The transformer architecture — attention plus feed-forward layers stacked repeatedly — is the engine behind every major LLM, and it was first described in the 2017 paper 'Attention Is All You Need'.
CJ explains how the transformer's self-attention mechanism lets each token gather context from every other token, resolves word ambiguity, and scales from a 52,000-parameter toy model to GPT's 120 billion parameters. ✦ AI generated
CJ · Syntax · 2026-07-10 · original ↗
starts at this moment · 31:04
By 2016, Google's translation models were built on a type of neural network called an LSTM. They worked, but they were slow. Each word had to wait for the previous one to finish processing. To help with longer sentences, researchers had bolted on an add-on called attention. Now, in 2017, Jakub Uszkoreit at Google proposed dropping the sequential part entirely using only attention. And eight researchers built it, all equal contributors, and they titled the paper 'Attention Is All You Need.'
verbatim transcript · starts at 31:04
31:042016, Google's translation models were built on a type of neural network called an LSTM, short for long short-term memory. LSTMs read text one word at a time, carrying a running memory of what they've seen so far. They worked, but they were slow. Each word had to wait for the previous one to finish processing. To help with longer sentences, researchers had bolted on an add-on called attention. Now, attention came
31:28from a 2014 paper by Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Older translation models compressed a whole sentence into one fixed-size vector before translating. Short sentences were fine, but long ones broke. And Bahdanau's idea was let the model look back at the input at each step and focus on the words that matter right now. And it worked, but it was still bolted onto the LSTM, so it was
31:52still slow. Now, in 2017, Jakub Uszkoreit at Google proposed dropping the sequential part entirely using only attention. And eight researchers built it, all equal contributors, and they titled the paper "Attention Is All You Need." Now, that paper has been cited over 100,000 times, and every one of those eight authors has left Google, and several of them founded billion-dollar companies. Now, the architecture they described is the engine running under everything. And
32:19the crazy thing is, like, that paper is only 15 pages long, and it's available for free for anyone to read. So, the blueprint for the technology powering a trillion-dollar industry is literally just a PDF that you can sit down and read right now. Now, underneath everything, a transformer is a neural network, but it's a very specific kind. And at the hardware level, it's almost entirely matrix multiplication. The intelligence
32:42isn't any clever logic. It's in the billions of numbers inside those matrices, all of which started random and got tuned during training to produce useful outputs. Each transformer block has two main operations. Attention, where tokens exchange information with each other, and a feed forward step, where each token gets processed on its own. Both have their own learned weights, and both are trained in the same way, by running predictions,
- ·Replaced slow sequential LSTMs with pure parallel attention
- ·Each token gathers context from every other token simultaneously
- ·Resolves word ambiguity via self-attention across the whole sequence
- ·Described in the 2017 paper 'Attention Is All You Need'
- ·Same architecture scales from toy model to production LLM
- ·52,000-parameter transformer proves the concept
- ·GPT-class models reach 120 billion parameters on same design