Tokenomics: Why making AI pay is tricky

Summary

The big AI firms sell paid-for versions of their tech 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. Employing more AI agents can be done with the click of a button, whereas expanding the human workforce would involve careful discussions over headcount and hiring, he says. That's not how anybody builds a budget." India's 'blue gold' starts a new drinks industry The ancient trick making food waste useful and tasty Could humanoid robots be heading for the battlefield?