Tokenomics | The new AI currency & your options explained

So as adoption for AI ramps up, this is something that developers and IT teams are feeling directly.

You know, costs can really spike in ways that aren't always obvious when you're building or scaling.

So how should we think about what actually is driving that?

Well, it's a combination of factors.

AI costs aren't just about how often you call a model.

They're shaped by how you build the experience.

Things like the prompt length, how much context you send, and which model you choose all add up quickly.

Tokens are what you build on, but the real driver is design, and that's how your app handles conversations, how context grows over time, and how everything's put together.

That's what ultimately determines your cost.

So why don't we break this down? So tokens are the new AI currency, but what are some of the ways that they actually drive costs, and what are they?

At the most basic level, tokens are the unit of text that AI models process and bill against.

A token can be a full word, part of a word, or even punctuation or white space.

Now, depending on your choice of model and the tokenizers they use, there can be nuances in how tokens are defined.

So for example, OpenAI uses tiktoken for their models.

I'll show you an example with GPT 5.4 mini model.

I have a prompt here that's ready to go, and when the response completes, you'll see the tokens consumed.

This is going to be broken down by the input tokens, so that's everything you send, including the instructions, and the output tokens, and that's everything the model generates in its response.

And here's the thing: input tokens and output tokens are priced differently.

These are charged per million, with output tokens typically costing three to five times more because generating text requires more compute than reading it.

A single interaction therefore incurs costs based on the total tokens processed, not just what the user explicitly types.