We investigate the nature of intelligence in Large Language Models (LLMs), arguing that their impressive capabilities stem from next-token prediction (NTP) combined with externally supplied cognitive structures, primarily Chain-of-Thought (CoT) prompting. It critically examines this "NTP + Schemata" model through the lens of Jean Piaget's theory of cognitive development, differentiating between assimilation (fitting new information into existing frameworks) and accommodation (altering frameworks to account for novel information). The analysis posits that while CoT facilitates assimilation by providing a reasoning template, current LLMs lack the capacity for true accommodation, highlighting a fundamental "assimilation/accommodation gap." This limitation is further underscored by their struggle with fluid intelligence tasks, such as those found in the Abstraction and Reasoning Corpus (ARC), which require dynamic schema creation rather than just pattern application. We conclude by exploring future directions, including neuro-symbolic AI and Piagetian-inspired learning, as potential pathways to bridge this gap and foster more adaptive machine intelligence.