Group 1 - The core viewpoint presented by Liu Gang is that "bubble" should be viewed as a neutral concept rather than inherently negative [2][6] - It is deemed insignificant to dwell on whether AI will become a bubble, as the formation phase of a bubble often coincides with the most significant market gains, and exiting too early may result in missed opportunities [2][6] - The key to assessing a bubble lies in whether "investment matches demand" and "investment exceeds one's own capabilities" [2][6] Group 2 - AI has profoundly changed the way research is conducted, allowing for rapid comparisons of historical meeting content and generating analysis reports, significantly enhancing work efficiency, making it possible for "everyone to be their own analyst" [2][6] - However, AI's complete replacement of human researchers faces three major bottlenecks: the "hallucination" problem of AI, the necessity for users to have basic knowledge of the inquiry field, and the difficulty in discerning the truth of AI's answers when questions exceed the user's knowledge [2][6] - Knowledge does not equate to wisdom; while AI can provide information, the critical aspect of problem-solving lies in human questioning and decision-making logic, emphasizing that "telling AI what to do" is more important than merely obtaining answers [2][6] - The financial market is significantly influenced by human emotions, and AI's binary calculations cannot accurately capture the market changes driven by emotional fluctuations, which remains a core advantage of human researchers [2][6] Group 3 - The transformation brought by AI to research and life is irreversible, and investors and researchers must actively adapt to this trend while recognizing the technological boundaries and leveraging human unique values in cognitive judgment, emotional perception, and wise decision-making [3][7]
中金刘刚：AI让“每个人都可以做自己的分析师”