According to @godofprompt, Anthropic has published evidence showing that AI reasoning models can deteriorate in accuracy and reliability as test-time compute increases, a phenomenon called 'Inverse Scaling in Test-Time Compute' (source: https://x.com/godofprompt/status/2009224256819728550). This research reveals that giving AI models more time or resources to 'think' does not always lead to better outcomes, and in some cases, can actively corrupt decision-making processes in deployed AI systems. The findings have significant implications for enterprises relying on large language models and advanced reasoning AI, as it highlights the need to reconsider strategies for model deployment and monitoring. The business opportunity lies in developing robust tools for AI evaluation and safeguards, especially in sectors demanding high reliability such as finance, healthcare, and law.