The big AI firms sell paid-for versions of their tech<\/p>\n
If you have used a free version of an ChatGPT or its AI rivals, then you are obviously getting a good deal.<\/p>\n
Firms like Microsoft, Google and Anthropic have invested hundreds of billions of dollars in developing Large Language Models (LLMs) the tech behind those services.<\/p>\n
So getting, ChatGPT, Claude or Gemini to help with your speech or holiday plans is a bargain.<\/p>\n
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.<\/p>\n
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.<\/p>\n
But setting a price for those services is surprisingly difficult.<\/p>\n
"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.<\/p>\n
Setting prices for AI services is difficult says Simon Gooch<\/p>\n
That's because of rapidly changing economics around tokens, the building blocks of LLMs and agentic AI.<\/p>\n
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.<\/p>\n
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.<\/p>\n
The problem is this process is not entirely predictable.<\/p>\n
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.<\/p>\n
Meanwhile, in agentic systems, businesses use multiple AI agents together to make decisions and take actions, further increasing both token use and unpredictability.<\/p>\n