AI Coding Costs Will Surpass Developer Salaries by 2028

TL;DR: Gartner warns that AI coding tool costs will surpass developer salaries by 2028, driven by increased token consumption and lack of vendor transparency. Companies will need to implement governance to control spending.

What happened?

A recent Gartner report, covered by TechRadar, predicts that costs of artificial intelligence (AI) coding tools will surpass the average developer salary by 2028. This phenomenon is due to the increase in license volume and growth in token consumption, as vendors adopt usage- and outcome-based pricing models. Each code generation, refinement, and debugging consumes tokens, driving up costs as AI becomes integrated into daily workflows.

Why is it important?

This paradigm shift threatens to reverse the productivity savings promised by AI. Companies that adopted tools like GitHub Copilot or ChatGPT for coding now face rising costs that can erode margins. Senior Principal Analyst Nitish Tyagi notes that 'software engineering leaders are increasingly concerned, as token-driven spending becomes harder to justify, with budgets often exhausted sooner than expected.' The lack of visibility into how tokens are counted exacerbates the problem, making cost prediction and usage optimization difficult.

Consequences for companies and developers

For companies, the cost escalation will force stricter governance over AI usage. Tyagi warns that 'token discipline will not emerge through developer choice alone,' as developers prioritize speed and convenience over cost efficiency. Organizations will need to establish usage policies, limit token access, or even restrict certain features. This could slow AI adoption and protect human roles, but also hinder innovation.

For developers, pressure to reduce costs could limit their autonomy and creativity. Tools that were once allies could become luxuries, and token efficiency could become a valued skill. Additionally, over-reliance on AI could atrophy fundamental coding skills.

What should readers know?

Readers should understand that AI in coding is neither free nor fixed-cost. As vendors shift to token-based pricing, companies must closely monitor consumption and negotiate contracts that offer transparency. It is also crucial to evaluate the real return on investment (ROI): if costs exceed developer salaries, the current business model may be unsustainable. Finally, AI usage governance will be a key differentiator for companies aiming to maintain profitability.

'Token discipline will not emerge through developer choice alone' — Nitish Tyagi, Senior Principal Analyst, Gartner.

Historical context and comparisons

This phenomenon echoes the dot-com bubble, where technology investment outpaced immediate returns. However, unlike then, generative AI offers real productivity improvements, but at a cost that may not be sustainable. It also resembles cloud computing adoption, where initially low costs skyrocketed with usage, leading to optimization practices like FinOps. The lesson is clear: without governance, technology adoption can generate hidden costs.

Implications for the future of work

If AI costs exceed salaries, companies might reconsider mass automation of development roles. However, AI does not fully replace developers; it assists them. Therefore, the most likely scenario is hybridization where developers focus on high-value tasks while AI handles routine work, but with strict cost control. This could lead to a reduction in the development workforce or the creation of new roles like 'AI cost engineer.'