Group 1 - The core argument of the articles revolves around the ineffectiveness of large models in stock trading, despite their initial promise and hype in the financial sector [2][3][4] - The introduction of BloombergGPT marked a significant moment in the integration of AI into finance, but its high cost and exclusivity limited its accessibility to smaller institutions [2][3] - The shift from relying on AI for stock predictions to using it as a research and analysis tool reflects a broader trend in the industry, where AI is seen as an assistant rather than a decision-maker [4][15][18] Group 2 - The financial market is characterized by a low signal-to-noise ratio, making it challenging for AI to identify reliable predictive signals [6][7] - The concept of Alpha, or the ability to consistently outperform the market, is undermined by the rapid discovery and exploitation of signals by market participants, leading to the decay of predictive models [8][9][10] - The articles emphasize that AI should be viewed as a cognitive enhancement tool rather than a replacement for human judgment in trading decisions [17][19][20] Group 3 - The evolution of AI in finance has led to a focus on enhancing research capabilities, such as faster data processing and analysis, rather than direct trading predictions [15][16] - The future of successful trading lies in the combination of strategic human decision-making and efficient AI tools, rather than blind reliance on AI for stock trading [18][20]