Core Insights - The article discusses the emergence of OpenAI's Sora in 2024, which is expected to lead a new era in video generation, significantly improving the quality and efficiency of video production, particularly in the fields of film, e-commerce, and advertising [1][11] - It highlights the competitive landscape in the AI video generation market, with Chinese companies like Kuaishou leading in annual recurring revenue (ARR) and market share by 2025 [3][28] Technology Path and Evolution - The evolution of video generation technology has gone through three main stages: image stitching, mixed architectures (self-regression and diffusion), and the convergence towards the DiT (Diffusion Transformer) path following the release of Sora [4][6][7] - Sora's introduction in February 2024 marks a significant improvement in content generation quality, with major companies adopting DiT as their core architecture [2][11] Market Potential - The global AI video generation market is projected to reach approximately $6 billion in 2024, with the combined P-end (Prosumer) and B-end (Business) market potentially reaching $10 billion in the medium term [3][22] - The article emphasizes the high growth potential of the market, particularly in the P-end and B-end segments, driven by the demand for cost-effective content creation tools [21][23] Competitive Landscape - By 2025, Kuaishou is expected to capture around 20% of the global market share in video generation, leading the industry, while other Chinese companies like Hailuo, PixVerse, and Shengshu are also performing well [3][28] - The competition is characterized by a mix of strong players, with a focus on different aspects of video generation technology, indicating a diverse and competitive market landscape [27][28] Future Directions - The future of video generation technology is anticipated to focus on end-to-end multimodal models, which will enhance the capabilities of video generation systems by integrating various data types [15][16] - The article suggests that the integration of understanding and generation in multimodal architectures will be a key area of development, potentially leading to improved content consistency and model intelligence [17][18]