中金 | 复盘互联网Dot-com浪潮：对AI应用有何启示？

Core Viewpoint - The article analyzes the historical development of the internet since the 1990s and the Dot-com bubble, drawing parallels to the current trends in AI development, suggesting that understanding past trends can provide insights into future industry and market dynamics [1][7]. Industry Perspective - The challenge lies in grasping the "timing" and "development path" of the industry. While the trends in the internet industry can be anticipated, accurately pinpointing the timing and specific forms of development is challenging. For instance, the World Wide Web and PCs were not initially mainstream forms [3][19]. - The early internet's core features included open cooperation, network effects, and decentralization, which ultimately shaped its evolution. The transition from localized networks to a unified internet infrastructure was not initially predictable [11][12]. - The early internet's leading companies leveraged their resource advantages to dominate the market, a trend that may re-emerge in the current AI landscape [19]. Market Perspective - The Dot-com bubble was a culmination of a long bull market in the U.S., with significant growth in internet penetration from 0% to 30% between 1990 and 1998. This period saw a surge in IPOs for internet-related companies [20][34]. - The valuation logic for companies shifted during the bubble, with non-rational factors dominating market trends. After the bubble burst, the market returned to fundamentals, leading to a significant drop in bandwidth costs by 90% and a talent surplus in computing [20][29]. Insights - The current AI trend is seen as entering an application phase, with the ultimate goal being AGI (Artificial General Intelligence). However, there is no consensus on the path or timeline to achieve this [4][36]. - The emergence of open-source AI technologies like DeepSeek is likened to the early internet's transition to open applications, potentially democratizing access to AI capabilities [38][45]. - The article suggests that the current AI development phase may mirror the early internet era, where initial applications are being developed, and the market is still defining its standards and models [39][41]. Conclusion - The historical analysis indicates that while identifying major trends is relatively straightforward, determining the timing and specific forms of development is complex. The interplay of necessity and randomness plays a crucial role in shaping industry trajectories [19][34]. - The article emphasizes that the aftermath of the Dot-com bubble laid the groundwork for sustainable business models and infrastructure, which could similarly apply to the current AI landscape as it matures [35][42].