How Language Models Evolved: A Series of Fixes to Broken AI Systems

The development of modern large language models like ChatGPT was not the result of deliberate top-down design, but rather decades of iterative problem-solving where each solution exposed a new limitation. Early rule-based systems failed because natural language has too many exceptions and requires probabilistic reasoning, leading researchers to shift toward learning patterns from large text datasets. Statistical n-gram models improved on rules but treated every word as an unrelated identifier, meaning knowledge about one word could not transfer to similar words. Word embeddings, popularized by word2vec in 2013, solved that by representing words as numerical vectors so that semantically similar words clustered together, even revealing unexpected mathematical relationships between concepts. However, static vectors could not capture context-dependent word meanings, pushing researchers toward sequential and eventually attention-based architectures to address each successive flaw.