The Unluckiest Person: Zero API Calls, Secretly Charged 1 Billion Yuan by Anthropic

marsbitPublicado a 2026-07-13Actualizado a 2026-07-13

Resumen

A South Korean developer, remy_notes, was shocked to receive two invoices from Anthropic totaling over $17.8 million within 24 hours, despite being on a free plan with zero API usage and no linked credit card. His bank blocked the attempted charges. This case highlights a broader issue of billing errors in the AI industry. Auditing firm Vaudit, reviewing millions in AI bills, found significant erroneous overcharges, often related to issues like clients being billed for newer, pricier models instead of older ones they used, or being charged for failed requests and retry loops. While Anthropic claims overcharging is not widespread, major providers have refunded a large portion of disputed amounts. The incident adds to growing distrust, following previous billing controversies like the "HERMES.md bug" and a class-action lawsuit over opaque usage calculations for Anthropic's subscription plans. The complexity of AI pricing models creates a "black box" for users, leading to a new competitive frontier: billing transparency. As users turn to audits and lawsuits, clear and fair pricing is becoming a crucial part of the product experience.

On July 8, the well-known Korean developer remy_notes posted two Anthropic invoice screenshots on Threads: the first for $1.669 million (approximately 11.3 million RMB), the second for $16.62 million (approximately 110 million RMB). The two invoices were issued less than 24 hours apart, with the amount increasing nearly tenfold.

He initially thought it was a phishing email, but the sender address and payment links pointed to Anthropic's official Stripe domain.

His KB National Bank debit card intercepted two overseas charge requests from the US merchant 'ANTHROP' on the same day—both were denied due to insufficient single-transaction limits.

His account was on a free plan, with no credit card linked, and the backend showed zero API usage.

As of publication, Anthropic has not publicly explained the cause of this "phantom bill."

More Than One "Phantom Bill"

The figure of $16.6 million is shocking enough, but what truly deserves attention is the systemic issue emerging from it.

From March to June, AI bill auditing company Vaudit reviewed $34 million in AI bills from 60 companies and discovered approximately $1.7 million in erroneous overcharges, most of which were related to Anthropic's Claude Code.

Vaudit's clients include Panasonic, HP, and Honda, among others.

Vaudit CEO Michael Hahn

Vaudit CEO Michael Hahn described several common overcharging patterns to The Information:

Customers using older, cheaper models but being billed for the more expensive new models;

AI Agents or chatbots failing to complete requests or returning error messages, yet still incurring charges;

Agents repeatedly retrying tasks after failure (retry storms), with each retry being billed.

An Anthropic spokesperson responded, stating the company does not charge for unfulfilled requests or error messages, does not route user requests to older models, and that "overcharging does not seem to be a widespread issue."

However, following appeals from Vaudit and clients, Amazon, Google, Microsoft, Anthropic, and OpenAI refunded about 80% of the disputed amounts.

Hahn said these companies were "very cooperative" in handling billing disputes.

A noteworthy detail: some of Vaudit's audited clients did not purchase services directly from Anthropic but through cloud platforms like AWS, Google Cloud, or Microsoft Azure.

After the billing passes through an additional layer, the responsibility attribution becomes murkier.

Why AI Bills Are Becoming Increasingly Incomprehensible

The developer community's distrust of Anthropic's billing didn't accumulate overnight.

In April this year, developers discovered a billing vulnerability dubbed the "HERMES.md bug": when code commit records contained specific strings, Anthropic's backend would silently route requests that should have been deducted from the Max plan quota to a more expensive "additional usage" billing channel.

Users paid a $200/month subscription fee but were actually billed separately at the API rate.

Anthropic characterized it as an "authentication routing issue" but initially refused refunds, leading many developers to initiate credit card chargebacks.

On June 14, Washington D.C. user Karl Kahn filed a class-action lawsuit in the US District Court for the Northern District of California, alleging that Anthropic's usage promises for the Max 5x ($100/month) and Max 20x ($200/month) subscription plans were misleading.

Kahn claimed that during his use of the Max 20x plan, a single 5-hour programming session consumed 15% of his weekly quota.

The lawsuit argues that Anthropic's usage calculation method is opaque, making it nearly impossible for users to verify whether they are receiving the promised service.

The pricing changes for Claude 5 further amplified this distrust.

Released on June 9 as part of subscription plans, this Anthropic's most powerful publicly available model to date was stripped from them just 13 days later, converted to a usage credit system billed at the API rate ($10 per million input tokens, $50 per million output tokens) – twice the price of Claude 4.8.

Extended reading: Sudden! Anthropic Globally Disables Claude 5

It was also taken offline globally for 19 days due to US government export controls.

After restoration on July 1, the free window provided by Anthropic was only 6 days, later extended to July 12.

When "Safety" Doesn't Cover the Wallet

Anthropic's core brand narrative is "AI safety."

But for paying users, the definition of safety is expanding: it shouldn't only mean the model won't output harmful content; it should also include not being charged an inexplicable fee without your knowledge.

This isn't just Anthropic's problem.

AI service billing structures are inherently complex – model tiers, token usage, API call counts, retries, cloud platform markups, different paths for direct purchase and resale – all layered together, forming a black box that average users can almost never independently verify.

The emergence of auditing companies like Vaudit is itself a signal: AI bills have become so complex that a professional third party is needed to understand them.

Competitors are already sensing the opportunity.

At the June Build conference, Microsoft released MAI-Thinking-1. AI head Mustafa Suleyman told Bloomberg, "Many people are urgently looking for alternatives to Anthropic's expensive models."

OpenAI is reportedly considering significantly lowering token prices.

DeepSeek V4's output token price is about 1/90th of Claude Sonnet 4.6's, starting to be included in developers' cost comparison options.

Over the past year, AI companies won users with model capabilities.

In the next phase of competition, billing transparency will become part of the product experience.

When users start auditing your bills and questioning your pricing promises with class-action lawsuits, "our model is smarter" is no longer enough.

The problem Anthropic faces today, the entire industry will encounter – whoever makes their billing clear first will get the ticket to the next round of trust.

References:

https://www.internationalcyberdigest.com/anthropic-tried-to-phantom-charge-16-6m/

https://www.theinformation.com/newsletters/applied-ai/anthropic-customers-find-errant-charges-auditing-startup-says

This article is from WeChat public account "New Zhiyuan", author: ASI Revelation

Criptos en tendencia

Preguntas relacionadas

QWhat is the main issue reported by Korean developer remy_notes regarding Anthropic's billing?

AKorean developer remy_notes reported receiving two massive invoices from Anthropic within 24 hours—one for about $1.669 million and another for $16.62 million—despite having a free plan with zero API usage and no credit card linked. His bank blocked the attempted charges due to insufficient limits.

QWhat systemic billing problems did the AI audit company Vaudit discover among Anthropic and other AI providers?

AVaudit audited $34 million in AI bills across 60 companies from March to June and found about $1.7 million in erroneous overcharges, mostly related to Anthropic's Claude Code. Common issues included charging for newer, more expensive models when older ones were used, billing for failed or incomplete requests, and charging for repeated retries by AI agents.

QWhat legal action has been taken against Anthropic related to its billing practices?

AOn June 14, a user named Karl Kahn filed a class-action lawsuit in the U.S. District Court for the Northern District of California. The lawsuit alleges that Anthropic misled customers about usage limits and costs for its Max subscription plans, and that its billing calculations are opaque, making it nearly impossible for users to verify if they received the promised service.

QHow did Anthropic's handling of its Fable 5 model contribute to user distrust?

AAnthropic initially included its powerful Fable 5 model in subscription plans upon release on June 9, but removed it just 13 days later. It was then made available only via a much more expensive API-based credit system (costing twice as much as Opus 4.8). Coupled with a 19-day global takedown due to U.S. export controls and a very short free usage window, these actions eroded user trust in the company's pricing stability and transparency.

QAccording to the article, why is billing transparency becoming a key competitive factor in the AI industry?

AAs AI services grow more complex—with layered pricing based on models, tokens, API calls, retries, and cloud platform markups—their bills have become a 'black box' that users cannot easily verify. The emergence of audit firms like Vaudit and user lawsuits signal that transparent and understandable billing is now part of the product experience. The article suggests that in the next phase of competition, companies that master clear billing will gain a significant trust advantage.

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The article argues that as real-world, cash-flow-focused businesses enter the blockchain space, they are increasingly choosing the Ethereum L1 + L2 architecture as the optimal infrastructure solution, in contrast to earlier crypto projects built primarily around token sales. It uses Robinhood as a prime example: after testing its stock tokenization product on Arbitrum One, Robinhood launched its own dedicated blockchain, "Robinhood Chain," which is built as an Ethereum L2 using Arbitrum's technology, relying on Ethereum for data availability (via blobs), using ETH as its native gas token, and employing a standard bridge to Ethereum. The author, Ryan Berckmans, distinguishes between two types of participants with different incentive structures: 1. **The "Old Crypto Economy":** Projects whose primary goal is to create and sell a token, with value derived from utility expectations, speculative "monetary premium," or distant cash-flow promises. Their technology stack choices are often flexible and driven by grants, copycat opportunities, or the need for a new token narrative. 2. **The Emerging "Real-World On-Chain Economy":** Traditional businesses using blockchain to improve existing services or create new cash-flow streams. Their goal is to maximize business profits, not token appreciation. For them, blockchain is infrastructure, and they prioritize low risk, security, user reach, operational control, liquidity, and interoperability. For these real-world enterprises, building a standalone L1 is costly, creating a new "security and liquidity island." Ethereum's L1+L2 model splits their core needs: the L1 provides a highly decentralized, neutral, and liquid global settlement layer, while L2s offer a market of customizable, high-performance, operator-controlled execution environments. An Ethereum L2 grants most benefits of an independent chain (high TPS, control, custom features) while inheriting Ethereum's security, seamless access to its ecosystem and assets, and native, low-trust bridging. The piece concludes that this shift in market participants profoundly benefits Ethereum and ETH. As businesses build on Ethereum (directly on L1, via their own L2 like Robinhood Chain, or on shared L2s like Base), they onboard users, embed ETH into products, and deepen its network effects and monetary premium. The choice is driven not by ideological belief but by rational commercial judgment, making Ethereum L1+L2 the current default optimal solution.

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