The emergence of generative models that produce new video content is reshaping the landscape of digital media production. These systems accept conditioning signals such as text, image, or additional video and deploy a range of core technologies—including generative adversarial networks (GANs), diffusion models, autoregressive methods, and multi-frame synthesis—to produce moving imagery. In the AI-generated video paradigm, a user specifies content, style, and purpose using artificial intelligence (AI) text prompts, which guide the generation of short clips in a variety of visual stylesAlthough other media types, such as text or image, have received greater attention, video, as a time-varying medium, introduces additional complexity. Consequently, the topic of AI-generated video has yet to reach a comparable maturity level, and the field remains fertile ground for exploration and research The technology holds considerable promise for entertainment, advertising, education, and training tasks requiring a substantial amount of visual material. Nevertheless, the ease and scale of production risk generating harmful content including misinformation and deepfakes Both technical and operational factors influence the development, deployment, and use of AI video generation; these aspects can either limit or enable the breadth of applications while simultaneously heightening exposure to ethical, reputational, and legal risks. Understanding these underlying dynamics facilitates informed decision-making about the directions and modalities of AI video generation.
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