The Consciousness Bottleneck in Artificial Intelligence: Thermodynamic Necessity and the Architectural Path to AGI

Current large language models fail at novel strategy games and fluid reasoning tasks despite massive scale. We propose this stems from fundamental architectural limitations, not insufficient training or data. The main argument presented is that achieving open-ended artificial general intelligence (AGI) will require the implementation of architectures that are functionally indistinguishable from consciousness. Through a synthesis of thermodynamic and information-theoretic analysis, it is proposed that the well-documented capability asymmetry in current AI (its superhuman performance in pattern recognition set against its fundamental limitations in fluid intelligence, genuine creativity, and open-ended strategy) is not a matter of insufficient scale but a direct consequence of missing architectural features. Specifically, it is established that cognitive capabilities essential for general intelligence necessitate recursive relevance realization, strange loop dynamics, and operation at thermodynamic criticality. While no claim is made that these features guarantee phenomenal experience, it is contended that they are necessary for the computational functions that consciousness enables. To substantiate this, a framework is presented for measuring these consciousness-relevant features, offer falsifiable predictions regarding the capability ceilings of current models, and propose concrete directions for experimental validation. Preliminary empirical indicators suggest specific quantitative boundaries warrant investigation (Φ ≈ 20–30 bits, recursive depth 2–3 levels, temporal duty cycle 10–20%). The central implication is that the pursuit of AGI is, by necessity, the pursuit of architectures that will exhibit the functional properties of consciousness, regardless of whether they possess subjective awareness.