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Training and use

The BasicsUpdated on 16 August 2026we are coded

Building the model is a one-time job. Every question you ask it afterward is a new electricity bill - and that one never stops.

Checked on16 August 2026
In short: training is the one-time build of the model - months of work, piles of money, and then it's done. Use is something else entirely: every time you ask it a question, the machine fires up again, and that costs power and money all over again. Companies call it inference. That's why the bill never closes - it starts fresh with every message you send.

Picture the difference between building a factory and the electricity bill that comes after. The build is one-time - you dig foundations, install machines, train people to run them. It's expensive, it takes time, but once it's done, the factory just stands there. Electricity is different - it flows only while the machines run, and stops the moment you switch them off. You pay it fresh every month, no matter how many times you've run production.

Training an AI model is exactly that build. The company gathers a huge amount of text, images, code - and uses it to teach the machine to recognize patterns in language. This happens once. It takes months, needs rooms of computers running around the clock, and costs sums that are hard to picture. Once it's done, the model is ready - it already knows how to write, to answer, to connect thoughts.

Use is the electricity bill. Every time you open a chat and type a question, somewhere in a distant server room chips fire up to calculate your answer word by word. It's not a memory, not a lookup in a ready-made store - the machine literally computes the answer from scratch every time, as if hearing it for the first time. That exact payment - for the moment of answering itself - is called inference. And it never stops, because every question is a new expense.

Here's your own example. When you ask AI to write an email to a client, this is exactly what happens: the machine switches on, computes every word, spends energy - then stops until you call it again. Do it once a day, and the cost is small. Have a whole company do it thousands of times an hour, and the cost turns into a serious business conversation - which is exactly why companies charge a subscription, not a one-time fee.

The factory is built once. The electricity flows every time you switch it on - and that's where the real, ongoing bill for artificial intelligence sits.

The expense that doesn't stop

People talk about AI as if a model, once built, is finished business - a ready product on a shelf. It isn't. Every question of yours is a new ignition of the machine, a new slice of electricity, paid for by someone. That's why prices for these services don't drop just because the model already "exists" - it still has to fire up fresh every time you call it.

That's why I'm skeptical of every promise of "free forever" AI. The build is a one-time cost, but use is a constant one - and someone always pays it. Either you, through a subscription, or the company, through investor money that runs out sooner or later.

The visual is generated code art. No third-party images.
Official primary sources
→NVIDIA: What is AI inference (official explainer)