Just as a shopper weighs calories against nutrients, employees could weigh a prompt’s cost against what it delivers.
Over the past year, tech-forward companies have gorged themselves on AI, gobbling up more and more tokens, the base unit of AI use. The scale is staggering. Google alone now processes more than 3.2 quadrillion tokens a month, roughly seven times what it handled a year earlier. Meanwhile, Uber burned through its entire artificial intelligence budget for 2026 by April—four months into the year.
After encouraging heavy AI use through token leaderboards and tokenmaxxing, tech companies are ready to pull back, and rightly so. Companies with no handle on their AI usage see worse returns. One study found that organizations with full visibility into their AI operating costs were five times as likely as others to report established ROI. Companies want more value from their AI, but struggle to get the employees they pushed toward it to change their behavior.
The central problem is that most folks simply don’t know how much energy and computing resources even the smallest prompt consumes. AI became easy to use before its cost became easy to understand.
However, the most effective AI-using companies won’t go on a crash diet. Instead, they’ll use AI tools that give their employees access to clear information about the true cost of each prompt before they run it—a “nutrition label,” so to speak.
While improvements in AI infrastructure may make systems more efficient, and token limits can prevent the most egregious overconsumption, neither addresses the underlying problem: Employees can’t judge whether a prompt’s computational effort fits their intended outcome.
The issue is not just that employees are using AI for tasks that don’t need it, like checking the weather. It’s also that wasted AI use often accumulates through ordinary, everyday misalignment: unclear requests, failed outputs, repeated attempts, and conversations burdened with irrelevant context.
A single unnecessarily complex AI workflow using multiple tools, repeated attempts, and excess context might run a bill of $2.25. A company might be willing to stomach this cost in the name of “experimentation,” but the same employee running two of these inefficient prompts a day would rack up $99 a month. Repeat this usage pattern across 1,000 employees for one year and that’s $1.19 million from inefficient prompting.
Not only is the cost untenable for companies, the sheer energy usage is untenable as well. While each AI model has its own efficiency level, analyzing publicly available Nvidia chip specifications, industry-standard power usage effectiveness benchmarks, and average estimates on how fast AI models handle data reveals that a single token uses approximately 200 joules of energy. That’s enough to power a 10W LED bulb for 20 seconds. At the highest level of AI complexity, employing a team of multiple AI agents can use more than 1 million tokens, or the equivalent of nearly two days of household electricity.
As AI systems become increasingly autonomous, the opaque nature of prompts’ monetary and energy costs won’t survive for much longer. Companies will demand more transparency from frontier AI models.
OpenAI’s head of enterprise, Alexander Embiricos, noted that their business customers have shifted from asking “What can AI do?” to “Can I audit the efficiency of this model?” AI makers will soon provide the stats to do so.
