Discussion on the Business Model of AI Computing Power Platform: Taking IO and ATH as Examples

Recently, two AI concept projects have successively conducted token issuances, sparking widespread discussions in the industry about the business model of AI Computing Power platforms. This model is essentially a classic platform-based business model.

On one hand, there are AI startups and game rendering companies that require a large amount of Computing Power; on the other hand, there are individuals or institutions with idle high-performance graphics cards. If there is a platform that can connect the demands of these two parties, it creates a typical platform business model.

This model is beneficial for both parties: AI companies can obtain the required Computing Power at a lower cost, while graphics card owners can earn revenue from idle resources. It is precisely because of this market opportunity that some platforms have emerged, aiming to connect idle graphics card resources with the demands of AI companies.

The popularity of this model is mainly based on the following reasons:

1. Cost considerations: AI companies, especially startups, find it difficult to afford the high costs of purchasing a large number of graphics cards.
2. Flexibility Requirements: These companies prefer to flexibly rent Computing Power resources based on demand.
3. Market Restrictions: The current global supply of graphics cards is tight, and there are even some trade restrictions, which increases the difficulty of direct purchases.

However, this platform model faces a typical "which came first, the chicken or the egg" problem: AI companies need sufficient Computing Power resources on the platform, while graphics card owners expect enough orders. To break this cycle, some platforms have introduced cryptocurrency mechanisms.

A platform has chosen a "chicken first" strategy, attracting a large number of graphics cards through token subsidies. Another platform has taken a different approach by launching virtual and physical mining machine products to maintain and expand its user base. This practice effectively increased users' sunk costs and enhanced user stickiness.

In terms of business models, these platforms usually allow payments using fiat or stablecoins, while also providing the option to pay with the platform token, offering certain discounts. This practice neither forces users to use the platform token nor does it assign practical functionality to the token, which is beneficial for the decentralized holding of the token.

In terms of ecological construction, different platforms have adopted different strategies. Some platforms have introduced the role of inspectors, responsible for monitoring the working status of graphics cards and the order processing situation, and earning token rewards through this work. This approach not only increases the reliability of the platform but also provides profit opportunities for early participants.

It is worth noting that although these platforms have a competitive relationship to some extent, there is also room for cooperation among them since they all handle standardized GPU resources. Some platforms have even engaged in token swaps, demonstrating a spirit of collaboration within the industry.

Overall, the emergence of AI Computing Power platform has provided new opportunities for AI startups and GPU owners, as well as injected new momentum into the development of the entire AI industry. As this model continues to improve and evolve, we can expect to see more innovations and breakthroughs.