Tokenomics: Why making AI pay is tricky

If you have used a free version of an ChatGPT or its AI rivals, then you are obviously getting a good deal.

Firms like Microsoft, Google and Anthropic have invested hundreds of billions of dollars in developing Large Language Models (LLMs) the tech behind those services.

So getting, ChatGPT, Claude or Gemini to help with your speech or holiday plans is a bargain.

But, naturally, those firms want to recoup their investment, so they offer paid-for versions of their AI, which have extra features for tasks like coding or billing.

Meanwhile, third party firms are building and selling services based on AI agents, usually based on an LLM, which are trained to do specific tasks.

But setting a price for those services is surprisingly difficult.

"Trying to tie someone into a cost model for the next 12 months, two years, three years, it doesn't make any sense, honestly, because we don't know," says Simon Gooch at Saviynt, an identity management company which is incorporating agentic AI into its services.

Setting prices for AI services is difficult says Simon Gooch

That's because of rapidly changing economics around tokens, the building blocks of LLMs and agentic AI.

When a user asks an LLM, like ChatGPT or Anthropic's Claude to answer a question, generate software code, or automate a process, that prompt is broken down into mathematical chunks called tokens, which can be processed by the model.

The LLM's response also comes in the form of tokens, which are converted back into text, software code, or a set of commands to automate a process.

Subtle variations in the prompt can produce different answers. The same prompt will not always produce the same answer. Different models will produce different answers.

Meanwhile, in agentic systems, businesses use multiple AI agents together to make decisions and take actions, further increasing both token use and unpredictability.

While the cost of individual tokens – or the credits used to pay for them - has plummeted in recent years, according to analysis by Goldman Sachs, the number of tokens consumed by businesses, and consumers, has skyrocketed.

The bank forecasts that, external token consumption will increase 24 times between 2026 and 2030 to 120 quadrillion tokens a month, as companies shift from to use AI agents.

But companies, and individuals, using AI systems often have a tenuous grasp on just how many tokens they are burning through – until they either run out or get their monthly bill.

Even Microsoft has reportedly reined back, external its engineers' use of some third party coding tools, while Uber