AI in Web3 : Beyond the Hype
How is AI used in web3?
AI and web3 intersect in several ways, laying the foundation for an innovative new set of use cases for the internet of the future. But AI means a lot of things to different people. In order to separate buzzword-laden noise from genuine signal, you have to understand the different types of AI technologies and their practical applications.
Below, explore some of the current use cases for AI in web3 and how blockchains are well-suited for AI innovation.
Top concepts in AI
AI models are systems that simulate human intelligence, enabling machines to perform tasks requiring human-like understanding and decision-making. Here is a breakdown of several model types and enablements:
- Generative AI: Creates new content like images, text, videos, or music by learning patterns from existing data.
- Natural Language Processing (NLP) Models: Enables machines to understand, interpret, and respond to human language, allowing for interaction through plain English instead of code.
- Large Language Models (LLMs): Advanced NLP models trained on vast text data to handle complex tasks like translation, summarization, and copywriting.
- Reinforcement Learning (RL): AI learns desired behaviors by interacting with an environment, receiving rewards or penalties based on actions.
- Graph Neural Networks (GNNs): Mimics brain structure to process and analyze graph-structured data, capturing relationships and interactions between data sets.
These types of AI models and techniques can be integrated across the entire web3 tech stack, from user-facing applications to core infrastructure.
AI in the web3 application layer
AI is valuable when integrated within the web3 application layer and manifests in tools that enable users to more easily create content, automate the development of smart contracts, improve gaming experiences and more. Here’s a few examples:
- NFT Creation: Generative AI is used to mass-produce unique digital assets like NFTs by converting inputs (e.g., data or text) into art, with some collections generating up to 10,000 unique pieces.
- Smart Contract Automation: LLMs are sometimes used to assist in creating smart contracts by providing code edits, advice, and automated documentation, reducing errors and speeding up development.
- Natural Language Queries: Some AI tools enable users to query blockchain data in plain English, making it more accessible to non-technical users.
- AI-Enhanced Gaming: AI adapts gameplay based on user behavior and creates personalized, immersive environments. It’s also used to craft diverse in-game NFTs and “loot”.
AI in the web3 infrastructure layer
AI is also seamlessly integrated into web3's infrastructure layer, where decentralized networks provide the necessary computational power and storage for training and running models. This integration is central to the emerging Decentralized Physical Infrastructure Networks (DePIN) category, in which many protocols and networks pool resources for tasks like AI training and inference.
These decentralized compute resources also support LLMs, distributing infrastructure costs across network participants. In the context of web3, the infrastructure layer grants access to compute power in a permissionless way, which lends many of the value propositions of web3 to individuals and developers looking to make use of AI.
AI in web3: a look forward
As we evaluate the intersection of AI and web3, it's clear that there are quite a few current and practical uses of AI in web3. But while there is clearly current utility pointing to an immense potential for AI’s use within the industry, we are still in the early stages of exploring how AI can create value.
About The Graph
is the source of data and information for the decentralized internet. As the original decentralized data marketplace that introduced and standardized subgraphs, The Graph has become web3’s method of indexing and accessing blockchain data. Since its launch in 2018, tens of thousands of developers have for dapps across 70+ blockchains - including Ethereum, Arbitrum, Optimism, Base, Polygon, Celo, Fantom, Gnosis, and Avalanche.
As demand for data in web3 continues to grow, The Graph enters a with a more expansive vision including new data services and query languages, ensuring the decentralized protocol can serve any use case - now and into the future.
Discover more about how The Graph is shaping the future of decentralized physical infrastructure networks (DePIN) and stay connected with the community. Follow The Graph on , , , , , and . Join the community on The Graph’s , join technical discussions on The Graph’s .
oversees The Graph Network. The Graph Foundation is overseen by the . , , , , , , and are eight of the many organizations within The Graph ecosystem.
