Why LLMs Still Fall Short of AGI: The Gap Between Data and Human Judgment

Large language models have rapidly evolved since ChatGPT's viral launch in 2022, progressing from question-answering to reasoning and agentic action. However, a fundamental gap remains: humans continuously process an effectively infinite amount of sensory and contextual information to decide what should be done, while LLMs rely on a compressed, text-based slice of reality provided by a user. The chat interface essentially acts as a lossy compressor, where the human distills a complex situation into a few sentences before the model can act — meaning critical context is often lost or unstated. Even as AI agents gain the ability to browse documents, observe screens, and use tools, directly perceiving information does not automatically confer the kind of judgment humans exercise. The author argues that until models can independently interpret open-ended, real-world context the way humans do, the current human-AI dynamic remains a partnership rather than true general intelligence.