Disney and OpenAI have made a surprise deal - what happens next? Disney's famous Mickey Mouse character will soon be available for use in AI-generated videos The world's best-known AI company and the world's best-known entertainment firm have come to a surprise agreement to allow AI versions of some of the most iconic characters in film, TV and cartoons to be used in generative AI videos and images. Social media is dead - here's what comes next The Walt Disney Company has signed a deal with OpenAI that will allow the AI firm's Sora video generation tool and ChatGPT image creator to use more than 200 of Disney's most iconic characters. Meanwhile, Disney remains in dispute with another AI firm, Midjourney, over alleged infringement of their intellectual property (IP), claiming Midjourney aims to "blatantly incorporate and copy Disney's and Universal's famous characters" into their image generating tool. The characters now deemed fair game for OpenAI users include the likes of Mickey and Minnie Mouse, Simba and Mufasa from and Moana, as well as Marvel and Lucasfilm characters, including some of's most well-known names.

Disney is hedging against the future. OpenAI is clearing a path for Sora. And together they've made a blueprint for how AI and Hollywood can move forward. On Thursday, Disney and OpenAI announced a deal that might have seemed unthinkable not so long ago. Starting next year, OpenAI will be able to use Disney characters like Mickey Mouse, Ariel, and Yoda in its Sora video-generation model .

A cease-and-desist letter accuses the search giant's AI tools of training on and copying protected works. The letter includes examples of images from several Disney properties including Deadpool, Moana, Star Wars and others, reproduced by Google's AI tools. Disney is demanding that Google implement guardrails within all its AI products to prevent further infringement. Today Disney with OpenAI to license its characters for use in Sora, OpenAI's video generator. The deal will see Disney invest $1 billion in OpenAI (a paltry sum by), with the option to purchase additional equity at a later date.

Disney has agreed to invest $1bn (£740m) in OpenAI as part of a deal which will let people use many of its iconic characters in the chatbot ChatGPT and video-generation tool Sora. It is the first major studio to license parts of its catalogue to the tech giant, in a move which could have major implications for the studio's future plans. It means fans will be able to generate and share pictures and videos of more than 200 characters from Disney's franchises, including Pixar, Marvel and Star Wars. The move comes as OpenAI faces mounting questions about how its rapidly advancing tech is used - and as anxiety in Hollywood increases over the impact of AI on the creative industries. According to a blog post announcing the news, the list of eligible characters include those from Disney films Zootopia, Moana and Encanto - as well as characters like Star Wars' Luke Skywalker and Marvel's Deadpool.

GPU prices could follow RAM's big rise It looks like Disney wasted no time delivering on CEO Bob Iger's promise to bring AI-generated content to Disney+. On Thursday, the company announced the start of a three-year licensing agreement with OpenAI to bring more than 200 of its beloved characters, including those from Star Wars and Pixar, to the Sora app and ChatGPT. With the deal in place, OpenAI users will be able to prompt ChatGPT to generate images that tap into Disney's intellectual property, with costumes, props, vehicles and environments covered. The agreement does not include voices or "talent likenesses," meaning Sora users won't be able prompt the app to make a video with Black Widow and get something with Scarlett Johansson in it. Instead, both Sora and ChatGPT will only have access to animated and illustrated versions of Marvel and Star Wars characters like Black Panther, Captain America, Han Solo, Darth Vader and others.

Mickey Mouse and Minnie Mouse floats at the Magic Kingdom Park at Walt Disney World in Orlando, Florida, on 3 April 2025. Mickey Mouse and Minnie Mouse floats at the Magic Kingdom Park at Walt Disney World in Orlando, Florida, on 3 April 2025. Walt Disney has announced a $1bn equity investment in OpenAI, enabling the AI start-up's Sora video generation tool to use its characters. Users of Sora will be able to generate short, user-prompted social videos that draw on more than 200 Disney, Marvel, Pixar and Star Wars characters as part of a three-year licensing agreement between OpenAI and the entertainment giant. A selection of the videos made by users will also be available for streaming on the Disney+ platform. Bob Iger, Disney's CEO, hailed a deal which paired his firm's "iconic stories and characters" with OpenAI's AI technology.

The Download: solar geoengineering's future, and OpenAI is being sued Solar geoengineering aims to manipulate the climate by bouncing sunlight back into space. In theory, it could ease global warming. But as interest in the idea grows, so do concerns about potential consequences. A startup called Stardust Solutions recently raised a $60 million funding round, the largest known to date for a geoengineering startup. My colleague James Temple has a new story out about the company, and how its emergence is making some researchers nervous. So far, the field has been limited to debates, proposed academic research, and--sure--a few fringe actors to keep an eye on.

The heirs of an 83-year-old Connecticut mother who was killed by her son in August filed a lawsuit against OpenAI and Microsoft, claiming ChatGPT amplified his "paranoid delusions."

Abstract--Generative Artificial Intelligence Models like Large Language Models (LLMs) and Large Vision Models (VLMs) exhibit state-of-the-art performance across a wide range of tasks but remain vulnerable to hardware-based threats, specifically bit-flip attacks (BF As), posing a serious risk to their security in safety-critical applications. Existing BF A discovery methods--gradient-based, static analysis, and search-based--lack generalizability and struggle to scale, often failing to analyze the vast parameter space and complex interdependencies of modern foundation models in a reasonable time. This paper proposes FlipLLM, a reinforcement learning (RL) architecture-agnostic framework that formulates BF A discovery as a sequential decision-making problem. FlipLLM combines sensitivity-guided layer pruning with Q-learning to efficiently identify minimal, high-impact bit sets capable of inducing catastrophic failure. We demonstrate the effectiveness and generalizability of FlipLLM by applying it to a diverse set of models, including prominent text-only LLMs (GPT -2 Large, LLaMA 3.1 8B, and DeepSeek-V2 7B), VLMs such as LLaV A 1.6, and datasets, such as MMLU, MMLU-Pro, VQA v2, and T extVQA. Our results show that FlipLLM can identify critical bits that are vulnerable to BF As up to 2.5 faster than SOT A methods. We demonstrate that flipping the FlipLLM-identified bits plummets the accuracy of LLaMA 3.1 8B from 69.9% to 0.2%, and for LLaV A's VQA score from 78% to almost 0%, by flipping as few as 5 and 7 bits, respectively. Further analysis shows that applying standard hardware protection mechanisms, such as ECC SECDED, to the FlipLLM-identified bit locations completely mitigates the BF A impact, demonstrating the practical value of our framework for guiding hardware-level defenses. FlipLLM offers the first scalable and adaptive methodology for exploring the BF A vulnerability of both language and multimodal foundation models, paving the way for comprehensive hardware-security evaluation. Generative Artificial Intelligence models like Large Language Models (LLMs) [1] and Large Vision Models (VLMs) represent a transformative advancement in artificial intelligence, finding integration into mission-critical systems spanning healthcare, finance, and autonomous navigation [2], [3]. Their effective deployment mandates reliable and secure operation across diverse hardware infrastructures, from expansive cloud accelerators to resource-constrained edge devices.

This chapter argues that the reliability of agentic and generative AI is chiefly an architectural property. We define agentic systems as goal-directed, tool-using decision makers operating in closed loops, and show how reliability emerges from principled componentisation (goal manager, planner, tool-router, executor, memory, verifiers, safety monitor, telemetry), disciplined interfaces (schema-constrained, validated, least-privilege tool calls), and explicit control and assurance loops. Building on classical foundations, we propose a practical taxonomy-tool-using agents, memory-augmented agents, planning and self-improvement agents, multi-agent systems, and embodied or web agents - and analyse how each pattern reshapes the reliability envelope and failure modes. We distil design guidance on typed schemas, idempotency, permissioning, transactional semantics, memory provenance and hygiene, runtime governance (budgets, termination conditions), and simulate-before-actuate safeguards.