With an annual revenue of 13 million, will Spheron's "revenue reaching target and then issuing tokens" set a new benchmark in the AI field?

Another project has already announced its TGE with ARR results! Recently, Spheron Network announced its TGE with over $13 million in ARR revenue.

With revenue now generated and the ecosystem established, it's time to discuss TGEs. Will this become the standard for TGEs in the AI infrastructure sector?

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 power, the platform also integrates supporting services such as IPFS storage, ENS domain name management, and Arbitrum-based smart contract deployment, providing relatively complete infrastructure support for AI developers.

From a technical perspective, Spheron has built a relatively complete product portfolio, covering every aspect from computing power supply to application scenarios.

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

This design significantly lowers the threshold for computing power provision, incorporating even scattered personal devices. Through a business-to-consumer (B2C) revenue-sharing model, a decentralized computing network is rapidly formed.

KlippyAI, an AI video creation tool, targets consumer users directly, using $SPON tokens for payment. It has already generated nearly 5,000 AI video NFTs on Base L2.

Unlike most agents that rely on developer accounts to access computing resources, Skynet attempts to enable AI agents to pay for computing power directly with tokens, while providing a one-click service from wallet creation to contract deployment.

In addition, products such as Supernodez (node-as-a-service), Aquanode (AI inference workloads), and Spheron Console (one-click GPU access), along with Fizz Nodes, form a complete closed loop from supply to demand.

2) Judging by operational data, Spheron has demonstrated considerable network scale. 44,000 active nodes are distributed across over 170 countries, providing the computing power of over 8,300 GPUs and over 600,000 CPUs, and paying out over $500,000 in node rewards weekly. Notably, AI businesses account for $7.6 million of the $13 million+ in ARR, demonstrating that AI applications are generating real, paid demand.

However, the sustainability of this two-sided marketplace model ultimately depends on whether both supply and demand can maintain synchronized growth.

Computing power suppliers receive token rewards, demanders pay tokens for service usage, and the platform collects service fees—while this sounds promising, it faces numerous challenges in practice: Can the service quality of a decentralized network be sustained? How long can the cost advantage over giants like AWS and Google Cloud last?

3) AI agent infrastructure is indeed a large, early-stage market. Spheron has a certain advantage in terms of time window by deploying related services ahead of time. However, competition in this sector is far more intense than expected.

Platforms such as Hyperbolic, IO.NET, VANA, and Sahara AI each have their own differentiated positioning, all focusing on AI infrastructure services.

The market landscape is far from settled, and the ultimate winner may not be the one with the most advanced technology, but rather the one with the most balanced performance across a range of dimensions, including product iteration speed, ecosystem development capabilities, and service stability.

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

At the very least, it will allow the market to focus more on actual products and revenue, rather than pure hype.