DGrid AI: Reconstructing AI Infrastructure with On-Chain Verification and Open Markets
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.
- Three Fundamental Issues of AI Infrastructure
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?
- For developers: Unable to verify service quality, making it difficult to optimize product experience
- For enterprises: High trust costs when dealing with sensitive data and core business
- For the industry: Lack of quality standards leads to "bad money driving out good" - fraudulent platforms have lower costs
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?
- The platform has pricing power, and users have weak bargaining ability
- Single point of failure risk: Platform service interruptions directly affect business
- Innovation is limited: Unable to flexibly try new models or fine-tune models for vertical domains
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?
- High-quality vertical models struggle to reach genuine demand
- Institutions and individuals with idle computing power cannot monetize their resources
- How much of the fees paid by users actually flows to service providers? It cannot be verified
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's Answer: A Comprehensive View of the Technical Architecture
DGrid AI is not simply about "putting AI on the chain," but building decentralized AI infrastructure around three core capabilities:
1. Unified Calling Layer: AI Gateway
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:
- Intelligent routing: Automatically selects the optimal model based on task type, cost budget, and latency requirements
- Load balancing: Automatically switches to backup nodes when a certain upstream service provider fails
- Unified billing: Standardizes the pricing methods of different models through the Compute Unit mechanism
- Zero migration cost: Only need to modify the base_url parameter, no need to restructure existing code
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.
2. Quality Verification Layer: Proof of Quality (PoQ)
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:
- Random blind testing: DGrid maintains a question bank and randomly selects questions to conduct blind tests on the model services provided by nodes
- Multi-dimensional evaluation: Evaluates output quality, response speed, stability, format compliance, and other dimensions
- On-chain evidence: Verification results are recorded on-chain, serving as the basis for node reputation scoring and incentive distribution
- Penalty mechanism: If a node commits fraud (claims to provide GPT-5.6 but actually calls a lower version), it will be detected and have its stake confiscated
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?
3. Open Market Layer: Model Marketplace
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:
- Free listing: Any individual or institution with model resources or computing power can become a supplier
- Self-pricing: Providers set prices based on costs, performance, and market demand
- PoQ endorsement: Listed models will be verified by PoQ, with quality signals being publicly transparent
- On-chain settlement: Calling revenue is automatically distributed through smart contracts, without platform custody
Key Value:
- Vertical fine-tuned models (medical, legal, financial) can directly reach paying users
- Institutions with idle GPUs can deploy computing power as inference services and monetize
- Users can filter the most suitable models based on performance, price, and use
From an architectural perspective, DGrid is a three-layer protocol stack:
- Top layer: Unified calling interface (AI Gateway), solving developer experience issues
- Middle layer: Quality verification mechanism (PoQ), addressing trust issues in the open market
- Bottom layer: Open market protocol (Marketplace + $DGAI), solving supply and value distribution issues
- In-Depth Analysis of the PoQ Mechanism: How to Establish Quality Standards in an Open Network?
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.
Why is PoQ Needed?
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:
- Some claim to provide Claude Opus 5, but actually call a cheaper model
- Some intentionally reduce service stability, causing disconnections during peak times
- Some return results that do not comply with formats, increasing downstream processing costs
The core problem PoQ aims to solve: How to verify the service quality provided by nodes without touching user privacy?
How Does PoQ Work?
Step 1: Question Bank Construction
DGrid maintains a question bank covering various task types:
- Code generation
- Text understanding
- Reasoning analysis
- Format conversion
- Multi-language translation
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:
- Quality Score: Accuracy, Completeness, Logic of Answers
- Speed Score: Response Delay, Time to First Byte
- Stability Score: Success Rate, Error Rate
- Compliance Score: Whether the Output Format Meets API Standards
Step Four: On-Chain Certification
The evaluation results are recorded on-chain, forming a credibility score for the nodes. This score:
- Public and Transparent: Anyone can query it
- Immutable: Historical records are permanently preserved
- Incentive Impact: High-quality nodes receive more $DGAI rewards
- Trigger Penalties: Fraudulent nodes will have their stakes confiscated
Three Key Designs of PoQ
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.
Product Matrix: A Complete Ecosystem Around Different Participants
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:
- SaaS products need to flexibly switch models (use Claude Opus 5 for code generation, GPT-5.6 for conversation, DeepSeek for cost optimization)
- AI Agents need to dynamically select the optimal model based on tasks
- Enterprises want to avoid vendor lock-in and maintain technology stack flexibility
2. For Model Providers: Model Marketplace
Core Value: Free listing, self-pricing, on-chain settlement, PoQ endorsement
Applicable Scenarios:
- Fine-tuning model teams in vertical fields like healthcare, law, finance
- Institutions or individuals with idle GPUs
- Suppliers with exclusive model resources or low-cost computing power
3. For Ordinary Users: AI Arena
Core Value: Anonymous model battles, real preference data, earn by participating
Applicable Scenarios:
- Enterprises compare different models' performance in real scenarios before purchasing AI services
- Ordinary users participate in model evaluations, contributing data to the ecosystem and receiving incentives
- DGrid uses real user feedback to optimize routing strategies and quality standards
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:
- Individual creators deploy dedicated AI assistants
- Agents establish trust relationships through on-chain identities
- As a member of the BNB Chain AI Landscape, integrate into the on-chain Agent ecosystem
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:
- AI Gateway solves the calling problem
- Model Marketplace addresses supply and distribution issues
- AI Arena resolves quality assessment and data feedback issues
- DClaw tackles Agent deployment and on-chain identity issues
These four products build a closed loop around "enabling AI services to circulate in an open network."
$DGAI Token Economy: More Than Just Incentives, But a Coordination Layer
$DGAI is the native token of the DGrid AI network, with a total supply of 1 billion coins.
Token Distribution
Only 15% is unlocked at TGE (Airdrops + Liquidity), with Team and Investor fully locked for one year.
Four Functions of $DGAI
- Node Staking
Node operators stake $DGAI as a service guarantee, and PoQ scores affect incentive weights. Users can delegate $DGAI to quality nodes to share profits.
- Service Payment
Users pay with $DGAI when calling AI services to enjoy discounts. This creates real demand for the token.
- Ecosystem Incentives
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.
- Protocol Governance
$DGAI holders can vote on: fee structure, PoQ rules, which new models to support, ecosystem incentive plans, and treasury usage.
Value Cycle
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.
Commercial Validation and Market Positioning: What Does $23M Revenue Mean?
Verified Data
- Revenue in the first half of 2026: $23M
- Number of paying users: 15,000+
- Users participating in AI Arena: 500,000+
- Financing scale: $5M seed round
What Does This Set of Data Indicate?
- Product Demand Has Been Verified
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.
- Extremely High Capital Efficiency
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.
- High Community Participation
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":
- Product Side: AI Gateway, Arena, and DClaw have been launched and accumulated real users
- Business Side: $23M revenue proves payment demand
- Technical Side: PoQ mechanism has been implemented, with five papers supporting its credibility
- Token Side: $DGAI economic model announced, TGE is about to launch
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?
Competitive Product Comparison Matrix: Where is DGrid's Differentiation?
Core Differentiation
vs. OpenRouter
- OpenRouter was recently acquired by Stripe for $7 billion, proving the market value of AI aggregation platforms
- However, OpenRouter is a closed relay station, while DGrid is an open market
- OpenRouter lacks quality verification, while DGrid has PoQ on-chain verification
vs. Akash/Render
- Akash provides raw computing power, while DGrid provides packaged AI services
- Akash is suitable for teams building their own services, while DGrid is suitable for developers seeking rapid integration
- Akash is a general computing power network, while DGrid is AI-specific infrastructure
vs. OpenAI/Anthropic
- Centralized platforms hold pricing power and quality interpretation rights
- DGrid makes quality verifiable through PoQ and returns pricing to the market through Marketplace
- As AI models proliferate and vertical models become increasingly important, the supply ceiling of an open market is far higher than that of a single supplier
Next Steps and Observation Indicators
Key Actions Ahead
- Launch of Node Network: Open Node Staking and Inference Services
- Expansion of Model Marketplace: Attracting More Model Providers to List
- Governance Launch: Gradually Opening Community Governance Rights
### Three Core Indicators to Assess DGrid's Progress
**1. Network Call Volume**
Is there a sustained growth in real AI service calls? This determines whether the demand for $DGAI payments truly exists.
Observation Method: Monitor the monthly/quarterly call volume data disclosed by the official sources.
**2. Diversity of Marketplace Supply Side**
How many model providers have listed their models? What percentage of vertical domain models is represented? This determines whether the open market is truly operational.
Observation Method: Monitor the number of models listed on the Marketplace, the number of providers, and the distribution of call volumes.
**3. Token Utilization Rate**
Is $DGAI genuinely being used for payments, staking, and governance, or is it merely a speculative asset?
Observation Method: Monitor on-chain data --- number of payment transactions, staking rate, and governance proposal participation.
Conclusion
AI infrastructure is undergoing a paradigm shift: evolving from centralized platform dominance to open protocols.
DGrid AI provides a complete example: * Solving developer call issues with AI Gateway * Addressing quality verification in the open market with PoQ * Resolving supply openness and value distribution issues with Model Marketplace * Connecting all participants with $DGAI to form a coordination layer
It does not start from the token to backtrack scenarios, but rather from real product demands --- $23M in revenue proves the demand for calls, 500,000 Arena users prove community participation, and 5 papers and actual deployments prove the feasibility of PoQ.
The core question is: Can DGrid turn "callable, verifiable, and settleable" from product features into a network protocol?
With the launch of $DGAI and the start of the node network, this question is about to be validated.
-- Price
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