A plain-English guide to how AI, machine learning, neural networks, and large language models actually relate. They're not competing ideas — they're nested inside each other. Written by an ex-Army officer who now builds them.
When I left the Army and walked into my first tech meetings, four words kept flying around the table as if they were interchangeable: AI, machine learning, neural network, LLM. Someone would say “let’s add some AI.” Someone else heard “let’s train a neural network.” A third person had already opened ChatGPT and started typing a prompt.
I did what most people do in that situation — nodded along, looked thoughtful, and went home to actually figure out what everyone had meant. Turns out they were each talking about a different thing, and nobody in the room had noticed. That bothered me enough to nail it down properly, because I was about to be the person who had to build these systems — and pay for the GPUs when we got it wrong.
So here’s the one idea that untangled the whole mess for me: these aren’t four rivals fighting for the same job. They’re nested. Each one lives inside the previous, like a set of Russian dolls.
AI ⊃ Machine Learning ⊃ Neural Networks ⊃ Deep Learning ⊃ LLMsOpen the biggest doll and you eventually reach the smallest. Let me open them one at a time.
Artificial intelligence is the broad ambition: get a machine to do something that would normally need a person’s judgment. That’s it. Notice what’s missing from that definition — learning. Nothing says the machine has to learn anything.
A chess program from the 1980s, running entirely on hand-written rules, is AI. The logic I wrote to route a complaint to the correct police station by matching its pincode to a jurisdiction table is AI too — and there isn’t a shred of “learning” in it. If-this-then-that, all the way down. I wrote every rule myself.
This is the part people forget: a huge amount of genuinely useful “AI” is just clever, explicit plumbing. We have a fancy name for it — rule-based or expert systems — but it’s a person sitting down and encoding what they know. AI is the umbrella. Some of what’s under it is genuinely smart. Some of it is a very well-organised pile of if statements, and there’s no shame in that.
Machine learning is the doll inside AI where the machine stops waiting for me to write the rules and starts working them out from data.
Take a spam filter. You don’t hand-code “any email with the word lottery is spam” — you’d be there forever, and the spammers would just change the word. Instead you show the system thousands of emails already labelled spam or not spam, and it learns the patterns on its own.
Here’s what surprised me when I got into it: most machine learning is not neural networks. Decision trees, random forests, gradient-boosted trees (the famous XGBoost), plain old logistic regression — boring names, absolute workhorses. For a lot of problems that look like a spreadsheet of numbers — fraud scoring, risk ranking, flagging which cases need a human’s eyes first — a gradient-boosted tree will still beat a neural network, train in a few seconds on a laptop, and cost nothing to run.
So: all machine learning is AI. Not all AI is machine learning.
Neural networks are a specific approach to machine learning, loosely inspired by the brain — layers of simple units, every connection carrying a weight, and you nudge those weights over and over until the output starts matching reality. (As an electronics guy, I’ll admit the “inspired by the brain” line is doing a lot of romantic heavy lifting. It’s really a lot of multiplication and a bit of calculus. But it works.)
Stack a couple of layers and you can learn simple patterns. Stack many layers and you get deep learning — and that’s where the real magic for messy, unstructured data lives. Images. Audio. Language. When one of my voice agents turns a citizen speaking Hindi into clean text, that’s a deep neural network doing the listening. No rule I could write by hand would survive contact with real accents, traffic noise, and people trailing off mid-sentence.
Neural networks are a subset of machine learning: a particular tool, not the whole toolbox.
A large language model is the smallest doll. Take one specific neural-network design — the transformer, which showed up in 2017 — make it very large, and train it on a frankly absurd amount of text with one almost-insultingly-simple goal: predict the next word.
Do that at the scale of billions of parameters and something unexpected falls out the other end. The thing that was only ever trained to guess the next word can suddenly draft, summarise, translate, and answer questions. ChatGPT, Claude, the Gemma model I run on Ollama for one of my demos — all LLMs. All the same trick, scaled up until it’s spooky.
So the full nesting, smallest to largest: an LLM is a kind of deep learning, which is a kind of neural network, which is a kind of machine learning, which is a kind of AI.
None of this is pedantry. Picking the wrong doll costs real money and real time — I’ve watched it happen.
The reflex of “let’s add AI” almost always means “let’s call an LLM.” Sometimes that’s exactly right. More often, the cheaper, older, more boring doll is the correct one — and knowing which is which turned out to be most of the job.
AI is the goal. Machine learning is AI that learns. Neural networks are how it learns the messy stuff. LLMs are that — scaled up until it can talk.
Keep that one line in your head and you’ll never mix the four up again. And the next time someone in a meeting says “let’s just add some AI,” you’ll be the person who asks the only question that actually matters: which doll do we need?
That’s a question the Army trained me to ask about everything, long before I ever touched a neural network — figure out what the job actually is before you reach for the expensive kit.