Conclusion First: “Waiting” is Not the Right Answer

DeepSeek has raised $15 billion (or $1.5 billion according to some reports), Cerebras has made inference of a 1 trillion parameter model practical, and Liquid AI has announced a latency stack of under 20ms.

The implications of these three pieces of news are straightforward. The “wholesale price” of AI inference is collapsing on a monthly basis.

“So let’s wait until it gets cheaper”—many small business owners think this way. I understand the sentiment. However, this is not the right answer. Let me explain why.

What’s Happening: A Look at the Numbers

First, let’s organize the facts.

Trends in OpenAI GPT-4 Inference Costs:

• March 2023 (at GPT-4 release): Input $30/1 million tokens
• May 2024 (GPT-4o): Input $5/1 million tokens
• Current 2025 (equivalent to GPT-4o mini): Input $0.15/1 million tokens

In just two years, this has become 1/200th of the original cost. This is not just a “price drop”; the structure has changed.

DeepSeek’s open-source model (DeepSeek-V3, R1) has enabled performance on par with GPT-4 to be run on in-house servers or in the cloud. A world where even API charges become unnecessary is on the horizon.

Cerebras’ wafer-scale chip processes inference of a 1 trillion parameter model at speeds up to 10 times faster than traditional GPU clusters. As speed increases, the time and cost required to run the same processes decrease.

Liquid AI’s latency of under 20ms is achieved through an architecture different from Transformers (Liquid Foundation Models). Even with smaller model sizes, performance remains high, meaning less hardware is required to operate them.

While the three companies seem to be doing different things, they are all heading in the same direction: “Dramatically reducing the cost of running AI.”

Three Reasons Why “Waiting” is Not the Right Answer

Reason 1: Costs Will Continue to Decrease, So You’ll End Up Waiting Forever

The decline in AI inference costs is driven by the evolution of semiconductors, competition among open-source models, and the emergence of new architectures. In other words, it will be even cheaper in three months. It will be even cheaper in six months.

“Let’s start after it gets cheaper” is synonymous with “I’ll never start.”

Think about what happened to the companies that kept using feature phones while waiting for smartphones to become cheaper.

Reason 2: The Cost of Learning is Higher than the Costs

When small businesses implement AI, the highest cost is not the API usage fees. It is the time and effort spent figuring out how to integrate it into their operations.

For example, consider a local manufacturing company (with 30 employees) that introduced an AI chatbot for customer service. The API cost was about 3,000 yen per month. However, organizing FAQs, adjusting prompts, explaining to employees, and designing operational flows took three months.

This “three months” will not shorten even if you wait. In fact, starting earlier means that when it’s time to switch to cheaper technology, you will know what to do.

Reason 3: Competitors Won’t Wait

For local SMEs, competitors are no longer just neighboring businesses. Companies that have automated quotes using AI or optimized inventory management with AI are competing in the same market.

“Waiting three months” means that during those three months, your competitors will gain experience in utilizing AI.

The Right Answer for SMEs is to “Start Small”

So, what should you do specifically?

Step 1: Look for Areas Where You Can Start for Under 10,000 Yen per Month

With the current AI inference costs, the following tasks can be automated or made more efficient for a few thousand yen per month:

• Automatic Meeting Minutes Creation: Using Whisper API (voice-to-text) + GPT-4o mini (summarization), you can turn a one-hour meeting into minutes in five minutes. For 20 meetings a month, the API cost would be around 500 to 1,000 yen.
• Initial Inquiry Routing: AI can classify inquiries from emails or forms and route them to the appropriate personnel. Monthly cost: 2,000 to 3,000 yen.
• Data Extraction from Quotes and Invoices: Extract necessary figures from PDFs or images and input them into a spreadsheet. Monthly cost: 1,000 to 2,000 yen.

The key is to choose tasks that “cost under 10,000 yen per month and yield over 50,000 yen in labor savings”. If you achieve a 5x ROI, even if costs halve further, you won’t regret not waiting.

Step 2: Design with “Switching in Mind”

The current AI industry landscape can change in three months. Therefore, it is crucial to have a design that avoids lock-in with specific vendors.

Specifically:

• Keep prompts (instructions) as internal documents.
• Clearly define the connection points between business processes and AI processing.
• Assume that “if this AI becomes unusable, we will switch to another AI.”

Creating a state where switching from OpenAI to DeepSeek or from Claude to Gemini can be done in a few days is the survival strategy for SMEs.

Step 3: Use the “Collapse of Wholesale Prices” as a Weapon

This is the crux of the matter. The plummeting costs of AI inference actually work to the advantage of small businesses over large corporations.

Why? Large companies have already built AI infrastructures costing tens of millions to hundreds of millions of yen. They have long-term contracts, dedicated teams, and deep integration with internal systems. Even if costs drop, switching is not easy.

On the other hand, small businesses are agile. If an API that cost 50,000 yen last month drops to 10,000 yen, they can switch the following month. When a new open-source model comes out, they can test it over the weekend and roll it out on Monday.

The “heaviness” of large corporations becomes a hindrance, while the “lightness” of small businesses becomes a weapon. This mirrors how the advent of the cloud put on-premises large corporations at a disadvantage.

Specific Cost Outlook: Predictions for Late 2025

Here’s a comparison of current inference costs and predictions for six months from now.

If you run DeepSeek’s open-source model locally, the API costs become zero. All you need is a decent GPU (around 100,000 to 200,000 yen) and electricity costs.

What once cost 3 million yen to implement AI can now start at 5,000 yen per month. In six months, it might be 2,000 yen. However, waiting six months for that 3,000 yen difference is foolish.

Summary: Three Actions to Take

1. Start one AI application that can be initiated for under 10,000 yen this month. Whether it’s meeting minutes or email routing, anything will do.
2. Avoid vendor lock-in and design for “switchability.” Preserve prompts and business flows as assets.
3. Every three months, assess whether there are “cheaper and faster options.” Directly link the price disruption in the AI industry to your own cost reductions.

The wholesale price of AI infrastructure is indeed collapsing. But that is not a reason to wait; it is a reason to start small and switch when prices drop.

The strength of small businesses lies in their speed of decision-making and agility. While large corporations are tied down by long-term contracts, small businesses can leverage the latest technology at the lowest prices. This is how SMEs can compete in this era of price disruption.

Don’t wait. Start small. And keep switching.