Before Transformers became the Rajinikanth (Before anyone learns AI, they should first know the man who said “If I say something once, it is like me saying it a 100 times.” Thats clarity, no Hallucination)  of AI architectures, the field ran on three big families of models. Each one tried to solve a different piece of the “how do we make machines understand patterns?” puzzle.

Let’s break them down in the simplest possible way.

RNN (Recurrent Neural Network)

What it is:

A model that processes sequences one step at a time, remembering what came before.

How it thinks:

“I saw this word earlier… let me keep that in mind.”

What it was used for:

• Early language models

• Time-series prediction

• Simple speech tasks

Why it struggled:

Memory fades fast. RNNs forget long sentences. If a sentence was 20 words long, it remembered… maybe 5. Think of it like a student who remembers the line “The words are dark lovely and deep” but forgets “but miles to go before I sleep”

LSTM (Long Short-Term Memory)

An advanced RNN that uses “gates” to decide what to remember and what to forget.

How it thinks:

“I’ll store important stuff and throw away the junk.”

What it was used for:

• Speech recognition

• Machine translation

• Predictive text

• Music generation

Why it was better than RNNs:

LSTMs could remember longer sequences. Instead of forgetting after 5 words, they could recall 20, maybe 30. They were like the topper kid who remembered the whole poem and recited it proudly on stage.

Why they still fell short:

They processed everything sequentially. No parallelism → slow, expensive, don’t scale.

CNN (Convolutional Neural Network),

What it is:

A network that looks for patterns in small patches of an image or signal and stitches them into bigger patterns.

How it thinks:

“I’ll check every small window, find edges, curves, textures, and build up the picture.”

What it’s used for:

• Image classification (cat/dog)

• Object detection

• Facial recognition

• Early medical imaging tasks

Why CNNs were kings for 10 years:

They are insanely good at visual patterns:

• they’re fast

• they’re parallel

• they reuse filters efficiently

They were the Aussie cricket team of computer vision – dominant, ruthless, unbeatable.

Why they didn’t become LLMs:

CNNs don’t have a natural way to handle long-range relationships in text. CNNs are great for “here’s a face,” but bad at “here’s a paragraph with meaning spread across 80 words.”

Why Transformers Replaced All Three

Transformers brought:

• Attention (focus on what matters)

• Parallelism (look at everything at once)

• Long-context handling

• Scalability

• MoE compatibility

• Cleaner training dynamics

RNNs forgot too easily. LSTMs remembered but were slow. CNNs saw patterns but not meaning. Transformers merged the best of all worlds. They became universal: text, vision, audio, protein folding, coding, reasoning… all with one architecture.