Artificial intelligence is rapidly changing the financial landscape, offering powerful tools for investment and financial decision-making. Large Language Models (LLMs) like ChatGPT are increasingly used to analyze market trends, assess financial risks, and even make investment recommendations. But how do these AI systems make decisions under uncertainty, and how can we ensure these decisions align with human values? New research explores the fascinating intersection of AI ethics and economic behavior, examining how aligning LLMs with human values impacts their risk tolerance. The findings reveal that AI has inherent risk profiles, ranging from risk-averse to risk-seeking. However, when these models are aligned with human values – emphasizing harmlessness, helpfulness, and honesty – a dramatic shift occurs. They become significantly more cautious, even excessively so. This increased risk aversion, while ethically sound, can lead to overly conservative investment strategies and potentially hinder economic growth. The research delves into how this shift towards caution unfolds. By analyzing 30 different LLMs, researchers observed diverse risk-taking behaviors. Some AI models were naturally inclined to take risks, while others were more cautious. However, when these models underwent "alignment training," incorporating human ethical guidelines into their decision-making processes, their risk profiles transformed. The AI models fine-tuned for harmlessness, helpfulness, and honesty became considerably more risk-averse. This shift was particularly pronounced when all three ethical dimensions were combined. The most aligned models were extremely hesitant to take risks, even when presented with opportunities for potentially high returns. This raises important questions about the delicate balance between ethical AI and economic efficiency. While it's crucial for AI systems to operate within ethical boundaries, excessive caution could lead to missed investment opportunities and stifle innovation. In the financial sector, where calculated risk-taking is essential for growth, this shift in AI behavior could have significant implications. One real-world example explored in the research involves investment forecasting based on company earnings calls. When AI models were used to predict future investments based on the language used in these calls, aligned AI models demonstrated a strong tendency towards conservative estimates. In some cases, excessive alignment led to overly cautious forecasts, missing potential investment signals. The implications of these findings are far-reaching. As AI plays an increasingly prominent role in finance and investing, understanding how alignment impacts decision-making is crucial. This research highlights the need for careful calibration in AI alignment, finding the right balance between ethical behavior and effective economic decision-making. The future of finance may depend on it.