Author: DODO Research
The fusion of AI and blockchain is currently a hot topic in the industry, with developers actively exploring the possibilities of this integration. Blockchain technology is now seen as an ideal solution to various issues such as the inability to efficiently utilize AI services and computing resources. Many projects in this field have already carved out their own paths of exploration.
Today, Dr. DODO will take you on a journey to discover some outstanding projects in the AI and computing power market arena.
AI + Blockchain
Projects focusing on AI in the blockchain space can mainly be divided into three tracks:
1. Computing power sharing: Blockchain technology can build a distributed cloud computing platform to share and efficiently utilize computing resources. Through smart contracts, idle computing resources can be leased to tasks in need, thus enhancing resource utilization and lowering costs. Representative projects include io.net and Aethir.
2. AI data security and verifiable computing: Both AI and blockchain technologies require handling large amounts of data. The blockchain storage network provides a secure way of storing and transmitting data, while AI analyzes this data to generate valuable information. The integration of these two technologies can protect user privacy while providing reliable data sources for AI. Currently, projects like Arweave represent this direction.
3. Decentralized AI: Deploying AI models on blockchain networks can realize decentralized artificial intelligence services, enhancing system reliability and stability while reducing the risk of single points of failure. Projects like Bittensor represent this direction.
io.net
Recently launched on Binance, io.net is currently a rising star in this track. io.net is a decentralized GPU network aimed at providing massive computing power for machine learning applications. Their vision is to unlock fair computing power access by assembling over a million GPUs from independent data centers, crypto miners, and projects like Filecoin. This approach makes computing more scalable, accessible, and efficient.
io.net offers a completely different cloud computing approach, utilizing a distributed and decentralized model to provide users with greater control and flexibility over computing power. Their service is permissionless and cost-effective. According to io.net, their computing power is 90% lower than centralized service providers like Amazon AWS. This combination makes io.net a leader among decentralized providers.
Aethir
Aethir offers a disruptive yet highly viable solution to the complex problem of efficiently utilizing global computing resources. Their network aggregates and intelligently redistributes new and idle GPUs from enterprises, data centers, crypto mining operations, and consumers. Aethir aims to increase global GPU computing availability by over 10 times.
A key feature of Aethir is its focus on reusing existing idle resources rather than requiring node participants to purchase new hardware. It is estimated that the underutilized GPU capacity of a device ranges from 50% to 75%, indicating a large amount of computing resources that can be tokenized. Aethir aims to leverage these abundant idle resources by targeting small and medium-sized data centers and enterprises.
Aethir’s token has already been listed on exchanges such as OKX and Bybit. They previously raised $9 million in Pre-A funding, with leading investments from renowned institutions like Sanctor Capital and Hashkey.
Arweave
AO is a distributed, decentralized, participant-oriented computing system based on Arweave. The core goal of AO is to provide a trustless, collaborative, and infinitely scalable computing service for blockchain-integrated applications. Compared to other high-performance blockchains, AO supports storing large amounts of data, such as AI models. Unlike Ethereum, AO allows an arbitrary number of parallel processes to run within computing units, coordinating through open message passing without relying on centralized memory space.
Arweave’s introduction of AO signifies a shift from decentralized storage to a broader decentralized cloud service domain. Their permanent on-chain storage aims to be not just for user data but also to serve as a permanent host for cloud computing, focusing on large-scale verifiable computing.
Arweave recently announced the token economics between $AR and $AO. According to official statements, $AO is a token with 100% fair distribution, with no pre-sale or pre-allocation. The total supply of $AO is 21 million, with a halving cycle of 4 years, distributed every 5 minutes at a monthly rate of 1.425% of the remaining supply.
Approximately 36% (the first four months at 100% plus the subsequent 33.3%) of $AO tokens are allocated every 5 minutes to $AR holders, incentivizing the security of the AO base – Arweave.
The remaining approximately 64% of $AO tokens are allocated to bridging users, providing external profits and incentives for introducing assets into AO.
Bittensor
The training of AI models requires a significant amount of data and computing power, but high costs have led to these resources being mostly monopolized by large enterprises and research institutions. This centralization restricts the use and collaboration of AI models, hindering the development of the AI ecosystem. Bittensor (TAO) aims to establish the world’s first blockchain neural network to enable network participants to exchange machine learning capabilities and predictions.
Bittensor hopes to promote the sharing and collaboration of machine learning models and services in a peer-to-peer manner. While the technical implementation of TAO presents challenges, it is still a distance away from actual application.
Author’s Opinion
These AI + blockchain projects are likely to change the landscape of computing resource allocation in the future. Decentralized ownership, collaborative cross-cluster decentralized regional deployments will pave the way for a new wave of economic and technological progress. These projects have ambitious goals, aiming to reshape the landscape of future cloud computing and AI applications, shaping a more interconnected, efficient, and innovation-driven global cloud economy. With countries actively promoting productivity transformation, these development directions are worth exploring in depth. However, due to the need for stronger technical and financial support in this field, the entry barriers for project owners are not low. Currently, these projects are still in the experimental stage, and whether they can be implemented as practical infrastructure for actual usage in the future remains to be seen.
