The Web3 Renaissance: How Tech Advancements Influence Decentralization

In the rapidly changing technology landscape, two significant trends have taken center stage: artificial intelligence and Web3. Web3 boasts a vocal community that advocates for a new generation of the Internet, and the boom of generative AI being a new popular kid on the block has initiated a shift in almost all industries. These technologies, separately, are set to reshape the trajectory of technological innovation, how industries generate revenues, and serve their customers. As we are still in the early stages of these tech advancements, most of our predictions and assumptions are about where the world will be in a few years.

On the application level, new technologies, such as AI and Web3, can individually be integrated into mature industries like banking or logistics, and already there are plenty of easily accessible cases of this integration. However, combining two technologies that are both trying to find their place in the market is more akin to a scientific experiment than a mere implementation test. Now, imagine the potential if these two were to be successfully combined - their integration could bring about unprecedented advancements. Right?

Can AI and Web3 actually work together?

Well, it’s definitely not that simple. Let’s dissect the two to understand their core ideas. Web3 is the next step in the evolution of the Internet, transitioning from the current Web 2.0, where Zuck and co. control all our data, to decentralized data ownership. I have touched on this in my previous article “The Future of Web3” where I presented an argument that virtual reality could be the cornerstone of a full-fledged shift to Web3. Decentralized ownership of data implies that no single entity has access to big data anymore, and companies can't use and sell our data as they wish. AI models, however, clash with this principle. Neural networks thrive on data to learn and become smarter and rely on massive computations to perform tasks quickly. So, at a fundamental level, AI and Web3 are opposed to each other.

There are already projects attempting to address this challenge. For instance, Render Network claims they are constructing a network for decentralized computation using idle GPUs as nodes. While it's primarily aim at the creative industry to render projects, a recent update to their whitepaper mentioned machine learning as another prominent use case. The idea sounds compelling on paper, but it would require a multitude of user GPUs operating simultaneously to match the performance of just one specialized GPU in the cloud. Factor in that these GPUs would need to run 24/7, 365 days a year, and it becomes improbable for regular users. This brings us to unsolvable (for now) challenge number 1 - decentralized AI-powered systems will be too slow.

Let's consider another example. It's widely recognized that the banking sector employs AI-driven anti-fraud mechanisms to detect and prevent malicious activities. The reason for its efficacy is straightforward: all financial transactions have a set of markers, from the sender’s and receiver’s names and locations to the specific reasons for the transactions. This information constitutes a vast dataset that enhances AI's capability to pinpoint anomalies. But is this applicable to crypto? One message frequently advocated by the Web3 community is that cryptocurrency represents a new financial paradigm, transferring monetary control from banks to users. Anti-fraud AI systems depend on analyzing vast amounts of transactional data in real-time to foresee and identify potential fraud. Yet, incorporating these mechanisms in a Web3 environment poses challenges. For starters, as I previously highlighted, Web3 emphasizes decentralized data ownership, which limits access to such datasets. In Web3, discerning the sender of a transaction, its purpose, and the recipient becomes more complex. This limitation reduces the available data, and the criteria to spot illegal transactions differ significantly vs. banks. Furthermore, the requirement for real-time anti-fraud AI assessments could drastically slow down transaction processing speed. This notion contradicts the essence of Web3, which advocates for seamless and swift transactions. This brings us to challenge number 2 - AI could slow down the blockchain. Moreover, entrusting centralized AI systems with the responsibility of detecting and halting fraudulent activities undermines the decentralized concept. This introduces the challenge of AI and Web3 number 3 - the risk of reverting trust back to a centralized system, even thought its an automated one.

Real applications of AI in Web3

Yet, despite these seemingly incompatible differences, AI and Web3 can coexist, though not as we might traditionally envision. Instead of embedding AI directly into the Web3 infrastructure, it can act as a complementary asset. Consider specialized neural networks designed explicitly for crypto and blockchain analytics. These could offer insights into market trends, user behaviors, or even forecast potential vulnerabilities within a blockchain – all while preserving the decentralized core of Web3. Projects like LayerAI (CryptoGPT) are heading in that direction, though tangible results remain to be seen.

Some argue that AI can analyze public blockchain’s data to predict market movements, advise users on their crypto investments, and recommend the best times to initiate transactions to reduce fees or optimize speed. The latter might not gain much traction, once Ethereum updates its chain, this concern will likely become obsolete. In these instances, AI doesn't need to be integrated at the core of the blockchain. it can instead function as an accessory, enhancing the user experience without breaching the foundational principal of Web3.

Furthermore, as we usher in a future where more services, platforms, and tools are built around the Web3 architecture, AI can play a crucial role in user education. Given the technical complexities and nuances associated with blockchain and crypto, AI-driven platforms can simplify and translate this information, ensuring that the average user isn't left behind in this new digital revolution. A good example is DeCipher - an AI-powered tool designed to transform the process of generating Smart Contract documentation. If this proves to be useful, the developers will be able to provide detailed documentation on their smart contracts, increasing their accessibility.

Let me share an insight into how AI can be integrated with Web3, drawing from our firsthand experience while developing Grap3. Our goal was to enable users to describe the requirements for a smart contract in simple terms. The system would then pinpoint the most suitable type of smart contract based on the user’s input and guide them through a series of specific scenarios, posing questions to gather all the necessary information. By the end of this process, the user receives a ready-to-use smart contract without having to write a single line of code. Although I can’t delve into the technicalities, it's worth mentioning that the system is powered by a neural network that leverages a linguistic model and Dialogflow for an effective Q&A experience.

In conclusion, while the direct integration of AI and Web3 presents challenges, given their fundamental differences, there are avenues to harness the strengths of both. We need to acknowledge that currently mature blockchains that have big user bases, thus substantial data sets, like Ethereum or Polygon, have yet to adopt or even announce plans for AI integration. The key lies in recognizing their distinct roles and crafting solutions that allow them to coexist, complementing each other without compromising their inherent values. As the technology landscape continues to evolve, this symbiotic relationship might just hold the key to unlocking new dimensions of digital innovation.