$SENT In-Depth Analysis: Interpreting the Technical Foundation and Market Pricing Logic of DePIN Projects Based on Development Stages

With the rise of the decentralized physical infrastructure network (DePIN) track, the market is shifting the concept of “decentralization” from the hardware resource layer to a more complex intelligent layer. Sentinel ($SENT) is a pioneering project attempting to build a decentralized AI economy in this context.

This article will comprehensively analyze its origin background, development stages, ecosystem construction, economic model, and market pricing logic.

What is the background of the $SENT (Sentinel) project? What core issues does it aim to solve?

Currently, the crypto world is undergoing a paradigm shift led by the DePIN track. Organizing and incentivizing physical world hardware resources in a decentralized manner has brought unprecedented efficiency and trustworthiness to fields like computing, storage, and bandwidth. However, as DePIN matures in the “hard resource” layers such as computing power and storage, a deeper demand has emerged: in the AI era, truly scarce assets are not only foundational computing power but also the “verifiable, composable intelligent capabilities themselves” built upon it. Sentinel was born in this context, aiming to extend the core DePIN principles—decentralization, token incentives, verifiable supply—from physical infrastructure to the “wisdom” and “model” resources of AI, creating a decentralized AI economy.

Specifically, Sentinel directly addresses two major pain points in the current AI field that mainstream paradigms cannot solve:

• The “black box” and “cut” dilemma of centralized AI: models are opaque, value distribution is concentrated, and developer-created value is largely captured by platforms.
• The “value capture” deficiency of open-source AI: developers lack sustainable monetization mechanisms for open-source AI, making long-term innovation difficult to sustain.
• Sentinel’s solution path: on-chain rights confirmation and economicization: through on-chain AI ownership technology, establishing property rights and traceable call records for AI models, building an open economy where value can be precisely and automatically fed back to creators.

What development stage is Sentinel currently in? How do its technical architecture and network operation mechanisms support its evolution?

Based on product and ecosystem maturity, Sentinel is currently in a critical stage of “early mainnet launch and ecosystem cold start.” Its core protocol layer has been deployed, the mainnet entry is open, and technical feasibility has entered a public validation cycle. The main task now is to verify the stability of its multi-agent network in real-world scenarios and to accumulate early ecosystem.

To support the transition from launch to future scaling, Sentinel has built a layered, modular decentralized AI infrastructure. Its technical architecture and the corresponding development stages are as follows:

Its core operation mechanism revolves around the GRID network. This network is a dynamic, composable marketplace and execution layer for intelligent agents, capable of automatically decomposing complex queries and scheduling the most suitable agent components to complete tasks. Its modular design offers high scalability, with future expansion into modalities like speech and vision to support complex business intelligence workflows.

How far has the Sentinel ecosystem been built? How are current application scenarios and real needs reflected?

Sentinel’s ecosystem has completed an initial structural setup and is transitioning from “ecosystem cold start” to “early network effects.” Its GRID network has gathered over 110 partners, forming an AI marketplace containing diverse agents, data, and models.

Current real needs and scenarios mainly involve three types of participants:

At present, the ecosystem has preliminarily validated its core value proposition (providing a market channel for open-source AI), but it is still a long way from generating “killer applications” with strong network effects and achieving “product-market fit.” It is on the brink of a critical value leap.

How is the $SENT token economic model designed? Analysis of issuance, distribution, and utility mechanisms within the ecosystem

The $SENT economic model aims to build a sustainable, self-reinforcing decentralized AI economy. Its total supply is fixed at 34,359,738,368 tokens, with the following distribution and release mechanisms:

This token release plan has bought valuable time for ecosystem cold start. Its real essence lies in the economic cycle created by token utility:

• Payment medium: users pay $SENT to invoke AI services, creating continuous demand.
• Revenue and staking: developers earn $SENT and can stake to increase service weight, locking liquidity.
• Governance: holders participate in decision-making, guiding protocol revenue to burn or redistribution, regulating the economy.

A closed-loop value flow: user payment → protocol fee → revenue distribution/burn → developer earnings and staking → network quality improvement → attracting more users. The long-term value of $SENT depends on its actual usage in these consumption scenarios.

Review of $SENT historical price trends: What are the main pricing drivers at different stages?

$SENT’s value discovery has gone through distinct multi-stage relay-driven phases:

This is a classic process from pure narrative to real-world validation. Currently, $SENT is in a critical transition from “liquidity-driven” to “data-driven.”

How does the market price $SENT? What are the long-term value supports and key variables?

Market pricing of $SENT oscillates between “short-term sentiment pricing” and “long-term fundamentals pricing.”

• Short-term (1-12 months): driven by sentiment and narratives, influenced by AI track hype, institutional halo, liquidity events.
• Long-term (over 1 year): driven by cash flow and usage rate, returning to its essence as the “fuel” of a decentralized AI economy.

Long-term value support can be examined through benchmarking and core formulae:

• Horizontal benchmarking: compare ecosystem breadth with Bittensor, resource monetization efficiency with Akash/Render.
• Valuation logic: a simplified framework is: Protocol annualized revenue × valuation multiple = fully diluted valuation. Here, protocol revenue is the total fees of the GRID network, and the valuation multiple is determined by growth potential and token economic design.
• Key variables:
  • Ecosystem growth quality vs. inflation: can real demand surpass token supply growth?
  • Technical reliability and governance: can the network stably support commercial applications, and can governance effectively incentivize builders?
  • Depth of “real consumption” scenarios: whether tokens are necessary and consumed in payment, staking, etc., and the implementation of deflationary mechanisms (like burns).

Summary

Sentinel is a typical “high uncertainty, high ceiling” infrastructure project. It is at a critical point of “basic infrastructure completion, waiting for explosive ecosystem applications.” Its tech stack and economic model are in place, but large-scale network effects remain to be validated.

Risks should not be underestimated: technical stability in large-scale application; ecosystem competition to form network effects; economic model management of inflation pressures before ecosystem revenue scales.

Therefore, for Gate users, $SENT is more suitable for mid- to long-term allocation rather than short-term speculation. Investors should view it as an early-stage layout for “decentralized AI infrastructure,” with key indicators being total network revenue, active agents, and real token consumption rate. This is a bold experiment applying DePIN philosophy to intelligence itself, and its long-term value realization depends on whether the team can successfully bridge from technical feasibility to economic sustainability.