Most people first heard the term “large language model” sometime around 2022, when ChatGPT became the fastest-growing consumer app in history. But the technology behind it did not appear overnight. Scientists and engineers have been working on teaching machines to understand and generate language for more than 70 years.
Here is how we got from a simple computer program in the 1960s to the AI tools reshaping business and daily life today.
The First Question: Can Machines Talk?
In 1950, British mathematician Alan Turing asked a simple but powerful question: could a machine ever converse well enough to fool a human into thinking it was a person? He proposed a test to measure this in a paper titled “Computing Machinery and Intelligence,” published in the journal Mind. It was a thought experiment, but it gave researchers a clear goal to work toward.
A few years later, in 1954, scientists at Georgetown University teamed up with IBM to build a program that translated more than 60 Russian sentences into English. The system ran on just 250 words and six grammar rules. The experiment was limited and carefully controlled. But it showed that computers could do something meaningful with language, and researchers began taking the idea seriously.
ELIZA: The World’s First Chatbot
The first real conversational program appeared in 1966. Joseph Weizenbaum, a researcher at MIT, created a program called ELIZA. He described it in a paper published in the Communications of the ACM. It worked by matching patterns in what a user typed and reflecting questions back at them, much like how a therapist might respond during a session.
ELIZA could not actually understand language. It was following rules, not thinking. But many people who used it were surprised by how human it felt. ELIZA proved that even a simple program could create the impression of real understanding, and that discovery would quietly shape AI research for the next 50 years.
Statistics Take Over
Through the 1980s and 1990s, researchers moved away from hand-coded rules and toward statistical methods. Instead of writing rules for every possible situation, they fed computers large amounts of text and let them find patterns in the data on their own.
One popular approach was called an n-gram model. It worked by tracking how often certain words appeared next to each other and using that information to guess the next word in a sentence. These models were limited compared to what exists today, but they were a genuine step forward. They powered early versions of spell-check, speech recognition, and machine translation tools that millions of people were already using without knowing it.
Neural Networks Enter the Picture
In the mid-1980s, a new type of model called a Recurrent Neural Network, or RNN, introduced a better way to process sequences of words. Unlike n-grams, RNNs could hold onto information from earlier parts of a sentence while working through later parts. It was a step closer to something that looked like reading comprehension.
The problem was that RNNs struggled with long pieces of text. By the time they reached the end of a paragraph, they had often forgotten the beginning. In 1997, researchers Sepp Hochreiter and Juergen Schmidhuber solved part of this problem with a design called Long Short-Term Memory, published in the journal Neural Computation. LSTMs were built to hold onto important details over longer stretches of text, and they became the standard tool for language tasks well into the 2000s.
The Word Becomes a Number
A key breakthrough came in 2013 when Google researcher Tomas Mikolov introduced a tool called Word2Vec. It converted individual words into numbers in a way that actually captured meaning. Words with similar meanings ended up sitting close together in this numerical space.
A famous example: the number for “king,” minus the number for “man,” plus the number for “woman,” lands you very close to the number for “queen.” This might sound abstract, but it gave machines a richer and more flexible way to work with language. The original Word2Vec paper was actually rejected at first before becoming one of the most influential publications in the field. Tools like Word2Vec became the building blocks for nearly everything that came after.
The Transformer Changes Everything
The single biggest leap in the history of language models came in 2017. A team of Google researchers published a paper with a bold title: “Attention Is All You Need.” In it, they introduced a new design called the Transformer architecture.
Unlike older models that read text word by word, Transformers could look at all parts of a sentence at the same time. This made them dramatically faster and far more capable. The paper did not make front-page news at the time, but it quietly redirected the entire field of AI research almost overnight.
GPT, BERT, and the Race Begins
In 2018, two landmark models launched within months of each other. Google released BERT, and OpenAI released the first version of GPT. Both were built on the Transformer design, but they took different approaches.
BERT was designed to understand language by reading text in both directions at once, forward and backward. It quickly improved Google Search results for millions of users worldwide. GPT was built to generate text, always predicting what word should come next. These two approaches, understanding versus generating, have defined the competition in AI ever since.
GPT-2 arrived in 2019 with 1.5 billion parameters, which are the numerical settings that shape how a model behaves. OpenAI delayed releasing the full version for nine months, concerned it could be used to spread misinformation. In their own testing, GPT-2’s text received a credibility score of 6.91 out of 10 from human evaluators. GPT-3 followed in 2020, scaled up to 175 billion parameters. It could write full essays, answer detailed questions, and produce working computer code with very little instruction.
The Public Gets Its Turn
In November 2022, OpenAI launched ChatGPT. It was built on an updated version of GPT-3 and refined through thousands of hours of feedback from human trainers. Within two months, it had 100 million users — a milestone that took TikTok nine months and Instagram two and a half years to reach. No consumer application had ever grown that quickly.
The floodgates opened. Google launched its own AI assistant. Meta released open models that any developer could download and modify. Anthropic, a company founded by former OpenAI researchers, introduced Claude. Microsoft invested billions and built AI tools into its Office and search products. Hundreds of startups entered the space almost overnight.
Where Things Stand Today
Today’s leading models, including OpenAI’s GPT-4, Anthropic’s Claude, and Google’s Gemini, can read images, write and debug code, summarize long documents, and hold conversations that feel natural. A newer category called reasoning models has started to emerge, designed to think through complex problems step by step rather than just producing fluent text.
The technology has moved well beyond the lab. It is being used in hospitals, law offices, customer service centers, and newsrooms. Businesses of every size are trying to figure out how to use it, build on top of it, or compete against it.
The story of large language models is not a story that started with a viral chatbot in 2022. It started with a mathematician asking a question more than 70 years ago. The answer is still being written.
