Core Insights - The article discusses the investment trends in AI for 2025, highlighting the experiences and observations of Jinqiu Fund over the past year in the AI startup and investment landscape [4][10]. Investment Focus - Jinqiu Fund emphasizes three key aspects: a focus on AI, a 12-year investment cycle, and an active investment strategy, having invested in over 50 AI projects in the past year, ranking among the top two in the industry [10][11]. - The majority of investments (56%) are in the application layer, with 25% in embodied intelligence, and 10% in computing infrastructure, reflecting a strategic focus on areas that support long-term model cost reduction [11][22]. Market Comparison - A comparison with 20 active VC and CVC firms shows that Jinqiu Fund is more heavily weighted towards application-focused investments, indicating a differentiated strategy in the AI investment landscape [14][16]. Trends in AI Development - The article outlines two major trends: the enhancement of intelligence and the reduction of costs associated with acquiring intelligence. The focus is on the transition from pre-training to post-training using high-quality datasets [25][32]. - The cost of acquiring intelligence is decreasing significantly, with a notable drop in the cost per token and the emergence of new benchmarks for model capabilities, leading to a more competitive environment for application-layer companies [33][34]. Opportunities in Application Layer - Jinqiu Fund has been closely monitoring opportunities in the application layer since the second half of last year, driven by the belief that the time for application-layer opportunities has truly arrived [38][39]. - The article suggests that key variables from the internet and mobile internet eras can be applied to analyze changes and opportunities in the AI application layer, emphasizing the importance of user data and context [41][47]. Embodied Intelligence - The future of embodied intelligence is seen as crucial for building physical world agent applications, although the foundational models have not yet reached a breakthrough moment akin to GPT [56][61]. - The article stresses the importance of hardware in the early stages of development, highlighting the need for effective collaboration between hardware and software to enhance algorithm development and deployment [58][61].