The Day AWS’s Invoice Transformed from a Few Cents to Billions of Dollars — Who Pays for the Damage When AI Makes a Mistake?

What if a cloud bill of $500 suddenly turned into $100 million one day?

This is no joke. It actually happened with AWS.

From a Few Cents to Billions of Dollars

A billing error occurred at Amazon Web Services (AWS), causing customer invoices to skyrocket from a few cents to billions of dollars. The cause was a bug in the system. In a cloud infrastructure where AI and automated billing engines are intricately intertwined, a single error generated astronomical figures.

AWS acknowledged the issue and is working on refunds for affected customers. Large corporations have the resources to notice the anomaly and inquire about it. But what about small and medium-sized enterprises (SMEs)?

Imagine a company with a monthly cloud bill of $500 receiving a $100 million invoice one morning. What if it was set up for automatic withdrawal? Their bank account could be frozen. They might not be able to pay salaries. Payments to suppliers would stop. Even if they are told, “We will refund you,” it means nothing if the company goes bankrupt in the meantime.

The issue to consider here is not just the superficial story of “AWS made a mistake.” It is the very structure that makes cloud billing a black box.

In traditional on-premise environments, costs are determined at the point of purchasing servers. If you buy a server for 1 million yen, you won’t receive a bill for more than that. However, the cloud operates on a “pay-as-you-go” model. While this system is flexible, it also means “you never know what might happen.”

AWS’s pricing structure spans hundreds of pages. To be honest, even engineers find it difficult to grasp the entirety of it. It is nearly impossible for SME owners to understand.

The U.S. Government Begins Using AI for Insurance Approvals and Denials

There is another significant piece of news. The U.S. government has launched a pilot program to incorporate AI into the pre-approval process for medical insurance.

Even if a doctor determines, “This patient needs this treatment,” the patient cannot receive treatment unless the insurance company approves it. This initiative aims to solve the problem of the excessively lengthy approval process using AI.

The goal is simple. AI will instantly approve cases that clearly should be approved — for instance, standard tests or common prescriptions. Tasks that human reviewers took 30 minutes to process can be handled by AI in a matter of seconds. Costs will dramatically decrease. It is estimated that 70-80% of the tens of millions of pre-approval requests each year can be processed automatically by AI.

The problem lies with the remaining 20-30%. When AI determines a case as “denied,” and that case should have been approved, what happens?

In fact, at UnitedHealthcare, a major insurance company in the U.S., the automatic denial of insurance claims using AI has become an issue. Reports indicate that a significant number of claims denied by AI should have been approved upon human review. Patients are unable to receive necessary treatments, and doctors are overwhelmed with extensive paperwork to obtain approvals.

The moment AI says “No,” a life-impacting decision is made. When that decision is wrong, who takes responsibility? The company that developed the AI, the insurance company that implemented it, or the government that approved it?

The Core Issue is the Lack of Clarity on “Who Bears the Cost When AI Makes a Mistake”

Both the AWS billing error and AI-driven insurance determinations stem from the same root.

“When AI or automated systems make a mistake, who bears the cost of that damage?”

You might think this is an issue for large corporations. However, it is SMEs that are directly impacted. There are three reasons for this.

1. Lack of Buffer
Large corporations can handle erroneous bills of several million yen, as their accounting departments can address the issue, legal can negotiate, and they can endure through cash flow. SMEs can face a cash shortfall from a single erroneous bill. If a company with a monthly revenue of 5 million yen receives a bill for 50 million yen, that could be the end.

2. Lack of Negotiating Power
Even if you call AWS and say, “There’s something wrong with the bill,” it can take days or even weeks to resolve. Large customers may have dedicated representatives, but a customer with a $500 monthly bill will be pushed to the back of the line. This is a structural issue.

3. No Alternative Options
If an SME that has its business systems on the cloud is forced to stop services due to billing issues, their operations will come to a halt. They lack the capacity to revert to on-premise systems. This is a classic case of vendor lock-in.

So, What Should SMEs Do?

Saying “Don’t use the cloud because it’s scary” is not a solution. The cost advantages of the cloud are overwhelming. If a company were to own its servers, it would incur initial costs of 3 million yen and annual operating costs of 1 million yen, whereas with the cloud, they can start for as little as 5,000 yen a month. This structure does not change.

The issue lies in the “how to use it.” Here are three specific actions that can be taken starting today.

① Set Up Billing Alerts (Time Required: 10 Minutes)

AWS, GCP, and Azure all have features that send alerts when billing exceeds a certain amount. Set a monthly budget and ensure notifications are sent at 50%, 80%, and 100%. This alone can prevent the scenario of “I didn’t realize I received a bill 100 times higher.” Setting this up takes just 10 minutes. Companies that haven’t done this should do it immediately.

② Set Usage Limits

Some cloud services allow you to set a cap on usage. While there is a risk that services may stop when the limit is reached, it is better than receiving a bill for billions of dollars. If you have to choose between “stopping” and “going bankrupt,” you should choose to stop.

③ Review the Contract’s “Response to Billing Errors”

Most SMEs do not read the terms of service for cloud services. It is essential to read them. The three points to particularly check are:

• Refund policy in case of erroneous billing
• Time frame for refunds
• Whether there is compensation for indirect damages (such as cash shortfalls) due to erroneous billing

In many cases, indirect damages are not compensated. This means, “We will refund you, but we don’t care if your company goes bankrupt because of it.” It is necessary to understand this fact and think about how to manage the risk.

Create a System That Does Not “Overtrust” AI Decisions

More SMEs are incorporating AI into their operations. Automated creation of quotes, automated responses to inquiries, automated inventory ordering. All of these dramatically reduce costs. Tasks that would cost 300,000 yen a month in labor can be done by AI for just 3,000 yen a month.

However, AI makes mistakes. This is not a technical limitation but a matter of probability. Even an AI with 99% accuracy will make a mistake once in every 100 attempts. If you process 1,000 cases a month, that means 10 mistakes. What happens when those 10 mistakes involve “billing amounts to clients” or “insurance determinations for patients”?

What is needed is to design systems with the assumption that “AI will make mistakes.”

• Have a human perform final checks for processes above a certain amount
• Log AI decision results and periodically sample check them
• Pre-determine response flows for when “AI makes a mistake”

This is the same concept as eliminating reliance on specific individuals. Just as it is dangerous to think, “We’re fine because we have experienced employees,” it is equally dangerous to think, “We’re fine because AI is smart.” We must ensure this through systems.

Invoices for “When AI Makes a Mistake” Will Reach the Weaker Side Structurally

The most severely affected by the AWS billing error are not large corporations but SMEs. The ones most troubled by AI insurance determinations are patients who have no alternative options.

The costs of technological errors structurally concentrate on the “weaker side” — those without negotiating power and those without buffers. This is not a new issue, but with the proliferation of AI and automation, the scale and speed of errors have become unprecedented.

When a human makes a mistake, it is usually just one error. When AI makes a mistake, that same error can instantly affect thousands or tens of thousands of cases. Moreover, the realization of that mistake often comes only after the invoice arrives.

What I want to convey to SME owners is not “Don’t use AI.” Instead, it is “Think ahead about what will happen to your company when AI makes a mistake.”

Set alerts. Establish limits. Read contracts. Create a checking system. All of these are mundane tasks, but they are the only way to protect your company from the invoices that come when AI makes a mistake.

Technology should be fully utilized. However, using it without preparation for when it breaks is like driving on a highway without a seatbelt.