AI Coding Costs Will Surpass Average Developer’s Salary by 2028

By 2028, AI coding costs will overtake the average developer’s salary due to rising large language model token consumption and the shift to consumption-based licensing models, according to Gartner. AI tokens are the units of data processed by GenAI models. Token consumption directly impacts the cost of AI coding tools, particularly under consumption-based pricing structures.

“Organizations 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 optimize 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.”

The shift from seat-based licensing to consumption-based pricing among AI coding agent vendors is introducing highly variable cost structures for software engineering workloads. Many vendors lack transparency into how token consumption is calculated and billed, limiting enterprises’ ability to accurately forecast and control costs.

Without clear visibility into token usage across development tasks, organizations risk budget overruns and reduced ability to track cost-to-value outcomes. “Most organizations still lack the maturity and frameworks to effectively measure cost versus business impact,” said Tyagi. “Software engineering leaders are increasingly concerned as token-driven AI spending becomes harder to justify, with budgets often being depleted earlier than expected.”

Beyond pricing and visibility challenges, how AI coding agents are used within organizations is further driving cost pressures. 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 optimize usage. In addition, AI coding vendors are yet to deliver mature, built-in cost optimization capabilities in AI coding agents, further contributing to cost escalation.

“AI coding costs will continue to rise as infrastructure investment and profitability challenges push model pricing higher,” said Tyagi. “At the same time, as more developers adopt AI tools, light users are expected to rapidly become mainstream users as familiarity and reliance increase, driving further growth in token consumption and overall spend.”