Large language models (LLMs) have taken the world by storm, demonstrating impressive abilities in generating human-like text, translating languages, and even writing different kinds of creative content. But how do these powerful AI systems relate to the human mind? New research explores this fascinating question, suggesting that LLMs closely resemble the intuitive, instinctive part of our thinking. Think of the 'gut feeling' you get about something—that's the kind of processing LLMs excel at. They identify patterns, draw associations, and make quick judgments based on vast amounts of data, much like our unconscious mind processes information. However, the research also points out a key difference. While LLMs are great at implicit processing, they lack the explicit, symbolic reasoning capabilities that characterize human thought. We can consciously manipulate symbols, apply logic, and engage in deliberate, step-by-step problem-solving. This type of thinking is still largely absent in LLMs. To bridge this gap, researchers are looking at 'dual-process' cognitive architectures—models of the human mind that incorporate both implicit and explicit processes. By combining the strengths of LLMs with symbolic reasoning systems, we can create AI that is not only powerful but also more closely resembles the flexible, adaptable intelligence of humans. This research offers a glimpse into the future of AI, one where LLMs play a vital role, not as replacements for human thought, but as powerful partners in enhancing our understanding of intelligence itself.