The ELIZA Effect and the Transformer Misinterpretation examines how contemporary artificial intelligence systems came to be widely—but incorrectly—interpreted as evidence of emerging machine cognition. Tracing the phenomenon back to Joseph Weizenbaum’s 1966 ELIZA program, the paper analyzes how human cognitive bias, historical drift away from foundational computational theory, and the unprecedented linguistic fluency of transformer models collectively produced a global misattribution of intelligence. The work integrates perspectives from computability theory, information theory, recursion, and thermodynamics to demonstrate that linguistic fluency—however convincing—does not imply understanding, reasoning, or conceptual grounding. It argues that the transformer architecture lacks the computational mechanisms required for cognition, and that empirical performance has been repeatedly mistaken for evidence of general intelligence due to the amplification of the ELIZA Effect at scale. This paper situates the transformer era within the broader history of AI, identifies the structural sources of misinterpretation, and outlines the theoretical constraints that any future architecture must satisfy to support genuine intelligence. It provides a rigorous foundation for re-evaluating the claims of AGI emergence and for redirecting AI research toward models grounded in computational and physical principles rather than surface-level behavioral outputs.