Rod Schneider

Posted on Jan 1

The Rise of the Transformer

#ai #programming #llm #developers

If you’ve used ChatGPT, Claude, or Gemini, you’ve already met the most influential idea in modern AI -- even if you didn’t know it.

It’s hidden inside a single letter:

GPT = Generative Pre-trained Transformer

That last word, Transformer, quietly reshaped the entire AI industry.

Not because it’s mystical.
Not because it mimics the human brain.
But because it turned out to be an astonishingly efficient way to work with language at scale.

This article tells the story of the Transformer -- without math, without jargon, and with enough intuition that everything else about modern AI suddenly makes sense.

🧩 GPT, Decoded (Before We Go Further)

Let’s briefly decode the acronym:

Generative → The model generates text by predicting what comes next
Pre-trained → It learns from massive amounts of existing text
Transformer → The architecture that makes this efficient and scalable

Everything impressive about modern language models sits on top of that last piece.

🧠 Before Transformers: How Machines Learned Before Language Models

Early machine learning systems were good at structured problems:

predicting house prices
estimating credit risk
classifying images

They worked by learning patterns between inputs and outputs.

But language is different.

Language is:

long
messy
contextual
dependent on what came before

Meaning isn’t just in words -- it’s in relationships between words.

Older systems struggled with that.

🔗 Neural Networks (A Very Gentle Explanation)

A neural network is just a system made up of many small decision units (called neurons) connected together.

Each one:

looks at numbers
applies a simple rule
passes the result forward

Stack enough of them together and you get something surprisingly powerful.

Input → [Small Decision] → [Small Decision] → Output

Add many layers, and you get deep learning.

But early neural networks still had a big weakness…

📜 The Big Language Problem: Sequences

Language arrives in order.

Consider:

“I went to the bank to deposit money.”

“I sat on the bank and watched the river.”

The word bank means different things depending on context -- sometimes far earlier in the sentence.

Older models tried to process language one word at a time, like reading a sentence through a narrow straw.

They struggled with:

long sentences
remembering earlier meaning
training efficiently on large data

Something better was needed.

🚀 2017: “Attention Is All You Need”

In 2017, researchers at Google published a paper with an unassuming title:

Attention Is All You Need

At the time, it looked like a clever optimisation.

In hindsight, it was the moment modern AI became possible.

🧠 What Is “Attention”? (In Plain English)

Attention means the model asks:

“Which parts of this text matter most right now?”

Instead of treating every word equally, it learns to focus.

Think of reading a sentence with a highlighter:

The cat that the dog chased climbed the tree.

When thinking about “climbed”, your brain naturally focuses on the cat, not the dog.

That’s attention.

🔍 Self-Attention Layer (Explained Simply)

A self-attention layer is a part of the model where:

every word looks at every other word
the model decides how strongly they relate

Word A ─┬─ looks at ─ Word B
        ├─ looks at ─ Word C
        └─ looks at ─ Word D

Each connection gets a weight:

strong connection → very relevant
weak connection → mostly ignored

⚖️ Weighted Understanding of Context

This just means:

The model combines information, giving more importance to relevant words and less to irrelevant ones.

Context = (Important words × big weight)
        + (Less important words × small weight)

This weighted combination lets the model understand meaning far more accurately.

🧱 Tokens: The Model’s Alphabet

Models don’t read words. They read tokens.

A token is:

a word
or part of a word
or punctuation

For example:

"Unbelievable!" → ["Un", "believ", "able", "!"]

Everything a model does is predicting the next token.

🧩 Embeddings: Turning Words into Meaningful Numbers

An embedding is how a model represents a token as numbers.

Think of it like a location on a map:

similar meanings → close together
different meanings → far apart

"cat"  → 📍 near "dog"
"bank" → 📍 near "money" OR "river" (depending on context)

Embeddings allow the model to reason about meaning mathematically.

🏗️ Feed-Forward Layers (The “Thinking” Part)

After attention figures out what matters, feed-forward layers do the actual processing.

They:

combine information
transform it
extract patterns

You can think of them as:

“Given what matters, what should I conclude?”

🏛️ Putting It All Together: The Transformer

A Transformer repeats the same structure many times:

Tokens
  ↓
Embeddings
  ↓
Self-Attention (what matters?)
  ↓
Feed-Forward Layers (what does it mean?)
  ↓
Repeat (many layers)
  ↓
Next Token Prediction

This structure turned out to be:

fast
parallelisable
scalable

And that changed everything.

📏 Why Context Windows Matter

A context window is how much text the model can see at once.

Bigger context windows mean:

better memory
better consistency
fewer hallucinations
better long-form reasoning

Small window → short attention span
Large window → sustained understanding

Transformers handle long context far better than older architectures.

📈 Why Models Scale So Well

Transformers scale beautifully because:

attention works in parallel
GPUs love parallel work
more data + more parameters = better performance

Older models slowed down as they grew.

Transformers sped up.

🔁 Why “Attention” Keeps Coming Up

Because attention is:

the mechanism that handles meaning
the reason context works
the key to scaling

Almost every modern LLM improvement still revolves around attention.

💸 Why Costs Dropped and Performance Exploded

Transformers made it possible to:

train faster
use cheaper hardware efficiently
reuse architectures across tasks

Without Transformers:

models would exist
but API costs would be 10×–100× higher
progress would’ve been much slower

🔀 What About Other Architectures?

There are alternatives:

State-space models

Track information over time more efficiently for very long sequences.

Hybrid architectures

Combine attention with other techniques.

Memory-augmented models

Explicitly store and retrieve information like a database.

Recurrent revivals

Older ideas (like RNNs) updated with modern improvements.

So far:

none have clearly beaten Transformers overall
many borrow ideas from Transformers

🏁 First Takeaway

Transformers didn’t invent intelligence.

They invented efficiency.

They let us:

train larger models
use more data
lower costs
scale faster

That’s why nearly every modern language model stands on their shoulders.

And while something else may replace them someday, this is the architecture that launched the current AI era.

One clever idea.
Repeated many times.
At massive scale.

Transformers vs the Brain (Spoiler: Not the Same)

Every time someone says “AI works like the human brain”, a neuroscientist quietly sighs and an ML engineer reaches for a beer.

Yes, neural networks borrow words like neurons and attention.
No, they are not miniature digital brains.

Transformers -- despite their name -- are not thinking, understanding, or conscious in any human sense. They’re doing something both far simpler and more alien.

Let’s clear this up once and for all.

🧠 Why People Think Transformers Are Brain-Like

The confusion is understandable.

Transformers:

talk like humans
answer questions
reason through problems
remember context
appear to “think”

And we describe them using brain-ish language:

neurons
attention
memory
learning

But this is mostly metaphor. Helpful metaphor -- but metaphor nonetheless.

🔌 What a Transformer Actually Is

A Transformer is:

A very large mathematical system trained to predict the next token in a sequence.

That’s it.

No goals.
No beliefs.
No awareness.
No internal model of the world.

Just probability -- scaled to absurd levels.

🧩 Tokens vs Thoughts

Let’s start with the most fundamental difference.

The brain works with experiences and meanings

Humans think in:

concepts
memories
sensory impressions
emotions
goals

Transformers work with tokens

Tokens are chunks of text:

words
parts of words
punctuation

"Thinking deeply" → ["Think", "ing", " deep", "ly"]

The model’s entire job is:

Given these tokens…
What token is most likely to come next?

No matter how intelligent the output sounds, the mechanism never changes.

🧠 Human Neurons vs Artificial “Neurons”

The term neural network is where a lot of confusion starts.

Human neurons:

are biological cells
fire electrically and chemically
adapt continuously
interact with hormones and emotions
operate asynchronously

Artificial neurons:

are tiny math functions
take numbers in
output numbers
run on silicon
update only during training

Human neuron ≠ Artificial neuron

The resemblance is poetic, not literal.

🔍 “Attention” Is Not Human Attention

This one causes the most misunderstanding.

Human attention:

is shaped by emotion
is influenced by survival instincts
can be voluntary or involuntary
is deeply tied to consciousness

Transformer attention:

is a mathematical weighting
assigns importance scores
has no awareness
does not “focus” in any felt sense

Human: "This matters because I care"
AI:     "This matters because math says so"

Same word. Very different phenomenon.

📦 Memory: Persistent vs Disposable

Human memory:

persists across time
shapes personality
fades imperfectly
influences future decisions

Transformer “memory”:

exists only in the context window
disappears after the response
does not accumulate experience

You remember conversations from years ago.
A transformer forgets everything after it replies.

No learning happens during a conversation.

🧠 Learning: Ongoing vs Frozen

Humans learn continuously.

Transformers do not.

Human learning:

updates beliefs constantly
adapts in real time
integrates new experiences

Transformer learning:

happens only during training
requires massive datasets
is frozen at inference time

Chatting ≠ learning

If a model appears to “learn” mid-conversation, that’s just pattern continuation, it isn't memory formation.

🧩 Reasoning: Simulation vs Deliberation

Transformers don’t reason the way humans do.

Human reasoning:

uses mental models
checks beliefs against reality
understands causality
can doubt itself

Transformer “reasoning”:

simulates reasoning patterns
produces structured explanations
follows statistical regularities

It doesn’t reason.
It imitates the *shape* of reasoning.

That imitation can be incredibly convincing, but it’s not the same thing.

🤖 Why Transformers Still Feel Smart

Here’s the important part.

Even though Transformers aren’t brains, they can:

model language extremely well
compress enormous amounts of knowledge
reproduce reasoning patterns accurately
generate useful, novel combinations

Language encodes a huge amount of human intelligence.

If you learn language well enough, intelligence leaks out.

📈 Why Scaling Works (and Brains Don’t Scale Like That)

Transformers get better by:

adding more parameters
adding more data
adding more compute

Brains don’t scale that way.

You can’t just:

add 10× neurons
train on the entire internet
run thoughts in parallel

Brains: efficient, adaptive, embodied
Transformers: brute-force statistical monsters

Different strengths. Different tradeoffs.

🔀 What Transformers Lack That Brains Have

Transformers do not have:

consciousness
self-awareness
intrinsic goals
grounding in physical reality
lived experience
emotional states

They don’t want anything.

They don’t know anything.

They don’t understand or care, in the human sense.

🏁 Second Takeaway

Transformers are not artificial brains.

They are:

extraordinarily powerful pattern learners
unmatched language compressors
highly efficient sequence predictors

Their intelligence is functional, not experiential.

That doesn’t make them less impressive.

It just makes them different.

Understanding that difference is the key to:

using them safely
trusting them appropriately
not over-anthropomorphizing them

And perhaps appreciating just how strange and remarkable this new kind of intelligence really is.

Why Human Developers Will Always Be More Valuable Than AI Developers

Every few months we get a fresh round of takes that sound like:

“Junior devs are cooked.”
“AI will replace programmers.”
“Software engineers are basically prompt typists now.”

And yes, frontier LLMs can write code that would’ve earned you a standing ovation in 2016. They can scaffold apps, refactor modules, generate tests, and explain your own bug back to you with unsettling calm.

But here’s the thing:

AI can generate code. Humans build software.

Those are not the same job.

Human developers won’t be made obsolete by AI developers.
They’ll become more valuable -- because the hard parts of software were never just typing code.

🧠 First, Let’s Define “AI Developer”

When people say “AI developer,” they usually mean one of these:

An LLM in an IDE (Cursor, Copilot, Claude Code, etc.)
An agentic tool that plans, writes, tests, and iterates
A swarm of agents doing “parallel work” (tickets, PRs, triage, etc.)

All of these are real. All are powerful.

But they share one core limitation:

They do not understand reality. They understand patterns.

They are, at their core, token predictors built on Transformers -- excellent at generating plausible sequences.

That’s a superpower.

It’s also exactly why human developers remain irreplaceable.

🤖 LLM Intelligence vs Human Intelligence (The Crucial Difference)

LLMs can simulate reasoning, but they don’t own it.

Humans do a bunch of things LLMs can’t truly do:

Humans have…

Grounding (we live in the real world and can check reality)
Goals (we want outcomes, not just plausible text)
Judgment (we decide what matters and what’s acceptable)
Accountability (we take responsibility when things break)
Taste (we know when something is “good,” not just “works”)
Ethics (we can reason about harm and obligations)
Context beyond text (politics, incentives, hidden constraints, the “real story”)

LLMs have…

impressive language capability
compressed knowledge
pattern recognition at scale
speed
stamina

These are different forms of intelligence.

And software development rewards the human kind more than people admit.

🧩 Software Isn’t “Writing Code.” It’s Solving Reality Problems.

A lot of software work happens before the first line of code:

What problem are we solving?
Who is the user?
What does “good” look like?
What are the constraints?
What are the risks?
What are the second-order effects?

You can ask an LLM to answer these questions and it will respond confidently.

But confidence is not the same as correctness.

And plausibility is not the same as responsibility.

ASCII diagram: What people think vs what devs actually do

Myth:                    Reality:
-----                    --------
Write code               Understand problem
Ship feature             Negotiate constraints
Fix bug                  Diagnose systems
Done                     Own outcomes

An AI can help with the code.
A human is still needed for the software.

🧭 Humans Provide Direction, Not Just Output

LLMs are incredible workers. They are not good leaders.

They push forward. They generate. They comply.

But they don’t reliably ask:

“Are we solving the right problem?”
“Is this safe?”
“What happens in production?”
“What are the edge cases?”
“Is this approach maintainable?”

They can be prompted to do those things. Sometimes they do them well.

But here’s the subtle point:

A system that must be prompted to be wise is not wise.

Humans naturally maintain a mental model of reality and consequences.

That makes humans uniquely valuable as:

product owners
architects
tech leads
security reviewers
reliability engineers
governance and risk owners

Or simply: adults in the room.

🧯 The Hallucination Problem Is a Leadership Problem

Hallucinations aren’t just “AI being wrong.”

They are what happens when you optimise for plausible continuation, not truth.

Which means:

LLMs can sound authoritative while being incorrect
they can fabricate APIs, flags, file paths, and “facts”
they can misdiagnose root causes and build elaborate solutions to the wrong problem

Humans are valuable because we can do the opposite:

We can stop. Doubt. Re-check. Change course.

LLMs tend to patch forward. Humans can step back.

The most expensive bugs happen when “plausible” beats “true”

LLM: "This looks right."
Human: "But does it match reality?"

That question is worth more than another 10,000 tokens of generated code.

🧱 The Uniquely Human Value: Judgment Under Uncertainty

Real systems are full of uncertainty:

incomplete logs
ambiguous requirements
political constraints
competing stakeholder needs
time pressure
unclear risk tolerance

Humans are built for this kind of mess.

LLMs are built for:

generating clean-looking outputs from messy inputs

That’s helpful, but it can also be dangerous, because it creates the illusion of certainty.

A human developer contributes something that doesn’t fit neatly into a prompt:

situational awareness
tradeoff thinking
risk management
strategic restraint
knowing what not to build

Those are premium skills.

🛠️ Humans “Own the System.” AIs Don’t.

When production breaks at 2:17am, the question is not:

“Can the AI write a fix?”

The question is:

Who is on call?
Who has access?
Who understands blast radius?
Who can coordinate rollback?
Who can communicate impact?
Who can make decisions under pressure?

Ownership is not a code-generation task.

Ownership is a human role.

🎨 Taste: The Secret Weapon of Great Engineers

One of the most underrated differences:

Humans have taste.

Taste is how you know:

whether an API is pleasant
whether an architecture will age well
whether a codebase feels coherent
whether the product experience “clicks”
whether a solution is elegant or a future maintenance tax

LLMs can approximate taste by copying patterns from good code.

But human taste is grounded in:

lived experience
consequences
empathy with users and teammates
the memory of past disasters

Taste is the difference between “it works” and “it’s good.”

And great products are made by people with taste.

🧠 Humans Build Mental Models. LLMs Build Text.

Humans maintain internal models like:

“This service depends on that database.”
“This team won’t accept that change.”
“This vendor SLA is fragile.”
“This feature will spike support tickets.”
“This architecture will lock us in.”

LLMs can repeat those ideas if you tell them.

But they don’t reliably form or maintain those models over time.

They have no persistent memory, no lived reality, no embodied context.

That makes humans the long-term stewards of systems.

🧑‍⚖️ Governance: The Job That Only Humans Can Truly Do

As we deploy more agentic systems, the most important work shifts upward:

defining policies
setting guardrails
designing evaluation criteria
monitoring harms and failures
determining acceptable risk
auditing and accountability

You can’t outsource accountability to a token predictor.

Even when AI agents act autonomously, humans must govern them.

That governance role is not optional. It’s the price of building powerful systems.

✅ The Future: Humans + AI Is the Winning Team

The best framing isn’t “AI replaces developers.”

It’s:

AI makes developers dramatically more productive.
And therefore, the developers who can direct, supervise, and govern AI become dramatically more valuable.

What changes in practice

Junior work becomes faster, but also riskier without supervision
Senior judgment becomes the bottleneck (and therefore the multiplier)
Product and architectural leadership becomes more important, not less
“Knowing what to ask” and “knowing what to trust” becomes a core skill

The new hierarchy

Old world:              New world:
---------               ----------
Code speed              Judgment speed
Typing ability          Direction quality
Knowing syntax          Knowing systems + reality