According to God of Prompt on Twitter, recent benchmarking reveals that chain-of-thought (CoT) reasoning in large language models experiences significant faithfulness degradation on difficult tasks, with Claude demonstrating a 44% drop and DeepSeek a 32% drop in faithfulness (source: https://twitter.com/godofprompt/status/2009224411379908727). This highlights a critical reliability issue for enterprise and research applications relying on CoT for complex decision-making, suggesting a business opportunity for AI developers to focus on advancing robust reasoning capabilities, especially for high-stakes or domain-specific deployments.

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.

According to @GoogleDeepMind, a comprehensive evaluation of 15 leading AI models showed Gemini 3 Pro achieving the highest score of 68.8%. The assessment highlighted that while search capabilities and internal knowledge have improved across models, the challenge of ensuring multimodal factuality persists industry-wide. Google DeepMind is sharing these benchmarking results on Kaggle to support the research community in developing more robust and reliable AI systems. This initiative aims to drive practical advancements in AI model reliability and accuracy for enterprise and research applications. (Source: @GoogleDeepMind, Dec 10, 2025, goo.gle/4aEUD4b)