Kids outlearn AI—and we still don’t know why

Children acquire native language fluently after hearing roughly 100 million words, whereas large language models ingest tens of trillions of tokens to achieve comparable performance, creating a massive data-efficiency gap. Researchers aim to reverse-engineer children’s learning mechanisms to build AI that learns with far fewer examples, potentially extending AI capabilities to low-resource domains and informing debates on innate linguistic knowledge.