Integration of AI and Blockchain A New Trend Disrupting the Pattern of Computing Resource Allocation

Origin: DODO Research
The combination of AI and blockchain is currently a highly anticipated topic within the industry, with developers actively exploring the possibilities of this integration. Blockchain technology is now seen as an ideal choice to address various issues such as the inability to efficiently utilize AI services and computational resources. Many projects in this field have already carved out their own paths.
Today, let Dr. DODO take you through some standout projects in the AI and computational power market race.
AI + Blockchain
Projects focusing on AI in the blockchain space can mainly be divided into three tracks:
Computational resource sharing: Blockchain technology can build a distributed cloud computing platform to share and efficiently utilize computational resources. Through smart contracts, idle computing resources can be leased to tasks in need, thereby increasing resource utilization and reducing costs. Representative projects include io.net and Aethir.
AI data security and verifiable computing: Both AI and blockchain technologies deal with large amounts of data. Blockchain storage networks provide a secure way to store and transmit data, while AI analyzes this data to generate valuable insights. The integration of these two technologies can protect user privacy while providing reliable data sources for AI. Currently, projects like Arweave represent this direction.
Decentralized AI: Deploying AI models on blockchain networks can realize decentralized AI services, enhancing system reliability and stability while reducing the risk of single points of failure. Projects like Bittensor currently represent this direction.
io.net
Recently launched on Binance, io.net is currently a hot favorite in this arena. io.net is a decentralized GPU network designed to provide significant computational power for machine learning applications. Their vision is to unlock fair access to computational power by assembling over a million GPUs from independent data centers, crypto miners, and projects like Filecoin, making computing more scalable, accessible, and efficient.
io.net offers a completely different approach to cloud computing, utilizing distributed and decentralized models to provide users with more control and flexibility over computational power. Their services are permissionless and cost-effective. According to io.net, their computational power is 90% lower than centralized service providers like Amazon AWS, making io.net a top contender in the decentralized provider space.
Aethir
Aethir offers a disruptive yet highly feasible solution to the complex problem of effectively utilizing global computational resources. Their network aggregates and intelligently redistributes new and idle GPUs from enterprises, data centers, crypto mining operations, and consumers. There is a vast market opportunity to better allocate GPU capacity, and Aethir aims to increase the 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. Typically, the underutilized GPU capacity of a device ranges from 50% to 75%, indicating a significant amount of computational resources that can be tokenized. Aethir targets leveraging these abundant idle resources by focusing on small to medium-sized data centers and enterprises.
Aethir’s token has been listed on exchanges like OKX and Bybit after a Pre-A round raised $9 million, led by renowned institutions like Sanctor Capital and Hashkey.
Arweave
AO is a distributed, decentralized, participant-oriented computing system based on Arweave. AO aims to provide a trustless and collaborative computing service without practical scale limitations, offering a new paradigm for applications integrated with blockchain. Compared to other high-performance blockchains, AO supports the storage of large amounts of data, such as AI models. Unlike Ethereum, AO allows any number of parallel processes to run within a computational unit, coordinating through open message passing without relying on centralized memory space.
Arweave’s launch of AO signifies a move from decentralized storage to a broader decentralized cloud service field. Its permanent on-chain storage is no longer limited to user data but aims to be a permanent host for cloud computing, focusing on large-scale verifiable computing.
Arweave has also announced the token economy between $AR and $AO. According to official statements, $AO is a token with 100% fair issuance, without presale or allocation. The total supply of $AO is 21 million, with a halving cycle of 4 years, distributed every 5 minutes, with a monthly distribution of 1.425% of the remaining supply.
Approximately 36% (100% in the first four months plus 33.3% thereafter) of $AO tokens are allocated to $AR holders every 5 minutes, incentivizing the security of the AO base layer – Arweave.
The remaining 64% of $AO tokens are allocated to bridge users to provide external revenue and incentivize the introduction of assets into AO.
Bittensor
The training of AI models requires a significant amount of data and computational power, but high costs result in these resources being largely monopolized by large enterprises and research institutions. This centralization limits the use and cooperation of AI models, hindering the development of the AI ecosystem. Bittensor (TAO) is dedicated to establishing the world’s first blockchain neural network, enabling network participants to exchange machine learning capabilities and predictions.
Bittensor aims to facilitate the sharing and collaboration of machine learning models and services in a peer-to-peer manner. While technically challenging, TAO still has some distance to go before practical application.
Author’s Perspective
These AI + blockchain projects are likely to change the landscape of computational resource allocation in the future. Decentralized ownership and collaborative cross-cluster deployment 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, creating 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 barrier for project parties is not low. Currently, these projects are still in the experimental stage, and whether they can be implemented as practical infrastructure for actual use in the future remains to be seen.