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Apple's latest AI research challenges the hype around Artificial General Intelligence (AGI), revealing that today’s top models fail basic reasoning tasks once complexity increases. By designing new logic puzzles insulated from training data contamination, Apple evaluated models like Claude Thinking, DeepSeek-R1, and o3-mini. The findings were stark: model accuracy dropped to 0% on harder tasks, even when given clear step-by-step instructions. This suggests that current AI systems rely heavily on pattern matching and memorization, rather than actual understanding or reasoning. The research outlines three performance phases—easy puzzles were solved decently, medium ones showed minimal improvement, and difficult problems led to complete failure. Neither more compute nor prompt engineering could close this gap. According to Apple, this means that the metrics used today may dangerously overstate AI’s capabilities, giving a false impression of progress toward AGI. In reality, we may still be far from machines that can truly think. #AppleAI #AGIRealityCheck #ArtificialIntelligence #AIResearch #MachineLearningLimits