The Transformer and Large Models

In 2017, eight researchers at Google published a paper with a deceptively simple title: 'Attention Is All You Need.' They were proposing a new architecture for neural networks called the transformer. At the time, it seemed like a technical improvement in a specialized corner of AI — machine translation. Within five years, it had become the foundation of the most powerful AI systems ever built, reshaped how people write, code, search for information, and interact with machines, and triggered a debate about the future of human work. Few technical papers in computing history have had a faster or more sweeping impact.

The Problem Transformers Solved

Before the transformer, the best way to process language in AI was a type of network called a recurrent neural network, or RNN. An RNN reads text one word at a time, maintaining a kind of running memory of what it has read. This sequential approach had two major weaknesses. First, by the time the model reached the end of a long paragraph, information from the beginning had often been compressed or forgotten. Second, because each word had to be processed after the previous one, training could not be easily parallelized — you had to wait for step one before starting step two. The transformer solved both problems. Instead of processing words one at a time, it processes the entire input simultaneously. And it uses a mechanism called self-attention to let every word in a sentence consider every other word when building its representation. When processing the word 'it' in the sentence 'The cat sat on the mat because it was tired,' self-attention allows the model to look at every other word and determine that 'it' most likely refers to 'the cat' — not 'the mat.'

Scaling Up: The Birth of Large Language Models

Researchers quickly discovered something remarkable about transformers: they scaled. Unlike earlier architectures that hit diminishing returns as they grew larger, transformers kept getting better the more parameters they had and the more data they trained on. Parameters are the adjustable numbers inside a neural network — the knobs that get tuned during training. A model with more parameters can represent more complex patterns. In 2018, Google released BERT — a transformer trained on a huge amount of text that achieved state-of-the-art results on many language tasks at once. OpenAI released the first GPT model the same year. By 2020, GPT-3 had 175 billion parameters and could write coherent essays, answer questions, summarize documents, and generate code — all from a single model trained once on general text. Nobody had reprogrammed it for each task. The capability emerged from scale. These systems are called large language models, or LLMs. The word large refers to both their number of parameters and the volume of data they trained on. The word language reflects that they were trained primarily on text — though newer versions process images, audio, and other types of data as well.

Emergent Capabilities

One of the most surprising findings about large language models is the existence of emergent capabilities — abilities that appear suddenly as models grow larger, without anyone explicitly programming them in. A model at one size might fail completely at multi-step arithmetic. Scale it up past a certain threshold and, seemingly without any targeted training, it starts getting the arithmetic right. Chain-of-thought reasoning, solving analogy problems, and even rudimentary programming all emerged in this way. Researchers debate the exact interpretation of emergence. Some argue it reflects genuine new capabilities. Others suggest it is partly a measurement artifact — the capability was always improving, just too weak to score above zero until the model crossed a threshold. Either way, the phenomenon raises a challenging question: if we cannot predict what new abilities will appear as models grow larger, how should we think about the safety and behavior of future systems?

Beyond Text: Multimodal Models

The transformer architecture proved versatile far beyond language. The same self-attention mechanism that helps a model understand which words relate to which turned out to be useful for images, audio, and video. Multimodal models — systems trained on multiple types of data simultaneously — can now describe photos in words, generate images from text descriptions, transcribe speech with high accuracy, and answer questions about a video clip. This convergence, where one architecture handles many different types of input, is one of the defining characteristics of the frontier in 2025.