Beyond Capability: Why Technical Progress in Large Language Models Does Not Necessarily Produce Better Human Experiences

This working paper examines the relationship between technical capability and perceived usefulness in Large Language Models (LLMs). Public discussions often assume that improvements such as larger context windows, more memory, faster inference, multimodal functionality, and expanded tool access will necessarily produce better human experiences. The paper questions this assumption and argues that technical capability and human usefulness are distinct dimensions. A system may become faster, more powerful, and more feature-rich while users may still experience its responses as inaccurate, superficial, repetitive, or insufficiently helpful. The paper proposes that the long-term significance of conversational artificial intelligence may depend not only on what systems can technically do, but also on whether human beings experience interactions as meaningful, reliable, intellectually stimulating, and practically valuable. This distinction between capability and usefulness is presented as a central philosophical and practical challenge for evaluating future artificial intelligence systems.