Interpreting AI+Web3 narrative logic and potential investment opportunities

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In the past six months, the tokens of many projects in the AI ​​sector have continued to rise. Tokens such as $TAO, $WLD, $RNDR, $AGIX, and $FET have increased several times. This prosperity has benefited from OpenAI, NVIDIA and other Web2 projects. Driven by the innovation of AI companies, AI projects in the encryption field are booming, showing jaw-dropping vitality. It is particularly worth mentioning that the combination of AI and cryptocurrency is not a fad, but an emerging track for sustainable development. AI + web3 has become one of the most exciting innovation frontiers in the technology field. With the development of AI technology With continued development and iteration, we will witness more funds and attention continuing to flow into this field, bringing multiple rounds of development opportunities to AI + web3. Therefore, deploying AI sector tokens is not only a feasible option, but also a necessary strategic move.

AI+Web3 narrative logic and its potential tokens

There is no doubt that artificial intelligence will be a key force in driving the cryptocurrency bull market. According to current market trends, the AI+Web3 industry chain can be divided into infrastructure, middle layer and application layer. The infrastructure layer lays the supporting foundation, the middle layer connects technology and business, and the application layer provides users with intelligent experiences, jointly promoting industry change and digital transformation. Now, let us explore more Alpha targets from the perspectives of the infrastructure layer, middle layer and application layer, earn more profits, and share the joy brought by this bull market.

1. Infrastructure layer

The AI ​​industry is developing rapidly and the demand for high-performance computing and storage infrastructure is increasing. Web3 technology is expected to build a decentralized resource network, reduce infrastructure costs, and meet various AI application needs. Therefore, decentralized AI infrastructure is one of the most anticipated development directions at present. Its representative projects include Render Network ($RNDR) dedicated to rendering services; Akash ($AKT) provides decentralized cloud services and computing hardware networks; in the storage field, Filecoin ($FIL) and Arweave ($AR) are still well-known decentralized storage networks. Recently, they have also launched a network focusing on the AI ​​field. New products for storage and computing services. In addition to the above established projects, there are also new projects such as AI Agent service platform Olas Network ($OLAS), AI data annotation and training platform AIT Protocol ($AIT), AI data tokenization platform LayerAI ($LAI), etc., the center ized GPU network io.net ($IO).

2. Middle layer

The middle layer mainly refers to the use of Web3 related technologies to improve the current situation and existing problems in the specific process of AI work production. It mainly includes three parts: data acquisition stage, data preprocessing stage and model verification and inference stage.

1) Data acquisition stage: Use decentralized data identity to build a more open data network and data trading platform. By combining cryptography technology and blockchain features to protect user privacy and data confirmation, and at the same time encouraging users to share high-quality data through incentives to expand data sources and improve data acquisition efficiency. Representative projects include AI identity project Worldcoin ($WLD), data trading platform Ocean Protocol ($OCEAN), etc.

2) Data preprocessing stage: Create a distributed AI data annotation and processing platform, and use an economic model to motivate crowdsourcing mode to promote more efficient and lower-cost data preprocessing and provide support for the subsequent model training stage. Representative projects such as Public AI, etc.

3) Model verification and inference stage: During the model verification and inference process, Web3 technology is combined with cryptographic technologies such as ZK and homomorphic encryption to verify the model’s reasoning. (Representative projects that combine Web3 technology in the model verification and inference stage include bittensor($TAO), Privasea($PRVA), Modulus, etc.

Many middle-tier projects focus more on developer tools, often providing additional services to existing developers and project parties. In the current early stages of AI development, market needs and business practices for these projects are still evolving.

3. Application layer

At the application level, AI technology is increasingly integrated into the Web3 trend. Combining AI technology can not only improve efficiency, but also optimize product experience. By leveraging AIs content generation, analysis, and prediction capabilities, we can apply it to gaming, social networking, data analysis, financial forecasting, and more. At present, AI+Web3 applications are mainly divided into three categories.

1) AIGC type: This type of application uses AI generative technology to allow users to generate text, pictures, videos, Avatar and other content through conversations. These technologies can be demonstrated as stand-alone AI agents or integrated directly into products. Representative projects include NFPrompt ($NFP), SleeplessAI ($AI), etc.

2) AI analysis: In this type of project, the project party uses its accumulated data, knowledge base and analysis capabilities to train vertical AI models to achieve data analysis, judgment, prediction and other functions, and provide them as products. to users. In this way, users can easily obtain advanced analytical functions of AI, such as data analysis, information tracking, code auditing and modification, financial forecasting, etc. Representative projects include Kaito, Dune, etc.

3) AI Agent Hub class: This type of project aggregates various AI agents and usually provides users with the ability to create customized AI agents without coding, similar to GPTs. New representative potential projects include My Shell, MetaSecond.AI ($MTSD), etc.

There is no obvious leading project in the application layer yet, but in the long run, the AI+Web3 application field must be a sector with extremely high potential and a broader ceiling. In this field, competition is no longer limited to technological innovation, but focuses more on the accumulation of products and technical capabilities. Especially products that can provide a better experience in AI will gain greater competitive advantages in this field.

Conclusion

There is no doubt that AI + web3 has become an investment target not to be missed in 2024 in the eyes of many people. With the vigorous development of AI, investors need to conduct careful evaluation from a more comprehensive perspective in order to carefully select Those “gold targets” that have greater potential and are more worthy of early intervention. Now, we have jointly entered a new era in the field of AI+Web3, and we hope that this field will shine even more dazzlingly. 2024 will be the year when we witness the peak of technological innovation, and this does not stop at the realization of technology, but also a stirring thinking about the future world.