Core Insights - AI has emerged as a revolutionary tool for investment research efficiency, enabling rapid analysis of vast financial texts and automated decision-making in asset allocation and policy analysis, significantly shortening research cycles [2][3] - The historical reliance and data limitations are the core obstacles for AI to generate excess returns, as AI models are trained on historical data and excel at summarizing the past but struggle to predict future structural turning points lacking historical precedents [2][4] - A "human-machine collaboration" model is essential to address model risks and regulatory requirements, as complete reliance on AI's "black box" decisions faces challenges from model failure and increasingly stringent financial regulations [2][10] AI Empowerment in Investment Research - Major Wall Street firms, such as Citadel, have positioned AI assistants as "super co-pilots" for investment managers, focusing on rapid information processing and automated analytical support [3] - AI enhances macro and policy analysis efficiency by deep processing unstructured data, allowing for a comprehensive understanding of policy context and sentiment [3] - In complex asset allocation frameworks, AI optimizes traditional model weight distributions and strategy backtesting by quickly analyzing vast structured and unstructured data to uncover market volatility patterns and asset interrelationships [3] Limitations of AI - AI's retrospective learning model limits its ability to identify future structural turning points that lack historical precedents, as emphasized by Citadel's founder Ken Griffin [4][7] - AI faces inherent challenges in speed of response, prediction accuracy, and model generalization, often referred to as the "impossible triangle" [4][5] - When dealing with assets characterized by long-term trends or non-converging data, AI's predictive capabilities are fundamentally challenged, necessitating the incorporation of forward-looking data to compensate for its retrospective focus [7][8] Risks of AI Models - AI may generate illusory correlations, leading to "hallucination" risks where it produces content that lacks factual basis due to its focus on statistical fluency rather than factual accuracy [8][10] - Over-reliance on limited historical patterns can result in overfitting, where models perform well on training data but fail in real market conditions [8][10] - The "black box" nature of AI conflicts with regulatory demands for transparency and traceability in investment decision-making, creating significant pressure during compliance reviews [10][11] Systemic Risks and Homogenization - Strategy homogenization can lead to resonance risks, where widespread adoption of similar AI models results in correlated trading signals that amplify market volatility during stress periods [11] - The collective failure of models in the face of unknown market conditions can exacerbate downturns, as seen in the "volatility crisis" of 2018, where similar quantitative strategies triggered large-scale sell orders [11] AI's Role in Investment Research - AI is a powerful cognitive extension tool but not a substitute for human cognition, as it lacks the ability to define problems and create paradigms [12][17] - The future investment research paradigm will require deep collaboration between human insights and AI capabilities, with humans taking on roles as architects, validators, and ultimate responsibility bearers [18][19]