With the rapid development of AI Agent (Artificial Intelligence Agent) technology, new forms of black and gray market business have begun to emerge around its upstream and downstream business models. In this system, black and gray market actors are using computing power—the core resource supporting the operation of AI Agents—as an arbitrage target, acquiring and centrally utilizing it in bulk through technical means. These activities are evolving into an arbitrage model characterized by organization, scale, and technical sophistication. The basic logic is: Utilizing common platform growth strategies (such as free quotas for new users, referral rewards, and membership benefits), they acquire computing power resources in bulk through technical means, then resell them at a lower cost, profiting from the price difference. In this process, such behavior not only impacts the platform's operational mechanisms but, under certain conditions, may also raise criminal risks. This article attempts to dissect common AI Agent computing power arbitrage paths from a behavioral perspective, and analyzes the potential legal risks from a practical standpoint. In the AI Agent industry, computing power is essentially a quantifiable and consumable cost resource. Many platforms lower the barrier to entry by offering free quotas and referral rewards to acquire users. Many people consider registering multiple accounts to utilize the free quotas of different platforms; at this stage, most people don't see anything wrong with it. However, if it gradually becomes not just for personal use, but rather involves acquiring these resources in bulk, centrally controlling multiple accounts to run computing power, and even taking orders, charging fees, and providing services to others to earn the price difference, then the nature of the whole thing has changed. It is precisely in this process of change that what originally seemed like simply exploiting platform rules has begun to be understood as a computing power-centric arbitrage method, and under certain conditions, it may fall under the scope of criminal evaluation. Below, we will analyze the risks of this type of behavior using several typical models. Model 1: Exploiting the Platform's New User Growth Mechanism to Obtain Computing Power Resources Currently, mainstream platforms typically offer free trial periods to new users and set up referral reward mechanisms to attract users. Under this mechanism, some people have begun to use automated tools (such as scripts and simulators) to register accounts in bulk, repeatedly and massively acquiring the computing power resources provided by the platform, or continuously acquiring invitation reward points or computing power by repeatedly registering new accounts and binding invitation codes. Many people might think this is simply "using" the platform rules to the extreme, and not a big problem. However, in actual assessment, the key is not whether these rules were used, but whether the platform's verification mechanisms (such as device identification and SMS verification) were repeatedly bypassed through technical means, and whether a method of continuously acquiring resources was formed. If the behavior has evolved from occasional use to batch operation through tools, stable acquisition of resources, and even further to providing services or monetization, then its nature may change. In some cases, such behavior may be evaluated from the perspective of "bypassing the system to obtain platform resources," involving the crime of illegally obtaining computer information system data. If the relevant behavior relies on programs or tools specifically designed to bypass platform security measures, the creation and provision of such tools may also be included in the evaluation scope of the crime of providing programs or tools for intruding into or illegally controlling computer information systems. In cases where platform rewards are repeatedly obtained by fabricating the identity of a "new user" and used for possession and monetization, there is also a risk of being analyzed from the perspective of fraud.
2. Mode Two: Reselling Computing Power by Splitting High-Tier Platform Benefits
Some platforms offer premium membership accounts (such as ChatGPT Plus and Team Edition), corresponding to higher computing power limits or multiple seat usage rights. Based on this, some individuals split the usage rights of a single account and provide them to multiple downstream users through "carpooling" or overselling, profiting from the price difference.
Many people might think this is simply a reuse of purchased benefits, at most a violation of the platform's user agreement. However, in actual judgment, it still needs to be considered in conjunction with the specific source and usage method.
If it is simply sharing or allocating usage based on normally purchased accounts, it generally stays at the level of breach of contract or unfair competition, and cases that directly escalate to the criminal level are relatively rare.
However, if the source of the relevant accounts is questionable—for example, if they were acquired at a low price through abnormal means, or if they are associated with the aforementioned bulk acquisition of resources and then monetized through carpooling, resale, or other means—then this step is no longer simply "sharing and using" and may be evaluated within the overall chain. In this case, whether the perpetrator was aware of the account's source, participated in subsequent monetization, and profited from it will become important factors in assessing the risk. Under certain circumstances, it may also be analyzed and determined from the perspective of "concealing or disguising the proceeds of crime." 3. Mode Three: Reselling and Arbitrage Using Platform Interface Capabilities This mode can be understood as follows: the platform provides a "service capability limited to internal use," while the black market transforms this capability into resources that can be sold externally. Analogously, it's closer to a structure where the platform is like a "self-service restaurant," allowing users to use services according to rules (e.g., generating content for free on a webpage), but not allowing them to package these capabilities and take them away or provide external API calls. The platform can bear this cost based on the premise that most users' usage is dispersed and limited, making the overall cost controllable. The so-called API reverse parasitism essentially involves adding a layer of "proxy acquisition and resale" structure outside this system: using technical means to obtain the platform's internal call paths and verification methods, transforming previously fragmented usage behaviors into centrally scheduled call capabilities, and then charging externally based on the number of calls as an "interface service." In this process, the platform bears the computational power consumption, while the intermediate layer completes resource integration and external charging. In other words, operations that could originally only be completed within the platform interface are transformed into capabilities that can be called in batches by programs, forming an interface service that can be charged externally. In practice, if the relevant behavior involves bypassing the technical measures set by the platform to restrict access (such as authentication mechanisms, token verification, etc.) and extracting and reusing interface logic, it may be analyzed from the perspective of copyright infringement. If it further provides services to the outside world in the form of "API relay" or "interface service" and continues to generate revenue, it also risks being evaluated from the perspective of illegal business operations. When the relevant request behavior reaches a high intensity and causes significant impact on the operation of the platform system or even damages its function, it may also involve the crime of damaging computer information systems. 4. **Criminal Lawyer Risk Warning** In summary, the "computing power arbitrage" behavior in the AI Agent field has gradually evolved from scattered operations to a multi-layered model encompassing account acquisition, rights splitting, and interface resale. Against the backdrop of a continuously improving digital economy and legal environment, the regulation of this new type of online black and gray industry is becoming increasingly stringent. Technology itself has no inherent attributes; the key lies in its usage and the actual effects it produces. For practitioners, it is more important to focus on their own position in the overall chain, and the nature and risks inherent therein.
Anais
Kikyo
Weatherly
Catherine
Joy
Alex
Miyuki
Weiliang