The subscription fee for AI is still the price of a cup of coffee. But will that still be the case in the era of agents?

$20 a month.

Currently, AI subscription fees feel roughly equivalent to a whole fried chicken in Korea or a few cups of Starbucks coffee in the U.S. An article by BZCF compared the impact of a $20 monthly subscription for services like ChatGPT Plus or Claude Pro, noting that while this represents about 0.5% of a monthly salary in the U.S., Singapore, and Germany, and around 0.75% in Korea, it can feel like as much as 7% to 20% in some developing countries . Even the same $20 can be a productivity tool in one country but a burdensome fixed cost in another.

However, there is a more important question.

Will this price be able to stay at $20 per month in the future?

The AI subscription fees we currently pay are mostly based on "conversational AI." It follows a structure where a person asks a question, a model answers, and then the person asks another question. However, AI is increasingly transforming into an agent. AI no longer just provides answers. It formulates plans, searches, reads files, executes code, calls tools, retries if it fails, and verifies results. On the surface, it may look just like "a single question," but something completely different is happening internally.

From a single API call to a single small project

Existing AI usage was simple.

The user asks a question. → The model answers. That's it.

In this structure, costs are also relatively predictable. There are input and output tokens, and the cost is calculated based on their sum. However, AI agents are different. If a user says, “Review this code,” the agent does not simply respond.

Read the repository → check change files → review test logs → find relevant documentation → reopen suspected sections → create modifications → verify again. Although the user made a single request, multiple model and tool calls occur internally.

According to Anthropic’s documentation on tool usage , requests to use tools include not only standard input and output tokens but also the tool name, description, schema, tool_use block, and tool_result block as tokens. In other words, “AI uses a tool” means that it is not simply adding a single feature, but that additional context is attached to every call.

In chat-based AI, a single question can be resolved with a single answer. In agent-based AI, a single question is broken down into dozens of intermediate actions. And most of those intermediate actions are not free. They will become even less free in the future.

The token cost feels like compound interest rather than linear.

The problem is that the agent carries over the previous work context.

In the first call, only the user's request is included. In the second call, the request and the first result are included together. In the third call, the request, the first result, the second result, and the tool response are included together. The context expands in this way.

Here is a simple example.

A standard API call can end up costing 3,000 tokens. However, if the agent works in 10 steps and the context grows with each step, the total token usage could be tens of thousands to hundreds of thousands of tokens, rather than 3,000. Therefore, agent costs should not be calculated based on the "number of questions," but rather on "work units."

GitHub also explained that if agent workflows are automatically executed for every Pull Request, API costs can accumulate unnoticed. In particular, if MCP tool schemas are included in the context of every request, 10 to 15 KB of schema can be added per turn in cases with many tools, such as GitHub MCP Server, and 38 unused tools can also be attached as costs for every request.

The AI agent is not a competent intern, but rather closer to an intern who rereads the entire meeting minutes every time they speak.

They do the work well. However, they read, think, call in tools, and read again every time. That is why the cost increases.

The $20 monthly subscription fee is already hitting its limit.

From a consumer's perspective, AI subscription fees currently still appear affordable. OpenAI offers ChatGPT Plus at $20 and divides its Pro line into $100 and $200 plans. Plus is described as suitable for light usage, the $100 Pro for actual project-based use, and the $200 Pro for parallel projects and high-intensity workflows.

This change is not simply a price increase. The price tag is changing because the way it is used has changed.

During NVIDIA's Q1 2026 fiscal year earnings announcement, Jensen Huang stated, "The volume of AI inference token generation has increased tenfold in just one year." He went on to explain that as AI agents become mainstream, the demand for AI computing will accelerate. In other words, this means that it is not simply an increase in people using chatbots more, but rather that as agents begin to perform tasks, inference itself becomes a new industrial demand.

If you use AI as an occasional conversational tool, a price of $20 per month is sustainable. However, once you start making AI work, $20 per month may no longer be a natural price.

The agent burns infrastructure before the model.

The AI industry has long discussed model performance: Who is smarter? Who understands longer texts? Who codes better? Who reasons better? However, the real questions of the agent era are slightly different.

At what price can that performance be provided?

No matter how good the model is, it is not sustainable if too many tokens are burned to process a single task. As the number of users increases, losses grow, and as the number of enterprise clients increases, the burden on the infrastructure grows.

Therefore, going forward, the competitiveness of AI companies will not be determined solely by model performance.

How much tokens are conserved. How well GPUs are utilized. How well the cache is designed. How much tool calls are reduced. How well small and large models are routed. How efficiently idle resources are recycled. What infrastructure is it operating on?

The same goes for GPUs.

GPUs are behind the token costs. And GPUs are expensive. A bigger problem is that expensive GPUs are not always busy working.

According to Cast AI's 2026 Kubernetes Optimization Report , the average GPU utilization in the clusters analyzed was around 5%. CPU utilization was 8%, and memory was 20%; GPUs were idle for most of the time, despite being a particularly expensive resource.

95% means that only costs are being incurred.

For AI companies to provide agents, they must handle more inference, more context, and more tool execution. However, if the GPUs behind them are only working 5%, it is difficult to keep subscription fees low. Currently, investment capital, large infrastructure contracts, cloud credits, and price subsidies may be absorbing these costs. But the story changes once usage explodes.

In conversational AI, costs are incurred only when the user asks a question. In agentic AI, costs are incurred even when the user is away. Scheduled tasks, automated code reviews, background research, documentation cleanup, email processing, CRM updates, data analysis, and report generation. As AI takes over the work, more tokens flow and GPUs run longer.

A “Cup of Coffee” AI May Not Last Long

Right now, AI subscriptions feel like the price of a cup of coffee.But that pricing is designed for an AI that people ask questions occasionally. The AI of the future will not simply be something people ask. It will become an AI that keeps working on behalf of people. From that point on, the unit of cost will no longer be a question. It will be a task. And the unit of a task will no longer be an answer. It will be a reasoning loop. The unit of a reasoning loop will be tokens and GPU time.

That is why the future of AI subscription pricing is not simply a question of whether it will be $20, $100, or $200 per month.

Is your AI service running on sustainable infrastructure?

The core issue in the AI agent era is not only who owns the largest GPU cluster.What matters is whether the necessary inference can be executed in the right place, at the lowest possible cost.

From this perspective, distributed cloud is not just a technology trend. It is an operating model that changes the cost structure of AI services.

The cost structures of two companies will diverge significantly over time: one that sends every request to expensive centralized GPUs, and another that combines centralized cloud, distributed cloud, edge GPUs, caching, and smaller models depending on the nature of each task.

The more agents work, the more tokens they consume.The more tokens are consumed, the more GPUs are needed.And the more GPUs are needed, the more important it becomes where infrastructure is located and how it is allocated.

In the end, in the AI agent era, model performance alone will not be enough to win.

Only companies that can run their models on sustainable infrastructure will survive.