人间清醒朱啸虎：AI应用即将大爆发，下个“小红书”今年应该已经成立了！

Core Viewpoint - The AI industry's potential is shifting from large models to application layers, with significant opportunities emerging in smaller, more efficient models and practical applications [5][6][7]. Group 1: AI Model Limitations and Opportunities - The capabilities of large models like GPT-5 have reached a ceiling, leading to a trend towards model miniaturization, which can enhance user experience and reduce costs [7][9]. - The explosion of AI applications is evident, particularly in text, voice, and video, with practical applications being more commercially viable than large model development [9][10]. Group 2: Building Non-Technical Moats - AI applications are fundamentally "shell applications" that rely on underlying model capabilities, making it difficult to create barriers based solely on AI technology [12][13]. - Entrepreneurs are encouraged to focus on "boring" but valuable areas, integrating workflows and editing capabilities to create long-term competitive advantages [14][15]. Group 3: Commercialization and Investment Standards - Retention is the key metric for evaluating AI projects, with many companies failing to maintain user engagement after initial interest [20][21]. - "Boring technology" that addresses practical needs is more likely to succeed in commercialization, as seen in applications like meeting minutes and customer service agents [22][24]. Group 4: Global Opportunities for Chinese Entrepreneurs - Chinese entrepreneurs excel in consumer applications and have advantages in supply chain efficiency, particularly in hardware integration [30][32]. - Embracing an "overseas" strategy can help Chinese teams avoid direct competition with large firms and tap into less saturated markets [32][33]. Group 5: Future Directions and Advice for Entrepreneurs - The focus should be on integrating AI with specific industry needs, creating non-technical barriers, and leveraging hardware to enhance user experience [36][38]. - Companies should prioritize solving real-world problems to generate commercial value, rather than solely competing in the large model space [38].