Centralized AI platforms face issues like quality black boxes. DGrid AI reconstructs decentralized AI infrastructure using on-chain verification.
Written by: Zhang's AI Diary
AI models are powerful enough, but the AI infrastructure remains untrustworthy.
When you call Claude Opus 5 or GPT-5.6 through a third-party platform, you cannot verify whether the platform actually called these top models or switched to a cheaper version; you cannot confirm whether the returned results are original outputs or have been processed in some way; and you cannot know if the platform's pricing is reasonable, as the entire supply chain is completely opaque to you.
This is not a trust issue, but a mechanism issue.
In today's world where AI services have become critical infrastructure, the three issues of quality black boxes, vendor lock-in, and value enclosure remain fundamentally unresolved. Most developers and enterprises still rely on centralized platforms, compromising on transparency, flexibility, and bargaining power.
DGrid AI aims to enable truly callable, verifiable, and settleable AI services through on-chain quality verification, an open model marketplace, and a token economy.
As of the first half of 2026, DGrid has served over 15,000 paying users, generating $23M in verification revenue, and the AI Arena has attracted over 500,000 users to participate in model evaluations. With the announcement of the $DGAI token economic model and the launch of TGE, DGrid is evolving from an "AI service product" to a "decentralized AI infrastructure protocol."
This article will dissect DGrid's technical architecture, mechanism design, commercial validation, and market positioning from three fundamental issues.
Issue 1: Quality Black Box - You cannot verify whether the services provided by the platform are genuine
Scenario: A company calls Claude Opus 5 through an aggregation platform to process sensitive documents, paying the top model price. But in reality, did the platform really call Opus 5? Will it automatically downgrade to a cheaper model during peak times? Were the returned results processed or reviewed in any way?
The essence of the problem: Centralized platforms control the entire process of model calls, leaving users at a complete information disadvantage. The platform is both the service provider and the quality judge, leaving users with the choice to either "trust" or "not use."
Why is this issue serious?
Issue 2: Vendor Lock-In - You are trapped in a few centralized entry points
Scenario: A SaaS product initially chooses a model vendor and deeply integrates their API into the business logic. Six months later, the vendor significantly raises prices or adjusts service terms, but the migration cost is extremely high - requiring code restructuring, retesting, and redeployment.
The essence of the problem: Each model vendor has its own API specifications, authentication methods, billing logic, and throttling strategies. Once developers choose a platform, the switching cost increases exponentially with the depth of integration.
Why is this issue serious?
Issue 3: Value Enclosure - You cannot participate in the underlying value distribution of AI services
Scenario: A medical AI team fine-tunes a model for clinical diagnosis scenarios, achieving results far superior to general models. However, they lack distribution channels and can only list it on a centralized platform, accepting the revenue-sharing ratios and traffic distribution rules set by the platform. Most of the revenue generated from model calls is taken by the platform.
The essence of the problem: Centralized platforms control model entry, pricing power, and data control. Model providers, developers, and users cannot directly participate in value distribution or influence platform rules.
Why is this issue serious?
The commonality among these three issues is that centralized platforms are both rule-makers and stakeholders, leaving users and service providers in a passive position.
DGrid's answer is to reconstruct AI infrastructure using decentralized mechanisms, making quality verifiable, supply open, and value traceable.
DGrid AI is not simply about "putting AI on the chain," but building decentralized AI infrastructure around three core capabilities:
Problem Solved: Vendor Lock-In
DGrid AI Gateway provides OpenAI-compatible API interfaces, allowing developers to access over 200 models, including Claude Opus 5, GPT-5.6, Gemini Pro, MiniMax, DeepSeek, Kimi, GLM, and other mainstream commercial models with a single API key.
Core Mechanism:
Key Value: Developers are no longer locked into a single vendor and can switch models, compare performance, and optimize costs at any time, maintaining maximum flexibility.
Problem Solved: Quality Black Box
PoQ is DGrid's unique on-chain quality verification mechanism and currently the only quality verification protocol actually deployed in AI infrastructure, supported by five professional technical papers.
Core Mechanism:
Key Design: PoQ only verifies the services claimed to be provided by nodes, without touching the user's real calling data, thus protecting privacy while establishing quality standards.
This addresses the core issue of the open market: How to prevent "substituting inferior for superior" when anyone can list models?
Problem Solved: Value Enclosure
DGrid Model Marketplace allows any model provider to list models, set their own prices, and directly receive calling revenue, with settlements completed through on-chain smart contracts.
Core Mechanism:
Key Value:
From an architectural perspective, DGrid is a three-layer protocol stack:
PoQ is DGrid's core technological innovation and the key to whether the entire open market can be established. This part deserves in-depth dissection.
In centralized platforms, quality control is the responsibility of the platform, and users can only choose to "trust the platform." But in an open market, anyone can list models, and without a verification mechanism, the market will quickly degrade:
The core problem PoQ aims to solve: How to verify the service quality provided by nodes without touching user privacy?
Step 1: Question Bank Construction
DGrid maintains a question bank covering various task types:
Each question has a standard answer or scoring criteria.
Step 2: Random Sampling
The system periodically randomly selects questions from the question bank and initiates "blind test calls" to nodes. Nodes do not know whether this is a test request or a real user request.
Step 3: Multi-Dimensional Evaluation
Returned results will be scored from multiple dimensions:
Step Four: On-Chain Certification
The evaluation results are recorded on-chain, forming a credibility score for the nodes. This score:
1. Privacy Protection: Only Verify Nodes, No User Data Access
The verification requests of PoQ are initiated by the system, using question bank data, and will not use users' real call data for verification or on-chain recording. This ensures user privacy.
2. Anti-Cheating: Randomness + Blind Testing
Nodes cannot know in advance which call is a test and cannot predict the question content. The only way to achieve a high pass rate is to continuously provide high-quality services.
3. Incentive Compatibility: Quality Linked to Earnings
PoQ scoring is not a simple "pass/fail" but affects the incentive weight of the nodes. The higher the quality of the node, the more $DGAI rewards it receives, creating a positive cycle.
PoQ essentially uses a combination of "continuous random audits + on-chain reputation + economic incentives" to make the service quality in the open market quantifiable, traceable, and punishable.
This is also why DGrid fundamentally differs from projects like OpenRouter and Infura on a technical level: the latter rely on platform credit, while DGrid relies on verifiable mechanisms.
DGrid's products are not single-point tools but a complete ecosystem built around four types of participants:
1. For Developers: AI Gateway
Core Value: One API call for 200+ models, zero migration costs, intelligent routing
Applicable Scenarios:
2. For Model Providers: Model Marketplace
Core Value: Free listing, self-pricing, on-chain settlement, PoQ endorsement
Applicable Scenarios:
3. For Ordinary Users: AI Arena
Core Value: Anonymous model battles, real preference data, earn by participating
Applicable Scenarios:
Current Data: Over 500,000 users participating, generating a large amount of human preference annotation data.
4. For Agent Developers: DClaw
Core Value: One-click deployment of Agents, on-chain identity, self-calling services
Applicable Scenarios:
DClaw integrates with the BNB Chain's ERC-8004 Agent Identity standard, allowing Agents to have on-chain identities, discoverability, and reputation accumulation capabilities.
From a product logic perspective:
These four products build a closed loop around "enabling AI services to circulate in an open network."
$DGAI is the native token of the DGrid AI network, with a total supply of 1 billion coins.
Only 15% is unlocked at TGE (Airdrops + Liquidity), with Team and Investor fully locked for one year.
Node operators stake $DGAI as a service guarantee, and PoQ scores affect incentive weights. Users can delegate $DGAI to quality nodes to share profits.
Users pay with $DGAI when calling AI services to enjoy discounts. This creates real demand for the token.
Nodes, model providers, Agent developers, and Arena participants receive $DGAI rewards based on their contributions. Incentive distribution is not egalitarian but differentiated based on quality and call volume.
$DGAI holders can vote on: fee structure, PoQ rules, which new models to support, ecosystem incentive plans, and treasury usage.
Users call services (paying $DGAI)
↓
Nodes provide inference (receiving $DGAI)
↓
PoQ verifies quality (affecting incentive weights)
↓
High-quality nodes receive more incentives
↓
More nodes → Better services → More users
↓
Token demand increases → Ecosystem expansion
Key Question: Can $DGAI transform from an "incentive asset" to a "utility asset"? That is, the token's value comes not only from incentive distribution but also from the real payment demand generated by AI service calls.
Verified Data
What Does This Set of Data Indicate?
While most AI x Crypto projects are still telling stories, DGrid has already proven the demand for payment through real revenue. AI Gateway and Premium services have been adopted by real users and enterprises.
Leveraging $5M in financing to generate $23M in revenue shows capital efficiency far above the industry average. This indicates that the project has self-sustaining capabilities and does not rely entirely on financing and token incentives to maintain operations.
With 500,000 users participating in AI Arena, it shows that DGrid not only has paying users but also active community participants contributing data to the ecosystem.
Key Turning Point
DGrid is currently at a critical juncture in transitioning from a "centralized product" to a "decentralized network":
The upcoming challenge: Can existing revenue and users be converted into sustained call volume for a decentralized network, supply and demand flow in the Marketplace, and real use cases for $DGAI?
vs. OpenRouter
vs. Akash/Render
vs. OpenAI/Anthropic
Key Actions Ahead
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