> For the complete documentation index, see [llms.txt](https://davidadeola.gitbook.io/influx-ai-whitepaper/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://davidadeola.gitbook.io/influx-ai-whitepaper/3.0-data-isolation-consraints/3.1-data-isolation-and-utilization-issues.md).
# 3.1 Data Isolation and Utilization Issues
Traditional enterprises' data management results in isolated data silos, which hinder the comprehensive utilization of data. This has created a significant need for a new model to reshape enterprise-user relationships and release the full value of data.
Currently, most innovations in AI and Web3 are concentrated at the data and infrastructure layers, with few applications at the user level. Web3 companies have attempted to address data silos, such as SWIFT connecting traditional and on-chain financial data, and GXC enabling peer-to-peer data transactions. However, these attempts have been limited by the singular focus on Web3 technology, which, while enabling trusted data exchanges, lacks robust data processing and presentation capabilities.
Traditional AI approaches, such as recommendation algorithms used by traditional companies, can mine user data to understand needs but are constrained by their limited reasoning capabilities and inability to switch learning tracks freely. Moreover, data remains under the control of individual enterprises, creating barriers that prevent the full activation and utilization of its value.\
\
**AI+Web3 reviews**\
\
At present, most of the innovations of web3+AI are concentrated in the data layer and infrastructure layer, and there are few innovative applications in the application layer. According to the classification of mainstream media, the current AI+Web3 applications can be divided into three categories:\
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1\) AIGC category, which uses AI generative technology to allow users to generate text, pictures, videos, Avatar and other content through conversations. It is presented as a separate AI agent or directly integrated into the product. Representative projects include NFPrompt, SleeplessAI, etc.
2\) AI analysis: Project parties integrate their own accumulated data, knowledge base, and analytical capabilities to train vertical AI models that can perform analysis, judgment, and prediction, and provide them to users as products, so that users can obtain AI analysis capabilities with a low threshold, such as data analysis, information tracking, code auditing and modification, and financial forecasting. Representative projects include Kaito, Dune, etc.
3\) AI Agent Hub, an aggregation of various AI Agents, usually provides users with the ability to create customized AI Agents without code, similar to GPTs. Representative projects include My Shell, Fetch.ai, etc.
4\) AI+other tracks, such as AI+DePIN, AI+Game, etc.
There are no top projects in the application layer yet, but in the long run, it is definitely a sector with a higher ceiling and great potential to be tapped. The best way to integrate AI+Web3 is definitely not the current simple integration and data decentralization of new language models. These insignificant innovations are difficult to gain an ecological niche in the high-speed competition among giants.
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