Tom Hundley
How a single paper from Google in 2017 made the modern AI revolution possible.
To understand why the Transformer is special, you have to understand the world before it. In 2016, if you wanted to do language translation (e.g., English to French), you used Recurrent Neural Networks (RNNs) or LSTMs (Long Short-Term Memory networks).
These architectures processed text sequentially. To understand the word bank in a sentence, the model had to process every word coming before it, one by one.
This created two massive problems:
Then came Attention Is All You Need.
In June 2017, researchers at Google Brain published the now-famous paper Attention Is All You Need. They proposed an architecture that threw away the recurrence entirely.
Instead of processing words one by one, the Transformer processes the entire sentence at once.
The core magic of the Transformer is the Self-Attention Mechanism.
Imagine reading the sentence: The animal didnt cross the street because it was too tired.
As a human, you know that it refers to the animal, not the street.
For an old RNN, this was hard. But the Transformer calculates an Attention Score for every word against every other word in the sentence simultaneously.
Since the Transformer looks at all words at once (a Bag of Words approach), it technically doesnt know the order. Man bites dog and Dog bites man look the same to the raw attention layer.
To fix this, the authors added Positional Encodings—mathematical vectors added to the word embeddings that tell the model, This word is at position 1, this is at position 2.
This simple hack allowed the model to have its cake and eat it too: massive parallel processing plus understanding of word order.
The original Transformer had two parts:
Note: Modern LLMs like GPT-4 are mostly Decoder-only Transformers. They just predict the next token, over and over again.
The true revolution of the Transformer wasnt just accuracy; it was scalability.
Because the architecture is parallelizable, labs could train models on datasets that were previously impossible to handle. They could scale from 100 million parameters to 100 billion parameters.
We discovered a Scaling Law: with the Transformer architecture, if you add more data and more compute, the model gets smarter. It didnt plateau like LSTMs did.
Every major AI breakthrough of the last 5 years—BERT, GPT-3, PaLM, Llama, Claude—is built on this foundation. We are still mining the insights from that 2017 paper.
When you use ChatGPT, you arent just talking to a bot. You are interacting with a massive, parallelized attention machine that is calculating the relationship between every word in your prompt and everything it has ever learned, all in milliseconds.
This article is a live example of the AI-enabled content workflow we build for clients.
| Stage | Who | What |
|---|---|---|
| Research | Claude Opus 4.5 | Analyzed current industry data, studies, and expert sources |
| Curation | Tom Hundley | Directed focus, validated relevance, ensured strategic alignment |
| Drafting | Claude Opus 4.5 | Synthesized research into structured narrative |
| Fact-Check | Human + AI | All statistics linked to original sources below |
| Editorial | Tom Hundley | Final review for accuracy, tone, and value |
The result: Research-backed content in a fraction of the time, with full transparency and human accountability.
Were an AI enablement company. It would be strange if we didnt use AI to create content. But more importantly, we believe the future of professional content isnt AI vs. Human—its AI amplifying human expertise.
Every article we publish demonstrates the same workflow we help clients implement: AI handles the heavy lifting of research and drafting, humans provide direction, judgment, and accountability.
Want to build this capability for your team? Lets talk about AI enablement →
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