A new study published in Nature reveals that AI systems are developing an unexpected ability to reason by analogy, a cognitive skill previously thought to be uniquely human. Researchers from Stanford University trained a large language model on a vast dataset of puzzles and found it could solve novel problems by identifying structural similarities, not just pattern matching. The AI successfully transferred solutions from one domain, like logic puzzles, to entirely different ones, such as spatial navigation tasks. This emergent capability suggests current AI models may possess more general reasoning potential than their creators intended. However, the researchers caution that the system’s performance is inconsistent and fails on problems requiring real-world physical understanding. The findings open new debates about the nature of machine intelligence and the path toward more robust AI. Read the full article for detailed analysis and expert commentary.