Will Venters, Associate Professor of Digital Innovation and Information Systems at the London School of Economics, said companies can be caught out as they experiment with or implement AI internally, as staff burn through tokens.
“People are finding it really hard to manage that cost… it’s a non-deterministic output, so it’s a non-deterministic value,” he said.
Companies are finding ways to work around this.
Oliver King-Smith, founder of engineering software firm smartR AI, says smaller organizations can “can fly under the radar and use [flat fee] personal accounts which I am sure the big vendors don’t like.”
But, he says, “This has to end at some point in time, because the big guys are taking a bath on those accounts.”
Once the big AI platforms start facing pressure from shareholders to show a profit, he predicts: “They will start clamping down.”
King-Smith says companies should also think more carefully about what AI models to use.
Companies also needed to be much more precise with their prompts, says Rob Steele, CFO at UK accounting software firm iplicit.
“You wouldn’t send someone in your family out to get the weekly shop without any kind of detailed instructions as to what you expect in that shopping basket, right?”
The situation can become difficult to control when companies build AI into a product that could be rolled out to thousands of users, Venters points out.
AI costs could start to balloon. For example, managers may realise they need tokens not just for core software development, but for other tasks such as testing, security, or for implementing guard rails.
“It’s particularly hard when you’re looking at agentic processes,” Ventners says.
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.
Venters points out, while token costs might be unpredictable, it might be that the company is ultimately getting more value from their token use with AI.
“It’s not quite the same as a calculator,” he says. “The more you give it, the more expensive it is, but the better the result may be.”
But companies still need to pass those costs onto their own customers.
“Nobody’s really figured it out,” says Bill Peterson, senior director of product marketing, at Sumo Logic.
The software firm is previewing new security services based on agentic AI, he explains, but is in discussion with corporate customers about how to charge for them.
“We’re still having some fun conversations about this internally,” he says drily.
Options could include simply raising prices across the board, he says, paying by results, or charging for “bundles” of incidents.
But whatever price structure it chooses could be upended if and when the large language model providers change their own pricing strategies.
“You get into variable pricing, and it’s changing every couple of months” he says. “Customers don’t like that. That’s not how anybody builds a budget.”
