清华孙茂松：对工业界而言，大厂可以Scaling，其他玩家重在垂直应用

Core Insights - The rapid development of AI and large models has created a competitive landscape where companies are driven by fear of missing out (FOMO) and are compelled to invest heavily in scaling their models and capabilities [2][6][40] - The emergence of capabilities in large models is characterized by non-linear changes, leading to significant uncertainty but also the potential for breakthroughs that can surpass expectations [3][19][15] - The relationship between language, knowledge, and action remains a fundamental challenge for AI, with the goal of achieving a true integration of these elements [15][38][37] Group 1: Development of AI and Large Models - The AI field has evolved significantly over the past eight years, transitioning into the era of pre-trained models and large models since around 2017 [11][10] - Key milestones in this development include the release of models like GPT-3 and ChatGPT, which have demonstrated remarkable capabilities in various tasks [16][24] - The ability of large models to perform well on complex tasks has increased dramatically, with benchmarks being surpassed in text, code, and multi-modal models [20][26][25] Group 2: Challenges and Risks - The costs associated with scaling AI models are becoming increasingly high, raising concerns about the sustainability of such investments [42][43] - There is a significant risk that the pursuit of scaling could lead to diminishing returns, especially if performance begins to plateau [40][41] - The uncertainty surrounding the limits of Scaling Laws poses a challenge for companies, as they must balance the need to invest in AI with the potential for wasted resources [7][68] Group 3: Strategic Recommendations - Companies with substantial resources may continue to pursue large-scale developments, while the majority should focus on niche applications to minimize risks and maximize potential [60][74] - The strategy of "致广大而尽精微" (to strive for greatness while paying attention to details) is recommended, emphasizing the importance of vertical applications in AI [63][69] - There is potential for new AI algorithms to emerge from specific vertical applications, suggesting that focusing on detailed, specialized work can also lead to broader advancements [71][74]