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.'

The complexity in LLMs isn't intelligence emerging in silicon; it reflects our own. These models are deep because they encode the vast, causally powerful structure of human language and culture. We are looking at a high-resolution imprint of our own collective mind.

Human understanding is the ability to connect new information to a global, unified model of the universe. Until recently, AI models were isolated (e.g., a chess model). The major advance with large multimodal models is their ability to create a single, cohesive reality model, enabling true, generalizable understanding.

Given the strong parallels in how AI models and brains represent information, studying AI provides a powerful, accessible, and faster proxy for neuroscience research. As Max Hodak's friends at top AI labs joke, it is simply "way easier to do neuroscience on the models."

Language is just one 'keyhole' into intelligence. True artificial general intelligence (AGI) requires 'world modeling'—a spatial intelligence that understands geometry, physics, and actions. This capability to represent and interact with the state of the world is the next critical phase of AI development beyond current language models.

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 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.

While biology (birds) provides initial inspiration for flight, progress eventually requires engineering machine-specific solutions (jet engines). Similarly, AI learned foundational principles from human cognition, but its recent breakthroughs come from non-biological methods like massive scaling. The focus should be on universal "laws of thought," not just mimicking biological hardware.

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.