The emergence of GPT has attracted global attention to large language models. All walks of life are trying to use this black technology to improve work efficiency and accelerate industry development. Future 3 Campus teamed up with Footprint Analytics to conduct in-depth research on the infinite possibilities of the combination of AI and Web3, and jointly released a research report titled Analysis of the Current Situation, Competitive Landscape, and Future Opportunities of the Integration of AI and Web3 Data Industry. The research report is divided into two parts. This article is the second part, compiled by Future 3 Campus researchers Sherry and Humphrey.
Summary:
The data combination of AI and Web3 is promoting the improvement of data processing efficiency and user experience. The current exploration of LLM in the blockchain data industry mainly focuses on improving data processing efficiency through AI technology, using the interactive advantages of LLM to build AI Agents, and using AI for pricing and transaction strategy analysis.
At present, the application of AI in the field of Web3 data still faces some challenges, such as accuracy, interpretability, commercialization, etc. There is still a long way to go to completely replace human intervention.
The core competitiveness of Web3 Data Company is not only the AI technology itself, but also the ability of data accumulation and in-depth analysis and application of data.
AI may not be the solution to the problem of commercializing data products in the short term, and commercialization will require more productization efforts.
1. Current status and development path of the integration of Web3 data industry and AI
1.1 Dune
Dune is currently the leading open data analysis community in the Web3 industry. It provides tools for querying, extracting and visualizing large amounts of data on the blockchain, allowing users and data analysis experts to use simple SQL queries to query on-chain data from Dunes pre-populated database. , and form corresponding charts and opinions.
In March 2023, Dune proposed plans for AI and future integration with LLM, and released its Dune AI product in October. The core of Dune AI-related products focuses on using LLMs powerful language and analysis capabilities to enhance Wizard UX and better provide users with data query and SQL writing on Dune.
(1) Query explanation: The product released in March allows users to obtain natural language explanations of SQL queries by clicking a button. It is designed to help users better understand complex SQL queries, thereby improving the efficiency and accuracy of data analysis.
(2) Query translation: Dune plans to uniformly migrate different SQL query engines on Dune (such as Postgres and Spark SQL) to DuneSQL, so LLM can provide automated query language translation capabilities to help users make a better transition to benefit DuneSQL. Product promotion.
(3) Natural language query: Dune AI released in October. Allows users to ask questions and obtain data in plain English. The goal of this feature is to make it easy for users without SQL knowledge to obtain and analyze data.
(4) Search optimization: Dune plans to use LLM to improve search functions to help users filter information more effectively.
(5) Wizard knowledge base: Dune plans to release a chatbot to help users quickly browse blockchain and SQL knowledge in Spellbook and Dune documents.
(6) Simplify SQL writing work (Dune Wand): Dune launched the Wand series of SQL tools in August. Create Wand allows users to generate complete queries from natural language prompts, Edit Wand allows users to make modifications to existing queries, and Debug functionality automatically debugs syntax errors in queries. The core of these tools is LLM technology, which can simplify the query writing process and enable analysts to focus on the core logic of analyzing data without worrying about code and syntax issues.
1.2 Footprint Analytics
Footprint Analytics is a blockchain data solutions provider that uses artificial intelligence technology to provide a no-code data analysis platform, unified data API products, and the Web3 project BI platform Footprint Growth Analytics.
Footprints advantage lies in the creation of its on-chain data production line and ecological tools. It establishes a unified data lake to open up on-chain and off-chain data and a metadata database similar to on-chain industrial and commercial registration, ensuring that users can obtain data when analyzing and using it. functionality, ease of use and quality. Footprint’s long-term strategy will focus on technical depth and platform construction to create a “machine factory” capable of producing on-chain data and applications.
The combination of Footprint products and AI is as follows:
Since the launch of the LLM model, Footprint has been exploring the combination of existing data products and AI for the first time to improve the efficiency of data processing and analysis and create more user-friendly products. In May 2023, Footprint has begun to provide users with natural language interactive data analysis functions, and has upgraded to high-end product functions based on its original no-code basis. Users do not need to be familiar with the tables and designs of the platform, that is, Quickly obtain data and generate charts through conversations.
In addition, the current LLM + Web3 data products in the market mainly focus on solving the problems of lowering the user threshold and changing the interaction paradigm. Footprint’s focus in the development of products and AI is not only to help users solve the data analysis use experience The focus is also on accumulating vertical data and business understanding in the crypto field, as well as training language models in the crypto field to improve the efficiency and accuracy of vertical scenario applications. Footprint’s advantages in this regard will be reflected in the following aspects:
Data knowledge quantity (quality and quantity of knowledge base). Efficiency of data accumulation, source, volume, category. Especially the Footprint MetaMosaic sub-product embodies the relationship graph and the accumulation of static data of specific business logic.
knowledge architecture. Footprint has accumulated more than 30 public chain abstract structured data tables based on business sections. Knowledge of the production process from raw data to structured data can in turn enhance the understanding of raw data and better train models.
type of data. There is a clear gap in training efficiency and machine cost between training from non-standard and unstructured raw data on the chain and training from structured, business-meaning data tables and indicators. A typical example is that more data needs to be provided to LLM. In addition to professional data based on the encryption field, these data also require more readable and structured data, and a larger number of users are used as feedback data.
Crypto money flow data. Footprint abstracts the capital flow data closely related to investment. It includes the time, subject (including flow direction), token type, amount (token price at the associated time point), business type, and token, The subjects tag can be used as the knowledge base and data source of LLM, which can be used to analyze the main funds of the token, locate the distribution of chips, monitor the flow of funds, identify changes on the chain, track smart funds, etc.
Injection of private data. Footprint divides the model into three major layers. One is the base large model with world knowledge (OpenAI and other open source models), the vertical model in subdivided fields, and the personalized expert knowledge model. It allows users to unify and manage their knowledge bases from different sources on Footprint, and use private data to train private LLM, which is suitable for more personalized application scenarios.
During the exploration of Footprint combined with the LLM model, we also encountered a series of challenges and problems, the most typical of which were insufficient tokens, time-consuming prompts, and unstable answers. In the vertical field of on-chain data where Footprint is located, the greater challenge is that there are many types of data entities on the chain, a huge amount, and rapid changes. In what form they are fed to LLM, the entire industry needs more research and exploration. The current tool chain is still in its early stages, and more tools are needed to solve some specific problems.
In the future, Footprint’s technology and product integration with AI will include the following:
(1) In terms of technology, Footprint will use the LLM model to explore and optimize in three aspects
Support LLM to perform reasoning on structured data, so that a large amount of structured data and knowledge in the encryption field can be applied to LLMs data consumption and production.
Help users build personalized knowledge bases (including knowledge, data and experience), and use private data to improve the ability of optimized crypto LLM, allowing everyone to build their own models.
Let AI assist analysis and content production. Users can create their own GPT through dialogue, combined with capital flow data and private knowledge base, to produce and share crypto investment content.
(2) In terms of products, Footprint will focus on exploring AI product applications and innovation in business models. According to Footprints recent product promotion plan, it will launch an AI crypto content generation and sharing platform for users.
In addition, for future partner expansion, Footprint will explore the following two aspects:
First, strengthen cooperation with KOLs to facilitate the production of valuable content, community operations, and knowledge monetization.
Second, expand more cooperative project parties and data providers, create an open and win-win user incentive and data cooperation, and establish a mutually beneficial and win-win one-stop data service platform.
1.3 GoPlus Security
GoPlus Security is currently the leading user security infrastructure in the Web3 industry, providing various user-oriented security services. It has been integrated by mainstream digital wallets, market websites, Dex and various other Web3 applications on the market. Users can directly use various security protection functions such as asset security detection, transfer authorization, and anti-phishing. The user security solutions provided by GoPlus can comprehensively cover the entire user security life cycle to protect user assets from threats from various types of attackers.
The development and planning of GoPlus and AI are as follows:
GoPlus’s main exploration in AI technology is reflected in its two products: AI automated detection and AI security assistant:
(1) AI automated detection
GoPlus will begin to develop its own automated detection engine based on AI technology in 2022 to comprehensively improve the efficiency and accuracy of security detection. GoPluss security engine adopts a multi-layered, funnel-style analysis method, using multiple links such as static code detection, dynamic detection, and feature or behavior detection. This composite detection process enables the engine to effectively identify and analyze the characteristics of potential risk samples to effectively model attack types and behaviors. These models are key for the engine to identify and prevent security threats. They help the engine determine whether risk samples have certain attack characteristics. In addition, the GoPlus security engine has accumulated a wealth of security data and experience after a long period of iteration and optimization. Its architecture can quickly and effectively respond to emerging security threats, ensuring that various complex and new attacks can be discovered and blocked in a timely manner. We protect user safety. Currently, the engine uses AI-related algorithms and technologies in multiple security scenarios such as risky contract detection, phishing website detection, malicious address detection, and risky transaction detection. The use of AI technology can reduce risk exposure more quickly, improve detection efficiency, and reduce detection costs; on the other hand, it reduces the complexity and time cost of manual participation and improves the accuracy of judgments on risk samples, especially for those who were originally manually For new scenarios that are difficult to define or difficult for the engine to identify, AI can better gather features and form a more effective analysis method.
In 2023, as large models evolved, GoPlus quickly adapted and adopted LLM. Compared with traditional AI algorithms, LLMs efficiency and effectiveness in data identification, processing and analysis have been significantly improved. The emergence of LLM has helped GoPlus accelerate its technological exploration in AI automated detection. In the direction of dynamic fuzz testing, GoPlus has adopted LLM technology to effectively generate transaction sequences and explore deeper states to discover contract risks.
(2) AI security assistant
GoPlus is also leveraging LLM-based natural language processing capabilities to develop AI security assistants to provide instant security consultation and improve user experience. Based on the GPT large model, the AI assistant developed a set of self-developed user security agents through the input of front-end business data, which can automatically analyze, generate solutions, disassemble tasks, and execute based on problems to provide users with the required security services. . AI assistants can simplify communication between users and security issues and lower the threshold for users to understand.
In terms of product functions, due to the importance of AI in the security field, AI has the potential to completely change the structure of existing security engines or virus anti-virus engines in the future, and a new engine architecture with AI as the core will emerge. GoPlus will continue to train and optimize the AI model in order to transform AI from an auxiliary tool into the core function of its security detection engine.
In terms of business model, although GoPluss services are currently mainly for developers and project parties, the company is exploring more products and services directly for C-end users, as well as new AI-related revenue models. Providing efficient, accurate and low-cost C-side services will be GoPluss core competitiveness in the future. This requires the company to continue research and conduct more training and output on large AI models that interact with users. At the same time, GoPlus will also work with other teams to share its security data and promote AI applications in the security field through cooperation to prepare for possible industry changes in the future.
1.4 Trusta Labs
Founded in 2022, Trusta Labs is a data startup company in the Web3 field driven by artificial intelligence. Trusta Labs focuses on using advanced artificial intelligence technology to efficiently process and accurately analyze blockchain data to build the blockchain’s on-chain reputation and security infrastructure. Currently, Trusta Labs business mainly includes two products: TrustScan and TrustGo.
(1) TrustScan, TrustScan is a product specially designed for B-side customers. It is mainly used to help Web3 projects conduct on-chain user behavior analysis and refined stratification in terms of user acquisition, user activity and user retention to identify high value and real users.
(2) TrustGo, a product for C-end customers, provides MEDIA analysis tools that can analyze and evaluate on-chain addresses from five dimensions (amount of funds, activity, diversity, identity rights, and loyalty) , this product emphasizes in-depth analysis of on-chain data to improve the quality and security of transaction decisions.
The development and planning of Trusta Labs and AI are as follows:
Currently, Trusta Labs two products use AI models to process and analyze the interaction data of addresses on the chain. The behavioral data of address interactions on the blockchain are all sequence data. This type of data is very suitable for training AI models. In the process of cleaning, sorting and labeling data on the chain, Trusta Labs hands over a lot of work to AI, which greatly improves the quality and efficiency of data processing, while also reducing a lot of labor costs. Trusta Labs uses AI technology to conduct in-depth analysis and mining of address interaction data on the chain. For B-side customers, it can effectively identify the most likely witch addresses. In multiple projects that have used Tursta Labs products, Tursta Labs has better prevented potential Sybil attacks; for C-side customers, through TrustGo products, existing AI models are used to effectively help users gain a deeper understanding of themselves. on-chain behavioral data.
Trusta Labs has been paying close attention to the technical progress and application practice of the LLM model. With the continuous reduction of model training and inference costs, and the accumulation of a large amount of corpus and user behavior data in the Web3 field, Trusta Labs will find the right time to introduce LLM technology and use the productivity of AI to provide more in-depth data mining and analysis for products and users. Function. On the basis of the rich data currently provided by Trusta Labs, we hope to use AIs intelligent analysis model to provide more reasonable and objective data interpretation functions for data results, such as providing qualitative and quantitative interpretations for B-end users. The analysis of Witch accounts allows users to better understand the analysis of the reasons behind the data, and can also provide more detailed material support for B-side users when explaining their complaints to their customers.
On the other hand, Trusta Labs also plans to use open source or relatively mature LLM models and combine it with intent-centered design concepts to build AI Agents to help users solve on-chain interaction problems more quickly and efficiently. In terms of specific application scenarios, in the future, through the AI Agent intelligent assistant based on LLM training provided by Trusta Labs, users can directly communicate with the intelligent assistant through natural language, and the intelligent assistant can smartly feedback information related to the data on the chain. It also provides suggestions and plans for subsequent operations based on the provided information, truly realizing one-stop intelligent operations centered on user intentions, greatly reducing the threshold for users to use data, and simplifying the execution of on-chain operations.
In addition, Trusta believes that with the emergence of more and more AI-based data products in the future, the core competitive element of each product may not be which LLM model is used. The key factor of competition is a deeper understanding of the mastered data and the Interpretation. Based on the analysis of the mastered data and combined with the LLM model, a more smart AI model can be trained.
1.5 0xScope
0xScope, founded in 2022, is an innovative platform with data at its core, focusing on the combination of blockchain technology and artificial intelligence. 0xScope aims to change the way people process, use and view data. 0xScope currently launches: 0xScope SaaS products and 0xScopescan for B-side and C-side customers respectively.
(1) 0xScope SaaS products, an enterprise-oriented SaaS solution, empower enterprise customers to conduct post-investment management, make better investment decisions, understand user behavior, and closely monitor competitive dynamics.
(2) 0xScopescan, a B2C product that allows cryptocurrency traders to investigate fund flows and activities on selected blockchains.
The business focus of 0xScope is to use on-chain data to abstract a common data model, simplify on-chain data analysis work, and transform on-chain data into understandable on-chain operation data, thereby helping users conduct in-depth analysis of on-chain data. Using the data tool platform provided by 0xScope can not only improve the quality of data on the chain and mine the information hidden in the data, thereby revealing more information to users, the platform also greatly lowers the threshold for data mining.
The development and planning of 0xScope and AI are as follows:
0xScopes products are being upgraded in combination with large models, which includes two directions: first, further reducing the user threshold through natural language interaction mode; second, using AI models to improve data cleaning, parsing, modeling and analysis Processing efficiency of other links. At the same time, 0xScopes products will soon launch an AI interactive module with Chat function. This function will greatly reduce the threshold for users to query and analyze data, and they can interact and query the underlying data only through natural language.
However, in the process of training and using AI, 0xScope found that it still faces the following challenges: First, AI training costs and time costs are high. After asking a question, the AI takes a long time to respond. Therefore, this difficulty will force the team to streamline and focus business processes and focus on QA in vertical areas, rather than making it an all-round super AI assistant. Second, the output of the LLM model is uncontrollable. Data products hope to give accurate results, but the results given by the current LLM model are likely to be somewhat different from the actual situation, which is very fatal to the experience of data products. In addition, the output of large models may involve users’ private data. Therefore, when using the LLM model in products, the team needs to impose greater restrictions on it to make the results output by the AI model controllable and accurate.
In the future, 0xScope plans to use AI to focus on specific vertical tracks and conduct deep cultivation. Based on the large amount of accumulated on-chain data, 0xScope can define the identity of users on the chain. In the future, it will continue to use AI tools to abstract user behavior on the chain, and then create a unique data modeling system. Through this set of data The mining and analysis system reveals the information hidden in the data on the chain.
In terms of cooperation, 0xScope will focus on two types of groups: the first type, the objects that the product can directly serve, such as developers, project parties, VCs, exchanges, etc., this group needs the data provided by the current products; the second type, Partners who have needs for AI Chat, such as Debank, Chainbase, etc., can directly call AI Chat as long as they have relevant knowledge and data.
2. VC insight——AI+Web3 data company’s commercialization and future development path
Through interviews with four senior VC investors, this section will look at the current status and development of the AI+Web3 data industry, the core competitiveness of Web3 data companies, and the future commercialization path from the perspectives of investment and market.
2.1 Current status and development of AI+Web3 data industry
Currently, the combination of AI and Web3 data is in a stage of active exploration. Judging from the development direction of various leading Web3 data companies, the combination of AI technology and LLM is an essential trend. But at the same time, LLM has its own technical limitations and cannot yet solve many problems in the current data industry.
Therefore, we need to realize that it is not to blindly combine with AI to enhance the advantages of the project, or to use AI concepts for hype, but to explore application areas that are truly practical and promising. From a VC perspective, the current combination of AI and Web3 data has been explored in the following aspects:
1) Improve the capabilities of Web3 data products through AI technology, including AI technology helping enterprises improve the efficiency of internal data processing and analysis, and correspondingly improve the capabilities of automated analysis and retrieval of user data products. For example, Yuxing of SevenX Ventures mentioned that the main help of using AI technology for Web3 data is efficiency. For example, Dune uses the LLM model for code anomaly detection and converts natural language into SQL to de-index information; there are also projects using AI for security warnings, AI The anomaly detection effect of the algorithm is better than that of pure mathematical statistics, so it can do security monitoring more effectively; in addition, Zi Xi from Matrix Partners mentioned that companies can pre-label data by training AI models, which can save a lot Labor costs. Despite this, VCs all believe that AI plays an auxiliary role in improving the capabilities and efficiency of Web3 data products, such as pre-annotation of data, and may ultimately still require manual review to ensure accuracy.
2) Use LLM’s advantages in adaptability and interaction to create AI Agent/Bot. For example, large language models are used to retrieve the entire Web3 data, including on-chain data and off-chain news data, for information aggregation and public opinion analysis. Harper from Hashkey Capital believes that this type of AI Agent is more focused on information integration, generation, and interaction with users, and will be relatively weak in information accuracy and efficiency.
Although there have been many cases of applications in the above two aspects, the technology and products are still in the early stages of exploration, so continuous technical optimization and product improvement will be required in the future.
3) Use AI for pricing and trading strategy analysis: There are currently projects in the market that use AI technology to estimate prices for NFTs, such as NFTGo invested by Qiming Venture Partners, and some professional trading teams use AI for data analysis and transaction execution. In addition, Ocean Protocol recently released a price prediction AI product. This type of product seems very imaginative, but still needs to be verified in terms of product, user acceptance, and especially accuracy.
On the other hand, many VCs, especially those who have invested in Web2, will pay more attention to the advantages and application scenarios that Web3 and blockchain technology can bring to AI technology. Blockchain has the characteristics of being publicly verifiable and decentralized, as well as the privacy protection capability provided by cryptography technology. Coupled with Web3s reshaping of production relations, it may bring some new opportunities to AI:
1) AI data confirmation and verification. The emergence of AI has made data content generation ubiquitous and cheap. Tang Yi from Qiming Venture Partners mentioned that it is difficult to determine the quality and creator of digital works and other content. In this regard, the verification of data content requires a completely new system, and blockchain may be able to help. Zi Xi from Jingwei Venture Capital mentioned that there are data exchanges that put data in NFT for trading, which can solve the problem of data confirmation.
In addition, Yuxing of SevenX Ventures mentioned that Web3 data can improve AI fraud and black box problems. Currently, AI has black box problems in both the model algorithm itself and the data, which will lead to deviations in output results. The data of Web3 is transparent, the data is publicly verifiable, and the training sources and results of the AI model will be clearer, making AI more fair and reducing bias and errors. However, the current amount of data in Web3 is not large enough to empower the training of AI itself, so it will not be realized in the short term. But we can use this feature to upload Web2 data to the chain to prevent AI deep forgery.
2) AI data annotation crowdsourcing and UGC community: At present, traditional AI annotation faces problems of low efficiency and quality, especially in the field of professional knowledge, which may also require cross-disciplinary knowledge. It is impossible for traditional general data annotation companies to cover , often need to be done internally by a professional team. Introducing data annotation crowdsourcing through the concepts of blockchain and Web3 can greatly improve this problem. For example, Questlab invested by Matrix Partners uses blockchain technology to provide crowdsourcing services for data annotation. In addition, in some open source model communities, the concept of blockchain can also be used to solve the economic problems of model creators.
3) Data privacy deployment: Blockchain technology combined with cryptography-related technologies can ensure data privacy and decentralization. Zi Xi from Matrix Partners mentioned that a synthetic data company they invested in generates synthetic data for use through large models. The data can be mainly used in software testing, data analysis, and AI large model training. The company involves many privacy deployment issues when processing data. Using the Oasis blockchain can effectively avoid privacy and regulatory issues.
2.2 How AI+Web3 data companies build core competitiveness
For Web3 technology companies, the introduction of AI can increase the attractiveness or attention of the project to a certain extent. However, the current products of most Web3 technology companies that combine AI are not enough to become the companys core competitiveness. They are more about providing A more friendly experience and improved efficiency. For example, the threshold for AI Agent is not high. Companies that do it first may have a first-mover advantage in the market, but it does not create barriers.
What really creates core competitiveness and barriers in the Web3 data industry is the teams data capabilities and how to apply AI technology to solve problems in specific analysis scenarios.
First of all, the teams data capabilities include data sources and the teams ability to perform data analysis and model adjustment, which is the basis for subsequent work. In interviews, SevenX Ventures, Matrix Partners and Hashkey Capital all unanimously mentioned that the core competitiveness of AI+Web3 data companies depends on the quality of data sources. On this basis, engineers are also required to be able to skillfully perform model fine-tuning, data processing and analysis based on data sources.
On the other hand, the specific scenarios in which the team’s AI technology is combined are also very important, and the scenarios should be valuable. Harper believes that although the current combination of Web3 data companies and AI basically starts with AI Agent, their positioning is also different. For example, Space and Time, invested by Hashkey Capital, cooperated with chainML to launch the infrastructure for creating AI agents. The DeFi agent is used in Space and Time.
2.3 The future commercialization path of Web3 data companies
Another topic that is important to Web3 data companies is monetization. For a long time, the profit model of data analysis companies has been relatively simple. Most ToC is free, and ToB is mainly profitable. This relies heavily on the willingness of B-side customers to pay. In the Web3 field, the willingness of enterprises to pay is not high, and the industry is dominated by start-up companies, making it difficult for project parties to support long-term payment. Therefore, Web3 data companies are currently in a difficult commercial situation.
On this issue, VCs generally believe that the current combination of AI technology is only used internally to solve production process problems and does not change the essential problem of difficulty in monetization. The threshold for some new product forms such as AI Bot is not high enough, which may enhance users willingness to pay in the toC field to a certain extent, but it is still not very strong. AI may not be the solution to the commercialization problem of data products in the short term. Commercialization requires more productization efforts, such as finding more suitable scenarios and innovative business models.
In the future path of combining Web3 with AI, using Web3s economic model combined with AI data may produce some new business models, mainly in the ToC field. Zi Xi from Jingwei Venture Capital mentioned that AI products can be combined with some token gameplay to increase the stickiness, daily activity and emotion of the entire community. This is feasible and easier to monetize. Tang Yi from Qiming Venture Partners mentioned that from an ideological point of view, the value system of Web3 can be combined with AI and is very suitable as an account system or value conversion system for bots. For example, a robot has its own account, can make money through its intelligent part, and pay to maintain its underlying computing power, etc. But this concept belongs to the imagination of the future, and practical application may still be a long way away.
In the original business model, that is, users pay directly, there needs to be strong enough product strength to make users have a stronger willingness to pay. For example, higher-quality data sources, the benefits brought by data exceed the cost paid, etc. This is not only based on the application of AI technology, but also on the capabilities of the data team itself.
This article is jointly published by Footprint Analytics, Future 3 Campus, and HashKey Capital.
Footprint Analytics is a blockchain data solutions provider. With the help of cutting-edge artificial intelligence technology, we provide the first code-free data analysis platform and unified data API in the Crypto field, allowing users to quickly retrieve NFT, GameFi and wallet address fund flow tracking data of more than 30 public chain ecosystems.
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Future 3 Campus is a Web3.0 innovation incubation platform jointly launched by Wanxiang Blockchain Labs and HashKey Capital, focusing on the three major tracks of Web3.0 Massive Adoption, DePIN, and AI, with Shanghai, Guangdong-Hong Kong-Macao Greater Bay Area, and Singapore As the main incubation base, it radiates the global Web3.0 ecology. At the same time, Future 3 Campus will launch an initial seed fund of US$50 million for Web3.0 project incubation, truly serving innovation and entrepreneurship in the Web3.0 field.
HashKey Capital is an asset management institution focused on investing in blockchain technology and digital assets, with current asset management scale exceeding US$1 billion. As one of the largest and most influential blockchain investment institutions in Asia, and also the earliest institutional investor in Ethereum, HashKey Capital exerts a leading goose effect, linking Web2 and Web3, and connecting with entrepreneurs, investors, communities and regulatory agencies. Join hands to build a sustainable blockchain ecosystem. The company is located in Hong Kong, Singapore, Japan, the United States and other places. It has taken the lead in deploying more than 500 global invested companies across Layer 1, protocols, Crypto Finance, Web3 infrastructure, applications, NFT, Metaverse and other tracks, and is representative. Invested projects include Cosmos, Coinlist, Aztec, Blockdaemon, dYdX, imToken, Animoca Brands, Falcon X, Space and time, Mask Network, Polkadot, Moonbeam and Galxe (formerly Project Galaxy), etc.
