In today’s Finshots, we explore why the AI boom is poised to create its own FinOps moment.
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story
Silicon Valley spent two years convincing companies to increase their use of AI. Now it’s the companies that listened that are in trouble.
A few weeks ago, Microsoft was scaling back access to Anthropic’s Claude Code for engineers. Around the same time, Uber revealed that it has exhausted its entire 2026 AI budget within the first four months of this year. Andrew McDonald, the company’s chief operating officer, acknowledged that it has become difficult to translate the increased usage of tokens (units of data used in AI models) into meaningful results. It simply means that the cost was not worth the result.
And some companies, like Amazon, are starting to back away from this.Token maxing” culture, and employees were encouraged to make the most of AI wherever possible.
Even if AI improves employee productivity, encouraging people to use AI should lead to better outcomes.
But what happens if an AI bill is introduced?
It’s a question that’s becoming increasingly difficult to ignore. According to a recent report, one AI consultant told Axios about a client who spent: Over $500 million You will be able to serve Claude users in one month. Nearly $17 million is spent every day just to keep the model running.
To understand why this is important, we need to talk about tokens.
Think of a token like a taxi meter that never stops. Every time you ask an AI agent a question, upload a document, or make a follow-up request, the meter moves. One ride may be cheap. But when thousands of employees take similar rides all day, every day, vehicle fees become something entirely different.
To users like you and me, tokens feel like something invisible. But for AI companies, they are anything but.
Behind every token there are real-world costs such as GPU time, power, and data center capacity. And since most AI providers charge companies based on the amount of tokens they consume, tokens are the closest thing the industry has to a common currency.
The more a company uses AI, the more tokens are spent. And the more tokens you spend, the more AI will charge you.
Of course, none of this means spending on AI is bad. After all, if AI allows software engineers to complete a 10-hour task in two attempts, the benefits will be worth the cost.
But when something becomes cheap and easily accessible, people usually end up using it far beyond their plans.
This is not the first time something like this has happened. When cloud computing was new, it was based on the idea that businesses would pay for the computing power they actually used, instead of purchasing expensive servers.
Netflix For example, after years of managing its own data centers, the company became one of the leading companies in migrating its infrastructure to Amazon Web Services (AWS). The move allows streaming giants to expand computing power whenever demand spikes, and scale back when demand doesn’t.
To put this in perspective, if 50 million people decide to watch the season finale of a hit show on the same night, Netflix doesn’t suddenly need to build a new data center. You can take advantage of more cloud resources. It sounded great. No more guessing future demands or purchasing hardware years in advance. And as you can imagine, companies loved it.
However, companies soon discovered a small problem. While it’s easy to spin up new resources, keeping track of them hasn’t been easy. That’s simply because computing resources have become easier to access, and usage has exploded. Developers spun up new workloads, and forgotten virtual machines continued to run for months. This meant that storage costs were ballooning in the background.
Over time, many companies found that managing cloud costs became a challenge of its own.
The situation has deteriorated so much that McKinsey has determined that some companies may almost be able to cut back. 20% You can figure out your cloud spending percentage if you know where to look.
But abandoning the cloud was not a solution or even an option. In fact, companies wanted to do more with it. It can be argued that the success of the cloud has created entirely new challenges.
Therefore, the only solution was to create an entirely new discipline to manage it.
Today, the field has a name. it is called finops.
But no one sat down one day and decided to invent FinOps. This plan emerged as businesses found themselves in an unusual situation: a cloud cost crisis.
And it gradually transformed into its own industry. Today, companies can hire FinOps specialists, software vendors sell cloud cost management tools, and professionals can even earn FinOps certifications. In other words, what started out as a billing headache ended up becoming a career path.
So let’s get back to AI. Similar to its predecessor, cloud computing, AI is becoming easier to access, scale, and utilize.
Even today, most companies are still measuring their AI adoption. They want to know how many employees are using AI tools, how often they use them, and whether AI is part of their daily workflow.
This is a perfectly reasonable goal when the technology is new.
However, a mature technology cannot be judged solely by its usage. They must be judged by their results.
This simply means that if companies want their employees to use AI, they may need to ask and answer harder questions. In other words, which AI uses are actually worth paying for?
But it’s not just about AI costs and budgets. Over the past two years, many companies have cut jobs in the belief that AI will enable their remaining employees to do more work with fewer people. fair enough. But like any investment, you have to show results.
Jefferies estimates that AI-related spending could have an impact. $4.7 trillion This means huge amounts of money are being poured into technologies whose business value companies are still figuring out.
And as spending increases, so does the pressure to prove that AI actually works.
Otherwise, companies could find themselves in an awkward situation with fewer employees, higher AI costs, and no real performance improvement.
So perhaps AI will have to prove that the value it creates is worth the cost, just as cloud computing once did for FinOps. And we’ll have to wait and see how that plays out.
Until next time…
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