We asked tech CEOs, journalists, entertainers, students, and more about the promise and peril of artificial intelligence. The future never feels fully certain. But in this time of rapid, intense transformation--political, technological, cultural, scientific--it's as difficult as it ever has been to get a sense of what's around the next corner. Here at WIRED, we're obsessed with what comes next. Our pursuit of the future most often takes the form of vigorously reported stories, in-depth videos, and interviews with the people helping define it.

Ubisoft, the video games publisher behind the Assassin's Creed series, has cancelled projects and announced a restructuring that will close several studios as a result of several years of weak results and disappointing sales. Ubisoft, the video games publisher behind the Assassin's Creed series, has cancelled projects and announced a restructuring that will close several studios as a result of several years of weak results and disappointing sales. The video game publisher behind the Assassin's Creed series has cancelled six projects including a remake of Prince of Persia: The Sands of Time as it fights to stay competitive in the global gaming market. Ubisoft announced a sweeping reorganisation and said it would cancel six games, sending its shares to their lowest level in more than a decade on Thursday. Ubisoft is abandoning development of six titles, including a highly anticipated remake of Prince of Persia - a series that dates back to 1989 and received an ill-fated Hollywood adaptation in 2010 - and delaying a further seven. Studios in Halifax, Canada and Stockholm are being closed, with restructuring to follow in other countries, it said.

Ubisoft has cancelled six video games - including its long-awaited Prince of Persia: The Sands of Time remake - as part of a major reset of its operations. The French developer and publisher, known for popular games such as Assassin's Creed, Far Cry and Just Dance, has closed two studios and delayed seven titles as part of its changes. Ubisoft boss Yves Guillemot said the move would create the conditions for a return to sustainable growth. The firm's shares plunged by 33% on Thursday morning following the announcement. The move comes at a time when studios are increasingly turning to video game remakes and remasters, with new versions of Super Mario Galaxy, Oblivion and Metal Gear Solid 3 proving popular in 2025.

Cloudflare CEO Matthew Prince claims the internet infrastructure company's efforts to block AI crawlers are already seeing big results. As the large language models powering generative AI tools slurp up ever more data across the web, Cloudflare cofounder and CEO Matthew Prince said at WIRED's Big Interview event in San Francisco on Thursday that the internet infrastructure company has blocked more than 400 billion AI bot requests for its customers since July 1. The action comes after the company announced a Content Independence Day in July--an initiative with prominent publishers and AI firms to block AI crawlers by default on content creators' work unless the AI companies pay for access. Since July 2024, Cloudflare has offered customers tools to block AI bots from scraping their content. Cloudflare told WIRED that the number of AI bots blocked since July 1, 2025 is 416 billion.

Saudi crown prince to visit U.S. with defense, AI and nuclear energy on agenda In his upcoming visit to the White House, the crown prince is seeking security guarantees and wants access to artificial intelligence technology and progress toward a deal on a civilian nuclear program. RIYADH/WASHINGTON - A visit by Saudi Arabia's de facto ruler to the White House for talks on Tuesday with U.S. President Donald Trump aims to deepen decades-old cooperation on oil and security while broadening ties in commerce, technology and potentially even nuclear energy. It will be the first trip by Crown Prince Mohammed bin Salman to the U.S. since the 2018 killing of Saudi critic Jamal Khashoggi by Saudi agents in Istanbul, which caused a global uproar. U.S. intelligence concluded that the crown prince approved the capture or killing of Khashoggi, a prominent critic. The crown prince, widely known by his initials MBS, denied ordering the operation but acknowledged responsibility as the kingdom's de facto ruler.

Efficiently solving real-world problems with LLMs increasingly hinges on their ability to interact with dynamic web environments and autonomously acquire external information. While recent research like Search-R1 and WebDancer demonstrates strong performance in solving web tasks, they heavily rely on additional tools to convert the interactive web environment into static text content. This is in contrast to human browsing behaviors, which involve diverse interactions with the browser, such as scrolling, clicking, and typing. In this paper, we propose BrowserAgent, a more interactive agent that solves complex tasks through human-inspired browser actions. BrowserAgent operates directly on raw web pages via Playwright through a set of predefined browser actions. We adopt a two-stage training (Supervised Fine-Tuning (SFT) and Rejection Fine-Tuning (RFT)) to improve the model's generalization abilities. Despite using significantly less training data than Search-R1, BrowserAgent achieves more competitive results across different Open-QA tasks. Additionally, we introduce an explicit memory mechanism to store key conclusions across steps, further enhancing the model's reasoning capabilities for long-horizon tasks. Notably, BrowserAgent-7B can achieve around 20\% improvement over Search-R1 on multi-hop QA tasks like HotpotQA, 2Wiki, and Bamboogle. These results indicate that BrowserAgent can serve as a more advanced framework for more interactive and scalable web agents.

Reasoning-augmented search agents such as Search-R1, trained via reinforcement learning with verifiable rewards (RLVR), demonstrate remarkable capabilities in multi-step information retrieval from external knowledge sources. These agents address the limitations of their parametric memory by dynamically gathering relevant facts to address complex reasoning tasks. However, existing approaches suffer from a fundamental architectural limitation: they process search queries strictly sequentially, even when handling inherently parallelizable and logically independent comparisons. This sequential bottleneck significantly constrains computational efficiency, particularly for queries that require multiple entity comparisons. To address this critical limitation, we propose ParallelSearch, a novel reinforcement learning framework that empowers large language models (LLMs) to recognize parallelizable query structures and execute multiple search operations concurrently. Our approach introduces dedicated reward functions that incentivize the identification of independent query components while preserving answer accuracy through jointly considering correctness, query decomposition quality, and parallel execution benefits. Comprehensive experiments demonstrate that ParallelSearch outperforms state-of-the-art baselines by an average performance gain of 2.9% across seven question-answering benchmarks. Notably, on parallelizable questions, our method achieves a 12.7% performance improvement while requiring only 69.6% of the LLM calls compared to sequential approaches.

How to Do It is Slate's sex advice column. Send it to Jessica and Rich here. I am a newly divorced bisexual dad who's moved to a city adjacent to my 20-year-old son's college. He's extremely shy and hasn't talked about sex with me in years. He identifies as queer but has provided no more detail than that.

As investor enthusiasm for artificial intelligence, and lately for a Trump Presidency, has been driving the stock market to record highs this year, Jeremy Grantham has been having flashbacks. At the end of the nineteen-nineties, the veteran value investor--one that looks for undervalued stocks--shied away from soaring Internet and technology stocks, believing that their prices had departed from financial reality, and that the market was heading for a crash. Far from thanking him for sounding the alarm, many clients of G.M.O., a Boston-based investment-management firm that Grantham had co-founded, held it responsible for making them miss out on a vertiginous rise in the Nasdaq, which went up by about a hundred and sixty per cent between 1998 and 1999. Some withdrew their money from the company. "We started off in a good position, and in two years we lost almost half of our business," Grantham recalled.

Iterative retrieval refers to the process in which the model continuously queries the retriever during generation to enhance the relevance of the retrieved knowledge, thereby improving the performance of Retrieval-Augmented Generation (RAG). Existing work typically employs few-shot prompting or manually constructed rules to implement iterative retrieval. This introduces additional inference overhead and overlooks the remarkable reasoning capabilities of Large Language Models (LLMs). In this paper, we introduce Auto-RAG, an autonomous iterative retrieval model centered on the LLM's powerful decision-making capabilities. Auto-RAG engages in multi-turn dialogues with the retriever, systematically planning retrievals and refining queries to acquire valuable knowledge. This process continues until sufficient external information is gathered, at which point the results are presented to the user. To this end, we develop a method for autonomously synthesizing reasoning-based decision-making instructions in iterative retrieval and fine-tuned the latest open-source LLMs. The experimental results indicate that Auto-RAG is capable of autonomous iterative interaction with the retriever, effectively leveraging the remarkable reasoning and decision-making abilities of LLMs, which lead to outstanding performance across six benchmarks. Further analysis reveals that Auto-RAG can autonomously adjust the number of iterations based on the difficulty of the questions and the utility of the retrieved knowledge, without requiring any human intervention. Moreover, Auto-RAG expresses the iterative retrieval process in natural language, enhancing interpretability while providing users with a more intuitive experience\footnote{Code is available at \url{https://github.com/ictnlp/Auto-RAG}.