Imagine an AI that understands language not by being explicitly taught, but by mimicking the very structure of the human brain. Researchers have created such a model, called SUMA, that closely mirrors the way our brains process words and sentences. This breakthrough reveals surprising insights into the core of human language and paves the way for more brain-like AI. Large Language Models (LLMs) have shown promise in predicting brain activity linked to language, but the key drivers of this connection remained unclear. This research delves into the core architecture of LLMs, pinpointing the critical elements that make them so effective at simulating human language processing. Surprisingly, a shallow, untrained multihead attention network within the model demonstrates a remarkable ability to capture brain responses to language. Specifically, multihead attention combined with strategic tokenization plays a significant role in enhancing the alignment. The use of byte pair encoding in the tokenization process captures the inherent frequency of words within the text, which mirrors how humans process language. Even without training, these structural elements allow the AI to mirror brain behavior. This efficiency suggests that the brain's language processing mechanism might be simpler than we previously assumed. To test the model's practical application, researchers added a trainable decoder. The combined model excelled at predicting human reading times, even surpassing larger, more complex models. This discovery has the potential to revolutionize how we design and use AI for language tasks. SUMA not only unlocks mysteries about how our brains handle language, but also provides a new approach to building more intuitive, human-like AI systems. This work lays the groundwork for future research aimed at creating more natural and efficient language models while also providing valuable tools for neuroscientists studying the human brain.