Uber Burned Its Entire Annual AI Budget in Four Months. Corporate America Is Rationing Tokens Now.

Executives at Uber, Meta, Microsoft, Salesforce, and DoorDash have launched AI cost-cutting campaigns after bills doubled or tripled, or blew through annual budgets in as little as three to four months. Uber has introduced hard usage limits on AI tools (WSJ).

Read that timeline again. Eighteen months ago the corporate posture was flood the org with AI tools and encourage experimentation. Now it's rationed access and per-seat justification. That's a fast reversal for something that was supposed to be existentially strategic.

The WSJ frames the cause as token pricing while OpenAI and Anthropic balance supply against demand. The documented response is more interesting than the diagnosis: enterprises are adding cheaper models alongside the frontier tiers rather than defaulting to the most expensive option, including Chinese open weights that can be downloaded and customized. The article draws the structural contrast explicitly. US frontier models are closed and tightly controlled. Chinese labs ship cheap open weights.

Cisco's Jeetu Patel put numbers on the shape of the problem: fewer than 1% of potential users are on agents today, and if agents genuinely beat chatbots on productivity, supply stays short (SiliconANGLE). Continuous agent operation replaces episodic chatbot usage, which makes token consumption the defining budget constraint rather than a line item. Cisco's own mitigation isn't frontier-everything. Their Antares models locate 70% of code vulnerabilities locally, reserving frontier calls for the remaining 30%.

The culture caught up before the CFOs did. A r/ChatGPT meme titled "POV: Using GPT-5.6 Sol Ultra to Rename a Variable" pulled 625 upvotes (r/ChatGPT). It's sentiment, not data, but practitioners have clearly internalized model-tier routing as an economic decision rather than a default-to-strongest habit. And a solo team published a $801.94 breakdown across 53 AI coding sessions in a single month building a finance app, itemizing where the spend created value versus waste (r/SaaS). Self-reported and single-source, but it's one of the only per-session dollar figures published by someone who isn't a vendor.

Here's what builders should do, and I mean this week: instrument per-task token cost. Not per-month, not per-seat. Per task. You cannot tier your models if you can't see which tasks are expensive, and almost nobody has that number. I added it to my own pipeline after watching a research run cost 6x what I expected because one agent was retrying a failed tool call in a loop.

Then route. Cheap model for classification, extraction, formatting, renaming variables. Frontier model for the thing that actually needs judgment. The r/startups thread arguing YC has lost prestige funding AI wrappers and paying in service credits landed the same week (r/startups), and the two together sketch a real squeeze on thin-margin wrappers whose unit economics assumed cheap tokens forever.