be shown by the recommendation algorithm, which can impact both the diversity and quality of their content. In this work, we investigate the resulting supply-side equilibria in personalized content recommender systems. We model the decisions of producers as choosing content vectors and users as having preferences, which contrasts with classical low-dimensional models. Multi-dimensionality and heterogeneity creates the potential for, where different producers create different types of content at equilibrium. Using a duality argument, we derive necessary and sufficient conditions for whether specialization occurs. Then, we characterize the distribution of content at equilibrium in concrete settings with two populations of users. Lastly, we show that specialization can enable producers to achieve, which means that specialization can reduce the competitiveness of the marketplace. At a conceptual level, our analysis of supply-side competition takes a step towards elucidating how personalized recommendations shape the marketplace of digital goods.