Another project is entering TGE with an ARR report! Recently, Spheron Network announced its TGE with over $13 million in ARR revenue.

With revenue generated and the ecosystem running, will this standard of discussing TGE only after achieving ARR become the norm for future AI infrastructure projects?

Let's take a closer look at the Spheron project:

1) Spheron Network is a decentralized computing network that aggregates global GPU/CPU resources to provide services for high-performance computing tasks such as AI training, inference, and rendering.

In addition to computing resources, the platform also integrates IPFS storage, ENS domain management, and smart contract deployment based on Arbitrum, providing relatively complete infrastructure support for AI developers.

From a technical architecture perspective, Spheron has built a relatively complete product matrix that covers all aspects from computing power supply to application scenarios.

Fizz Nodes serve as the core infrastructure of the entire network, allowing individual users—especially gamers—to contribute idle GPUs/CPUs to the network and earn rewards through a simple onboarding process.

This design significantly lowers the threshold for computing power supply, incorporating decentralized personal devices and quickly forming a decentralized computing network through a B2C revenue-sharing model.

KlippyAI, as an AI video creation tool, directly targets end users, charging service fees in $SPON tokens. It has already generated nearly 5,000 AI video NFT data on Base L2;

Meanwhile, Skynet differentiates itself from most agents by allowing developer accounts to access computing resources, attempting to enable AI agents to pay for computing power directly with tokens while providing one-click services from wallet creation to contract deployment.

Additionally, products like Supernoderz (nodes as a service), Aquanode (AI inference workloads), and Spheron Console (one-click GPU access) together with Fizz Nodes form a complete closed loop from supply to demand.

2) From operational data, Spheron has already demonstrated a certain scale of the network. There are 44,000 active nodes distributed across more than 170 countries, providing over 8,300 GPUs and more than 600,000 CPUs of computing power, with weekly node rewards exceeding $500,000. Notably, in terms of revenue structure, $7.6 million of the $13 million+ ARR comes from AI business, indicating that AI applications indeed generate real paid demand.

However, the sustainability of this bilateral market model fundamentally depends on whether both supply and demand sides can maintain synchronized growth.

The computing power suppliers receive token rewards, while the demand side pays tokens to use services, and the platform charges service fees—this sounds great, but in practice, it faces several challenges: Can the service quality of a decentralized network remain stable? How long can the cost advantages over giants like AWS and Google Cloud be maintained?

3) The AI agent infrastructure is indeed a large market in its early stages, and Spheron has a certain advantage in timing by laying out related services in advance. However, the competition in this space is far more intense than expected.

Platforms like Hyperbolic, IO.NET, VANA, and Sahara AI each have differentiated positioning and are all making strides in AI infrastructure services.

The market landscape is far from being set, and the eventual winner may not be the one with the most advanced technology, but rather the one that performs most balanced across dimensions such as product iteration speed, ecosystem building capability, and service stability.

Anyway, from a broader industry trend perspective, if "entering TGE with ARR" truly becomes the new standard for the AI infrastructure sector, it may not be a bad thing for the entire industry.

At least it can make the market pay more attention to actual products and revenue, rather than purely speculative concepts.

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