Large language models (LLMs) are getting eerily good at mimicking human language, but do they actually process information like our brains? A fascinating new study used fMRI brain scans to peek inside the “minds” of LLMs like BERT and the Llama family, and the results are surprising. Researchers analyzed the activity of individual artificial neurons within these models as they processed stories. They discovered that groups of these neurons fire in patterns strikingly similar to activity in specific brain networks associated with language processing, working memory, and even the default mode network—the brain's “daydreaming” state. This suggests LLMs aren't just parroting back text; they may be developing internal representations of information that resemble how humans understand the world. Even more intriguing, the study found that as LLMs become more sophisticated (like progressing from Llama 1 to Llama 3), their functional organization becomes more brain-like, striking a balance between diverse computational behaviors and specialized functions. This means more advanced models not only perform better but also organize their internal processes in a way that more closely resembles the human brain. While the research is still in its early stages, it offers a tantalizing glimpse into the future of AI. By understanding how LLMs develop brain-like organization, we might unlock the secrets to building truly intelligent machines and, perhaps, even gain a deeper understanding of our own cognitive processes. However, challenges remain. Researchers need to explore how these functional organizations change with different models and tasks, pushing the boundaries of what we know about both artificial and biological intelligence.