A March survey by Faros found that among 20,000 developers, output was rising, but so were b...
"AI adoption sparks budget crises, forcing companies to reassess spending. Token costs skyrocket."
Uber blew through its entire 2026 AI coding budget by April, a stark reminder of the industry's scramble to manage AI's runaway costs. Across the industry, companies are starting to balk at the price of AI, prompting a market to form around giving companies the tools and language to track their spending.
The push for more AI adoption and increasingly autonomous agents has driven token consumption higher, with per-token prices falling but overall costs rising. Companies that gorged themselves on all-you-can-eat subscriptions in early 2025 are now scrambling to understand where their money is going, pull back spending, and figure out whether they can salvage some return on investment from the wreckage of their budgets.
Alexander Embricos, OpenAI's head of enterprise, noted that conversations with customers have shifted from discussing the capabilities of AI to focusing on cost control and visibility. "Six months ago, I would have a conversation with a customer and it would be all about 'What can it do? Is it good enough?' Now the conversations are about, 'hey, we're spending so much. What visibility do you have? What auditability do you have? What token controls do you have? What is the efficiency of your models?'"
The Linux Foundation has unveiled plans for the Tokenomics Foundation, a new standards body that aims to instill cost discipline around AI tokens, similar to what FinOps did for cloud spend. J.R. Storment, executive director of the FinOps Foundation, said that companies are facing existential crises due to token budget overruns, with some reporting being 3x over their entire 2026 token budget by April.
The fervent demands from CEOs to use the best models and move fast, costs be damned, have led to significant improvements in agentic tools, which have multiplied consumption. New models released in November, such as Anthropic's Claude Opus 4.5, OpenAI's GPT-5.1, and Google's Gemini 3 Pro, have brought substantial improvements, but also skyrocketing costs. One company reportedly found itself with a $500 million Claude bill after forgetting to set usage limits for employees.
Chris Reed, senior director of IT finance at Priceline, compared the situation to the crack-cocaine epidemic, noting that companies are getting hooked on AI and then struggling to control costs. "They let you try it to get you hooked on it, and now you're kind of beholden to it." Vitaly Gordon, CEO of engineering operations platform Faros AI, said that he recently spoke to a CTO who was unsure whether to stop an engineer who spent $40,000 on tokens in a month or encourage others to follow suit.
A March survey by Faros found that among 20,000 developers, output was rising, but so were bugs and rewrites. Jellyfish, an engineering management platform, similarly found that engineers who used the most tokens were about twice as productive as those who used AI less, but they spent 10x the number of tokens to get there. Nicholas Arcolano, head of research at Jellyfish, noted that expenditure on AI is exploding due to agentic features, with per-developer consumption rising about 18.6x in nine months.
The productivity case for extreme AI spending is murkier than the spending suggests, with most companies still unable to measure the ultimate business value of shipped code. "Whether extreme spend pays off comes down to the ultimate business value of shipped code, which most companies still can't measure," Arcolano said. The sheer scale at which AI is being used today makes tracking costs a significant challenge, with J.R. Storment comparing it to a hundreds-of-millions-of-rows-a-month data problem.
As companies struggle to come to terms with their AI spending, the market for cost management tools is forming rapidly. Startups, established vendors, and the new Tokenomics Foundation are all racing to provide companies with the tools and language to track their spending and regain control over their budgets. The question remains whether these efforts will be enough to prevent further budget crises and ensure that companies can harness the power of AI without breaking the bank.
