$29 for a Product Shipment. Zero Labor Costs.

Hiring one engineer for a month costs at least 500,000 yen. Outsourcing can run into the millions. In the world of “software development” where this was the norm, a developer recently made headlines on X by coordinating five AI agents to ship a product for just $29.63 (about 4,500 yen).

$29. It’s less than the cost of a single night out.

What does this mean? We can’t just stop at saying “that’s amazing.” When costs drop to one-hundredth of what they were, what happens, and what breaks? For small businesses in rural areas, is this truly a weapon, or is it an invisible trap?

What Happened—Five AI Agents Became a “Team”

What this developer did was simple. Instead of relying on a human team—PM, designer, front-end, back-end, QA—to handle the various stages of development, they assigned each role to AI agents with specific functions.

Specifically, they combined multiple AI tools like Cursor, Claude, and ChatGPT to manage everything from requirements definition to coding, testing, and deployment in one go. The only tasks left for humans were to provide instructions on “what to create” and to conduct the final quality check.

The cost breakdown was simply the total API usage fees for each AI service, amounting to $29.63. Labor costs were zero.

Looking at this figure alone, it seems like a dream come true for small businesses. Development of their own products, which they had previously given up on due to “not being able to afford development costs,” can now be tested for under 5,000 yen. Trying it ten times in a month would cost 50,000 yen. That’s 600,000 yen a year—less than the monthly salary of a single full-time engineer.

What Changes When Costs Drop to One-Hundredth?

The important point here is not just that things have become cheaper. When the cost structure changes, the very way of competing changes.

For example, when small businesses attempted software development in the past, it looked like this:

• Outsourcing estimates: 3 to 5 million yen
• Development period: 3 to 6 months
• What if it fails? → A fatal blow

As a result, small businesses developed under the pressure of needing to “hit it big on the first try,” leading to conservative specifications, and by the time the product was launched, the market needs had shifted—this has been a recurring losing pattern.

What happens when it costs $29?

• The cost of “testing” disappears. Create ten products and one success is enough.
• The definition of “failure” changes. A 5,000 yen failure is not a failure but an experiment.
• Speed becomes a weapon. While large companies are going through bureaucratic processes, ten variations can be launched in the market.

A structure is emerging where rural small businesses can compete with large corporations. Because larger organizations have more layers, decision-making is slower. Small businesses can “think of something today and launch it tomorrow.” AI agents further accelerate that speed.

However, There Are Traps: Three “Invisible Costs”

Before jumping at the $29 figure, there are points to consider calmly.

Trap 1: Memory Loss Issue—Starting Over Each Time

Many current AI agents forget context when sessions end. ChatGPT’s “memory feature” is essentially a simple notepad and cannot retain the overall design philosophy or the history of past decisions for an entire project.

What does this mean? As projects become more complex, the cost for humans to “re-teach the AI context” increases. The $29 cost was possible because it was a small-scope product. If one attempts to create something complex like a business system, this “re-teaching cost” will balloon exponentially.

A realistic countermeasure at this point is to prepare structured documents for project design and rules to be read by the AI each time. This is tedious but effective. However, only those who understand “what should be created” can write those documents.

Trap 2: The “Invisible Pay-Per-Use” Token Costs

The fees for using AI agents are essentially pay-per-use based on tokens (≈ character count). Simple tasks may cost just a few cents, but as complexity increases and debugging requires more trial and error, token consumption can skyrocket.

In fact, for Claude 3.5 Sonnet, the cost is $3 for 1 million input tokens and $15 for 1 million output tokens. For GPT-4o, it’s $2.5 for input and $10 for output. Even if a single interaction is inexpensive, if AI agents interact with each other dozens of times, costs multiply.

There’s a possibility that the $29 project did not account for the “cost of trial and error learned through multiple iterations.” If you judge solely by the cost of the “one successful attempt,” you may misjudge the actual operational costs.

Trap 3: The Gap Between “Working Code” and “Usable Product”

Code written by AI is “functional.” However, “functional” and “usable” are different. Security, error handling, usability, maintainability—human judgment is still needed to ensure these aspects.

Especially for small businesses providing services to customers, saying “it worked, but customer data was leaked” is not acceptable. Creating something for $29 and then facing a lawsuit costing hundreds of thousands of yen is not an exaggeration.

So, How Should Small Businesses Use This?

This is not a call to stop using it because there are traps. It’s about using it correctly while being aware of the traps.

Step 1: Start with “Internal Tools”

Starting with internal efficiency tools rather than customer-facing products is the least risky approach. Automating daily report aggregation, generating estimates automatically, organizing inventory data—testing AI agents in these areas where “failure won’t be fatal” is advisable.

With a budget of 50,000 yen per month, create one internal tool each month. In six months, that’s six tools. If just two of them become established, the annual cost savings could amount to hundreds of thousands of yen.

Step 2: Separate “What AI Handles” and “What Humans Control”

Let AI handle coding. However, humans must decide “what to create,” “for whom to create it,” and “what level of quality is necessary.” Blurring this line can lead to a pile of “tools nobody uses” produced by AI.

Step 3: Set a “Ceiling” for Costs Before Starting

Set limits like “up to 50,000 yen per month” or “up to 10,000 yen per project.” The pay-per-use nature of AI agents can balloon unexpectedly. Establish budget limits before you find yourself pale at the sight of your credit card bill.

The Real Question Is Not Whether to Use AI

In an era where products can be shipped for $29, we are no longer at the stage of debating whether to use AI.

The real question is this: “What will your company create in a world where development costs have dropped to one-hundredth?”

Ideas that were previously abandoned due to “cost not aligning” are all back on the table. Rural small businesses are uniquely positioned to understand on-the-ground challenges. Those who know these challenges can prototype solutions at nearly zero cost. This is a strength that large corporations in Tokyo do not possess.

However, I reiterate: $29 is the price for “one experiment” and not for “building a business.” Between experimentation and business, there are gaps in quality control, customer support, and continuous improvement that AI alone cannot fill.

Start by creating one thing. For 5,000 yen. Whether it becomes a weapon or a trap depends on how you use it.