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A thought-provoking discussion has emerged on Reddit, questioning the widespread assumption that Large Language Models (LLMs) are the definitive route to achieving Artificial Superintelligence (ASI). The user’s argument centers on the data efficiency disparity between LLMs and human learning. They highlight that humans, particularly children and PhD students, acquire deep expertise with far less data than is required to train these massive models. This observation raises concerns that current AI research, focused on scaling up LLMs, might be overlooking a fundamental connection between neural networks and the mechanisms of human intelligence. The debate originated in a post on the r/artificial subreddit.