The quest to understand AI models is mirroring neuroscience's historical path. Researchers first saw representations as distributed, then focused on single neurons ('neuron doctrine'), and now study how populations of neurons encode complex concepts—replicating neuroscience's shift to the 'population doctrine.'

Early AI pioneers modeled neural network algorithms on the hierarchical structure of visual neurons discovered by neuroscientists like Hubel and Wiesel in the 1950s. This direct inspiration from biology was a pivotal starting point for modern AI, bridging neuroscience and computation.

Today's AI, particularly neural networks, stems from a long tradition in cognitive science where psychologists used mathematical models to understand human thought. Key advances in neural nets were made by researchers trying to replicate how human minds work, not just build intelligent machines.

The structural similarity between an LLM's 'J-space' cognitive architecture and theories of human cognition suggests that treating models as human-like is a surprisingly effective way to design experiments and gain insights, challenging the view that they are completely alien.

The geometric similarities between how AI models and the brain represent concepts are a strong signal that both fields are converging on a fundamental truth. This alignment provides a practical tool for BCI development and a deeper understanding of intelligence itself, suggesting it's like a law of physics.

The debate over AI consciousness isn't just because models mimic human conversation. Researchers are uncertain because the way LLMs process information is structurally similar enough to the human brain that it raises plausible scientific questions about shared properties like subjective experience.

Neural networks, like brains, emerge from countless small nudges during training rather than a premeditated architectural design. The field of interpretability, therefore, functions like neuroscience, attempting to reverse-engineer what this 'evolutionary' process has learned.

New AI models are moving away from brute-force computation. By selectively focusing on relevant data, much like the human brain indexes memories, they can achieve massive performance gains and cost reductions, overcoming a major bottleneck in current architectures.

A novel training method involves adding an auxiliary task for AI models: predicting the neural activity of a human observing the same data. This "brain-augmented" learning could force the model to adopt more human-like internal representations, improving generalization and alignment beyond what simple labels can provide.