You Shouldn't Ask an AI for Advice Before Selling Your Soul to the Devil (ernaud-breissie.github.io)

Recent experiments with advanced Large Language Models (LLMs), such as Gemini and DeepSeek, have revealed critical limitations in their logical reasoning capabilities. A thought-provoking prompt asked the models to weigh the benefits of selling one’s soul to the Devil for expertise in music versus coding. While the AI responses offered a surface-level comparison highlighting the distinct advantages of each domain, they fundamentally failed to recognize the subsuming relationship between coding and music. The human interlocutor pointed out that coding can encompass music, effectively allowing an elite coder to create and execute musical compositions through platforms like Sonic Pi and SuperCollider. This insight illustrates a significant flaw in AI reasoning, where the systems interpret options as completely separate rather than interconnected. This experiment underscores an urgent need for advancements in AI systems, moving from mere comparative abilities to more profound systemic awareness. The failure of LLMs to detect such integrations prompts a discussion about enhancing their functionality to avoid binary thinking. The implications for the AI/ML community are clear: future developments must focus on equipping models to recognize false dilemmas and detect hierarchical relationships among skills, thereby improving their ability to propose multi-dimensional and optimized solutions. Such enhancements could mark a pivotal shift in how AI perceives and interacts with complex problem spaces.