How do you view the problems existing in the AI+web3 integration track?

Author: Haotian Source: X, @HAOTIANCRYPTOINSIGHT

are all looking forward to AI+Web3 becoming the catalyst for this round of bull market, as can be seen from the high valuations and heavy bets given by VCs. The question is, what are the current problems in the AI+Web3 integration track? Combined with this systematic report by @web3caff_zh, let me talk about my views:

1) AI training requires large-scale data, and Web3 is precisely used for data tracking and the incentive effect derived from it. In the long run, AI is bound to need the help of web3, but it needs to be clarified that web3 can only solve limited problems of AI.

For example, the main driving forces of traditional large-scale data training, continuous algorithm optimization, computer vision, speech recognition technology, game AI and other core areas still rely on large-scale centralized computing power and chip, algorithm and other hardware and software adaptation optimization. Directions such as deep learning convolutional neural networks, reinforcement learning, and brain-like computing models that expand the boundaries of AI capabilities have no possibility of web3 gaining a foothold in the short term;

2) Generative AI only occupies a small branch of the AI ​​sector, but it has accelerated the integration of AI and web3. Because generative AI is an AI inclusive technology that is more application-oriented. Ideally, the basic large model is generally completed by large companies using centralized computing power and adopting an open source policy to drive the upper-level application market. The overall AI market will gradually become long-tailed, and the importance of model fine-tuning and reasoning will be highlighted.

However, once the company that controls the core computing power and model resources changes the open source policy, it will have a direct impact on the overall AI market. To avoid this crisis, an infra that relies more on distributed computing power architecture and distributed reasoning collaboration architecture will become necessary.

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3) Web3 can play a key role in the construction of AI distributed frameworks. For example, during model training, blockchain can create a unique identifier for the data source and perform data deduplication to improve training efficiency. When computing power is insufficient, blockchain can use the Tokenomics incentive mechanism to build a distributed AI computing power network. In the parameter fine-tuning stage, blockchain can record different versions of the model, track the evolution of the model and perform fine control.

In the model reasoning stage, ZK, TEE and other technologies can be used to build a decentralized reasoning network to enhance communication and trust between models. In the edge computing and DePIN integration stages, web3 can help build a decentralized edge AI network and drive the combination of AI+DePIN Internet of Things.

4) When Vitalik talked about the integration of AI+Web3, he stated that AI can be integrated step by step as a participant in the Web3 world, so the integration of AI and web3 will definitely be very slow.

On the one hand, the mainstream web2 world is still paying attention to the effectiveness of AI and does not rely too much on the AI ​​behind-the-scenes collaboration framework, which is out of touch with web3; on the other hand, web3 is still in the construction stage of basic infra such as distributed computing power network, distributed reasoning architecture network, distributed Tokenomics application network, and distributed AI Agent tool collaboration network in the field of AI integration, and has not been fully verified and applied by the mainstream rigid demand group of web2.

In short, in a word, the general trend of AI+Web3 is correct, but the actual landing development is not so fast. It may take a cycle or even a cross-cycle to see significant progress, which requires more patience.