From Transistors to AI: How Computers Learned to Speak Human

How a 2017 research paper flipped the relationship between humans and computers

My wife asked me today a simple question: “What is AI, really?”

That question is harder to answer than it sounds, because the most important change isn’t that computers got faster or smarter. It’s that, for the first time, they learned how to understand human language.

Modern computing begins with a very simple idea: a transistor is an electronic switch. It’s either on or off. Those two states are what we call 1 and 0.

By wiring transistors together, engineers created logic gates like AND, OR, and NOT. Logic gates combine into circuits, circuits into CPUs, and suddenly we have machines capable of executing precise logical instructions at extraordinary speed.

But humans don’t think in 1s and 0s.

So we built layers of abstraction on top of that hardware. First came machine code and assembly. Then higher-level languages like C, C++, Java, and Python. Each layer made computers easier to program, but the relationship stayed the same: humans had to learn the computer’s language. Progress meant better tools, better languages, and more specialized engineers.

That pattern held for decades.

Then something flipped.

In 2017, Google published a paper called Attention Is All You Need. That paper introduced what’s known as the transformer architecture, which fundamentally changed how computers process language.

Instead of reading text one word at a time, transformers can look at entire sequences at once. They understand relationships between words, track context, and focus attention on what matters most in a sentence. This made it possible to train models on massive amounts of human text—books, articles, documentation, and code—and let computers learn how language works from real examples.

Not by hard-coding rules.
Not by manually defining grammar.
But by learning patterns from how humans actually communicate.

This is the foundation of modern large language models like ChatGPT.

Once a computer understands language, everything changes. It can understand instructions. It can understand code, which is also a form of language. And it can explain things back to us in plain English.

This is why non-programmers can suddenly build things that once required teams of engineers (this is often called “vibe coding”). You describe what you want, refine it conversationally, and the computer figures out how to translate intent into software, workflows, or analysis.

The historical relationship reverses. Instead of humans adapting to computers, computers adapt to humans.

When you combine language understanding with access to tools, memory, feedback loops, and the ability to run repeatedly and improve, you get systems that don’t just execute instructions. They assist thinking, iteration, and problem-solving.

All of this still runs on billions of transistors flipping on and off.

What’s changed is the height of the abstraction.

So when my wife asked, “What is AI, really?” the best answer I’ve found is this:

We didn’t make computers conscious (yet).
We made them fluent.