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 single accepted definition of Web3, this term is often used to refer to a decentralized Internet that leverages blockchain technology to place control of data back into the hands of users and reduce the power currently exercised by big technology companies. Web3 offers a potential solution to issues like lack of privacy, surveillance and misinformation created by a data hungry society where consumers are often the product. But Web3’s adoption has been stymied by significant user friction.
The integration of AI and blockchain technology can provide the necessary catalyst to spur the adoption of Web3. The technological synergies between AI’s ability to learn from data and make predictions and blockchain’s transparent and tamper resistant data processing capabilities can enhance the user experience of Web3 and reduce user friction. For example, decentralized AI built on blockchain can offer users tailored online experiences like music recommendations based on their past listening history, without requiring them to sacrifice privacy or control over their personal data.
Power Of AI And Blockchain In Web3
Blockchain and AI are complementary technologies that each offer a solution to a problem the other poses. In the realm of AI, access to high-quality data is critical to the design and development of effective and accurate AI algorithms. AI trained on flawed data will inevitably yield flawed results, also known as the “garbage-in; garbage-out” problem. Blockchain’s built-in consensus protocol, which is the way nodes on a blockchain agree on the “truth” of data, helps reduce the “garbage-in; garbage-out” problem by enabling verification of the authenticity, accuracy and integrity of data.
It can also help combat the concentration of power over AI in the hands of a few companies by distributing authority over data and algorithms. As the Securities and Exchange Commission chairman, Gary Gensler, said in a recent podcast on AI and the financial sector, “hundreds or thousands of financial actors relying on a central data or central choice model” can cause a “risk in this society and the financial sector at large.” As a decentralized and distributed system, blockchain platforms can be designed to allocate power in a way that mitigates the risk stemming from a few AI companies or models making opaque but consequential determinations.
Convergence Of AI And Blockchain
However, leveraging the power of AI and blockchain in a cohesive fashion is not easy and it has taken time to overcome technical challenges. One useful way to chart the co-development of these two technologies is to examine it through the lens of progression in three phases: data (Phase 1) to information (Phase 2) to knowledge (Phase 3). At its core, data consists of raw, alphanumeric values and information is data that has been structured and organized. Knowledge represents the collective insights and takeaways extracted from information. But sifting through mountains of data and information to extract actionable insights is challenging.
Until fairly recently, searching, indexing, and extracting data, especially across diverse formats like text, audio and images, was complex as blockchains were not originally designed to optimize searchability. However, companies like The Graph, which is often analogized to the “Google of Web3,” have largely addressed the Phase 1 challenge of harnessing indexable and searchable data from blockchains, without relying on centralized intermediaries.
In Phase 2, companies that can now access significant amounts of blockchain data, shifted their focus to organizing this data into coherent, analyzable information. This was challenging because while blockchains provide a public record of transactions between wallet addresses, those wallet addresses are, by design, not easily traceable to a real-world identity. A wallet address is a cryptographically generated string of characters and acts as a user’s pseudonym. Therefore, it was difficult to extract useful information from this data for due diligence and analytics purposes. However, many companies like Nansen have stepped in to address this void, providing a means to gather valuable information from blockchain data, which can then be used to train AI algorithms.
However, the next frontier, or Phase 3, is developing knowledge from the vast amounts of information offered by blockchain platforms. This challenge has not yet been fully addressed, as the task of meaningfully tying disparate pieces of data and information together is both time-consuming and manual. AI can be a powerful tool to automate the formidable task of extracting, organizing, storing, and disseminating an organization’s collective knowledge.
AI Can Accelerate Web3 Adoption
Generative AI has exploded in popularity recently partly because of its ability to offer tailored experiences based on user prompts. As Han Jin, the CEO of AI-driven Web3 firm Bluwhale, said, “For Web3 to go mainstream, the next generation of consumer-facing applications using blockchain must at least match the user experiences of Web2. Personalization will not be optional but essential.” This approach allows decentralized applications to more effectively engage their current audience and attract new users, thereby optimizing marketing spend.
Knowledge graphs or as Jin refers to them, “a decentralized AI brain scaling across blockchains” may be the missing component to bring the personalized experiences of Web2 into Web3. Knowledge graphs are data science tools that map relationships between objects, facts, events, situations and other data. A knowledge graph is often employed alongside AI, as it aids in imparting meaning and introducing structure to a diverse dataset. Search engines often use knowledge graphs to allow computers to understand context for people’s queries and tie together billions of facts about people, places and things.
However, much like the core infrastructure of Web2, many knowledge graphs are constructed by centralized entities, siloed to their specific organizations, and not broadly shared. Decentralized knowledge graphs, like those being built by OriginTrail, can make knowledge graphs more accessible by leveraging open, permissionless blockchain networks where the public can contribute, maintain and verify knowledge.
Emerging Technologies Are The Future
By utilizing cutting edge tools like knowledge graphs, integrating AI and blockchain can serve as the foundation for Web3 built on trustworthy data. This new decentralized internet can help combat issues prevalent in our current centralized internet such as disinformation, surveillance, risks to privacy and security, and overall loss of agency over our personal data.
