This episode cuts through the confusion and endless debates surrounding AGI by applying a quantifiable, human-centric definition: AGI is defined as an AI that can match or exceed the cognitive versatility and proficiency of a well-educated human adult.

We explore a robust framework that measures AI progress against the gold standard of human intelligence: the Cattell-Horn-Carroll (CHC) theory of cognitive abilities. This detailed, hierarchical model assesses performance across 10 core domains derived from human cognition, ensuring an "apples-to-apples comparison".

The results for modern large language models show a stunning rate of progress. We reveal how GPT-5 (2025) achieved a remarkable total AGI score of 58%, demonstrating near-superhuman proficiency in complex symbolic manipulation. It scored a perfect 10% in Mathematical ability (M), capable of solving challenging multivariable calculus integrals, and a perfect 10% in Reading and Writing ability (RW), mastering complex comprehension and high-quality essay generation.

However, this session dives into the core paradox: despite these incredible peaks, GPT-5's overall score points to a fundamental structural gap. The system exhibits a highly uneven or "jagged cognitive profile".

We highlight the most critical bottleneck revealed by this framework: Long-Term Memory Storage (MS). Both GPT-4 and GPT-5 scored a stark 0% in this domain, suffering from a kind of functional amnesia that prevents stable learning and consolidation of new information based on recent experiences. This deficit suggests a major persistent architectural limitation, severely limiting its use for anything requiring personalization or ongoing context.

Tune in as we analyze these critical deficits, including struggles in novel Reasoning (R), integrated Visual Processing (V), and basic cognitive Speed (S). We also unpack the concept of "capability contortions," where AI uses massive context windows to simulate memory, masking its underlying inability to truly learn and update its knowledge base dynamically.

Discover why simply improving existing strengths won't lead to AGI, and why fixing these "broken parts" of the AI engine is the true challenge facing researchers today.

Keywords: AI, Artificial Intelligence, LLMs, Large Language Models, AI Consciousness, Machine Thinking, AI Understanding, Philosophy of AI, Chinese Room Argument, John Searle, Self-Awareness, Machine Learning, Deep Learning, Technological Singularity, AI Limitations, Genuine Intelligence, Simulated Intelligence, AI Ethics, Future of AI, Apple AI Research, Symbolic Reasoning, Syntax Semantics.