穿越周期的早期投资：从赛道思维到认知红利｜甲子引力

Core Insights - The article discusses the shift from "track thinking" to "cognitive dividends" in early-stage investment, emphasizing the need for investors to develop a deep understanding of people, cycles, and non-consensus views in a crowded market [1][2]. Group 1: Investment Strategies - Investors are moving away from simply betting on popular sectors and are focusing on building their own cognitive models and project radars to identify unique opportunities [1][2]. - The importance of maintaining a "feel" for the market and establishing positive feedback loops during industry downturns is highlighted as key to capturing the next big opportunity [1][2]. Group 2: Key Investment Areas - Major investment themes identified include AI applications, AI-driven consumer electronics, embodied intelligence, and energy systems related to AI [8][9]. - The focus on AI hardware and AI for Science is emphasized, with a recognition of the rapid evolution of sectors like quantum technology and biomanufacturing [9][10]. Group 3: Cognitive Differentiation - Investors are encouraged to develop unique cognitive perspectives that differentiate their investment decisions, even when consensus exists around certain sectors [12][21]. - Examples of successful investments based on unique cognitive insights include early support for companies that later gained significant market traction, despite initial skepticism from the broader investment community [14][15]. Group 4: Project Sourcing and Influence - The role of personal influence and brand visibility in attracting quality projects is discussed, with a focus on how public engagement can enhance investment opportunities [25][26]. - The importance of continuous learning and sharing insights through platforms like podcasts and articles is noted as a way to build a network of potential investment opportunities [27][28]. Group 5: Future Outlook - The consensus among investors is to continue focusing heavily on AI-related investments, with specific attention to foundational AI technologies and applications [32][33].