Disney has entered a three-year exclusive licensing deal with OpenAI, allowing the AI company to use more than 200 of its iconic characters from franchises like Disney, Marvel, Pixar, and Star Wars in its Sora video generator. The partnership, announced by CEO Bob Iger, grants OpenAI the sole legal right to incorporate these characters into AI-generated videos for now. After the exclusive period ends, Disney is free to pursue similar agreements with other AI firms. The move positions OpenAI as a high-profile player in generative AI content creation, while giving Disney a chance to test the waters of AI without fully committing. Iger emphasized that Disney aims to embrace technological change rather than resist it, stating, “We don’t intend to try” to stop progress. Notably, the same day Disney announced the deal, it sent a cease-and-desist letter to Google, accusing the tech giant of copyright infringement in its AI content efforts. Google has not confirmed the allegations but said it will engage with Disney. Meanwhile, at MIT, new faculty member Yunha Hwang is pioneering research at the intersection of computational science and microbiology. As a Samuel A. Goldblith Career Development Professor in the Department of Biology and an assistant professor in Electrical Engineering and Computer Science and the MIT Schwarzman College of Computing, Hwang focuses on understanding microbial life—particularly in extreme environments. With over 99.999% of Earth’s species being microbial, and less than 1% of known genes having verified functions, the field remains largely unexplored. Hwang’s work centers on genomic language modeling, a form of large language model trained on DNA sequences rather than human language. This approach allows researchers to analyze vast microbial genomes in silico, bypassing the challenge that most microbes cannot be grown in labs—a major bottleneck in microbiology. Hwang’s research tackles “microbial dark matter”—organisms so genetically distinct they defy classification. By using machine learning to detect patterns in genomic sequences and their surrounding context, she aims to infer protein function and evolutionary relationships beyond traditional methods based on sequence similarity. She argues that the genomic neighborhood—what genes sit next to each other—holds crucial clues about biological function, especially in systems where proteins work together. Her goal is to build computational tools that can interpret this complex biological language, enabling deeper insights into microbial metabolism and adaptation. The implications of this work are vast. Microbes are nature’s most efficient chemists, driving processes like carbon sequestration, nitrogen fixation, and nutrient cycling—key to planetary health and climate stability. Understanding their genetic blueprints could unlock sustainable solutions for producing new materials, pharmaceuticals, and biofuels. On the medical front, insights into microbial behavior could improve our ability to combat infectious diseases and manage the human microbiome. As climate change accelerates, understanding microbial ecosystems becomes increasingly urgent. Hwang’s interdisciplinary approach reflects a growing trend in science: combining computational power with biological discovery to unlock mysteries hidden in vast datasets. Her work exemplifies how AI and genomics can converge to reveal the hidden logic of life at the smallest scales—offering not just scientific breakthroughs, but practical tools for a more sustainable and resilient future.