# AI Is Too Expensive

Edward Zitron's May 2026 piece updates the [[subprime-ai-crisis|"subprime AI" thesis]] with the numbers that came out in the intervening two months: the Musk-OpenAI trial testimony revealing Microsoft's $87B sunk into OpenAI infrastructure, Anthropic's March affidavit showing $3 spent for every $1 of compute revenue, RPOs jumping by hundreds of billions but with almost all of the jump coming from Anthropic and OpenAI themselves, and a new section of evidence: enterprises blowing through their entire annual token budgets in months. The thesis hasn't changed; the supporting numbers got worse.

## The hyperscaler arithmetic

Zitron stacks the capex numbers:

- Microsoft has spent $293.8B in capex since FY23 (back half of 2022). Bloomberg, via Musk-OpenAI trial testimony from executive Michael Wetter, reveals that ~$87B of that — about 30% — went specifically into OpenAI infrastructure. Per Zitron's Azure sources, this is the vast majority of Microsoft's operational capacity.
- 2026 hyperscaler capex plans: Microsoft $190B, Google $185B, Meta $125B+, Amazon $200B+. Roughly $700B in 2026, and another ~$1T projected for 2027.
- Combined revenue of Microsoft, Meta, Amazon, Google FY2025: $1.599T for *every product*. They need at least $3T in AI-specific revenue to break even, $6T to come out ahead.
- Microsoft 365 Copilot has 20M paid subscribers — absolute maximum revenue $7.2B/year if every one paid $30/month (they don't; discounting is multi-year). Total Microsoft AI revenue FY2025 Zitron estimates at ~$17.9B against $88.2B capex.

The pull-through math: at $42-44M per megawatt, OpenAI's claimed 1.9GW of capacity (end of 2025) is ~$79.8B of infrastructure cost. Microsoft has invested nearly four times that. Most of what Microsoft has spent is for OpenAI alone.

## The four-conditions test

For any of this to make sense as an investment, *all four* must be true:

1. AI revenues have to explode.
2. Capex has to stop being invested.
3. GPUs need to be margin-positive, including debt service.
4. AI revenue has to stay consistent before *and* after capex stops.

Zitron walks through why each condition fails. If condition 1 fails alone, the best case is break-even. If condition 2 fails (capex keeps growing), revenues have to roughly double Microsoft/Meta/Google's entire businesses and triple AWS. Condition 3 is unproven and contested; the on-the-record claims ("Anthropic is profitable on inference") rely on misquoted Altman remarks or hedged Amodei statements about "stylized facts." Condition 4 is the worst: even if revenue is good now, the question is whether it survives the capex stop.

## RPOs — the demand illusion

Remaining performance obligations (RPOs) are the multi-year contracted revenue commitments on hyperscaler balance sheets. They jumped dramatically in Q1 2026:

- Microsoft RPOs: $392B → $625B, driven by OpenAI's $250B "incremental Azure spend" lockup (October 2025) and the $30B from Anthropic (November 2025). Without those two, RPOs would have been flat.
- Amazon RPOs: $244B → $364B, driven by Amazon's $100B expansion of its OpenAI compute deal and the Anthropic 5GW capacity partnership.
- Google RPOs: $242.8B → $467.6B, driven mostly by Anthropic's $200B TPU spend commitment. Without it, the jump is $24.8B (and a chunk of that is the Meta TPU rental deal).

Zitron's reading: outside of Anthropic and OpenAI, these three companies aren't significantly increasing their AI revenues. They are extending each other credit through two intermediary companies. Per The Information, more than 50% of hyperscalers' revenue backlog comes from Anthropic and OpenAI alone.

## Anthropic and OpenAI's four-year hole

What Anthropic appears to owe over the next four years:

- $200B to Google
- $100B to Amazon
- $30B to Microsoft
- $30B to CoreWeave
- $20B to xAI ($5B/year × 4)
- ≥$20B in non-compute operating expenses

Total: ~$380B over four years. Anthropic per its March 2026 affidavit had spent $10B on training and inference against ~$5B in total lifetime revenue — $3 spent per $1 earned. The Information reports its gross margins were 40% in 2025, 10 points below the optimistic projection, with inference costs 23% higher than expected.

OpenAI per The Information plans to burn $852B through 2030, with gross margins falling from 40% in 2024 to 33% in 2025 — 13 points below projection because of last-minute compute purchases.

This is what Zitron has called the [[knife-catching-compute|knife-catching problem]]: order compute years ahead and you can bankrupt yourself if revenue doesn't materialise (Amodei: "there's no force on earth, there's no hedge on earth that could stop me from going bankrupt"); buy at the last minute and you pay spiked spot prices that destroy whatever margin you might have had. Both companies are in the bad version of this trap.

## Enterprises blowing through token budgets

The new evidence in this piece is enterprise [[ai-token-budget-explosion|token-budget overruns]]. Anthropic moved enterprise customers to token-based billing a month or two before publication, and the stress test is producing predictable results:

- ServiceNow CIO Kellie Romack (talking to the Information's Laura Bratton): the company is unsure whether it can keep employees on Claude Enterprise for the rest of 2026 without containing costs.
- Salesforce CEO Marc Benioff: $300M planned Anthropic token spend in 2026.
- Stripe (per sources to Zitron): ~5,000 technical staff burning an average of $94K/day in tokens — $2.8M/month, $33.6M/year. Against ~$765M in technical headcount, this is ~4.4% of headcount cost. Goldman reports AI costs approaching 10% of total headcount across the industry, "on track to be on par with headcount costs in the next several quarters."
- Uber and other organisations are exhausting yearly token budgets in months.

## Zillow is AI Chernobyl

The case study Zitron leans on:

- Zillow spent $1M on AI services in Q1 2026, then $749K in April alone (Cursor, Anthropic, AWS Bedrock).
- By mid-May, total AI spend was over $3M; Cursor budget consumption hit 85% of the annual $1.1M.
- 2026 trajectory: $7-10M total AI spend, against $23M FY2025 net income.
- Internal slide deck "AI-Native Engineering" promises a "path to an agentic Zillow." Talks about engineers moving from "soloist" → "conductor" → "composer."
- The "2027: A Tuesday" slide describes a hypothetical engineer who never opens a codebase, just specs and eval dashboards.
- Reality, per Zitron's sources: engineers still open IDEs, code is "slowly becoming AI slop," human review work *increased* by ~50% even with engineering headcount flat. Software reviewer load up 29,000 hours per month — about 19 extra review hours per engineer per month, "just looking at extra code written by LLMs."
- Blind anonymous posts from Zillow engineers: "the slop is job security"; "people are burning tokens just to hit internal AI adoption targets."

This is the rare named case where the [[ai-great-leap-forward|"corporate AI mandate"]] dynamic is being documented from the inside in near-real-time.

## Anthropic's transparency problem

Per Bratton's Information reporting, Anthropic does not offer:

- Telemetry data of the kind ServiceNow, SAP, Microsoft, Workday provide (which user, which tool, how much, how)
- Service-level agreements
- Granular billing decomposition

Zitron's reading: with multi-million-dollar contracts, no SLA, no telemetry, and a model whose token consumption is non-deterministic per task, the conditions are set up for either deliberate or accidental cost amplification with no customer-side audit path. He stops short of accusing Anthropic of anything specific — but he's clear that the structure of the relationship is unusual for enterprise software.

## The unmeasurable ROI

Zitron's structural argument: AI token budgets are inherently bullshit because tokens-per-task isn't a fixed quantity. Tokens vary across models, across users, across runs, even with identical prompts. Sample-of-one measurements are useless. KPI replacements all gameable:

- "Burn as many tokens as possible" — Amazon and Meta employees already gaming token-leaderboards.
- "Use AI every day" — no success criterion.
- "Ship more software" — emphasises velocity over quality.
- "More pull requests" — any number an engineer can manipulate, they will.

Without per-task token measurability, every AI budget is a dart thrown blindfolded. Without a usable KPI, "productivity" claims can't be falsified — which is why the [[business-idiot|Business Idiot]] class loves the technology.

## The Business Idiot turn

Zitron's closing argument is structural rather than financial: AI is grift-shaped *because* the modern executive is grift-receptive. LLMs do an "impression of work" the same way most executives do an impression of work. They'll say yes to anything, never push back on timelines, produce "convincing-sounding responses" instead of asking why the engineer just said no. See [[business-idiot]] for the standalone concept.

The connection to the rest of his analysis: a Business-Idiot–run economy is what makes the demand side of the bubble look real. Enterprise spend looks like demand. It's not — it's executives "eating the cost while employees experiment" (Workato CIO) with no return-on-investment measurement and no exit plan.

## Where this leaves the bubble thesis

Zitron's prediction in [[subprime-ai-crisis]] was that the collapse sequence runs through (1) lab cost escalation → (2) startup margin compression → (3) consumer rate-limit revolt → (4) lab compute over-commitment. This piece adds a fifth: (5) enterprise token-budget revolt, where CIOs and CFOs are forced into the cost-containment conversation in 2026, not 2027.

The first CFO to publicly cut AI spend, Zitron predicts, sends the rest of them running for the doors.

## Cross-references

- [[subprime-ai-crisis]] — the March 2026 flagship piece this updates
- [[edward-zitron]] — the entity page, updated to list this piece
- [[ai-bubble-pale-horses]] — the checklist; this piece confirms several signals (capex commitments, lab over-commitment, RPO concentration, enterprise spend backlash starting)
- [[knife-catching-compute]] — the Amodei-quote structural problem
- [[ai-token-budget-explosion]] — the new enterprise-side dynamic
- [[business-idiot]] — Zitron's structural account of why this works on executives
- [[ai-great-leap-forward]] — the mandate side; Zillow's "AI-Native Engineering" deck is the genre exemplar
- [[hold-on-to-your-hardware]] — the consumer-hardware-shortage consequence
- [[ai-backlash-polling-2026]] — the demand-side polling that says explosive revenue growth isn't coming from consumers
- [[ai-subsidy-economics]] — the underlying mechanism
- [[ai-sycophancy-loop]] — the loop AI plays on Business Idiots specifically
- [[titit-local-ai]] — Neward's case that local models are the rational response to this entire structure