AI coding token costs are on track to rival human payroll | CIO
Editions
Search
Menu
Topics
Close
Analytics<br>Artificial Intelligence<br>Business Operations<br>Careers<br>Cloud Computing<br>Data Center<br>Data Management<br>Digital Transformation<br>Diversity and Inclusion<br>Emerging Technology<br>Enterprise Applications<br>Enterprise Buyer’s Guides<br>Generative AI<br>Industry<br>Innovation<br>IT Leadership<br>IT Management<br>IT Operations<br>IT Strategy<br>Networking<br>Project Management<br>Security<br>Software Development<br>Vendors and Providers
AfricaAfrica
AmericasCanada<br>United States
AsiaASEAN<br>India<br>Middle East<br>日本 (Japan)<br>대한민국(Korea)
EuropeDeutschland (Germany)<br>España (Spain)<br>Ireland<br>Italia (Italy)<br>Netherlands<br>United Kingdom
OceaniaAustralia<br>New Zealand
by Taryn Plumb
AI coding token costs are on track to rival human payroll
News
Jun 24, 20266 mins
Growing use of coding agents and consumption-based pricing models could push per-developer AI spending to unprecedented levels over the next two years, says Gartner.
Credit: thanmano / Shutterstock
Enterprises may soon be paying as much for their developers’ AI token usage as they do for their salaries.
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. Rather than the flat per-seat SaaS model of the past, enterprises now pay for developer token use as well.
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.
Lack of visibility, immature oversight
Enterprises are quickly moving from experimentation to scaled deployment of AI coding agents, but many still underestimate token costs, Tyagi noted.
This is because cost structures for software engineering workloads are “highly variable,” he pointed out, and there isn’t a lot of transparency into how token consumption is calculated and billed.
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.
Added to this, light users such as non-developers will increase their usage as they become more familiar with, and even reliant on, AI tools, driving up token consumption and spend even more.
Tyagi said that, while AI is incredibly valuable, he sees no “direct relationship” between the number of tokens developers consume and their productivity gains. Rather, applying context engineering principles to optimize or reduce token consumption increases quality.
“Tokenmaxxing is not directly related to higher productivity gains,” Tyagi said, “but optimizing token consumption is.”
Still, this in no way means that organizations should move away from AI coding agents, he emphasized. Optimizing token consumption simply means spending only as much as needed without compromising the quality and value brought by AI.
“Without a governed engineering operating model, costs can escalate faster than the productivity gains these tools are designed to deliver,” Tyagi said.
How enterprises can control token usage
The traditional ‘lines-of-code-written’ productivity metric no longer applies when AI can almost instantaneously produce entire Python libraries. Rather, value should be measured in quality, speed, and customer satisfaction metrics, Tyagi said.
For instance: How quickly are developers able to release important features? How much time is reduced between app development and feedback from business, product, and development teams? Shipping features quickly while maintaining quality can create competitive advantage and improve user and customer experience, he...