Here is the line from Gartner that should make any leader put down their coffee: by 2028, the money your company spends on AI coding tools could overtake what you pay the average software developer. Not approach it. Overtake it. And the reason is not that the tools got dramatically better. It is that almost nobody is watching how much they cost to run.
Most people picture AI tools the way they picture software: pay a fixed licence, use it as much as you like. That is not how this works anymore. AI coding assistants charge by the "token", which is roughly a chunk of text the model reads or writes. Every question you ask, every file the tool reads to understand your code, every answer it generates, all of it burns tokens, and tokens cost money. The more your team leans on the tool, the bigger the bill. There is no flat fee protecting you.
That shift from fixed to usage-based pricing is the whole story. Gartner predicts AI coding costs will overtake the average developer's salary by 2028, driven by a surge in token consumption as companies move from a few people experimenting to whole teams relying on these tools daily. The pattern is predictable. Light users become heavy users. Heavy use becomes the default. Spend climbs quietly in the background while everyone celebrates how much faster the work feels.
And the token bill is only the cost you can see. There is a second one arriving behind it. When AI writes a great deal of code quickly and nobody on the team fully understands how it works, you have bought something cheap to generate and expensive to own. Months later someone has to debug it, extend it, or explain why it does what it does, and that is slow, costly human work. Fast code you do not understand is a loan, not a saving, and the repayment lands long after the demo that impressed everyone.
And here is the human part, which is the part I find most honest in the Gartner analysis. The problem is not greedy engineers. It is that people, sensibly, optimise for getting their work done. Nitish Tyagi, the Gartner analyst behind the forecast, puts it plainly: "developers tend to optimize for speed and convenience over cost efficiency". Of course they do. You would too. Nobody opens their editor in the morning thinking about token budgets. They think about shipping the feature. So the cost discipline will never come from individual choice. It has to be designed into how the work is organised.
This is where I want to pull leaders out of the weeds of software and into the bigger picture, because this is not really a coding story. The same meter is running on Copilot licences, on enterprise AI assistants, on every chatbot and agent your business is rolling out priced by usage (or at least can be moved to a meter if the powers that be decide to make it so). If you are a Chief Digital Officer or a Head of Strategy, this lands on your desk first. The alternative is your CFO discovering it for you in a budget review, and that is a far less comfortable conversation.
Gartner's recommendation is to stop treating AI use as a free-for-all and instead sort work into three clear lanes. It is worth using their structure, because it is a useful way to think.
Developer-led. A person does the work, with the AI offering suggestions at most. This is for the high-stakes, high-judgement tasks where you want a human firmly in control and the token cost is incidental.
Developer-with-agent. A person and the AI work together, the human steering and reviewing while the tool handles the heavy lifting. This is the middle ground, and it needs the most attention, because it is easy to let the tool run further and spend more than the task warrants.
Fully agent-led. The AI handles a task end to end with little human involvement. Reserve this for simple, repetitive, low-risk work where the economics genuinely stack up, and route those jobs to cheaper, smaller models rather than the most expensive one by reflex.
Underneath all three sits a simple governance habit: review token spend the way you already review time and budget. Gartner suggests folding token usage into the regular retrospectives teams already hold. That is not exotic. It is just deciding to look.
There is a sharper edge to this, though, and it is worth naming. Once your whole team depends on a tool priced by usage, the company selling it holds the lever, not you. They set the price per token, and they can move it. Usage-based pricing is not just a budgeting quirk, it is a question of who has power in the relationship. The more deeply a tool is woven into how your people work, the harder it is to walk away when the terms change. So watching the meter is only half the job. The other half is watching who controls it, and making sure you are never so locked in that a price rise becomes a crisis rather than a decision.
Now for the part that worries me more than the bill. In the next few years the AI will get faster and more capable, and yes, it will write a great deal more of the code. Some leaders will read that as permission to gut their engineering teams and hand the work to AI, maybe even let non-technical staff build products with it. Resist that. The AI has no subjective experience. It has never lived through a failed rollout at two in the morning, never felt the cold drop of a compromised system, never been the engineer who deleted the production database and learned, permanently, what that costs. Those scars are not trivia. They are exactly the judgement that stops a small mistake becoming a catastrophe.
AI magnifies human capability, it does not manufacture it from nothing. People who could never build digital products before will genuinely be able to do more, and that is worth celebrating. But your developers hold the skills and knowledge that let technology scale safely, and as cyber-security threats grow heavier every year, you will need people of a technical disposition more, not fewer. So before you find yourself, eighteen months from now, competing in a tight market to rehire the very people you let go, think twice. The cost of the meter is recoverable. The cost of losing your technical memory is not.
A powerful tool in an undisciplined system does not save you money, it accelerates the waste. The productivity promise of AI was never really about the technology. It was always about whether you have the operating discipline to use it well, and the human judgement to know when not to. The companies that win the next three years will not be the ones with the cleverest models. They will be the ones who watched the meter, kept control of it, and kept their people.
Because most AI coding tools have moved from fixed-price licences to usage-based pricing, where you pay per "token", the small chunks of text the model reads and writes. As whole teams adopt the tools and use them daily, token consumption climbs sharply, and Gartner predicts the total cost could overtake an average developer's salary by 2028.
No, and treating it as a reason to cut technical staff is a mistake. AI will write more of the code, but it has no lived experience of failed rollouts, security breaches, or production disasters, and that hard-won judgement is what keeps systems safe at scale. As cyber-security threats grow, you will need technically skilled people more, not fewer.
The hidden cost is maintenance and ownership of code your team did not write and may not fully understand. AI can generate working code quickly, but if nobody grasps how it works, the bill arrives later in slow debugging, extension, and risk. Cheap to produce is not the same as cheap to own, and that gap rarely shows up on the invoice.
It belongs to senior leadership, specifically roles like the Chief Digital Officer or Head of Strategy, not individual developers. Gartner's analysis is clear that developers naturally optimise for speed and convenience over cost, so discipline will not emerge from personal choice. It has to be designed into how work is governed, or the CFO will impose it later under worse conditions.
Avoid lock-in by keeping awareness of how deeply each tool is woven into your workflows and never becoming so dependent that a price rise turns into a crisis. Usage-based pricing hands the pricing lever to the vendor, so governance means watching not just what you spend but who controls the meter, and preserving your ability to switch or scale back.