Google Explains What AI Inference Really Is And Why It’s So Expensive

Google just published a new explainer that tackles a question most people using AI never stop to ask: what happens after you hit “enter” on a chatbot?

In its latest Ask a Techspert post, Google engineer Dale Markowitz takes readers behind the scenes of AI inference, the process that powers every ChatGPT reply, image generation, or translation request. If training is how models learn, inference is what they do when they’re put to work.

And it turns out, inference isn’t cheap. Every time someone asks a question, the model needs to run through billions of calculations. That process demands massive computing power, especially when using large language models like Gemini or GPT-4.

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Markowitz compares AI inference to searching through a library the size of the internet almost instantly. While training a model might take weeks and millions of dollars, running inference happens in real time, thousands of times per second, across the globe. That’s why companies like Google, Microsoft, and OpenAI build dedicated infrastructure to keep up.

The post also points out that inference costs often determine what features AI tools can offer, or which ones stay behind a paywall. Bigger models give better answers but need far more resources to run.

This isn’t just a technical detail. This inference shapes everything from chatbot speed to product pricing. And now, Google wants more people to know what’s under the hood.

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