Photo-Illustration: Intelligencer; Photo: Getty Images

For a couple of years, the main problem AI firms had was convincing people — or, more specifically, people in charge of companies — to pay for their products. Artificial-intelligence applications were gaining popularity, but they were expensive and not yet easy to deploy in clearly and measurably productive ways. Lots of companies wanted to use AI, insofar as they understood it as a theoretical source of efficiency and a tech megatrend that they didn’t want to miss, but early AI pilots and half-baked enterprise tools didn’t make a strong case for spending more.

In late 2025, though, the case became much stronger: AI coding improved massively as tools like Claude Code and OpenAI’s Codex went from coding assistance tools to code-writing tools. This elevated so-called vibe-coding from a fascinating curiosity into a plausible way of working. Here, for companies developing and maintaining software, was a clear and actionable use case, a place to spend real money with the expectation of specific returns, and a way to satisfy a strategic need to be doing something — anything! — not to fall behind. A few months later, some companies have a different problem with their AI deployments: Now they’re spending too much.

This has all been good news for Anthropic, which became the standard-bearer for AI coding, surpassed OpenAI’s valuation, and reportedly logged its first profitable quarter this year, far ahead of projections. If the trend continues, the company will ride enterprise AI-coding adoption to a massive IPO this year. (The enormous surge in spending with the frontier AI companies has also quieted talk of a bubble, which AI executives themselves were fretting about as recently as December.)

But this period of rapid adoption, for all its surface-level obviousness — you can just talk through software development now, and the code writes itself — led to some strange behaviors in the broader tech industry. Top-down AI usage mandates became common practice, and companies including Meta and Amazon created internal leaderboards to rank and incentivize AI usage. This resulted in some well-publicized episodes of “tokenmaxxing,” where employees at these companies blew through billions of tokens — the basic unit of information that a model receives and generates — to unclear ends, throwing AI agents at pointless tasks, using the most expensive models to do simple work, and defaulting to AI for work where other tools might suffice. (Why check your weather app when you can send Claude to check for you? Better yet: Why not whip up a whole new weather app?)

BREAKING: CEO discovers tokens cost money pic.twitter.com/4rhm6ayAJU

— Alberta Tech (@albertadevs) June 2, 2026

This was a brief and manic phase that both Amazon and Meta distanced themselves from quickly. (“Please don’t use AI just for the sake of using AI,” an Amazon executive told employees last month.) But a less cartoonish version of the top-down deployment model has become common practice: Companies like Salesforce set “minimum” and “ideal” AI usage targets, while countless other firms, large and small, are demanding proof from employees that they were at least doing something with these new tools. It would be fair to say policies like this haven’t been popular among employees. Most of the angst comes from the association of AI deployment with job cuts, a prospect that executives are so excited about that some of them have been doing it in advance and at scale.

But it would also be fair to say that top-down policies like this have worked in that they’ve habituated a significant portion of the code-writing workforce to working with generative AI. Now that these companies are getting what they wanted in that sense — a part of their workforce testing AI tools to see if it can become more productive, reduce its size, or both — they’re back to making more straightforward calculations about whether all this investment is worth it. After reports that Uber blew through its allotted AI budget for the year before the end of Q1, the company is instituting limits on AI usage for its employees, according to Bloomberg. According to The Wall Street Journal, they’re not alone:

Use of artificial intelligence by big companies is exploding—and the soaring cost has some of them pumping the brakes in a way that could complicate AI’s triumphal march across the economy… Executives across industries this year have urged employees to integrate AI tools into their work, spending freely to encourage experimentation and seeking to send a message to Wall Street that their companies won’t be left behind in a coming wave of disruption.

It’s tempting to overstate the swing here. Uber is a tech company, of course, and one that deals regularly with plenty of software-shaped problems that could plausibly be made easier by code-generating AI. But it’s also a mature company that makes money from a platform that’s already been built, and which contains millions of drivers and riders — its biggest bottleneck for growth, in other words, probably isn’t that it can’t produce software quickly enough. It also makes sense that companies with a general interest in AI as a labor-saving tool would underestimate just how costly going all-in on new AI tools actually is: Compared to 2024, for example, the amount of compute used by the top-end models from Anthropic and OpenAI is astronomically higher in 2026. Concerns about cost are “all of a sudden a huge issue,” Sam Altman says, but why wouldn’t they be? AI firms finally found a product corporate America wanted to buy. It just happened to be much more expensive than corporate America expected.

It also turned out to be more expensive than AI firms expected, which has led to some adjustments in pricing. The days of unlimited (and heavily subsidized) personal and enterprise accounts are ending, giving way to more tightly defined usage tiers and metered billing. Last month, Microsoft warned customers that change was coming. “Today, a quick chat question and a multi-hour autonomous coding session can cost the user the same amount,” the company said. “GitHub has absorbed much of the escalating inference cost behind that usage, but the current premium request model is no longer sustainable. Usage-based billing fixes that.” The change happened at the beginning of June, which trickled down to actual workers — stuck a few caverns below the discursive object of “AI” in the Platonic caverns of corporate America — in some fairly strange ways. Reports of usage limits and new policies flooded programmer sub-Reddits:

4 days into june and we used like 75% of copilot credits for the department… It’s going to be a manual coding month.

I work at a big telecom. They’re cutting back and placing strict budgets of like $30 per developer unless it’s really needed for your workflow.

My usage costs have more than tripled for no reason and my usage is the same as last month.

I work at fang+ and was told this week we have a budget of 1500/month.

“The whiplash is pretty crazy from the guidance we were receiving just a month or two ago,” wrote one tech employee. Another agreed:

I went through 70% of the monthly usage yesterday before I realized it. It’s hilarious because about 2 months ago our leadership decided to go all in on AI. Borderline AI psychosis – devs should be using AI every day, don’t write tests by hand when you can use AI, AI code reviews everywhere, AI, AI, AI.

These companies — both the AI firms and their customers — are clearly still in a discovery phase: AI firms are figuring out what their debt and customers will force or allow them to charge; their customers are trying to nail down what these tools, now that they’re at least partially deployed, are actually worth to them. For now, despite the headlines about spending caps, these are mostly stories about companies spending a lot on AI with near-term intentions to spend more. But the next few months could tell a different tale, particularly as far cheaper models — which have tended to run a few months to a year behind the frontier, depending on who you ask — start to pass through the good-at-coding threshold that triggered this surge in adoption. Remember DeepSeek, the vastly cheaper Chinese AI model that triggered a temporary tech sector sell-off in 2025? According to fintech company Ramp, which tracks AI spending, corporate customers are giving it a second look, as well as “using open source models,” which they can run themselves, “in a shift away from OpenAI and Anthropic.” Meanwhile, popular AI coding company Cursor has been marketing a version of Kimi, from Chinese company Moonshot, as “frontier-level at coding” at a small fraction of the cost.

It won’t be long, in other words, until the latest new, dazzling, and expensive use case for AI — writing decent code — is thoroughly commoditized, alongside the growing list of other tasks that once sat at the frontier of AI capability. (Asking a chatbot to do research on the web, to choose a mundane example of a function users take for granted today, was limited to paid ChatGPT users just last year.) After finally succeeding in their push for corporate adoption, and getting a taste of real revenue, AI firms could be looking at a different sort of competition: a good old-fashioned price war.

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