在全球人工智能浪潮持续深化的2025年，智能陪伴赛道正展现出巨大的市场潜力。 据Grand View Research数据显示，2024年，全智能陪伴市场规模已超过 281 亿美元，预计到 2030 年将 突破 1400 亿美元，复合年增长率高达 30%。 在这个千亿美金市场中，中国与新加坡正在成为产业发展的两个关键市场：中国作为全球第二大AI伴 侣应用市场，拥有最活跃的应用创新生态和完整的产业链基础；新加坡则凭借其国际化环境、高老龄化 程度和先进的AI治理体系，成为AI伴侣产品进行全球化验证的重要试验场。 基于两国市场的互补性，由通商中国主办，中新人工智能协会（CSAIA）作为官方合作伙伴的2025通商 中国青年论坛中，特别设置了"AI陪伴与人形机器人：探索产业新趋势"专题讨论，本次讨论汇聚了中新 两国产学界的典型实践代表。 邹春慧：商汤的核心逻辑是构建了 "一基两翼" 的产品体系：一基是指商汤人工智能基础设施包括智算 中心 SenseCore和大模型SenseNova 为核心底座，以此支撑两大应用方向 —— 生产力工具：通过 AI 辅助编码、数据分析，提升企业端效率，目前已服务超千家金融、制造企业； 新加 ...

Core Viewpoint - The article emphasizes the challenges faced by AI companies in global expansion, particularly in infrastructure, talent, and business models, with a focus on the integration of high-quality large models and products for effective globalization [2][6]. Group 1: Development and Technical Route of Mureka Model - The Mureka model was initiated in 2020, leveraging existing music processing technologies and data accumulated from a music social product, Starmaker, which holds a significant market position overseas [7]. - The decision to enter the music generation field was based on the observation that the scale of music data is smaller compared to text and video data, leading to lower required investment and training resources [7]. - The company initially explored various technical routes for music generation, ultimately adopting the Diffusion Transformer (DIT) approach, which significantly improved the model's performance [9]. Group 2: Global Promotion Challenges and Non-Acquisition Growth - After developing the model, the company faced challenges in global promotion, particularly the reliance on user acquisition (UA) models, which are less effective in the AI startup landscape [11]. - Non-acquisition growth strategies include leveraging core technological breakthroughs for viral growth, SEO for user acquisition, and GEO optimization, which are essential for companies to explore beyond traditional UA methods [12][13]. Group 3: Product Judgement Standards and Market Opportunities - The article outlines two core judgments for the feasibility of To B products: they serve as "efficiency multipliers" and act as "workflow adhesives" to enhance automation [15]. - For To C products, the focus is on reducing production costs significantly, with AI music generation costing less than 0.1 RMB per song compared to traditional methods costing around 100,000 RMB [16]. - The article highlights the growing competitiveness of Chinese open-source large models, indicating that small and medium enterprises can leverage these models to build new ecosystems and tap into vast market opportunities [17].