Our Take
Gartner is pricing a future where inference cost per developer becomes a line-item headcount decision, not a tool purchase—but the prediction rests on token-consumption trends, not on demonstrated unit economics or model pricing commitments.
Why it matters
Engineering leaders planning 2025 budgets need to understand whether AI coding assistants remain a marginal expense or become a major operating cost. This shifts the ROI calculation from productivity gains to cost management.
Do this week
Finance and engineering leads: model your actual token spend per developer over the next 12 months using your current tool's usage logs, then stress-test that curve against Gartner's assumptions before committing to multi-seat contracts.
Gartner's Cost Projection
Gartner forecasts that by 2028, the annual cost of AI coding assistants will exceed the average developer's salary (per the analyst report). The projection is driven by surging token consumption as more developers adopt tools like GitHub Copilot, Cursor, and similar models. Gartner does not name a specific dollar figure in the available excerpt, nor does it isolate which model providers or pricing tiers underpin the forecast.
The analyst firm attributes the acceleration to increased usage density and token-per-request growth as codebases expand and teams integrate AI into more development workflows. The implication is that current per-seat pricing models will become unsustainable if token consumption continues its current trajectory.
The Budget Squeeze Is Real, the Timeline Is Speculative
If true, this projection rewrites the business case for AI coding tools. Companies currently justify these expenses as productivity multipliers, not headcount replacements. A cost that rivals salary fundamentally changes the conversation: teams will start asking whether the tool is worth the human it could hire instead.
The risk in Gartner's forecast is that it assumes token prices remain flat and usage grows unabated. Model providers may cut prices to defend market share, or usage may plateau as developers hit practical productivity ceilings. The four-year timeline also presumes no major shifts in model architecture or deployment (on-device inference, for instance, would cut API costs dramatically).
What is not speculative: token consumption is accelerating, and vendors are not yet competing aggressively on inference pricing for IDE-integrated tools. If that changes slowly, Gartner's warning has teeth.
Audit Your Actual Spend Now
Most teams do not track token consumption per developer in real time. Start collecting baseline data on your current tooling (GitHub Copilot usage logs, Cursor telemetry, whatever you use). Calculate cost-per-developer-per-month and extrapolate over 24 months.
Compare that curve against your headcount budget. If the trend line crosses 10% of average salary by 2026, Gartner's warning becomes tactical: you will need to negotiate volume discounts, switch to on-device models, or reduce adoption. If it stays below 5%, you have breathing room.
Do this before your 2025 budget is locked. Waiting until 2027 to discover the cost is unsustainable means you have already committed capital and developer workflows to a tool you may need to rip out.