Recently, with the listing of AI-based tokens such as io.net and Aethir on top exchanges such as Binance and OKX, more and more projects combining with AI have emerged in Web3. Can Web3 use its own technological advantages to promote the AI ​​revolution and subvert the industry? What are the application scenarios that can be implemented in the process of combining Web3 with AI? The author will share with you the AI ​​applications in Web3 through a series of articles.

1. Competition in AI Big Models

The competition for large AI models mainly focuses on three aspects: computing power, algorithms, and data.

If traditional technology giants want to enter the AI ​​big model field, they must first consider the high training and debugging costs of the big models in the early stage. Tian Qi, chief scientist of Huawei Cloud's artificial intelligence field, mentioned in his speech at the AI ​​Big Model Technology Summit that the single cost of big model development and training is as high as 12 million US dollars. OpenAI CEO Sam Altman also mentioned that the training cost of GPT-4 exceeds 100 million US dollars, of which GPU computing power costs account for the majority of the entire training cost.