Gartner: Your AI Coding Assistant Might Soon Cost More Than Your Salary
Oh, look, another tech dream turns into a financial nightmare. Gartner just dropped a reality check: soon, those shiny AI tools will be burning through more cash than the actual humans writing the code. Happy burning, everyone!
The honeymoon phase with AI coding assistants is officially hitting the billable hours. Gartner predicts that by 2028, the sheer volume of token consumption will push the cost of AI-powered development past the average salary of a software engineer. Companies are sprinting from experiments to massive deployment, but they’ve completely underestimated that these LLM agents aren’t just writing code—they’re eating through budget line items like a hungry shark in an aquarium.
The root of this fiscal headache is the shift toward usage-based pricing. Vendors have moved away from flat-rate seats to models where every query and context window adds up. Since developers are wired to prioritize speed and functionality over counting every single token, they aren't exactly incentivized to be frugal. Without a strict operational model, these tools are currently scaling costs much faster than they are actually boosting productivity.
To stop the bleeding, Gartner suggests treating LLM usage like a serious engineering discipline rather than a magic wand. This means setting up request routing—sending simple tasks to cheap, tiny models while saving the expensive heavyweights for actual complex logic. It also requires training staff to clean up their prompts and enforce strict token caps, because apparently, an autonomous agent given a blank check is just as reckless as an intern with a company card.
When the software that was supposed to save money ends up costing as much as the person it replaced, it’s not really automation—it’s just a very expensive, glorified calculator. The irony of paying a premium to have a machine hallucinate code while the real engineers spend their time managing a budget instead of building features is the peak of modern tech efficiency.
Source: Gartner
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