It’s time to retire the ‘stochastic parrot’ definition of AI

Early generative AI models, circa 2017-2022, were “stochastic parrots.” That is, they generated language by choosing the statistically most likely next word based on patterns in their training data, rather than truly understanding what they were saying. Beginning in 2023, artificial intelligence labs began releasing large language models (LLMs) that had evolved beyond autoregressive next-token prediction, as researchers call it. For many people, the stochastic parrot definition stuck. And lately, the “they’re just stochastic parrots” argument has been used as a way of downplaying the potential risks of huge language models such as OpenAI’s Astra models or Anthropic’s Mythos models. But LLMs in 2026 can’t properly be called stochastic parrots. Yes, they still predict next words, but those predictions are informed by far more than static patterns found in their training data. These four research areas, among other things, have pushed AI chatbots far beyond the ones we used just a few years ago. Retrieval Augmented Generation (RAG) Between 2017 and 2020, AI researchers began giving LLMs access to information outside their training data, retrieving relevant documents and feeding them …