TL; DR
We have discussed how AI and Web3 can leverage each other's strengths across various vertical industries such as computational networks, agency platforms, and consumer applications. When focusing on the vertical domain of data resources, emerging Web3 projects provide new possibilities for data acquisition, sharing, and utilization.
Traditional data providers struggle to meet the demand for high-quality, real-time verifiable data from AI and other data-driven industries, especially in terms of transparency, user control, and privacy protection.
Web3 solutions are committed to reshaping the data ecosystem. Technologies such as MPC, zero-knowledge proofs, and TLS Notary ensure the authenticity and privacy protection of data as it flows between multiple sources, while distributed storage and edge computing provide greater flexibility and efficiency for real-time data processing.
Among them, the emerging infrastructure of decentralized data networks has spawned several representative projects: OpenLayer (modular authentic data layer), Grass (utilizing user idle bandwidth and a decentralized crawler node network), and Vana (user data sovereignty Layer 1 network), which open new prospects for AI training and applications in different technical paths.
Through crowdsourced capacity, a trustless abstraction layer, and token-based incentive mechanisms, decentralized data infrastructure can offer solutions that are more private, secure, efficient, and cost-effective than Web2 supermassive service providers, while granting users control over their data and associated resources, building a more open, secure, and interoperable digital ecosystem.
1. Wave of Data Demand
Data has become a key driver of innovation and decision-making across various industries. UBS predicts that the global data volume is expected to grow more than tenfold from 2020 to 2030, reaching 660 ZB. By 2025, each person globally is expected to generate 463 EB (Exabytes, 1EB=1 billion GB) of data per day. The data-as-a-service (DaaS) market is rapidly expanding, with Grand View Research estimating the global DaaS market at $14.36 billion in 2023, and expected to grow at a compound annual growth rate of 28.1% to reach $76.8 billion by 2030. Behind these high-growth figures is the demand from multiple industries for high-quality, real-time trustworthy data.
AI model training relies on large amounts of data input for pattern recognition and parameter adjustment. After training, datasets are also needed to test the model's performance and generalization ability. Additionally, AI agents, as a foreseeable new form of emerging intelligent applications, require real-time reliable data sources to ensure accurate decision-making and task execution.
(Source: Leewayhertz)
The demand for commercial analysis is also becoming diverse and extensive, serving as a core tool driving business innovation. For example, social media platforms and market research companies need reliable user behavior data to formulate strategies and gain insights into trends, integrating diverse data from multiple social platforms to build a more comprehensive profile.
For the Web3 ecosystem, reliable real data is also needed on-chain to support some new financial products. As more new assets are being tokenized, flexible and reliable data interfaces are needed to support the development of innovative products and risk management, allowing smart contracts to execute based on verifiable real-time data.
In addition to the above, there are research, the Internet of Things (IoT), and so on. Emerging use cases show a surge in demand for diverse, authentic, real-time data across various industries, while traditional systems may struggle to cope with the rapidly growing volume of data and ever-changing demands.
2. Limitations and Issues of the Traditional Data Ecosystem
A typical data ecosystem includes data collection, storage, processing, analysis, and application. The centralized model is characterized by centralized data collection and storage, managed by core enterprise IT teams, with strict access controls implemented.
For example, Google's data ecosystem covers multiple data sources, from the search engine and Gmail to the Android operating system, collecting user data through these platforms, storing it in its globally distributed data centers, and then using algorithms to process and analyze it to support the development and optimization of various products and services.
For example, in financial markets, data and infrastructure LSEG (formerly Refinitiv) obtains real-time and historical data from global exchanges, banks, and other major financial institutions while leveraging its own Reuters News network to collect market-related news, using proprietary algorithms and models to generate analytical data and risk assessments as additional products.
(Source: kdnuggets.com)
Traditional data architectures are effective in professional services, but the limitations of centralized models are becoming increasingly apparent. Particularly in terms of coverage of emerging data sources, transparency, and user privacy protection, traditional data ecosystems face challenges. Here are several aspects:
Insufficient data coverage: Traditional data providers face challenges in rapidly capturing and analyzing emerging data sources such as social media sentiment and IoT device data. Centralized systems struggle to efficiently obtain and integrate 'long-tail' data from numerous small-scale or non-mainstream sources.
For example, the GameStop incident in 2021 revealed the limitations of traditional financial data providers in analyzing social media sentiment. Investor sentiment on platforms like Reddit quickly shifted market trends, but data terminals such as Bloomberg and Reuters failed to capture these dynamics in a timely manner, resulting in delayed market predictions.
Data accessibility is limited: Monopolies restrict accessibility. Many traditional providers open some data through API/cloud services, but high access fees and complex authorization processes still increase the difficulty of data integration.
On-chain developers find it difficult to quickly access reliable off-chain data, with high-quality data monopolized by a few giants, resulting in high access costs.
Data transparency and credibility issues: Many centralized data providers lack transparency about their data collection and processing methods, and there are no effective mechanisms to verify the authenticity and integrity of large-scale data. Verifying large-scale real-time data remains a complex issue, and the centralized nature also increases the risk of data tampering or manipulation.
Privacy protection and data ownership: Large tech companies commercially exploit user data on a large scale. Users, as the creators of private data, find it difficult to gain the value they deserve. Users often cannot understand how their data is collected, processed, and used, nor can they decide the scope and manner of data use. Excessive collection and use also lead to serious privacy risks.
For example, the Cambridge Analytica incident involving Facebook exposed significant gaps in traditional data providers regarding data use transparency and privacy protection.
Data silos: Furthermore, real-time data from different sources and formats is difficult to integrate quickly, affecting the possibility of comprehensive analysis. Much data is often locked within organizations, limiting cross-industry and cross-organization data sharing and innovation, and the data silo effect hinders cross-domain data integration and analysis.
For example, in the consumer industry, brands need to integrate data from e-commerce platforms, physical stores, social media, and market research, but this data may be difficult to integrate due to inconsistent platform formats or isolation. Additionally, shared mobility companies like Uber and Lyft collect large amounts of real-time data from users regarding traffic, passenger demand, and geographical location, but due to competitive relationships, this data cannot be proposed or shared for integration.
In addition, there are issues related to cost efficiency and flexibility. Traditional data vendors are actively addressing these challenges, but the emerging Web3 technologies provide new ideas and possibilities for solving these issues.
3. Web3 Data Ecosystem
Since the launch of decentralized storage solutions like IPFS (InterPlanetary File System) in 2014, a series of emerging projects have emerged to address the limitations of traditional data ecosystems. We see that decentralized data solutions have formed a multi-layered, interconnected ecosystem covering various stages of the data lifecycle, including data generation, storage, exchange, processing and analysis, verification and security, as well as privacy and ownership.
Data storage: The rapid development of Filecoin and Arweave proves that decentralized storage (DCS) is becoming a paradigm shift in the storage field. DCS solutions reduce single-point failure risks through distributed architecture while attracting participants with more competitive cost-effectiveness. With a series of scaled application cases emerging, DCS storage capacity has shown explosive growth (for example, the total storage capacity of the Filecoin network had reached 22 exabytes by 2024).
Processing and analysis: Decentralized data computation platforms like Fluence improve the real-time performance and efficiency of data processing through edge computing technology, particularly suitable for applications requiring high real-time performance such as IoT and AI inference. Web3 projects leverage technologies like federated learning, differential privacy, trusted execution environments, and fully homomorphic encryption to provide flexible privacy protection and trade-offs at the computational layer.
Data markets/exchange platforms: To facilitate the quantification and circulation of data value, Ocean Protocol creates efficient and open data exchange channels through tokenization and DEX mechanisms, for instance, helping traditional manufacturing companies (Daimler, the parent company of Mercedes-Benz) collaborate to develop data exchange markets for data sharing in their supply chain management. On the other hand, Streamr has created a permissionless, subscription-based data stream network suitable for IoT and real-time analysis scenarios, showing excellent potential in transportation and logistics projects (for example, in collaboration with the Finnish smart city project).
With the increasing frequency of data exchange and utilization, the authenticity, credibility, and privacy protection of data have become critical issues that cannot be ignored. This has prompted the Web3 ecosystem to extend innovation into the realms of data verification and privacy protection, giving rise to a series of groundbreaking solutions.
3.1 Innovations in Data Verification and Privacy Protection
Many web3 technologies and native projects are committed to solving data authenticity and private data protection issues. In addition to ZK, technologies like MPC are widely used, among which the Transport Layer Security protocol notarization (TLS Notary) is particularly noteworthy as an emerging verification method.
Introduction to TLS Notary
The Transport Layer Security protocol (TLS) is a widely used encryption protocol for network communication, designed to ensure the security, integrity, and confidentiality of data transmission between clients and servers. It is a common encryption standard in modern network communications, used in various scenarios such as HTTPS, email, instant messaging, etc.
When TLS Notary was born a decade ago, its initial goal was to verify the authenticity of TLS sessions by introducing a third party 'notary' outside the client (Prover) and server.
Using key splitting technology, the master key of the TLS session is divided into two parts, held by the client and the notary, respectively. This design allows the notary to participate as a trusted third party in the verification process without accessing the actual communication content. This notarization mechanism is aimed at detecting man-in-the-middle attacks, preventing fraudulent certificates, ensuring that communication data has not been tampered with during transmission, and allowing trusted third parties to confirm the legitimacy of communications while protecting communication privacy.
As a result, TLS Notary provides secure data verification and effectively balances verification needs with privacy protection.
In 2022, the TLS Notary project was rebuilt by the Ethereum Foundation's Privacy and Scaling Exploration (PSE) research lab. The new version of the TLS Notary protocol was rewritten from scratch in Rust, incorporating more advanced cryptographic protocols (such as MPC). The new protocol features allow users to prove the authenticity of the data they receive from servers to third parties without revealing the data content. While maintaining the original core verification functions of TLS Notary, it significantly enhances privacy protection capabilities, making it more suitable for current and future data privacy needs.
3.2 Variants and Extensions of TLS Notary
In recent years, TLS Notary technology has also been continuously evolving, producing multiple variants that further enhance privacy and verification functionalities:
zkTLS: A privacy-enhanced version of TLS Notary, which combines ZKP technology to allow users to generate cryptographic proofs of web data without exposing any sensitive information. It is suitable for communication scenarios requiring extremely high privacy protection.
3P-TLS (Three-Party TLS): Introduces three partiesâthe client, server, and auditorâallowing the auditor to verify the security of communications without revealing communication content. This protocol is very useful in scenarios requiring transparency while simultaneously demanding privacy protection, such as compliance reviews or financial transaction audits.
Web3 projects use these cryptographic technologies to enhance data verification and privacy protection, break data monopolies, solve data silos and trusted transmission issues, allowing users to prove ownership of social media accounts, shopping records used for financial lending, bank credit records, employment backgrounds, and educational credentials without disclosing their privacy, such as:
Reclaim Protocol uses zkTLS technology to generate zero-knowledge proofs of HTTPS traffic, allowing users to safely import activity, reputation, and identity data from external websites without exposing sensitive information.
zkPass combines 3P-TLS technology to allow users to validate real-world private data without leaks, widely used in KYC, credit services, and is compatible with HTTPS networks.
Opacity Network, based on zkTLS, allows users to securely prove their activities across platforms (such as Uber, Spotify, Netflix, etc.) without directly accessing these platforms' APIs, achieving cross-platform activity proof.
(Projects working on TLS Oracles, Source: Bastian Wetzel)
Web3 data verification is an important link in the data ecosystem chain, with vast application prospects. Its ecological prosperity is guiding a more open, dynamic, and user-centric digital economy. However, the development of authenticity verification technology is just the beginning of building a new generation of data infrastructure.
4. Decentralized Data Network
Some projects combine the above data verification technologies to explore more deeply in the upstream of the data ecosystem, namely data traceability, distributed data collection, and trusted transmission. Below, we focus on several representative projects: OpenLayer, Grass, and Vana, which demonstrate unique potential in building a new generation of data infrastructure.
4.1 OpenLayer
OpenLayer is one of the a16z Crypto Spring 2024 accelerator projects, as the first modular authentic data layer, committed to providing an innovative modular solution for coordinating data collection, verification, and transformation to meet the needs of both Web2 and Web3 companies. OpenLayer has attracted support from well-known funds and angel investors, including Geometry Ventures and LongHash Ventures.
Traditional data layers face multiple challenges: lack of credible verification mechanisms, reliance on centralized architectures leading to limited accessibility, lack of interoperability and liquidity between different systems, and no fair data value distribution mechanisms.
A more tangible problem is that AI training data is becoming increasingly scarce. Many websites on the public internet have begun to implement anti-scraping measures to prevent AI companies from mass data collection.
In terms of private proprietary data, the situation is even more complex. Many valuable data are stored in a privacy-protective manner due to their sensitive nature, lacking effective incentive mechanisms. In this situation, users are unable to safely obtain direct benefits by providing private data and are therefore reluctant to share these sensitive data.
To address these issues, OpenLayer combines data verification technology to build a modular authentic data layer and coordinates data collection, verification, and transformation processes in a decentralized + economic incentive manner, providing a more secure, efficient, and flexible data infrastructure for Web2 and Web3 companies.
4.1.1 Core Components of OpenLayer's Modular Design
OpenLayer provides a modular platform to simplify the processes of data collection, credible verification, and transformation:
a) OpenNodes
OpenNodes is the core component responsible for decentralized data collection in the OpenLayer ecosystem, collecting data through user mobile applications, browser extensions, and other channels. Different operators/nodes can optimize returns based on their hardware specifications to perform the most suitable tasks.
OpenNodes supports three main types of data to meet the needs of different types of tasks:
Publicly available internet data (such as financial data, weather data, sports data, and social media streams)
User private data (such as Netflix viewing history, Amazon order records, etc.)
Self-reported data from secure sources (such as data signed by proprietary owners or verified by specific trusted hardware).
Developers can easily add new data types, specify new data sources, demands, and data retrieval methods. Users can choose to provide de-identified data in exchange for rewards. This design allows the system to continually expand to accommodate new data needs, and diverse data sources enable OpenLayer to provide comprehensive data support for various application scenarios, while also lowering the barriers to data provision.
b) OpenValidators
OpenValidators are responsible for subsequent data verification, allowing data consumers to confirm that the data provided by users matches the data source completely. All provided verification methods can be subject to cryptographic proofs, and verification results can be confirmed afterward. The same type of proof can be provided by multiple different providers. Developers can choose the most suitable verification provider according to their needs.
In initial use cases, especially for public or private data from internet APIs, OpenLayer uses TLS Notary as a verification solution to export data from any web application and prove the authenticity of the data without compromising privacy.
Not limited to TLSNotary, thanks to its modular design, the verification system can easily integrate other verification methods to accommodate different types of data and verification needs, including but not limited to:
Attested TLS connections: Utilizing Trusted Execution Environment (TEE) to establish authenticated TLS connections, ensuring the integrity and authenticity of data during transmission.
Secure Enclaves: Using hardware-level secure isolation environments (such as Intel SGX) to process and verify sensitive data, providing a higher level of data protection.
ZK Proof Generators: Integrating ZKP, allowing verification of data properties or computational results without revealing original data.
c) OpenConnect
OpenConnect is the core module in the OpenLayer ecosystem responsible for data transformation, ensuring usability by processing data from various sources and ensuring interoperability between different systems to meet the needs of different applications. For example:
Convert data into on-chain Oracle format for direct use by smart contracts.
Convert unstructured raw data into structured data for preprocessing for AI training and other purposes.
For data from users' private accounts, OpenConnect provides data de-identification features to protect privacy and components to enhance security during the data sharing process, reducing data leakage and misuse. To meet the demand for real-time data in applications like AI and blockchain, OpenConnect supports efficient real-time data transformation.
Currently, through integration with Eigenlayer, OpenLayer AVS operators listen to data request tasks, responsible for fetching and verifying data, and then reporting the results back to the system. By staking or re-staking assets through EigenLayer, they provide economic guarantees for their actions. If malicious behavior is confirmed, there is a risk of forfeiture of staked assets. As one of the first AVS (Active Verification Services) on the EigenLayer mainnet, OpenLayer has attracted over 50 operators and $4 billion in re-staked assets.
Overall, the decentralized data layer built by OpenLayer expands the range and diversity of available data without sacrificing practicality and efficiency, while ensuring the authenticity and integrity of data through cryptographic technologies and economic incentives. Its technology has broad practical applications for Web3 Dapps seeking to obtain off-chain information, AI models that require real inputs for training and inference, and companies looking to segment and target users based on existing identities and reputations. Users can also monetize their private data.
4.2 Grass
Grass is a flagship project developed by Wynd Network, aimed at creating a decentralized web crawler and AI training data platform. By the end of 2023, the Grass project completed a $3.5 million seed round led by Polychain Capital and Tribe Capital. Soon after, in September 2024, the project welcomed an A-round financing led by HackVC, with participation from well-known investment institutions such as Polychain, Delphi, Lattice, and Brevan Howard.
We mentioned that AI training requires new data access, and one solution is to use multiple IPs to bypass data access permissions to feed data to AI. Grass thus created a distributed crawler node network, dedicated to collecting and providing verifiable datasets for AI training using users' idle bandwidth in a decentralized physical infrastructure. Nodes route web requests through users' internet connections, access public websites, and compile structured datasets. It uses edge computing technology for preliminary data cleaning and formatting to improve data quality.
Grass adopts the Solana Layer 2 Data Rollup architecture, built on Solana to improve processing efficiency. Grass uses validators to receive, verify, and batch process web transactions from nodes, generating ZK proofs to ensure data authenticity. Verified data is stored in the data ledger (L2) and linked to the corresponding L1 chain for proof.
4.2.1 Key Components of Grass
a) Grass Nodes
Similar to OpenNodes, C-end users install the Grass application or browser extension and run it, utilizing idle bandwidth for web-crawling operations. Nodes route web requests through users' internet connections, access public websites, and compile structured datasets, using edge computing technology for preliminary data cleaning and formatting. Users earn GRASS tokens as rewards based on the bandwidth and data they contribute.
b) Routers
Connecting Grass nodes and validators, managing the node network and relaying bandwidth. Routers are incentivized to operate and earn rewards, with the reward ratio being proportional to the total validated bandwidth relayed.
c) Validators
Receiving, verifying, and batch processing web transactions from routers, generating ZK proofs, and using a unique key set to establish TLS connections, selecting appropriate cipher suites for communication with the target web server. Grass currently employs centralized validators, with plans to shift to a validator committee in the future.
d) ZK Processors
Receive proofs of each node session data generated from validators, batch process the validity proofs of all web requests, and submit them to Layer 1 (Solana).
e) Grass Data Ledger (Grass L2)
Storing complete datasets and linking them to the corresponding L1 chain (Solana) for proof.
f) Edge Embedded Models
Responsible for converting unstructured web data into structured models usable for AI training.
Source: Grass
Comparative Analysis of Grass and OpenLayer
OpenLayer and Grass both leverage distributed networks to give companies access to open internet data and closed information that requires authentication. They incentivize data sharing and the production of high-quality data. Both are committed to creating a decentralized data layer to solve the issues of data access and verification, but have adopted slightly different technical paths and business models.
Differences in Technical Architecture
Grass uses a Layer 2 Data Rollup architecture on Solana, currently adopting a centralized verification mechanism, using a single validator. OpenLayer, as one of the first AVSs built on EigenLayer, utilizes economic incentives and penalty mechanisms to achieve a decentralized verification mechanism and adopts a modular design, emphasizing the scalability and flexibility of data verification services.
Product Differentiation
Both provide similar To C products, allowing users to monetize data through nodes. In To B use cases, Grass provides an interesting data marketplace model and uses L2 to verifiably store complete data, providing structured, high-quality, verifiable training sets for AI companies. OpenLayer does not currently have a dedicated data storage component but offers a wider range of real-time data stream verification services (Vaas), suitable for scenarios requiring rapid response, such as feeding prices for RWA/DeFi/prediction market projects as an Oracle, providing real-time social data, etc.
Therefore, today, Grass's target customer base mainly focuses on AI companies and data scientists, providing large-scale, structured training datasets, and also serving research institutions and enterprises that require substantial web datasets; whereas OpenLayer temporarily targets on-chain developers needing off-chain data sources, AI companies requiring real-time, verifiable data streams, and Web2 companies supporting innovative user acquisition strategies, such as verifying competitive product usage histories.
Potential Future Competition
However, considering industry development trends, the functionalities of the two projects may indeed converge in the future. Grass may soon provide real-time structured data as well. OpenLayer, as a modular platform, may also expand to data set management in the future and have its own data ledger, thus the competitive fields of the two may gradually overlap.
Moreover, both projects may consider adding the critical step of data labeling. Grass may advance more quickly in this regard, as they have a large node networkâreportedly over 2.2 million active nodes. This advantage gives Grass the potential to provide human feedback-based reinforcement learning (RLHF) services, optimizing AI models using large amounts of labeled data.
However, OpenLayer, with its expertise in data verification and real-time processing, may maintain an advantage in data quality and credibility due to its focus on private data. Additionally, as one of Eigenlayer's AVS, OpenLayer may have deeper developments in decentralized verification mechanisms.
Although the two projects may compete in certain areas, their unique advantages and technical routes may also lead them to occupy different niche markets within the data ecosystem.
4.3 VAVA
As a user-centric data pool network, Vana also aims to provide high-quality data for AI and related applications. Compared to OpenLayer and Grass, Vana adopts a markedly different technical path and business model. Vana completed a $5 million financing round in September 2024, led by Coinbase Ventures, previously having secured $18 million in Series A financing led by Paradigm, with participation from other notable investors including Polychain and Casey Caruso.
Originally launched in 2018 as an MIT research project, Vana aims to become a Layer 1 blockchain specifically designed for user private data. Its innovations in data ownership and value distribution allow users to profit from AI models trained on their data. The core of Vana lies in the trustless, private, and attributable Data Liquidity Pool (DLP) that facilitates the circulation and monetization of private data through innovative Proof of Contribution mechanisms.
4.3.1. Data Liquidity Pool
Vana introduces a unique concept of Data Liquidity Pool (DLP): As a core component of the Vana network, each DLP is an independent peer-to-peer network used to aggregate specific types of data assets. Users can upload their private data (such as shopping records, browsing habits, social media activity, etc.) to specific DLPs and flexibly choose whether to authorize these data for specific third-party use. Data is integrated and managed through these liquidity pools, with the data being de-identified to ensure user privacy while allowing data to participate in commercial applications, such as for AI model training or market research.
Users submit data to DLP and receive corresponding DLP tokens (each DLP has a specific token) as rewards. These tokens not only represent the user's contribution to the data pool but also grant users governance rights and future profit-sharing rights over the DLP. Users can not only share data but also obtain ongoing benefits from subsequent calls of that data (with visual tracking). Unlike traditional one-time data sales, Vana allows data to continuously participate in the economic cycle.
4.3.2 Proof of Contribution Mechanism
One of Vana's other core innovations is the Proof of Contribution mechanism. This is a key mechanism for Vana to ensure data quality, allowing each DLP to customize a unique contribution proof function based on its characteristics to verify the authenticity and integrity of data and assess the contribution of data to AI model performance enhancement. This mechanism ensures that users' data contributions are quantified and recorded, thereby providing rewards to users. Similar to 'Proof of Work' in cryptocurrencies, Proof of Contribution allocates benefits to users based on the quality, quantity, and frequency of the data they contribute. It is automatically executed through smart contracts, ensuring that contributors receive rewards that match their contributions.
Vana's Technical Architecture
1. Data Liquidity Layer
This is the core layer of Vana, responsible for data contribution, verification, and recording to DLPs, bringing data onto the chain as transferable digital assets. DLP creators deploy DLP smart contracts, setting the purpose of data contributions, verification methods, and contribution parameters. Data contributors and custodians submit data for verification, and the contribution proof (PoC) module executes data verification and value assessment, granting governance rights and rewards based on parameters.
2. Data Portability Layer
This is an open data platform for data contributors and developers, as well as the application layer for Vana. The Data Portability Layer provides a collaborative space for data contributors and developers to build applications using the data liquidity accumulated in DLPs. It provides infrastructure for user-owned model distributed training and AI Dapp development.
3. General Connectome
A decentralized ledger, also a real-time data stream graph that runs through the entire Vana ecosystem, uses proof-of-stake consensus to record real-time data transactions within the Vana ecosystem. It ensures the effective transfer of DLP tokens and provides cross-DLP data access for applications. Compatible with EVM, allowing interoperability with other networks, protocols, and DeFi applications.
Vana offers a relatively different path, focusing on the liquidity and value empowerment of user data. This decentralized data exchange model is applicable not only to AI training and data markets but also provides a new solution for cross-platform interoperability and authorization of user data within the Web3 ecosystem, ultimately creating an open internet ecosystem where users own and manage their data, as well as the smart products created from that data.
5. Value Proposition of Decentralized Data Networks
Data scientist Clive Humby once said that data is the oil of the new era. Over the past 20 years, we have witnessed rapid developments in 'refining' technologies. Big data analytics, machine learning, and other technologies have released unprecedented value from data. According to IDC's predictions, by 2025, the global data sphere will grow to 163 ZB, with most coming from individual users. As emerging technologies like IoT, wearables, AI, and personalized services become more prevalent, a significant amount of data needed for commercial use will also come from individuals.
Pain points of traditional solutions: Innovations unlocked by Web3
Web3 data solutions break the limitations of traditional facilities through distributed node networks, achieving wider and more efficient data collection, while also enhancing the efficiency of real-time access to specific data and the credibility of verification. In this process, Web3 technology ensures the authenticity and integrity of data while effectively protecting user privacy, thus creating a fairer data utilization model. This decentralized data architecture promotes the democratization of data access.
Whether it is the user node model of OpenLayer and Grass or Vanaâs monetization of user private data, they not only improve the efficiency of specific data collection but also allow ordinary users to share in the dividends of the data economy, creating a win-win model for users and developers, enabling users to truly control and benefit from their data and related resources.
Through token economics, Web3 data solutions redesign the incentive model, creating a fairer data value distribution mechanism. This has attracted a large number of users, hardware resources, and capital injection, thus coordinating and optimizing the operation of the entire data network.
Compared to traditional data solutions, they also possess modularity and scalability: for example, OpenLayer's modular design provides flexibility for future technological iteration and ecological expansion. Thanks to their technical characteristics, they optimize data acquisition methods for AI model training, providing richer and more diverse datasets.
From data generation, storage, verification, to exchange and analysis, Web3-driven solutions address many shortcomings of traditional facilities through unique technological advantages, while also empowering users to monetize their personal data, triggering a fundamental shift in the data economy model. With further developments and expanding application scenarios, decentralized data layers are expected to become a key infrastructure for a wide range of data-driven industries in conjunction with other Web3 data solutions.
