SUMMARYGartner estimates that enterprise spending on developer AI tokens could reach or exceed a typical software engineer’s monthly salary within two years, driven by wider use of generative AI and agentic tools plus consumption-based pricing. Senior principal analyst Nitish Tyagi warned that some developers and business users are already generating tens of thousands of dollars in monthly token charges, while many companies lack the governance and cost controls to manage the expense.
"Enterprises may soon be paying as much for their developers' AI token usage as they do for their salaries," writes InfoWorld:
According to Gartner, these costs will meet, or even exceed, the typical software engineer's monthly salary within the next two years. This is not only because developers are increasingly adopting generative AI and agentic tools, it reflects a trend toward consumption-based licensing models as vendors balance infrastructure investments with profitability... Gartner senior principal analyst Nitish Tyagi explained that it's important to note that Gartner's prediction is based on a global average salary of $2,000 per month; it doesn't mean AI token usage will exceed all salaries. For instance, in the US, yearly pay rates can be six digits or more. However, that kind of spend is not out of the realm of possibility, Tyagi emphasized. "I have heard scary numbers like 'My developer consumed $20K last month,' or 'A business user consumed $32K'."
If these amounts sound shocking, that's the point. "The goal is to alarm the industry about the impact of token cost if it is not governed and controlled," he said... AI coding vendors have yet to deliver "mature, built-in cost optimization capabilities," Tyagi said, and prices will likely only continue to rise as vendors further build out their models while at the same time trying to remain profitable. Thus, enterprises struggle to forecast and control costs, and, because AI is moving so fast, many organizations lack the "maturity and frameworks" to determine ROI, he noted. Agent-driven workflows are difficult to govern, context windows become bloated, budgets are wiped out earlier than anticipated, and token spend becomes hard to justify....
"Without a governed engineering operating model, costs can escalate faster than the productivity gains these tools are designed to deliver," Tyagi said.