The evolution from the current generation of the Internet, or “Web2,” to the next generation of the Internet, or “Web3,” represents a fundamental paradigm shift in how we manage and control information on the Internet. While there is no agreed-upon definition of Web3, the term is often used to refer to a decentralized internet that uses blockchain technology to put control of data back into the hands of users and reduce the power currently exercised by big tech companies. Web3 offers a potential solution to the problems of lack of privacy, surveillance, and misinformation caused by a data-hungry society where the consumer is often the product. But Web3 adoption is hampered by significant user friction.
The convergence of artificial intelligence and blockchain technology can provide the necessary catalyst to stimulate the adoption of Web3. The technical synergy between artificial intelligence’s ability to learn and predict from data and blockchain’s transparent, tamper-proof data processing capabilities can enhance Web3’s user experience and reduce user friction. For example, blockchain-based decentralized AI could provide users with tailored online experiences, such as music recommendations based on their past listening history, without requiring them to sacrifice privacy or control over their personal data.
The power of artificial intelligence and blockchain in Web3
Blockchain and artificial intelligence are complementary technologies, each providing solutions to problems posed by the other. In the field of artificial intelligence, access to high-quality data is critical to designing and developing effective and accurate artificial intelligence algorithms. AI trained with flawed data will inevitably produce flawed results, also known as “garbage input.” “Garbage output” problem. The built-in consensus protocol of the blockchain is the way for the nodes on the blockchain to agree on the “authenticity” of the data, helping to reduce “garbage”; by verifying the authenticity, accuracy and completeness of the data to solve the garbage problem. It could also help combat the concentration of AI power in the hands of a few companies by distributing power over data and algorithms. As SEC Chairman Gary Gensler said in a recent discussion of AI and According to the Finance Sector podcast, “hundreds of financial players relying on centralized data or central selection models” could lead to “risks for society and financial markets throughout the financial sector.” As a decentralized and distributed A blockchain platform can be designed to distribute power in a way that mitigates the risk of a small number of AI companies or models making opaque but important decisions.
The integration of artificial intelligence and blockchain
However, harnessing the power of AI and blockchain in a tightly integrated manner is not easy and will take time to overcome technical challenges. A useful way to chart the co-evolution of these two technologies is to examine it through a three-stage progression lens: data (stage 1) to information (stage 2) to knowledge (stage 3). Essentially, data consists of raw alphanumeric values, while information is structured and organized data. Knowledge represents the collective insights and gains extracted from information. But sifting through large amounts of data and information to extract actionable insights can be a challenge.
Until recently, searching, indexing, and extracting data, especially across different formats such as text, audio, and images, remained complex because blockchains were not originally designed to optimize searchability. However, companies like The Graph (often likened to the “Google of Web3”) have largely solved the first phase challenge of leveraging indexable and searchable data in blockchain, while No reliance on centralized intermediaries.
In the second phase, companies that now have access to large amounts of blockchain data will turn their focus to organizing this data into coherent, analyzable information. This is challenging because while blockchain provides a public record of transactions between wallet addresses, by design, these wallet addresses cannot be easily traced to real-world identities. The wallet address is a cryptographically generated string that acts as a pseudonym for the user. Therefore, it is difficult to extract useful information from this data for due diligence and analytical purposes. However, a number of companies like Nansen have stepped in to address this gap, providing a way to collect valuable information from blockchain data, which can then be used to train artificial intelligence algorithms.
However, the next frontier or “third phase” is developing knowledge from the vast amounts of information provided by blockchain platforms. This challenge has yet to be fully solved because the task of meaningfully combining disparate data and information is time-consuming and manual. Artificial intelligence can be a powerful tool that automates the difficult task of extracting, organizing, storing and disseminating an organization’s collective knowledge.
AI can accelerate Web3 adoption by delivering personalized experiences
Generative AI has exploded in popularity recently, in part because of its ability to deliver customized experiences based on user prompts. As Han Jin, CEO of AI-driven Web3 company Bluwhale, said: “For Web3 to become mainstream, the next generation of consumer-facing applications using blockchain must at least match the user experience of Web2. Personalization will no longer be Optional, but essential.” This approach allows decentralized applications to more effectively engage current audiences and attract new users, optimizing marketing spend.
Knowledge graphs, or as Jin puts it, “decentralized AI brains that scale across blockchains” may be the missing component to bring the personalized experiences of Web2 to Web3. Knowledge graphs are data science tools used to map relationships between objects, facts, events, situations, and other data. Knowledge graphs are often used with artificial intelligence as they help convey meaning and introduce structure to disparate data sets. Search engines often use knowledge graphs to allow computers to understand the context of people’s queries and connect billions of facts about people, places, and things.
However, like the core infrastructure of Web2, many knowledge graphs are built by centralized entities, siled to their specific organizations, and not widely shared. Decentralized knowledge graphs, such as those built by OriginTrail, can make knowledge graphs more accessible by leveraging an open, permissionless blockchain network where the public can contribute, maintain and verify knowledge.
The future of the Internet will be built on emerging technologies
By leveraging cutting-edge tools like knowledge graphs, integrating artificial intelligence and blockchain can serve as the foundation for Web3 based on trusted data. This new decentralized internet could help solve problems prevalent in the current centralized internet, such as disinformation, surveillance, privacy and security risks, and the overall loss of agency over personal data.