锦秋基金被投企业流形空间3个月融资亿元，证明世界模型也需要预训练 ｜Jinqiu Spotlight

Core Insights - The article discusses the emergence and potential of world models in AI, particularly focusing on the company Manifold AI and its CEO Wu Wei's vision for developing a robust world model that can understand and predict the physical world [7][10][22]. Investment and Company Overview - Jinqiu Fund has invested in Manifold AI, which has quickly raised over 100 million in seed and angel rounds within three months of its establishment [4][6]. - Jinqiu Fund emphasizes a long-term investment philosophy, seeking breakthrough technologies and innovative business models in general artificial intelligence startups [5]. Technology and Market Trends - The concept of world models is gaining traction, with significant discussions in Silicon Valley about their capabilities, including generative, multimodal, and interactive features [8][9]. - Wu Wei argues that world models can provide superior predictive capabilities compared to Vision-Language-Action (VLA) models, which are limited by their reliance on past experiences [18][22]. Technical Development and Challenges - The development of world models is still in its early stages, with various approaches being explored, including explicit physical modeling and latent space interaction [25][30]. - Manifold AI aims to create a "bodily world model" that can transfer and unify across different scales, contrasting with the top-down strategies of many international teams [33]. Strategic Focus and Market Positioning - Manifold AI prioritizes the robotics and drone sectors over autonomous driving due to the fragmented nature of these markets, which allows for more opportunities for innovation [43][44]. - The company is focused on enabling hardware to possess autonomous reasoning capabilities, moving away from human-controlled operations [46]. Future Goals and Product Development - The company plans to release its first generation of base models based on the World Model Architecture (WMA) by late 2025 to early 2026, aiming to drive advancements in Physical AI Agents [51]. - Wu Wei emphasizes the importance of pre-training models to understand physical world dynamics, which can reduce deployment costs significantly [37][40].