The integration of native artificial intelligence into Web 3 systems is a natural progression (AI).
Every software field is influenced by AI, therefore Web 3 shouldn’t be an exception. However, there are fundamental, technological hurdles to the adoption of AI technology in Web 3 stacks.
Beyond recognising their obvious benefits, it’s critical to consider how AI might join the Web 3 sector in the near future, as well as what main barriers are now impeding this.
To describe an upcoming trend in which much of the world’s software will be recreated with AI/ML as its primary building blocks, we might claim that “machine learning (ML) is devouring software.” Databases and identity management spring to mind when thinking about the ubiquitous components of software programmes. AI/ML models, which represent intelligence, are quickly becoming another essential building component of modern software systems.
Intelligence of Web3
The inclusion of machine learning (ML) in Web 3 will not be a one-off trend; rather, it will be diffused throughout several tiers of the Web 3 stack. Web 3 has three important levels where ML-driven intelligence might arise.
The current generation of blockchain systems has concentrated on developing critical distributed computing components that allow for decentralized financial transaction processing. Some of these fundamental building pieces are consensus techniques, mempool architectures, and oracles. The next generation of layer 1 (base) and layer 2 (companion) blockchains will naturally contain ML-driven features, much as key components of existing software infrastructures like networking and storage are becoming smarter. Consider a blockchain runtime that employs machine learning to forecast transactions to allow a massively scalable consensus process.
Among the most important parts of Web 3 stack that will begin to include machine learning capabilities are smart contracts. DeFi appears to be the archetypal manifestation of this tendency. We’re not far away from witnessing DeFi automated market makers (AMMs) or loan protocols with more advanced reasoning based on machine learning models. For example, consider a lending protocol that employs an intelligence score to balance the different sorts of loans from various wallets.
Decentralized applications are anticipated to become one of the most popular Web 3 methods for adding machine learning (ML) capabilities quickly. This tendency is currently evident in NFTs, but it will become more widespread in the future. The next-generation NFTs will move away from static pictures and toward intelligent artifacts. Many of these NFTs will be able to adjust their behavior in response to the tone of their public or the profile of their new owners.
Roadblocks and challenges of bottom-up adoption pattern
We can erroneously think that a bottom-up adoption pattern is most sensible when contemplating layers of Web 3 intelligence. Blockchain runtimes may learn, and some of that learning can impact higher levels of the stack, such as DeFi protocols or NFTs. However, substantial technological constraints would require a top-down, rather than bottom-up, adoption of machine learning technologies in Web 3 stacks.
The focus of the present wave of blockchain runtimes is at the root of these technological impediments. In theory, blockchains are based on a distributed computing model that integrates several nodes to do calculations that result in a consensus on transaction processing.
This differs from current ML models, which need complicated, long-running calculations for training and optimization and are primarily geared for centralized structures. Because of this friction, embedding native machine learning capabilities in blockchain runtimes, although viable, will necessitate some iteration.
Because DeFi protocols may rely on oracles and external intelligent agents that can fully benefit from existing ML platforms, they have less constraints when it comes to integrating ML capabilities. For dapps and NFTs, the restriction is essentially non-existent. From this viewpoint, we believe that the adoption of machine learning capabilities in Web 3 solutions will likely follow a top-down path, from dapps to protocols to blockchain runtimes, rather than the other way around.
Intelligent Web3 is already here
To illustrate the trajectory of futuristic technology trends, science fiction writer William Gibson remarked, “The future is already here — it’s just not equally distributed.” This concept is ideal for the convergence of AI and Web 3.
The fast advancement of machine learning research and technology over the last decade has resulted in an overwhelming amount of machine learning platforms, frameworks, and APIs that may be utilized to enhance We 3 products with intelligent capabilities. In Web3 apps, we are already seeing isolated examples of intelligence. As a result, we can confidently assert that the intelligent Web 3 is already here, although inequitably dispersed.