AI Agent Computing Power Arbitrage Model and Its Legal Risk Analysis

With the rapid development of AI Agent (artificial intelligence agents) technology, new black-and-gray business forms have also begun to emerge around the business models across its upstream and downstream.

In this ecosystem, black-and-gray industries are taking compute power—which is the core resource that supports the operation of AI Agents—as the target for arbitrage, using technical means to obtain it in bulk and centralize its use.

These related activities are evolving into an arbitrage model characterized by organizational, scalable, and technical features. Its basic logic is:

By using common growth strategies on platforms (such as free usage quotas for new users, invitation rewards, membership benefits, etc.), obtain compute resources through batch technical means, then resell them externally at a lower cost to earn the price difference.

In the process, these kinds of activities not only may disrupt a platform’s operations and regulatory mechanisms, but under certain conditions, they may also touch criminal risks.

This article attempts to start from behavior patterns, break down the currently common AI Agent compute arbitrage paths, and—combined with a practical perspective—analyze the legal risks they may face.

In the AI Agent industry, compute power is essentially a cost resource that can be quantified and consumed.

To obtain user scale, many platforms lower the usage threshold through free quotas, invitation rewards, and similar methods.

Many people will consider registering several additional accounts to use the free quotas from different platforms. At this stage, most people do not think there is anything wrong.

But if it gradually becomes not only for personal use, but instead for bulk acquisition of these resources—centralizing control over multiple accounts to run compute power, or even taking orders externally, charging fees, and helping others provide services to earn arbitrage profits—then the nature of the whole matter is already different.

It is exactly through this change that behaviors that originally seemed to be merely making use of platform rules begin to be understood as an arbitrage method centered on compute power, and under certain conditions, they may fall within the scope of criminal evaluation.

Below, by combining several typical models, we will break down the risks of this kind of behavior.

1 Model 1: Obtain compute resources by leveraging a platform’s new-user growth mechanisms

At present, mainstream platforms typically provide new users with free trial quotas to drive user growth, and set up an invitation reward mechanism.

Under this mechanism, some people start by using automation tools (such as scripts and emulators) to batch-register accounts, repeatedly and in large quantities obtain compute resources provided by the platform, or continuously obtain invitation-reward points or compute power through repeated registration of new accounts and binding of invitation codes.

Many people believe that this is simply using the platform’s rules “to the extreme,” and there is nothing wrong with it. But in actual determination, the key is not whether these rules are used, but whether technical means are used to repeatedly bypass the platform’s verification mechanisms (such as device identification, SMS verification, etc.), and whether a method of continuously obtaining resources is formed.

If the behavior has shifted from occasional use to stable resource acquisition through tool-based batch operations, or even further used to provide services externally or monetize, then its nature may change.

In certain cases, this kind of conduct may be evaluated from the perspective of “obtaining platform resources by bypassing the system,” involving the crime of crime of illegally obtaining computer information system data; if the relevant conduct relies on programs or tools specifically used to break through the platform’s protective measures, the production and provision of such tools may also fall within the scope of evaluation of the crime of providing programs or tools for intrusion and illegal control of computer information systems; and in situations where “new user” identities are fabricated repeatedly to obtain platform rewards for possession and monetization, there is also a risk of being analyzed from the perspective of fraud.

2 Model 2: Achieve compute resale by splitting platform high-tier benefits

Some platforms provide premium member accounts (such as ChatGPT Plus, team editions), corresponding to higher compute quotas or permissions for multiple seats. On this basis, some people split the usage permissions of a single account, and through “carpooling” or over-selling, provide access to multiple downstream users, earning the price difference from it.

Many people will think that this is simply reusing purchased benefits, at most a problem of violating the platform’s user agreement. But in actual determination, it still needs to be assessed in light of the specific source and usage method.

If it is only shared or proportionally allocated usage based on accounts obtained through normal purchase, it generally stays more in the realm of breach of contract or improper competition, and direct escalation to the criminal level is relatively rare.

But if there is a problem with the source of the relevant accounts—for example, they are acquired at a low price through abnormal means, or they are associated with the aforementioned conduct of batch obtaining resources—and then monetized externally through carpooling, resale, and similar methods, then that step is no longer merely “shared usage,” but may be evaluated as part of the overall chain of conduct.

In such circumstances, whether the actor is aware of the account source, whether they participate in the subsequent monetization, and whether they profit from it will all become important factors in judging risk. Under certain circumstances, it may also be analyzed and recognized from angles such as the crime of concealing or hiding proceeds of crime.

3 Model 3: Resale arbitrage by leveraging a platform’s interface capabilities

This kind of model can be understood as: the platform provides a kind of “service capability limited for internal use,” while what the black-and-gray industry does is convert this capability into a resource that can be sold externally.

By analogy, it is closer to a structure like this: the platform is like a “cafeteria,” allowing users to use services in the store according to rules (for example, free content generation on the web), but it does not allow those capabilities to be packaged and taken away or that interface calls be provided externally.

The reason the platform can withstand these costs is based on a premise: most users’ usage is dispersed and limited, and the overall cost is controllable. As for the so-called API reverse-replacement parasitism, in essence it adds an extra layer of a “collection-and-resale” structure outside this system: by using technical means to obtain the platform’s internal call paths and verification methods, the originally scattered usage behavior is transformed into centrally scheduled call capability, and then, in the form of “interface services,” charged externally based on the number of calls.

In this process, the platform bears the compute consumption, while the intermediate layer completes resource integration and charges externally. In other words, what was originally only possible to do within the platform interface is converted into a capability that can be batch-called by programs, and forms an externally chargeable interface service.

In actual determination, if the relevant conduct has already involved bypassing the technical measures set up by the platform to restrict access (such as authentication mechanisms, Token verification, etc.) and extracting and reusing the interface logic, it may be analyzed from the perspective of crime of infringing on copyright; if it further provides services externally in forms such as “API transit” and “interface services,” and continuously obtains benefits, there is also a risk of being evaluated from the perspective of crime of illegal business operations; and when the relevant request behavior reaches a high intensity, causing a significant impact on the platform system’s operation or even functional destruction, it may also involve crime of destroying computer information systems.

4 Criminal defense lawyer risk notice

Overall, in the AI Agent field, “compute arbitrage” behavior has gradually developed from scattered operations into multi-tier models that include account acquisition, splitting of benefits, and interface resale.

Against the backdrop of continuous improvement in the digital economy and the rule-of-law environment, regulation of this kind of new network black-and-gray industry is tightening. Technology itself has no inherent attributes; the key lies in the way it is used and the actual effects it produces.

For practitioners, what needs more attention is their position in the overall chain of conduct, as well as the nature and risks presented by that.