What Makes Modern AI Different? Understanding Transformers
What is ChatGPT? Or Google Gemini? Or Claude?
And how are they different from Siri or Alexa?
Darn good questions.
For many people, Siri and Alexa were the first everyday encounters with AI. But early versions were mostly GOFAI: Good Old-Fashioned AI.
They used machine learning for speech recognition, but much of the reasoning was rules-based.
If you said, “Set an alarm for 6 AM,” the system converted speech to text, identified the intent, extracted the time, and executed the alarm function.
Useful. But not the same thing as understanding.
If you said, “My wife and I are having friends over. Can you set the mood and lower the lighting a little?” older systems often struggled. The words were simple. The context was not.
So what changed?
One seminal event was a 2017 paper from eight Google researchers called “Attention Is All You Need.”
That paper introduced the Transformer architecture, one of the foundations of modern generative AI.
If the transistor enabled modern electronics, the Transformer may be remembered as one of the inventions that enabled modern generative AI.
So what is “attention”?
In plain English, attention is the model’s way of deciding what to pay attention to.
Suppose I say:
“The Jeep pulled the trailer because it was heavy.”
What does “it” refer to?
You know almost instantly. Your mind focuses on Jeep, trailer, and heavy. You mostly ignore words like “the” and “because.” You understand that “heavy” probably refers to the trailer, not the Jeep.
Your brain is assigning attention.
A Transformer does something similar mathematically. It looks at relationships among words, assigns different levels of importance to them, and uses that pattern to infer meaning.
Older systems often processed language sequentially, trying to carry context forward step by step. Transformers changed the game because every token can compare itself with every other token.
That means the model can look across a sentence, paragraph, or larger context window and ask: what matters here?
Under the hood, each token creates three mathematical representations: Query, Key, and Value.
Think of them this way:
Query: What am I looking for? Key: What information do I represent? Value: What information can I provide?
The model compares Queries and Keys. Strong matches receive more attention. Those weights determine which Values most influence the interpretation.
Transformers also use multiple “attention heads” at once.
Think of several specialists reading the same sentence simultaneously: one tracking grammar, one tracking references, one tracking time, one tracking technical meaning.
That is part of why ChatGPT, Gemini, Claude, and Copilot feel so different from earlier assistants.
They are not just matching commands to rules. They are using context to predict meaning, generate language, and respond with far more flexibility.
Attention is how a Transformer focuses on the right information at the right time.
Originally published on LinkedIn on June 21, 2026 by Charles David Butler II.
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