In recent years, the integration of AI and blockchain technology has become a hot topic in the industry. Developers are actively exploring the possibility of this combination, aiming to use blockchain technology to solve many problems in AI services and computing resource utilization. At present, many projects have made significant progress in this field. Today, Dr.DODO will give you an in-depth understanding of outstanding projects in the AI and computing power market.
Three major tracks of AI + blockchain
In the blockchain field, projects focusing on AI are mainly divided into the following three tracks:
1. Computing resource sharing
The distributed cloud computing platform built with blockchain technology can realize the sharing and efficient use of computing resources. Through smart contracts, idle computing resources can be leased to required computing tasks, thereby improving resource utilization and reducing costs. Representative projects include: io.net and Aethir.
2. AI Data Security and Verifiable Computing
Blockchain provides a secure way to store and transmit data, while AI generates valuable information by analyzing this data. Combining the two can provide a reliable data source for AI while protecting user privacy. Representative projects include: Arweave.
3. Decentralized AI
Deploy AI models on blockchain networks to achieve decentralized AI services. This approach improves the reliability and stability of the system and reduces the risk of single point failures. Representative projects include: Bittensor.
io.net
io.net, which was recently launched on Binance, is a star project in the current track. io.net is a decentralized GPU network designed to provide huge computing power for machine learning applications. Their vision is to unlock fair access to computing power and make computing more scalable, accessible and efficient by assembling more than one million GPUs from independent data centers, crypto miners and projects such as Filecoin.
io.net provides a new approach to cloud computing, using a distributed and decentralized model to provide users with more control and flexibility in computing power. Its services are permissionless and low-cost. According to official statements, their computing power costs are 90% lower than centralized service providers such as Amazon AWS. These advantages make io.net a leader among decentralized providers.
It will be taken
Aethir is committed to solving the problem of efficient use of global computing resources and provides a disruptive and highly viable solution. Their network aggregates and intelligently reallocates new and idle GPUs from enterprises, data centers, cryptocurrency mining operations, and consumers. Aethir hopes to increase the current global GPU computing availability by more than 10 times by better reallocating GPU capacity.
A key feature of Aethir is its focus on repurposing existing idle resources rather than requiring node participants to purchase new hardware. Typically, the underutilized GPU capacity of a device is estimated to be between 50% and 75%, indicating that there is a large amount of computing power resources that can be tokenized. Aethir aims to take advantage of these abundant idle resources by targeting small and medium-sized data centers and enterprises. Aethir's tokens have been listed on exchanges such as OKX and Bybit, and it has raised $9 million in a Pre-A round of financing led by well-known institutions such as Sanctor Capital and Hashkey.
Arweave
Arweave is a distributed, decentralized, participant-oriented computing system based on Arweave. Its core goal is to provide a computing service that does not require trust and collaboration, and has no practical scale limitations, providing a new paradigm for applications combined with blockchain. Compared with other high-performance blockchains, Arweave supports the storage of large amounts of data, such as AI models. Its permanent on-chain storage is not only used for user data, but also hopes to become a permanent host for cloud computing, focusing on large-scale verifiable computing.
Recently, Arweave announced the token economy between its dual tokens $AR and $AO. $AO is a 100% fair issuance token with no pre-sale and pre-allocation. The total supply of $AO is 21 million, with a halving cycle of 4 years, distributed every 5 minutes, and a monthly distribution of 1.425% of the remaining supply. About 36% of $AO tokens are distributed to $AR holders every 5 minutes to incentivize the security of Arweave, the base layer of AO. The remaining approximately 64% of $AO tokens are allocated to bridge users to provide external benefits and incentives for introducing assets into AO.
Bitten Sensor
Training AI models requires a lot of data and computing power, but the high cost has led to these resources being mostly monopolized by large companies and research institutions. This centralization limits the use and cooperation of AI models and hinders the development of the AI ecosystem. Bittensor (TAO) is committed to building the world's first blockchain neural network so that network participants can exchange machine learning capabilities and predictions. Bittensor promotes the sharing and collaboration of machine learning models and services in a peer-to-peer manner. TAO is quite challenging in technical implementation and is still some distance away from practical application.
in conclusion
These AI+blockchain projects are expected to change the future distribution of computing resources. Decentralized ownership, collaborative cross-cluster decentralized regional deployment will pave the way for a new wave of economic and technological progress. These projects are ambitious and hope to change the future landscape of cloud computing and AI applications and shape a more interconnected, efficient, and innovation-driven global cloud economy. In the context of countries actively promoting productivity transformation, these development directions are worth exploring in depth. However, since this field requires stronger technical support and more financial support, the entry threshold faced by the project is relatively high. It is still in the trial stage. Whether it can be implemented in the future and become an infrastructure that is actually used by people remains to be seen.
