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 for addressing various issues such as the inability to effectively 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 projects that are performing well in the AI and computing power market race.
AI + Blockchain
Projects focusing on AI in the blockchain field 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 for required computing tasks, thereby increasing resource utilization and reducing 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 insights. Integrating the two can protect user privacy while providing reliable data sources for AI. Projects like Arweave represent this direction.
3. Decentralized AI: Deploying AI models on a blockchain network enables decentralized AI services, enhancing system reliability and stability while reducing the risk of single-point failures. Projects like Bittensor represent this direction.
io.net
Recently launched on Binance, io.net is currently a hot topic in the computing power market. io.net is a decentralized GPU network designed to provide massive computing power for machine learning applications. Their vision is to unlock fair access to computing 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, using a distributed and decentralized model to provide users with more control and flexibility over computing power. Their services are permissionless and cost-effective. According to io.net, their computing power is 90% lower than centralized service providers like Amazon AWS, making io.net a standout decentralized provider.
Aethir
Aethir offers a disruptive yet highly feasible solution to the effective utilization of global computing resources. Their network aggregates and intelligently redistributes new and idle GPUs from enterprises, data centers, crypto mining operations, and consumers. The market opportunity for better redistributing GPU capacity is vast, and 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. Typically, the underutilized GPU capacity of a device is estimated to be between 50% to 75%, indicating a significant amount of computing power that can be tokenized. Aethir aims to leverage these abundant idle resources by targeting small to medium-sized data centers and enterprises.
Aethir’s tokens are now listed on exchanges like OKX and Bybit, having previously raised $9 million in Pre-A funding, with leading institutions like Sanctor Capital and Hashkey as investors.
Arweave
AO is a distributed, decentralized, participant-oriented computing system based on Arweave. The core goal of AO is to provide a trustless and collaborative computing service with no practical scale limitations, offering a new paradigm for applications combined with blockchain. 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 a computing 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 arena. Their permanent on-chain storage aims to become 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 100% fair issuance token with no pre-sale or allocation. The total supply of $AO is 21 million tokens, halving every 4 years, distributed every 5 minutes with a monthly distribution 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 AO’s underlying layer – Arweave.
The remaining 64% of $AO tokens are allocated to bridging users, providing external incentives and introducing assets into AO.
Bittensor
The training of AI models requires a significant amount of data and computing power, but high costs result in these resources being mostly monopolized by large enterprises and research institutions. This centralization limits 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 for participants to exchange machine learning capabilities and predictions.
Bittensor hopes to facilitate the sharing and collaboration of machine learning models and services in a peer-to-peer manner. While the technical implementation of TAO poses challenges, it is still a distance away from practical application.
Author’s Opinion
These AI + blockchain projects are likely to change the future landscape of computing resource allocation. Decentralized ownership and collaborative cross-cluster decentralized regional deployment will pave the way for a new wave of economic and technological advancement. 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 support and more funding, the entry barriers for project owners are not low. Currently in the experimental stage, it remains to be seen whether these projects can become practical infrastructure that people will actually use.
