Unlike other species, humans are born with "half-baked" brains that wire themselves based on the culture, language, and knowledge accumulated by all previous generations. This cumulative learning, not just individual experience, is the key to our rapid advancement as a species.

Contrary to some theories, there is little evidence for a distinct "language module" in the brain. Instead, Dr. Erich Jarvis explains that complex algorithms for producing and understanding language are built directly into the brain's existing speech production and auditory pathways.

A human child learns a language from five years of input, while an LLM requires the equivalent of 5,000. Professor Griffiths quantifies this gap as 4,995 years' worth of information, which represents the "priors" or inductive biases—innate structures and assumptions—that give humans a massive head start in learning.

Unlike other primates, the human brain continues its rapid, fetal-like growth trajectory for years after birth. This protracted development period makes children uniquely receptive to intense social learning and environmental influences, effectively functioning as "external fetuses."

People who are bilingual consistently outperform monolinguals on tasks requiring executive function, such as response inhibition. This cognitive advantage is thought to stem from the lifelong, unconscious practice of actively suppressing one language while speaking another, effectively training the brain's control networks.

Thought is fundamentally non-linguistic. Evidence from babies, animals, and how we handle homophones shows that we conceptualize the world first, then translate those concepts into language for communication. Language evolved to express thought, not to be the medium of thought itself.

Research shows children engage in more complex, "authentic communication" when playing with peers because they are constantly negotiating and problem-solving. In contrast, adult-child interactions are often didactic and less challenging, stunting the development of sophisticated language skills.

Despite AI's impressive capabilities, it lags significantly behind humans in learning efficiency. Today's models are trained on amounts of data that would take a person tens of thousands of years to consume, while a human child achieves language fluency in under ten years, indicating a fundamental algorithmic difference.

The idea that language creates thought is backwards. Pre-linguistic infants already have a sophisticated understanding of the world (e.g., cause and effect). They learn language by shrewdly guessing a speaker's intent and mapping the sounds they hear onto thoughts they already possess.