The Day Anthropic Was Stopped by the Government—Calculating the True Cost of ‘AI Vendor Dependency’ in Small and Medium Enterprises

What Would You Do If the AI Model You Use Disappeared Tomorrow?

Anthropic has halted the use of its AI model immediately after release at the request of the U.S. government, citing national security concerns.

For large corporations, this might be a simple matter of saying, “Let’s switch to another model.” They have dedicated engineering teams, a multi-vendor strategy, and the budget to accommodate such changes.

But that’s not the case for small and medium enterprises (SMEs) in rural areas.

A manufacturing company with 30 employees has finally integrated the Claude API into its operations. It automated tasks like generating quotes, drafting responses to inquiries, and summarizing daily reports. At a cost of 50,000 yen per month, they saved the equivalent of one administrative staff member’s workload. Just when they thought, “Now we’re going digital,” one morning the API stops working.

This is not a hypothetical scenario. The recent incident with Anthropic has proven that such occurrences can happen in reality.

The question is simple: What is the cost of relying on a specific AI vendor?

We Have Entered an Era Where AI Models Have a Lifespan of Less Than a Year

First, let’s clarify the premise.

The pace of generational change in AI models is accelerating. It took about a year to move from GPT-3.5 to GPT-4, and another year from GPT-4 to GPT-4o. The transition from Claude 2 to Claude 3 took about 10 months, and from Claude 3 to Claude 3.5, about 8 months. Now, Claude 4 has been released.

What happens when a new model is released? The APIs of the old models are gradually deprecated. OpenAI plans to discontinue some endpoints of GPT-3.5 Turbo in 2024, and Google is also phasing out versions of the original Gemini.

In other words, the model you are currently using may not exist in 12 months.

Moreover, the recent incident with Anthropic is not just a case of “generational change” but rather a “sudden death due to political judgment.” There was no warning. No transition period. Something that was working yesterday stops today.

Not only is the lifespan of models getting shorter, but the way they end is becoming unpredictable. This is the reality heading into 2025.

Calculating the True Cost of a “50,000 Yen AI”

Now, let’s look at the numbers specifically.

Assuming a small business with 30 employees and annual sales of 300 million yen has introduced an AI chatbot for customer service.

Normal Operating Costs (Annual)

While the API costs 50,000 yen per month, the actual annual cost is 1,520,000 yen. Even so, compared to the annual salary of one administrative staff member (around 3.5 to 4 million yen), it is significantly cheaper. That’s why they implemented it. Up to this point, it was a sound decision.

Additional Costs in the Event of Model Shutdown

The problem arises from here. One day, out of the blue, the model they are using stops working, just like in the case of Anthropic.

What was being operated at an annual cost of 1,520,000 yen incurs an additional cost of 1,830,000 yen due to just one shutdown. The amount exceeds the operational cost, disappearing in a single model shutdown.

In total, the annual “true total cost of ownership” amounts to 3,350,000 yen. This is nearly equivalent to hiring one administrative staff member.

Moreover, this assumes a “shutdown once a year.” Given the shortened lifespan of models, there is a possibility of two switches occurring within 12 months. In that case, the cost would be 5,180,000 yen. It becomes cheaper to hire a person, creating a reversal.

Saying “Then Don’t Use AI” Is Wrong—It’s a Design Issue

It would be simplistic to conclude here that “AI is dangerous” or “humans are best.”

The issue is not the use of AI itself. The problem lies in the design that directly ties the core of operations to a specific model from a specific vendor.

There are three specific measures that SMEs should take.

1. Introduce an “Abstraction Layer”

Instead of directly calling the API, place an intermediary layer within your company. For example, using a multi-model compatible gateway like LiteLLM or OpenRouter allows you to switch the backend model from Claude to GPT to Gemini without changing the application-side code.

The setup cost would be an additional 100,000 to 200,000 yen. This would compress the reconstruction cost of 600,000 yen during a switch to below 100,000 yen. This is a cheap insurance premium.

2. Manage Prompts as “Assets”

Even if the model changes, the prompts that describe the business logic remain as assets. However, the effectiveness of prompts varies by model. Therefore, test the same prompts with 2-3 major models and regularly compare the output quality.

This requires just 1-2 hours of work per month. The cost is nearly zero. However, when the time comes for a switch, the decision-making process can be shortened from days to hours.

3. Design Operations That Can Survive Even If AI Stops

Designing operations that come to a complete halt the moment AI stops is dangerous. AI should remain as a “draft,” “suggestion,” or “support,” with final decisions made by humans.

This should not change even as AI accuracy improves. This is because it is not a matter of accuracy but rather a matter of availability. No matter how smart the AI is, if the server goes down, the output will be zero.

The Reverse Structure That SMEs Can Achieve

In fact, SMEs are at an advantage in this issue.

When large corporations adopt AI, they deploy it on a large scale as a unified system across the entire company. Switching models for a system used by thousands can take months just for verification. Contracts with vendors are often tied for years.

SMEs are different. They are using it with just a few to several dozen people. The decision to switch can be made with a single word from the president. A testing period of one week is sufficient. Contracts are on a monthly basis.

Agility directly translates into risk hedging.

That’s why the initial design is crucial. As long as you create a structure that avoids vendor lock-in, you can adopt a strategy of “constantly switching to the latest and cheapest model.” This is a level of agility that large corporations cannot replicate.

So, What Should We Do?

To summarize:

1. Simulate the “shutdown scenario” of the AI you are currently using. If it stops tomorrow, how many hours will it take to recover? How much will it cost? If you don’t have this information, it’s the same as not managing costs.

1. Introduce an abstraction layer between the model and the application. With an investment of 100,000 to 200,000 yen, switching costs can be reduced to less than one-fifth. There’s no reason not to do it.

1. Don’t make prompts dependent on individuals. Many companies have prompts in production that no one knows who wrote. Document them, test them across multiple models, and manage them as assets.

1. Maintain business flows that can continue even if AI stops. Just because AI is convenient, the moment you delegate 100% of your operations to it, your company’s availability becomes tied to the availability of the AI vendor.

The incident with Anthropic may seem like a “fire on the other side of the river” for many SMEs. However, this is a structural issue. The same can happen with OpenAI or Google.

It’s not about not using AI; it’s about not being used by AI.

Behind the 50,000 yen API lies a risk of 3,350,000 yen. Knowing this and using it versus using it without knowledge makes a significant difference in the quality of management decisions.

Start by asking today within your company, “What would happen if our AI stopped tomorrow?” Companies that cannot answer this are already in the midst of risk.