The real scenarios of AI + Crypto today involve the tokenization of AI assets, including forms such as AI Agent meme tokenization, AI Agent platform tokenization, democratized AI narrative tokenization, and the tokenization of AI production materials, data, and computing power.
The applications of AI in the Crypto field and the applications of Crypto in the AI field that value investors are looking forward to are currently in very early exploratory stages. The application types with relative industry consensus include: AI Crypto Wallet, Crypto-specific LLM or AI Agent based on full-chain data, and AI governance for DAOs.
These applications are either in a minimally viable state or in a semi-paralyzed state. Previously, I experienced a Crypto-specific LLM, which often hallucinated by fabricating a CA when asked about today's hottest meme coins in a chat.
Therefore, some leading VC institutions are more optimistic about the deep coupling of AI Agents and Crypto, and they expect that in the future:
http://1. Assetization of AI Agents (AI Agent meme coins and AI Agent platform/protocol value coins);
http://2. AI Agent replaces Mev bot/on-chain arbitrage robots to become the main consumers of block space;
3. Deep integration with applications such as DeFi and Web3 games has led to the emergence of a new product paradigm.
At present, to build a Crypto-specific AI Agent, tools for one-click creation of Crypto-specific AI Agents are available from platforms like Coinbase, Virtual, Ai16z, Vvaifu, Clanker, etc. However, these Crypto-specific AI Agents are mainly used for AI meme coin issuance scenarios, with only a few, like aiXBT, used for crypto investment advisory scenarios.
We know that the underlying layer of AI Agents is LLMs, the underlying layer of LLMs is algorithms, computing power, and data. Open-source large models are already sufficiently usable, and computing power is not a major issue. What primarily limits the capabilities of Crypto-specific AI Agents is the lack of a labeled, complete, real-time, and high-quality full-chain data set.
Perhaps seeing this, CZ has recently been very bullish on the role of Crypto incentives in data labeling collaboration.
Currently, building a complete, real-time, and high-quality full-chain data set faces three major challenges: data being scattered across different chains making unified calls difficult, varying data quality making direct application challenging, and the difficulty in effectively measuring and distributing data value.
ChainBase, which has received investments from top institutions such as Jingwei Venture Capital and Tencent, attempts to provide a feasible solution with its innovative technical architecture and token economic model.
ChainBase's technical architecture consists of a four-layer stack + dual consensus mechanism:
Four-layer stack:
--Data processing layer - Responsible for cross-chain data access and real-time synchronization, currently supporting over 200 public chains.
--Consensus layer - Ensuring data state consistency based on the CometBFT consensus algorithm.
--Execution layer - The innovative ChainbaseDB supports parallel processing of data tasks.
--Co-processing layer - Community developers jointly govern data through the 'Manuscript' mechanism.
Dual consensus mechanism:
--Data consensus: Ensuring data authenticity through ChainBase AVS (Active Verification Service).
--Value consensus: Achieving reasonable distribution of data value through community governance.
This technical architecture design allows Chainbase to handle 560 million to 650 million full-chain data calls daily, achieving a data refresh interval of less than 3 seconds and maintaining PB-level data storage scale.
At the same time, ChainBase uses the native token $C as a resource coordination tool for the full-chain data production flow and the basic currency of the network.
$C, as a network incentive token, encourages global community participation in data contribution and maintenance. Currently, over 26,000 developers have created 27,000 projects on the platform.
$C serves as a data access license; developers need to pay $C to access ChainBase's data infrastructure. The daily average API call volume currently reaches 600 million, with data query volume exceeding 3000TB.
$C serves as an ecological value medium and can be used to pay for services of ecological projects like http://io.net and Aethir, connecting the AI infrastructure ecosystem.
In addition, to validate the feasibility of the ChainBase solution, Chainbase launched the first chain-native AI large model, Theia. This model has 8 billion general language model parameters, 200 million cryptocurrency-specific parameters, and a proprietary D2ORA algorithm.
Compared to traditional AI models, Theia Chat's unique advantage lies in its complete, real-time, and high-quality full-chain data training set. This enables it to provide users with more accurate on-chain insights.
In the future, whether Theia Chat opens its API to AI Agent developers or other LLMs use ChainBase's full-chain data training set to enhance their training, it will improve the performance and usability of AI Agents in the Crypto field, laying a good foundation for AI Agents to replace Mev bots/on-chain arbitrage robots as the main consumers of block space.
Above.
