Recently, OpenAI's GPT-5.2 soared past the human baseline in the ARC-AGI-2 benchmark test, notching up a record-breaking accuracy rate of 75%. The ARC-AGI-2 test is a pivotal benchmark for gauging AI's prowess in abstract, inductive, and transfer reasoning. It eschews reliance on memory or pattern matching, demanding genuine reasoning capabilities from AI. GPT-5.2's leap in performance stemmed from refining its software architecture, rather than a mere boost in computational muscle. Yet, large models continue to grapple with real-world hurdles, including subpar user experiences and imprecise task execution. Ilya Sutskever, OpenAI's ex-Chief Scientist, highlighted a 'high scores, low capabilities' phenomenon in current models. They ace benchmark tests but falter in real-world generalization. This 'performance paradox' underscores AI tech's application-level shortcomings. Model design must delve deeper into user needs to ensure seamless integration with actual work scenarios.