When the parameter scale of artificial intelligence (AI) models exceeds one trillion and the computing power is measured in FLOPS, an overlooked core bottleneck is emerging - data . Chainbase pointed out in its latest technical blog "Building the Hyperdata Network for AI": The next revolution in the AI industry will no longer be driven by model architecture or chip computing power, but depends on how we transform fragmented human behavior data into verifiable, structured, AI-ready capital . This insight not only reveals the structural contradictions in the current development of AI, but also outlines a new "DataFi era" - in this era, data is no longer a by-product of technology, but a core production factor that can be measured, traded, and value-added like electricity and computing power.
From computing power competition to data famine: Structural contradictions in the AI industry
The development of AI has long been driven by the dual cores of "model-computing power". Since the deep learning revolution, model parameters have jumped from millions (such as AlexNet in 2012) to trillions (such as GPT-4), and the demand for computing power has grown exponentially. According to OpenAI data, the cost of training an advanced large language model has exceeded US$100 million, 90% of which is spent on GPU cluster rental. However, when the industry focuses on "bigger models" and "faster chips", the supply-side crisis of data is quietly approaching.
Chainbase pointed out sharply in its blog that "organic data" generated by humans has reached its growth ceiling. Taking text data as an example, the total amount of high-quality text (books, papers, news) publicly crawlable on the Internet is about 10^12 words, and the training of a 100 billion parameter model consumes about 10^13 words of data - this means that the existing data pool can only support the training of 10 models of the same size. What's more serious is that duplicate data and low-quality content account for more than 60%, further compressing the supply of effective data. When the model begins to "swallow" the data generated by itself (such as articles written by AI and images generated by AI), the degradation of model performance caused by "data pollution" has become a hidden concern in the industry.
The root of this contradiction is that the AI industry has long regarded data as a "free resource" rather than a "strategic asset" that needs to be carefully cultivated. Models and computing power have formed a mature market system - computing power is priced by FLOPS on cloud platforms such as AWS and GCP, and models are charged by the number of API calls - but data production, cleaning, verification, and trading are still in the "wild era". Chainbase emphasizes: The next decade of AI will be the decade of "data infrastructure", and the on-chain data of encrypted networks is the key to solving this dilemma.
On-chain data: the “human behavior database” most needed by AI
In the context of data famine, the on-chain data of crypto networks is showing irreplaceable value. Compared with traditional Internet data (such as social media posts and e-commerce reviews), on-chain data naturally has the authenticity of "incentive alignment" - every transaction, every contract interaction, and every wallet address behavior are directly linked to real capital and cannot be tampered with. Chainbase defines it in a blog as "the most concentrated human incentive-aligned behavior data on the Internet", which is specifically reflected in three dimensions:
Real-world “intention signals”
The on-chain data records not emotional comments or random clicks, but decision-making behaviors that are voted with real money. For example, the behavior of a wallet exchanging assets on Uniswap, mortgaging loans on Aave, and registering domain names on ENS directly reflects the user's judgment of the project value, risk preference, and capital allocation strategy. This kind of data "backed by capital" is of great value for training AI's decision-making capabilities (such as financial forecasting and market analysis). In contrast, traditional Internet data is full of "noise" - such as false likes on social media and fake order comments on e-commerce platforms. These data not only fail to train reliable AI models, but also mislead model judgments.
Traceable “behavior chain”
The transparency of blockchain allows user behavior to be fully traced. The historical transactions of a wallet address, the protocols it has interacted with, and the changes in the assets held constitute a coherent "behavior chain". For example, by analyzing the operations of a certain address in the DeFi protocol from 2020 to the present, AI can accurately identify whether it is a "long-term holder", "arbitrage trader" or "liquidity provider", and build a user portrait based on this. This kind of structured behavioral data is the most scarce "human reasoning sample" of the current AI model.
“Unlicensed access” to the open ecosystem
Unlike the closed nature of traditional corporate data (such as bank transaction records and e-commerce user data), on-chain data is open and permissionless. Any developer can obtain raw data through a blockchain browser or data API, which provides a "barrier-free" data source for AI model training. However, this openness also brings challenges: on-chain data exists in the form of "event logs" (such as Ethereum's ERC-20 Transfer events and Uniswap's Swap events), which are unstructured "raw signals" that need to be cleaned, standardized, and associated before they can be used by AI models. Chainbase pointed out that the current "structured conversion rate" of on-chain data is less than 5%, and a large number of high-value signals are buried in billions of fragmented events.
Hyperdata Network: The “operating system” for on-chain data
To solve the problem of on-chain data fragmentation, Chainbase proposed Hyperdata Network, an "on-chain intelligent operating system" designed specifically for AI. Its core goal is to transform decentralized on-chain signals into structured, verifiable, real-time, and composable AI-ready data.
Manuscript: Open data standards allow AI to “understand” the world on the blockchain
One of the biggest pain points of on-chain data is "format confusion" - different blockchains (such as Ethereum, Solana, Avalanche) have different event log formats, and different versions of the same protocol may also have different data structures. As an open data schema standard, Manuscript unifies the definition and description of on-chain data. For example, it standardizes "user staking behavior" into structured data containing fields such as staker_address, protocol_id, amount, timestamp, reward_token, etc., ensuring that AI models do not need to adapt to the data formats of different chains or protocols, and can directly "understand" the business logic behind the data.
The value of this standardization lies in reducing the friction cost of AI development. Suppose a team wants to train a "DeFi user behavior prediction model", the traditional method requires connecting to the APIs of multiple chains such as Ethereum and Polygon, and writing different parsing scripts; based on Manuscript, all on-chain data has been pre-processed according to unified standards, and developers can directly call structured data such as "user pledge records" and "liquidity provision records", greatly shortening the model training cycle.
The core requirement of AI models for data is "trust" - if the training data is tampered with or contaminated, the model output will be worthless. Hyperdata Network ensures the authenticity of the data through Ethereum's AVS (Active Validator Set) mechanism. AVS is an extension component of the Ethereum consensus layer, consisting of 600,000+ ETH-collateralized validator nodes, which are responsible for verifying the integrity and accuracy of on-chain data. When Hyperdata Network processes an on-chain event, the AVS node cross-verifies the data's hash value, signature information, and on-chain status to ensure that the output structured data is completely consistent with the original on-chain data.
This "cryptoeconomic guarantee" verification mechanism solves the trust problem of traditional centralized data verification. For example, if an AI company uses on-chain data provided by a centralized organization, it needs to trust that the organization has not tampered with the data; while using Hyperdata Network, the authenticity of the data is endorsed by a decentralized network of verifiers, and any tampering will trigger the smart contract's penalty mechanism (such as deducting the mortgaged ETH).
Chainbase DA: High-throughput data availability layer
AI models, especially real-time interactive AI applications (such as trading robots and intelligent customer service), require low-latency, high-throughput data supply. The Chainbase DA (Data Availability) layer is designed specifically for this need. By optimizing data compression algorithms and transmission protocols, it can achieve real-time processing of hundreds of thousands of on-chain events per second. For example, when a large transaction occurs on Uniswap, Chainbase DA can complete data extraction, standardization and verification within 1 second, and push structured "large transaction signals" to subscribed AI models, enabling them to adjust trading strategies in a timely manner.
The high throughput is based on a modular architecture - Chainbase DA separates data storage from computing. Data storage is handled by a distributed node network, while computing is done through off-chain Rollup, thus avoiding the performance bottleneck of the blockchain itself. This design enables Hyperdata Network to support the real-time data needs of large-scale AI applications, such as providing on-chain data services for thousands of trading agents online at the same time.
The DataFi Era: When Data Becomes Tradable “Capital”
The ultimate goal of Hyperdata Network is to promote the AI industry into the DataFi era - data is no longer a passive "training material", but an active "capital" that can be priced, traded, and valued. Chainbase made an analogy in its blog: "Just like electricity is priced in kilowatts and computing power is priced in FLOPS, data must also be scored, ranked, and valued." The realization of this vision depends on Hyperdata Network's transformation of data into four core attributes:
Structuring: From “raw signals” to “usable assets”
Unprocessed on-chain data is like "crude oil" and needs to be refined to become "gasoline". Hyperdata Network converts it into structured data through the Manuscript standard. For example, "wallet address A deposits X tokens into protocol B at time T" is decomposed into multi-dimensional data including user portrait, protocol attributes, asset type, and timestamp. This structure allows data to be directly called by AI models, just as simple as calling an API interface.
Composable: The Lego Bricks of Data
In Web3, "composability" has given rise to the explosion of DeFi (such as the combination innovation of Uniswap+Aave+Curve). Hyperdata Network introduces this concept into the data field: structured data can be freely combined like Lego blocks. For example, developers can combine "user pledge records" (from Lido) with "price fluctuation data" (from Chainlink) and "social mentions" (from Twitter API) to train a "DeFi market sentiment prediction model." This combination greatly expands the application boundaries of data, so that AI innovation is no longer limited to a single data source.
Verifiable: Data “Credit Endorsement”
Structured data verified by AVS will generate a unique "data fingerprint" (hash value) and store it on the Ethereum blockchain. Any AI application or developer using the data can confirm the authenticity of the data by verifying the hash value. This "verifiability" gives the data a credit attribute - for example, a data set marked as "high-quality trading signals" can have its historical accuracy traceable through hash records on the blockchain. Users do not need to trust the data set provider, they only need to verify the data fingerprint to judge the data quality.
Monetizable: Data Value Monetization
In the DataFi era, data providers can directly monetize structured data through the Hyperdata Network. For example, a team developed a "smart contract vulnerability warning signal" by analyzing on-chain data. The signal can be packaged into an API service and charged by the number of calls; ordinary users can also authorize the sharing of their own anonymized on-chain data and receive data token rewards. In the Chainbase ecosystem, the value of data is determined by market supply and demand - high-accuracy transaction signals may be priced higher, while basic user behavior data may be charged on a per-call basis.
Chainbase's practice: DataFi infrastructure behind 500 billion calls
Chainbase did not build Hyperdata Network from scratch, but upgraded it based on its existing data infrastructure. The core data disclosed in the blog shows its leading position in the industry: 500 billion+ data calls, 20,000+ developer communities, and 8,000+ project integrations. Behind these numbers is Chainbase's years of deep cultivation in the field of on-chain data.
For example, DeFi protocol Aave obtains user lending behavior data through Chainbase's API and optimizes its risk assessment model; NFT market Blur uses the "floor price trend data" provided by Chainbase to develop smart pricing functions; traditional financial institutions such as JPMorgan Chase access on-chain data through Chainbase for market analysis of crypto assets. These practices verify the core value of Hyperdata Network - making on-chain data like water and electricity, the infrastructure for AI and Web3 applications.
In the future, Chainbase plans to further expand the coverage of Hyperdata Network, support more blockchain networks (such as Cosmos ecosystem, Polkadot parachain), and develop a "data scoring protocol" - automatically evaluate the quality of data sets (such as accuracy, timeliness, scarcity) through AI models, and provide a standardized pricing benchmark for the DataFi market. When the quality of data can be quantified and the value can be traded, a new "data capital" ecosystem will be formed faster.
Conclusion: Data Revolution, the Next Decade of AI
When we talk about the future of AI, we often focus on the "intelligence" of the model, but ignore the "data soil" that supports intelligence. Chainbase's Hyperdata Network reveals a core truth: the evolution of AI is essentially the evolution of data infrastructure. From the "limitedness" of human-generated data to the "value discovery" of on-chain data, from the "disorder" of fragmented signals to the "order" of structured data, from the "free resources" of data to the "capital assets" of DataFi, Hyperdata Network is reshaping the underlying logic of the AI industry.
In this DataFi era, data will become a bridge between AI and the real world - trading agents perceive market sentiment through on-chain data, autonomous dApps optimize services through user behavior data, and ordinary users gain continuous benefits through shared data. Just as the power network gave birth to the industrial revolution and the computing power network gave birth to the Internet revolution, Hyperdata Network is giving birth to the "data revolution" of AI. Chainbase is undoubtedly the key infrastructure builder of this revolution.
Chainbase wrote at the end of the blog: "The next generation of AI-native applications requires not only models or wallets, but also trustless, programmable, high-signal data. We are building it." This is not only the vision of a company, but also the inevitable maturity of the AI industry - when data is finally given the value it deserves, AI can truly unleash the power to change the world.
