Due to geographical and payment frictions, a massive gray market for AI shadow APIs has grown unchecked. They rely on invisible routing arbitrage to amass wealth, and this distortion has backfired on rigorous academic evaluations. When opaque operations become a pain point for the industry, how can we rebuild order? This article will dissect this hidden, highly profitable business and deduce the inevitable evolution of the AI computing power market: from stablecoin-driven computing power gateways like B.ai to verifiable settlement loops designed specifically for the Agent era by Cobo Pact.
Behind the grand narrative of AI's rapid advancement lies a frustrating fact: developers and researchers worldwide have never started on a level playing field.
While Silicon Valley teams can access the latest models with virtually zero friction, developers and researchers in many other regions are kept out by geographical restrictions, payment barriers, and account risk controls. However, the demand doesn't disappear because of access restrictions; it simply seeks alternative entry points. Thus, a vast third-party intermediary market, the Shadow API, has emerged. For restricted developers, this seems like a perfect hack: pay, get the intermediary address, modify a single line of the Base URL, and you can call GPT or Claude in a way similar to the official API. It's cheap, convenient, and close enough to a real-world experience. But this shortcut isn't free. This gray market, spurred by massive demand, is engaging in covert arbitrage based on information asymmetry. You might think you're calling a specific model, but the actual return could be a different version, a different supply source, or even a cheaper alternative model. As long as the output format is correct and the answer looks similar enough, most users will find it difficult to detect the difference. Even more serious is that when this unequal starting line, fueled by gray channels, begins to backfire on rigorous academia and fragile commercial systems, we are forced to confront an extremely urgent and real problem: given the unstoppable cross-border demand for AI computing power, how should the industry govern this gray supply chain, driven by real demand but lacking transparent delivery rules? Heavily Costly: A "Cascaded Failure" Spreading Across Academia In March 2026, the Helmholtz Centre for Information Security (CISPA) in Germany released an audit paper titled "Real Money, Fake Models: Deceptive Claims in Shadow APIs," which systematically compared the output differences between the official LLM API and the Shadow API. The results were not optimistic. The paper found that at least 187 academic papers used this type of Shadow API, of which 116 have been accepted by top conferences or journals such as ACL, CVPR, and ICLR, accounting for over 62%. Performance deviations for some services reached as high as 47%, and a significant proportion of models failed fingerprint authentication tests. This means that the Shadow API is no longer just a tool for fringe developers, but has entered the academic production chain. If these figures are accurate, it means that some AI research may not be testing the model claimed in the papers. If this deviation only occurs in everyday chatbots, it might only result in a temporary degradation in user experience. However, in rigorous engineering systems and academic evaluations, distortion of the underlying evaluation object is almost equivalent to a weakened foundation. Once the model's identity becomes unverifiable, the experimental subject becomes unstable; and with an unstable experimental subject, leaderboards, capability analysis, method comparisons, and subsequent citations will all be dragged into the same chain of uncertainty. This is also where the Shadow API truly lies in its danger. It doesn't just contaminate a single output, but the measurement standard itself. The "nearly 6,000 subsequent citations" mentioned in the paper illustrate that this type of risk will not remain on the paper but will continue to spread along reproduction, citations, and downstream applications, ultimately evolving into a potential cascading failure. In the past, when reproducing experiments, researchers mainly cared about the consistency of the Prompt, temperature parameters, and dataset. But after the intervention of the Shadow API, the reproducibility crisis in AI research has sunk from the model behavior itself to the infrastructure supply chain level. Now we must ask a more fundamental question: Is the model being called actually the one claimed in the paper? When the underlying interface is unverifiable, the so-called model capability evaluation becomes an indirect, blind test of a black-box relay chain. Deconstructing the Black Box: A "Route Arbitrage" Business Peeling back the veneer of the Shadow API access shortcut, the underlying mechanism is actually a route arbitrage business. This business is built on information asymmetry. The relay station controls invisible routing rights: users see an interface compatible with OpenAI or Anthropic formats, believing they are calling a specific model, but the actual backend, model version, billing method, and resource source are all hidden behind the gateway. Arbitrage opportunities arise from this unverifiable nature. The relay station sells access to cutting-edge models, but delivers potentially cheaper smaller models, open-source models, or downgraded versions. The price difference between the advertised model and the actual delivery is the source of profit. This arbitrage typically takes three forms. The most direct is model swapping. Users pay for the flagship model, but the gateway routes requests to cheaper, smaller models or low-cost commercial models. As long as the output doesn't give away the discrepancy, the price difference becomes the intermediary's profit. The second type is version arbitrage. Many research and production systems rely on specific model versions to ensure stable output and reproducible results. However, gateways can quietly redirect traffic to older, lighter, or cheaper versions. Users believe they are calling the specified model, but they are actually receiving a substitute with inconsistent behavior. The third type is resource pool overselling. Some intermediaries don't obtain model capabilities through legitimate enterprise APIs; instead, they pool C-end subscription accounts, reverse engineering entry points, and bulk accounts, then repackage them as stable developer services for sale. This model appears usable during off-peak periods, but once concurrency increases, it exposes problems such as long-tail latency, connection resets, context loss, and service drift. These three mechanisms together constitute the black box of the Shadow API. For ordinary users, model degradation might only result in a diminished experience; however, for research evaluation, production systems, and future Agent workflows, distortions in underlying behavior will continue to propagate along the system. A single replaced call can contaminate a set of experimental results; in a continuously operating Agent system, an abnormal route can be amplified into a series of errors by subsequent steps. Even more serious are high-risk scenarios. The audit paper points out that in tasks such as medical diagnosis, the official model and the intermediary model may give significantly different recommendations. In other words, the problem with the Shadow API isn't just about the stability of its response quality; it's that it has turned what should be a verifiable, reproducible, and reliable model interface into an operational black box where identity cannot be verified, expectations cannot be stabilized, and accountability is difficult to pinpoint. The existence of a large gray market often indicates that genuine needs are seeking an outlet. The proliferation of the Shadow API exposes structural frictions in accessing cutting-edge models. Geographical restrictions, payment thresholds, compliance constraints, and account risk control have collectively increased the cost of accessing the latest models. As long as the official channels are not smooth enough, the gray market for intermediaries will be difficult to eliminate. This has attracted more and more players to enter the market through API intermediaries, attempting to use this entry point to tell a bigger story. For Fu Sheng, the intermediary is Cheetah Mobile's entry point into the AI application layer and reshaping the capital market narrative; for projects related to the Trump family, it's more like a tool to drive traffic and use cases for WLFI tokens and USD1 stablecoins. For B.ai, which Sun Yuchen is betting on, the intermediary is just the front end; the more crucial aspect is importing the payment, deposit, and settlement demands generated by model calls into TRON's stablecoin network. Along this path, B.ai attempts to reorganize the fragmented, black-box model call requirements into a platform-based model gateway and settlement network. The core logic is not complex: if payment friction is one of the major causes of the Shadow API, then stablecoins and on-chain payments can theoretically remove a significant portion of this resistance. This allows developers excluded from traditional payment networks to purchase model services with a lower barrier to entry. Once the payment chain is established, the business model may shift from black-box to white-box operations. Black-box intermediaries profit from information asymmetry: packet swapping, downgrading, and overselling all occur at the routing layer, unobservable to users. Platform-based computing power gateways, theoretically, can shift their profit sources to large-scale distribution, routing efficiency, settlement services, and platform credibility. In other words, intermediaries no longer profit from things invisible to users, but from users' continuous and stable calls to their services. The greater potential lies in the settlement layer. AI computing power calls inherently possess the characteristics of small amounts, high frequency, and global reach, with each call corresponding to authentication, billing, and settlement. For stablecoin circulation networks like TRON, this provides a more productivity-oriented narrative: if a large number of developers and agents purchase model services through on-chain tracks in the future, TRON will not only be a transfer network but may also further assume the role of payment and clearing for AI computing power transactions. The commercial value here comes not only from API price differences. User deposits will create fund accumulation, model calls will generate continuous transaction fees, and gateway aggregation will bring a stable transaction flow. As AI computing power gradually becomes a high-frequency digital commodity, stablecoin networks have the opportunity to obtain a more realistic, higher-frequency, and more productivity-oriented payment scenario. Therefore, the significance of B.AI lies in attempting to solve three problems within the same framework: using aggregated routing to address model entry points, using stablecoins to reduce payment friction, and using on-chain identities and records to support future Agent autonomous calls. It aims to reorganize a portion of the computing power demand originally handled by the gray market into a billable, settlement-enabled, and auditable infrastructure. Endgame Thinking: From Platform Credit to Verifiable Settlement Justin Sun's bet on B.AI, in a sense, represents a platform-based upgrade of the Shadow API market: transforming the fragmented, hidden, and difficult-to-account-for black-box relay into a large-scale computing power gateway. The larger the platform, the greater its need for reputation; the stronger its financial resources, the greater its ability to procure real model resources; the more complete its transaction records, the easier it is to create a traceable performance history. This increases the cost of wrongdoing and shrinks the space for black market operations. However, platformization does not mean the disappearance of trust issues. Users still need to trust that the platform will route honestly, bill accurately, and fulfill contracts stably, and will not quietly change the rules when costs, traffic, or business incentives change. B.AI can move the market from trusting black boxes to trusting platform gateways, but a mature AI computing power market cannot stop there. The next step is to move from platform credibility to verifiable settlement. This is precisely the problem that Cobo and its Pact framework are trying to solve: reshaping the closed loop of authorization, verification, and settlement behind every instance of computing power consumption. Beforehand: Defining risk boundaries and writing rules into the wallet. Traditional API consumption is more like recharging first and then blindly opening a box. Users hand over their money to the platform first, and then can only passively accept how the service provider explains the route, how it deducts fees, and how it explains anomalies. The financial risk and verification costs basically fall on the user's side. Cobo Pact changes the location of funds and permissions. Funds don't need to be exposed all at once; instead, budgets, model requirements, and billing rules are written into Pact's risk control rules as preconditions. This is equivalent to equipping future AI agents with a smart meter. Agents can spend freely within preset boundaries, but service providers must meet agreed-upon conditions to be eligible for the corresponding funds. In-process: Penetrating the black box of calls, making the process an auditable transaction. The biggest frauds in the Shadow API often occur during the call process. Users see a compatible interface, but what actually happens might be model packet loss, version drift, token metering anomalies, or routing degradation. Traditional payment systems only care whether the money has been successfully deducted, not whether the delivery is genuine. Therefore, verification should be embedded in the call process itself. When the Agent initiates a request, the system not only monitors fund outflows but also simultaneously verifies model fingerprints, latency distribution, token counts, and output quality. If covert packet loss, performance drift, or billing anomalies occur, payments can be suspended, limited, or even triggered by circuit breakers. This transforms API consumption from a one-way black-box deduction into a transaction process of simultaneous invocation, auditing, and settlement. Service providers no longer simply receive payment and deliver; they need to continuously prove they meet the agreements throughout the delivery process. Post-event: Accumulating Performance Records and Using Credit to Improve Market Distribution
Post-event: Accumulating Performance Records and Using Credit to Improve Market Distribution
Auditing a single call is not enough. To truly improve the market, the key is to accumulate records from each call into credit.
In the Cobo Pact framework, each call can form a performance track: which Gateway was called, what the nominal model is, whether identity verification was passed, whether there were any disputes regarding token counting, whether any delays or anomalies occurred, whether circuit breakers were triggered, and how the final settlement was handled. These records are not only used for user reconciliation but can also influence market distribution.
Gateways that consistently deliver honestly should receive higher traffic weights; service providers that frequently experience model drift, performance anomalies, or billing disputes will be demoted or even eliminated. The market shouldn't rely on service providers' self-discipline, but rather use mechanisms to ensure that the cost of wrongdoing outweighs the benefits, and that stable fulfillment translates into real traffic. Conclusion: In the Agent era, verifiable settlement will become a necessity. If the current Shadow API primarily exposes the problem of human misguidance, then with the arrival of the Agent Economy, this problem will further evolve into the risks of automated payments and continuous settlement. In human-led transactions, there is still room for manual intervention after anomalies are detected. However, when machines begin to purchase computing power on a large scale and at high frequency automatically, false billing, model swapping, and service degradation can accumulate continuously without real-time monitoring. Individual losses may be small, but they can be rapidly amplified in high-frequency transactions. This places higher demands on the governance capabilities of the AI computing power market. The truly valuable capability is not just connecting model supply with developer demand, but transforming computing power consumption into a controllable, auditable, and accountable transaction system: limiting expenditures before transactions, verifying delivery during transactions, and suspending settlement in case of anomalies. Only when payment, authorization, and verification are written into the underlying transaction layer can the future Agent Economy operate securely in a more transparent and verifiable computing power network. Otherwise, the more autonomous the agent and the more frequently it makes calls, the greater the risk posed by black-box settlement.
Weatherly
Catherine
Anais
Kikyo