The Cost Has Dropped from Hundreds of Thousands to Just a Few Dollars. That Alone Changes the World.

Simulating a large number of LLM agents. For instance, testing how “1,000 consumer agents behave in the market.” Until now, such experiments required GPU environments costing hundreds of thousands of yen. Only university research labs, large corporate R&D departments, or well-funded startups with ample cloud budgets could afford this. In short, it was a “rich person’s game.”

Now, it can be done with just a single laptop and a few dollars.

“Poor Man’s Agentic Modeling”—literally translated as “the agent modeling of the poor.” This research has demonstrated the fact that costs can drop by three to four orders of magnitude. The issue is not that “amazing technology has emerged.” The essence lies in who can do what when costs dramatically decrease.

The Mechanism is Simple. Just “Ask in Advance.”

The core of this method is surprisingly simple.

In traditional LLM agent simulations, API calls are made to the LLM at each step of the simulation. If 1,000 agents move for 100 steps, that results in 100,000 API calls. Using a GPT-4 class model, that alone can cost tens of thousands of yen. Moreover, each time the simulation parameters are changed and re-executed, the same cost is incurred.

The “Poor Man’s” approach flips this idea on its head. Before running the simulation, the “personality,” “judgment criteria,” and “memory” of each agent are extracted from the LLM. Through hundreds to thousands of queries, the behavioral patterns of the agents are distilled into a low-parameter model. It’s akin to “interviewing the LLM in advance and creating lightweight avatars based on its responses.”

These avatars run on the laptop’s CPU. During the simulation, there’s no need for GPUs or cloud services. Even if the parameters are changed and the simulation is re-run 100 times, the additional cost is nearly zero.

In numerical terms, it looks like this:

• Traditional: Hundreds of thousands to millions of yen for a single simulation, requiring a GPU environment.
• Poor Man’s Method: A few dollars to tens of dollars for preparation, execution on a single laptop.

Costs drop by two to four orders of magnitude. This is not just a matter of being “cheaper.” It represents a structural change where “those who couldn’t do it before can now do it.”

Crushing the “Cost of Trial and Error”—The Concept of SpeedRunner

Another noteworthy research project is the “SpeedRunner” coding agent proposed in “Better, Faster, Stronger: Programmatic Skill Learning Best Reduces Agent Cost.”

The primary reason for the high operational costs of LLM agents is “trial and error.” Each time an agent faces a new task, it queries the LLM repeatedly, fails, and retries. Each instance of this trial and error incurs API costs.

SpeedRunner analyzes the trajectories of past successes and failures to extract skills as “programs.” When a similar task arises, instead of starting from scratch, it combines the extracted skill programs to respond.

In the context of small and medium-sized enterprises, this is akin to “documenting the know-how of veterans into manuals so that newcomers can replicate it.” It eliminates individual dependency and ensures reproducibility. This is fundamentally about systematization.

SpeedRunner has been tested in different environments and tasks, consistently achieving cost reduction while maintaining performance. An agent that “gets cheaper as it learns.” If this becomes practical, the variable costs of agent operation will drop dramatically.

Large-Scale Models Operating at the Edge—APEX Opens a “GPU-Free” World

The third research project, “APEX: Adaptive Expert Prefetching,” aims to disrupt cost structures from yet another angle.

Recent LLMs increasingly utilize an architecture known as “Mixture-of-Experts (MoE).” The entire model is enormous, but only a part of it (the expert modules) is used during each inference. The problem is that the entire massive model needs to be loaded into memory, which is far from sufficient on a laptop.

APEX predicts “which expert module will be needed next” and preloads only the necessary ones into memory. This reduces latency per token by up to 26% and improves the energy-delay product (EDP) by 41%.

In simpler terms, it has become possible to run large models at “usable speeds” even on devices with insufficient memory. This could eliminate the need to pay tens of thousands of yen monthly for cloud GPU instances.

So, Who Does This Ultimately Concern?

While I have presented three research projects, the common message is clear.

“The assumption that high-cost environments are necessary to use AI is beginning to crumble.”

What does this mean?

The advantage of the “AI experimental environments” that large companies built by investing millions of yen in GPU clusters is diminishing. Conversely, small teams that can rapidly test hypotheses on a single laptop are emerging as the ones who can win through speed.

For local small and medium-sized enterprises, this is not just someone else’s story.

For example, creating LLM agents for the personas of 1,000 customers and simulating their reactions to new products. What previously required paying hundreds of thousands of yen to external research firms for market research can now be tested internally for just a few dollars in a matter of hours.

For instance, accumulating sales talk patterns in a SpeedRunner-like manner to create agents capable of making proposals on par with veterans, even for new salespeople. The know-how that was previously individualized becomes reproducible as a system.

Or, running customer service AI on inexpensive edge devices placed in stores. No cloud connection, no monthly fees. It continues to operate with just the initial investment.

None of these are stories that can only be done by large companies. Rather, it is precisely because they are small and can make decisions quickly that they can experiment first.

“Just Trying It Out” Becomes the Strongest Strategy

These technologies include some that are still in the paper stage. Whether they can be immediately incorporated into a company’s operations is not guaranteed.

However, the direction is clear. The cost of running AI will continue to drop exponentially in the future.

What was said to cost 1 million yen last year may become 10,000 yen this year and 100 yen next year. In this rapid pace, waiting for “it to get a little cheaper” is actually the riskiest strategy.

Organizations that can act the moment costs drop and those that don’t even notice the drop. This difference is not a matter of technical capability. It’s a difference of whether there is a culture of “just trying it out.”

In an era where 1,000 AI agents can run on a single laptop, large corporate laboratories are no longer necessary. What is needed are curiosity and the courage to experiment.