Gartner: AI coding costs will surpass average developer’s salary by 2028

Gartner has predicted that by 2028, AI coding costs will overtake the average developer’s salary.

This has been attributed to rising large language model (LLM) token consumption and the shift to consumption-based licensing models.

AI tokens are the units of data processed by AI models, meaning token consumption, particularly with token-based consumption pricing models, directly impacts the cost of AI coding tools.

Gartner said many vendors lack transparency into how token consumption is calculated and billed, limiting enterprises’ ability to accurately forecast and control costs. 

Token overspending is often linked to how software engineering leaders govern usage, with common failure modes including ungoverned autonomy in agent-driven workflows, bloated context windows and the absence of structured feedback mechanisms to optimise usage.

Gartner also stated that AI coding vendors are yet to deliver mature, built-in cost optimisation capabilities in AI coding agents, further contributing to cost escalation.

To manage costs, Gartner recommends measures including establishing a use-case-driven decision framework, as well as aligning model selection with task complexity.

The research firm also advises mandating context engineering practices, implementing governance and cost controls, and embedding token usage reviews into development cycles.

“Organisations are rapidly moving from experimentation to scaled deployment of AI coding agents, but many are underestimating the financial impact of rising token consumption,” said Nitish Tyagi, senior principal analyst at Gartner.

“Token discipline will not emerge through developer choice alone, as developers tend to optimise for speed and convenience over cost efficiency. Without a governed engineering operating model, costs can escalate faster than the productivity gains these tools are designed to deliver.”

“Most organisations still lack the maturity and frameworks to effectively measure cost versus business impact. Software engineering leaders are increasingly concerned as token-driven AI spend becomes harder to justify, with budgets often being depleted earlier than expected.”