Interview with Footprint Analytics CEO Navy: The Integration Path of AI and Web3
Web3 is leading the next era of the Internet. However, the problem of fragmented and non-standardized on-chain data still exists. Footprint Analytics has launched an all-in-one data solution that utilizes AI technology to automatically collect, clean, and correlate blockchain data, and build cross-chain data standards, enabling developers to access and analyze data more conveniently.
"We aim to become the 'Google Analytics' of Web3, providing industry-leading growth analysis and operational analysis tools through coverage of 20+ public chains and structured data, helping Web3 projects achieve precision marketing," said Navy, founder and CEO of Footprint. "At the same time, we are also exploring other applications of AI, such as AI-powered data analysis dashboards, to further enhance the productivity of the blockchain industry."
Navy believes that the combination of AI and blockchain technology will be the catalyst for the large-scale adoption of Web3. On one hand, high-quality data is the foundation for training AI models, and on the other hand, AI can also help generate high-value data. "Data is the lifeline of this industry. We are trying to establish a positive feedback ecosystem where AI and blockchain technology complement each other, thereby driving the development of the entire Web3 industry."
Q1: Could you please help Navy introduce what Footprint Analytics is currently doing?
Footprint Analytics is dedicated to building a structured data platform that connects Web2 and Web3 data.
Our core value lies in data structuring. Although Web3 has advantages over Web2 in terms of on-chain data transparency, there are also challenges, such as the industry being in its early stages of development, lack of data standards, and lack of complete structure, making data application difficult.
Let's illustrate with an example. If you want to access Opensea's transaction data on multiple chains like Ethereum, Solana, and Polygon, you need to understand Opensea's business model, read smart contract code, and extract transaction data from each chain (such as Ethereum, Solana, Polygon, etc.).
However, there are some issues with this process. Firstly, it is complex and prone to errors. The entire process of obtaining data is complicated and prone to errors. Secondly, it has technical difficulties, as the ledger design and data structure of different chains are different. Lastly, it wastes resources. If 1000 people need to obtain this data, they would have to execute similar cumbersome procedures 1000 times, greatly reducing data acquisition efficiency and wasting computing resources.
Therefore, Footprint Analytics' mission is to abstract data from various areas, including GameFi, NFT, and DeFi, to establish data standards for the Web3 industry, and provide developers and industry participants with efficient and accurate data.
Up to now, we have launched more than 20 public chains, and our products mainly include 3 major categories:
Industry Solution - Footprint Growth Analytics: Provides customized solutions for marketing growth and operational analysis for Web3 projects. It can be compared to Web3 version of Google Analytics, helping projects achieve data-driven growth.
0-code Data Analysis Tool: Provides data analysis reports through simple questions and answers, similar to ChatGPT. I anticipate that using on-chain data will become extremely easy in the future, without the need to understand Web3 business logic or possess advanced programming skills, making the migration from Web2 to Web3 easier to achieve.
Free Multi-chain Cross-chain API: Provides seamless multi-chain data access for users through a unified API, enabling easy retrieval of data from multiple chains.
Q2: Currently, the integration of AI with Web3 has become an exciting trend in Web3 projects. Both GPT and AIGC have shown different creativity in combining with AI based on their respective situations.
Next, let's discuss from the perspective of data how AI can be integrated with Web3. This analysis can be done from both technical and application scenario angles, in order to demonstrate the ways and possibilities of this integration more clearly.
As a data platform, Footprint has a natural ability to integrate with AI. The infrastructure of AI mainly covers three key aspects: computing power, data, and algorithms. Among them, computing power is the cornerstone for training and executing AI models, data is the soul of AI, and algorithms determine the performance of AI, including model accuracy and application effects.
Among these elements, data is undoubtedly the most core and important one. Data is the lifeblood of industries and enterprises, and it involves critical areas such as privacy and compliance, and its value is immeasurable. Money may not even be able to buy data due to privacy and compliance issues. In this regard, AI is both a consumer and a producer of data.
Currently, Footprint has the following main aspects of application in the integration of data and AI:
In the data content generation phase, the application of AI plays a significant role on our platform. Firstly, we use AI to generate code for processing data, providing users with a more efficient data analysis experience.
Specifically, we focus on two aspects of innovation:
On the one hand, it is the organization and classification of "Reference data". Taking the newly deployed contracts on the blockchain as an example, our AI can automatically determine the contract belongs to which protocol, the type of contract, and even whether the contract belongs to LP or swap type in Dex platform. This intelligent organization and classification greatly enhance the availability of data.
On the other hand, based on these "Reference data", we can also generate higher-level domain data. For example, we can use AI to generate data in the fields of GameFi, NFT, etc., to provide users with richer data resources. This approach not only improves the quality of data content, but also allows users to have a deeper understanding of the data dynamics in various domains.
In terms of frontend user experience, we have implemented AI smart analysis functionality. As mentioned earlier, when users use Footprint for data analysis, they can have an experience similar to chatting with ChatGPT. Users can obtain corresponding analysis reports automatically by asking questions. The underlying logic is to convert the text into SQL statements, which greatly reduces the threshold for users to perform data analysis.
Finally, in terms of product user support, we have developed an AI customer service robot. We provide AI with structured data in the blockchain field, including various fields such as GameFi, NFT, DeFi, and build a dedicated AI customer service robot for Footprint. This AI customer service robot can provide immediate support to users, answer questions related to the use of Footprint, such as data types, data definitions, API usage, etc., greatly improving the efficiency of customer support and reducing manual input.
Of course, although the application of AI can improve productivity and help us solve most problems, it cannot solve all problems. Based on our experience in data processing, AI can assist in solving approximately 70% to 80% of problems.
Q3: What are the problems that may arise in the process of combining AI with Web3? Are there any technical difficulties, user experience, intellectual property compliance, ethical and moral issues, etc.?
From a macro perspective, regardless of the field in which AI is applied, an important consideration is the acceptance of AI fault tolerance. Different application scenarios have different requirements for fault tolerance. In this process, it is necessary to balance the accuracy and reliability of AI, as well as people's tolerance for errors.
In the medical field, the decision to trust AI or doctors may involve challenges in trust. In the investment field, AI can provide factors that affect the price trend of BTC, but people may still have doubts about the actual buy or sell decisions.
However, in the fields of marketing and operational analysis, such as user profiling and segmentation, accuracy requirements may not be high because minor errors will not have a significant impact. Therefore, fault tolerance is more acceptable in these scenarios.
Currently, Footprint remains primarily focused on data in the track of combining AI with Web3. Along the way, we have encountered some challenges:
The first is the issue of data production, how to provide high-quality data for AI to achieve more efficient and accurate data production capabilities. We can analogize the relationship between AI and data to the engine and fuel of a car. AI is the engine and data is the fuel it requires. Even with a good engine, performance cannot be maximized without high-quality fuel.
This involves how to generate high-quality data, such as quickly and automatically producing data in areas like GameFi, NFT, DeFi, and other fields. This includes organizing the relational structure of data, namely, the data graph. Specifically, we need to determine multiple pieces of information, such as the protocol, contract type, and deployer of a contract. The core goal of this process is to continuously provide high-quality data for AI to achieve more efficient and accurate data production capabilities, thereby forming a virtuous cycle.
The second is the issue of data privacy. Although Web3 is inherently decentralized and transparent, as the industry develops, we will also see scenarios similar to Web2 where the demand for data privacy becomes increasingly prominent. For example, the privacy protection of user identities, assets, and transaction information. This presents a challenge: the public nature of on-chain data gradually weakens, and the data available for AI also decreases. However, as the industry progresses, this problem will also be resolved, and homomorphic encryption technology is one of the solutions.
In summary, combining AI with Web3 will inevitably face a core problem: data acquisition, specifically how to obtain high-quality data and deliver it to AI. The ultimate challenge for AI lies in the accessibility of data.
Q4: Although AI is not a new term, the combination of AI and Web3 is still in its early stages. In Navy's opinion, what are the potential areas or ways of combining AI and Web3 in the future that could become breakthroughs, attracting a large number of users to enter Web3 and achieve widespread adoption?
I believe that to achieve widespread integration and adoption of Web3 and AI, two core issues need to be addressed. Firstly, we need to better serve the builders and developers of Web3, especially those in fields like GameFi, NFTs, and social media. Secondly, we need to lower the barriers for users to enter this field.
First, let's discuss serving developers. In the developer field, there are two main categories of applications.
One category is AI-driven developer platforms. These platforms use AI technology to automate the generation of code templates. Whether it's developing DEX platforms or NFT markets, code templates can be intelligently generated based on the specific needs of developers, significantly improving development efficiency. In the gaming field, developers can also use AI technology to create game models and generate game images faster, accelerating the game development and launch process. This kind of platform provides developers with more possibilities, allowing them to focus more on creativity and innovation without spending too much time on repetitive basic work.
Another category is AI-driven data platforms. These platforms automatically generate domain-specific data in various industries, such as GameFi, NFT, SocialFi, DeFi, etc., using AI technology. The goal of these platforms is to lower the threshold for developers to use data, making data analysis and application easier. Through AI technology, the platforms can automate the generation of various data, providing developers with rich data resources to better understand market trends, user behavior, etc. This data platform provides developers with more data support, making the threshold for using data extremely low, and promotes the birth of more innovative applications.
Reducing the threshold for application development has always been a key issue in the Web3 domain. For example, recently, some nearly zero-fee public chain solutions have emerged in the market, aiming to improve transactions per second (TPS). In addition, solutions like MPC wallets can effectively address the primary barrier of migrating from Web2 to Web3 and the difficulties encountered in the migration process.
Solving these problems not only relies on AI technology but also depends on the development and improvement of the entire Web3 ecosystem. Although AI plays an important role in enhancing efficiency and lowering thresholds, the fundamental construction and development of Web3 itself are still crucial factors in solving application development barriers.
Footprint Analytics is a structured data platform that connects Web2 and Web3. With cutting-edge artificial intelligence technology, we provide the first no-code data analytics platform and unified data APIs in the crypto field. Users can quickly retrieve NFT, GameFi, and DeFi data from over 26 public chain ecosystems.
Footprint Website: https://www.footprint.network
