Transformer — The Ancestor of Every AI Today

Instead of reading one word at a time in order like an RNN, look at the whole sentence at once and compute how related the words are to each other. This single idea, published by Google in 2017, became the skeleton of almost every AI model released today. Without knowing this architecture, you cannot properly explain GPT, Gemini, or Claude.

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Background / Why This Architecture Now

As of 2026, the phrase "new architecture" accompanies every new model release. In reality, the Transformer architecture introduced in 2017 on the official Google Research blog is still the skeleton of almost every frontier model. According to futureagi.com, the major models of 2026, including GPT-5.6, Claude Opus 5, Gemini 3.x, and Grok 4.5, are all based on "decoder-only Transformers," and differences in training data and post-training separate their performance more than differences in the architecture itself.

At the same time, as IBM summarizes, alternative architectures like Mamba draw attention as "the first competitive architecture that could replace the Transformer," and are sometimes reported as if an entirely new approach has arrived. Yet these alternatives too are mostly explained in terms of "what part of the Transformer was changed and how." In other words, without knowing the Transformer, you read half of today's AI news without a reference point.

Key Data & Current State

Item	Details
First published	June 2017, eight Google researchers (Vaswani et al.), paper "Attention Is All You Need"
Citations	More than 250,000 (per Wikipedia, 2026, among the top 10 most-cited papers published in the 21st century)
Leading replacement candidate	Mamba (state space model): memory use grows only linearly as sequences lengthen, up to 5x the throughput of a comparable Transformer
State in 2026	Most frontier models, including GPT-5.6, Claude Opus 5, and Gemini 3.x, adopt a decoder-only Transformer + mixture-of-experts (MoE) structure

Interpretation: The fact that it has gone unchanged for more than eight years is itself unusual. There are not many cases in deep learning where a core architecture has survived this long. At the same time, the emergence of alternatives like Mamba with clear efficiency advantages on long text is a signal that the Transformer is not all-powerful.

In-Depth Analysis

1) What Was the Problem Before the Transformer?

Before the Transformer, translation and language models mostly used RNNs (recurrent neural networks). The Google Research blog points to two limitations of RNNs. First, because they processed a sentence one word at a time from left to right, it was hard to learn relationships between words far apart. To decide whether "bank" connects to "river" or to "money," the model had to pass through every word in between in order. Second, the sequential approach could not properly exploit the advantages of parallel hardware like GPUs and TPUs. CNN (convolutional neural network) models were also tried, but they had a similar problem: the number of computational steps needed to combine distant information grew with the distance.

2) Self-Attention — the Core Idea in One Line

The heart of the Transformer is self-attention. As an analogy, to understand one word, the model scans every other word in the sentence at once, scores "how related is this to the word in question," and then mixes in each word's information in proportion to that score. Dropping the analogy for the actual structure: each word is converted into three vectors, query, key, and value; the dot product of one word's query with every other word's key yields relevance scores, which softmax turns into a probability distribution. A weighted sum of each word's value vector using those probabilities produces a new, "context-aware" representation of the word. Because the entire sentence is computed at once rather than passing through words in order as an RNN does, parallel processing becomes possible, and relationships between distant words are computed directly in a single step.

3) The Other Components — Multi-Head, Positional Encoding, Feed-Forward

Three more devices make up for what self-attention alone lacks. Multi-head attention splits this relevance computation into several parallel heads, so that different heads capture different kinds of association, such as grammatical relationships and semantic relationships. Positional encoding adds position information, built from sine and cosine functions, to each word vector to tell the model "which word in the sequence this is," since self-attention itself has no notion of order. Finally, after each attention layer comes a feed-forward neural network applied identically at every position, along with residual connections and layer normalization to stabilize training. These four, self-attention, multi-head, positional encoding, and feed-forward, form the standard Transformer block.

4) So How Do Today's AIs Modify and Use It?

The original 2017 paper described an encoder-decoder structure for translation, but most conversational AI today uses a "decoder-only" structure that stacks decoders without an encoder. Focusing solely on predicting the next word suits text generation better. On top of this, open-weight models like Llama, DeepSeek, and Mistral openly adopt the mixture-of-experts (MoE) structure, growing total parameters into the trillions while activating only a fraction (tens of billions) in any single computation to keep costs down. Closed models like GPT, Claude, and Gemini are presumed to use MoE as well, but their exact internal structure has not been disclosed. Meanwhile, architectures like Mamba that process sequences with a state space model (SSM) without attention have emerged; according to IBM, they are highly memory-efficient on long contexts but still fall short of the Transformer at in-context learning, the ability to learn on the fly from the context. Hence hybrid architectures that mix Transformer layers with Mamba layers, such as AI2's Jamba and IBM's Bamba and Granite 4.0, are emerging as a compromise.

In Practice — 3 Principles for Reading the Transformer Properly

1. Every time you see the phrase "new architecture," check what is actually new. Most major models in 2026 are still Transformer variants (decoder-only + MoE), and completely different structures (Mamba and the like) are so far mixed in only in hybrid form.
2. Distinguish model size (parameter count) from actual computation (active parameter count). In an MoE structure, even if total parameters are in the trillions, the parameters used at any one time can be far fewer, so "the number is big" alone is a poor basis for judging performance or cost.
3. When choosing a service that handles long documents or long conversations, look not just at "context length" but at whether it actually runs fast and reliably at that length. The Transformer has the structural property that computation grows quadratically with context, so whether a model uses methods that compensate (efficiency techniques, hybrid structures, etc.) affects perceived performance.