Software trends, which include cloud computing, networking and cyber security are being reimagined, with ML, machine learning, as best-in-class.
This is a logical next step for Web 3 platforms to incorporate native artificial intelligence (AI). However, there are foundational and technical barriers around Web 3 stacks for the adoption of AI technologies. The question becomes: How can AI enter the Web 3 space in the near future, and what major roadblocks are currently preventing this to materialize?
Currently, and definitely in our future, most of the world’s modern software applications will be rewritten with AI/ML as its core building blocks, especially when it comes to intelligence.
ML is the first-class citizen when it comes to trends such as cloud computing, networking and cyber security. Given that Web 3 is the next iteration of many of those software trends, ML will likely play a foundational role in the evolution of Web 3 technologies. The convergence of ML and Web 3 requires understanding both the trajectory of adoption of ML capabilities in Web 3 stacks and the fundamental challenges.
There are three key layers that ML-driven intelligence will emerge in Web 3.
The current generation of blockchain platforms has focused on building key distributed computing components that enable the decentralized processing of financial transactions. Consensus mechanisms, mempool structures (the dynamic staging area in front of the blockchain that enables transaction ordering, transaction fee prioritization, and general block construction) and oracles are some of these key building blocks. Just as core components of traditional software infrastructures such as networking and storage are becoming intelligent, the next generation of layer 1 (base) and layer 2 (companion) blockchains will natively incorporate ML driven capabilities. For instance, we can think of blockchain runtime that uses an ML prediction for transactions to enable a massively scalable consensus protocol.
Smart contracts and protocols are another component of the Web 3 stack that will start incorporating ML capabilities. DeFi seems to be the prototypical example for this trend. Soon a generation of DeFi automated market makers (AMMs) or lending protocols will emerge that incorporate more intelligent logic based on ML models. A great example is a lending protocol that uses an intelligent score to balance the types of loans from different types of wallets.
Decentralized applications (dapps) are likely to become among the most likely Web 3 solutions to rapidly add ML-driven features. We are already seeing this trend in NFTs, but it’s going to become increasingly pervasive. The next-generation NFTs will transition from static images to artifacts that exhibit intelligent behavior. Some of these NFTs will be able to change their behavior based on the mood of their audience or the profile of new owners.
In considering layers of Web 3 intelligence, we might naively assume that a bottom-up adoption trend is most logical. Blockchain runtimes can become intelligent, and some of that intelligence can influence higher layers of the stack like DeFi protocols or NFTs. Yet, there are serious technological limitations that would force a top-down, instead of bottom- up, adoption of ML technologies in Web 3 stacks.
The root of these technological roadblocks trace to the architecture of the current generation of blockchain runtimes. In principle, blockchains are designed around a distributed computing paradigm that coordinates different nodes to perform computations that lead to a consensus about the processing of transactions.
That approach contrasts to the state-of-the-art ML models that require complex, long-running computations for training and optimization which have been designed mostly for centralized architectures. This friction means that incorporating native ML capabilities in blockchain runtimes, although possible, is going to require some iterations.
DeFi protocols have fewer limitations from embracing ML features as they can rely on oracles and external intelligent agents that can fully benefit from existing ML platforms. And the limitation is almost non-existent for dapps and NFTs. From this perspective, we think the adoption of ML capabilities in Web 3 solutions is likely to follow a top-down trajectory going from dapps to protocols to blockchain runtimes instead of the opposite.
The intersection of AI and Web 3 is already here. As a society, we need to evenly distribute this intersection.
The rapid evolution of ML research and technology in the last decade has translated into an overwhelming number of ML platforms, frameworks and APIs that can be used to add intelligent capabilities to Web 3 solutions. We are already seeing isolated examples of intelligence in Web 3 applications. so we can safely say that intelligent Web 3 is already here, just not evenly distributed.