KEYTAKEAWAYS
-
Planck Network takes a rare heavy asset strategy with real revenue and large scale GPU deployment before its token launch, moving against the typical Web3 pattern of light infrastructure and early hype.
-
Its two layer design positions Planck as a trusted execution platform for AI rather than a simple compute network, offering cross chain coordination and verifiable AI inference for enterprise level adoption.
-
The USDC based revenue loop forms a practical token model where real income drives buybacks and staking rewards, creating a sustainable cycle but also exposing the project to execution and transparency risks.
CONTENT
A REVERSE BEGINNING
The current cycle is full of noise and fast stories. AI and DePIN have become the center of attention. Most new projects follow the same pattern. They launch a token first. They publish a strong vision. They promise a future network long before anything real exists. Many of them do not have customers. Many of them do not have true hardware. Even so they can attract early hype with a few slides or a simple test page. Planck Network is different. From the first week it moved in a direction that does not look like Web3. It reported over one point one to one point four million dollars of real revenue before its token generation event. It deployed more than sixty million dollars of enterprise GPUs in real data centers. These are H100 H200 and B200 machines. They are not home computers or idle cards. They are the same hardware used by global cloud providers. This shows that Planck did not try to build a network with scattered small nodes. It started by building real computing capacity. It chose the difficult and expensive path.
At the same time Planck completed full MiCA compliance before the token listing. This means it is allowed to provide AI and computing services to European companies. It is rare among Web3 projects. Most teams still worry about regulation. Many delay key features because of unclear rules. Planck did the opposite. It treated compliance like part of its product. This makes enterprise adoption much easier. The project looks more like an infrastructure company than a crypto experiment. Its early decisions show that it wants to be stable first then grow. It wants to be credible before talking about scale. Because of this Planck became a special case inside the industry. It did not enter with noise or pure promises. It entered with equipment contracts revenue and legal status. This is not common in Web3. It is a reverse beginning.
THE DESIGN BEHIND TWO LAYERS
Many discussions about Planck stay on the surface. People talk about its GPU native EVM chain. They talk about its low cost and strong marketing. These points are true but they are not the core. The real meaning of Planck is in its two chain structure. Planck Zero and Planck One are not simple labels. They describe a system built for trust and coordination in the AI world. Planck Zero is the coordination layer. It does not execute user transactions. It manages the entire network environment. It controls how resources move between chains. It secures new chains that join the ecosystem. It allows different chains and AI agents to send messages to each other. It is like the control tower of an airport. It does not fly planes. It guides every route in the air. It creates order. It creates safety. It creates a shared space where different actors can work together.
Planck One is the execution layer. It is an EVM chain but it does not act like a common L1. It is made for AI work. It allows AI tasks such as inference and verification to be confirmed on chain. This is important because AI today is a black box. No one can easily prove that a model processed something correctly. No one knows if the output is safe or biased. No one can check how the system made its decision. Planck wants to solve this. With Planck One complex tasks happen off chain on GPUs but their proof appears on chain. This creates a trusted execution path. It does not replace training. It does not compete with large model developers. It builds a layer where AI actions become transparent and verifiable. This design matches the needs of enterprises. They want to use AI. They need to trust outputs. They need to show regulators how data was handled. A chain that provides trusted results becomes useful.
This is why Planck does not behave like a light DePIN network. It does not rely on many home nodes. It does not try to build scale from hobby miners. It focuses on high quality GPUs and unified management. It wants control and order because it wants to fill a missing layer in the AI industry. It wants to be the trust layer. This makes its real competitors cloud companies not other crypto projects. It is trying to do something bigger. It is trying to offer credible execution for AI inside a network that does not rely on trust between participants.
A PRACTICAL ECONOMIC LOOP
Almost every DePIN token faces a similar problem. Users buy tokens to pay for service. Node operators sell tokens to earn money. This creates strong downward pressure. Many networks cannot escape this cycle. They use high emissions to support node rewards. When emissions fall the network becomes weak. When users stop growing the token loses value. This is why many DePIN models do not last long. The systems fall back to zero once the incentive stops.
Planck chose a different path. It lets enterprise customers pay in USDC. They do not need to touch the token. They do not need to worry about price moves. This removes friction. It solves a risk that keeps enterprises away from Web3. After receiving USDC the platform uses the revenue to buy PLANCK on the open market. These tokens go to stakers and supporters. This creates a simple and strong loop. Real revenue creates real buying pressure. Network growth becomes token demand. This looks more like a cloud business than a crypto network. It avoids heavy emissions. It avoids artificial rewards. It creates a system where token value comes from product usage not speculation.
This is one of the most solid models in the DePIN field today but it also comes with risk. The team holds almost forty percent of supply. This makes long term execution important. It makes transparency important. If growth slows or if unlocks create pressure the token may suffer. Another challenge is that core chain code is not fully open. For a project that talks about trust and transparency this becomes a point that will be watched carefully. The model is strong but the balance is fragile. It needs real growth to stay stable. It needs constant delivery to maintain confidence. If it works it can support a large valuation. If it breaks it will face harder challenges than other networks.
THE FIGHT FOR THE FUTURE
Planck’s future will not be decided by GPU quantity. It will not be decided by early token performance. It also will not be decided by how many platforms list it. The key years will be twenty twenty five and twenty twenty six. The main question is simple. Can Planck become one of the first networks to serve the enterprise AI market with a trusted chain based execution system. Enterprise demand for AI is growing. They want tools that can prove how decisions are made. They want systems that follow strict data rules. They want safe environments that can pass audits. Traditional clouds cannot fully solve this. They can offer speed and storage but not transparent validation. Chain based verification can provide this. It can turn model behavior into auditable records. It can create a history that regulators understand. It can build trust between companies that share data or models.
Planck wants to be the network that offers this service. Its roadmap points in this direction. It plans full mainnet delivery. It plans ISO SOC2 and GDPR certification. It plans to expand its validator system and staking design. If it completes these steps it becomes the first network that mixes chain security enterprise standards and AI verification. This is a new category. It is bigger than DePIN. It touches the cloud market. It touches the compliance market. It touches AI governance. But this is also a difficult path. Competitors like io net Render and Akash are also pushing forward. They work on the resource layer. Planck works on the execution layer. The layers are different. The potential is larger but the difficulty is higher.
The final result depends on delivery speed and market acceptance. If Planck finishes its networks in time and if enterprises test its AI pipeline it can move far ahead of the market. If delays happen or if enterprise adoption slows its strong model becomes a weakness. Its heavy structure demands stable growth. Its revenue loop demands scale. Its token design demands confidence. Planck is a high risk and high reward story. It is one of the few projects that combines real revenue strict compliance and ambitious engineering. It stands out because it is not built for hype. It is built for a future where AI actions must be proven and trusted. Whether it can reach that future will be the core question of the next year.
