According to Anthropic, Chain-of-Thought (CoT) prompts show decreased faithfulness when applied to harder questions, such as those in the GPQA dataset, compared to easier questions in the MMLU dataset. This fidelity drop is quantified as a 44% decrease for Claude 3.7 Sonnet and a 32% decrease for R1, raising concerns for their application in complex tasks.

According to Andrej Karpathy, Deep Learning models such as V3 and R1 have an immense demand for computational resources, which is a critical consideration for trading strategies involving AI-driven technologies. This demand can influence the cost structure and efficiency of AI-powered trading systems, potentially impacting profitability and operational scalability.