Are you really using Claude? DGrid wants to use on-chain verification to end the "black box era" of AI intermediaries

In the past year, Model as a Service (MaaS) has become one of the most vibrant tracks in the crypto world. From OpenRouter completing financing with a valuation of $1.3 billion to WLFI and Sun Yuchen entering the fray, more and more players are squeezing into this "selling water" business—aggregating mainstream AI models globally and reselling them to developers through a unified interface.

The track is hot, but a fundamental question has remained unanswered: When you pay to access a top-tier model, how do you know the platform actually provided you with that model and didn't secretly swap it for a cheaper, lower-spec version?

This is not an unfounded worry. For the vast majority of requests, the output difference between GPT-5.6 and some cheap model is not obvious, and users can hardly perceive it. For the platform, using a low-cost model to impersonate a high-cost model means the price difference is pure profit. This information asymmetry is a tacit gray area in the entire MaaS track.

Decentralized AI network DGrid AI aims to solve this problem. Its answer—and the revenue data of $23 million for the first half of 2026—may indicate that "verifiability" itself is a good business.

The Original Sin of the Intermediary: Black Box

To understand DGrid's differentiation, one must first grasp the operational logic of existing MaaS platforms.

Whether it's aggregators like OpenRouter with brand endorsement or various new intermediaries, they are essentially "black box intermediaries": the platform connects with various model providers and then packages and resells them to users. What users see is just an API entry; as for where the request is ultimately routed, whether the claimed model is used, or if it has been downgraded, it all relies on the platform's word.

Platforms with brands rely on "credibility"—you trust that they won't act maliciously. But credibility is not a mechanism; it cannot be verified or held accountable. Once a platform manipulates things due to cost pressures, users are the last to know.

As AI services evolve from a single centralized platform to an open network with highly decentralized model providers, nodes, developers, and users, this problem will be dramatically magnified. The more participants there are, the more black boxes exist, and the higher the trust cost.

DGrid's judgment is: In a sufficiently open AI network, trust can no longer come from brand endorsement but must come from a verifiable mechanism.

PoQ: Turning "Trust" into "Verifiable"

DGrid's solution is called Proof of Quality (PoQ).

Its approach is not to determine whether a certain AI response is "absolutely correct"—this is neither technically realistic nor meaningful. What PoQ aims to verify is another matter: Whether the model provider has honestly delivered the services it promised.

Specifically, PoQ conducts independent, random sampling of model providers connected to the network using a proprietary test set and then records the verification results on-chain. If a provider delivers a subpar model, misrepresents quality, or engages in false billing, the mechanism will detect and punish it.

There is an easily misunderstood but crucial detail: The subject of PoQ sampling is the "provider," not every single user call. The entire verification process uses the platform's own question bank, does not touch user call data, and will not put any user data on-chain. In other words, it protects the user's right to know without sacrificing user privacy.

This mechanism is not just a concept in a white paper. Core members of the DGrid team have PhDs from institutions like Stony Brook University and have published four academic papers on PoQ and related topics:

• Proof of Quality: https://arxiv.org/abs/2512.16317

• Optimistic TEE-Rollups: https://arxiv.org/abs/2512.20176

• Cost-Aware Proof: https://arxiv.org/html/2601.21189v1

• PoQ-Judge: https://arxiv.org/pdf/2606.11196

In the entire MaaS track, PoQ is currently the only mechanism that verifies model service quality on-chain. This is also the core barrier that distinguishes DGrid from all other intermediaries—others provide you with an API, while DGrid gives you an API plus a verifiable quality guarantee.

From "Intermediary" to "Open Market"

Beyond the trust mechanism, DGrid has also taken a different path in product form compared to intermediaries.

Intermediaries are closed: the platform decides which models to connect, how to price them, and users can only passively accept. DGrid, on the other hand, is an open market—model providers can freely join, set their own prices, and compete openly in the market, while users directly call through a unified AI Gateway, with PoQ ensuring quality in the background.

To put it another way, other intermediaries are more like "purchasing agents," where you can only trust that the agent provides you with genuine products; DGrid is more like an "open marketplace with on-chain quality inspection," where the quality and price are determined by market competition, and each product has a verifiable quality inspection record.

Around this market, DGrid has built a complete product matrix:

• AI Gateway: An API aggregating over 200 mainstream models like Claude, GPT, Gemini, MiniMax, GLM, intelligently routing based on cost, speed, and historical performance, with prices significantly lower than official ones.

• AI Arena: Users anonymously score blind tests, accumulating high-quality human preference data to feed back into routing optimization, with over 300,000 participating users.

• DClaw: One-click deployment of local AI assistants in minutes, allowing access to top models without configuration keys, supporting persistent memory and hot-swappable skills.

• Model Marketplace: Models can be freely listed, self-priced, and tokenized, opening direct revenue paths for providers.

• Dori: An intelligent recommendation agent that matches the optimal model solution based on natural language descriptions of needs.

$23 Million: A Verifiable Business Loop

A coherent mechanism ultimately needs to be validated by the market.

In the first half of 2026, DGrid's Genesis membership program accumulated revenue exceeding $23 million, with over 15,000 paid members. Users pay an annual fee of $1,580 to receive a monthly model usage quota of $300 (approximately 44% of the official price), exclusive NFT benefits, DClaw deployment capabilities, and DGAI token mining rewards.

These paying users are not just crypto-native players but also include many developers and enterprises with genuine AI usage needs. Their willingness to continue paying indicates that the combination of "low price + verifiable" meets real demand—after all, who wouldn't want to access genuine models at lower prices without the risk of being downgraded?

In Conclusion: Verifiability is the Ultimate Goal of the AI Network

The AI MaaS track will continue to thrive. More capital will flow in, more players will enter, and more platforms that "aggregate N models" will emerge.

But as the track returns to rationality, users and the market will ultimately ask a more fundamental question: What exactly am I getting for the money I paid?

For black box platforms that rely on brand credibility, this question can only be answered with "trust me." DGrid's answer is a verifiable, accountable, and settleable mechanism—turning "trust" into "verifiable."

In an increasingly open and decentralized AI world, this may be the truly sustainable answer. $23 million is the first vote of trust the market has cast for DGrid. The weight of this vote lies precisely in its verifiability on-chain.