The Impact of Web3 and AI on the Future of Fintech

Web3 and AI have been celebrated as the herald angels of the new digital revolution. Both have promised to change the way we interact with the internet and offer huge potential financial gains to those lucky enough to ride the wave to the top.

In fact, by 2025, the market size of Web3 and AI is set to grow to $39.7 billion and $126 billion, respectively. But what exactly is AI and how can it be used in Web3?

By Medb Kiely-Cuddy · Author at Blockmate.io

What is AI?

Artificial Intelligence (AI) is a cutting-edge technology that is quickly gaining attention in various industries due to its potential for limitless opportunities. While some may still consider it the new kid on the block, AI has already proven to be a game-changer.

At its core, AI involves the creation of intelligent machines that can simulate human intelligence, learning, and decision-making. These machines can be programmed to perform tasks independently or provide valuable information for decision-making purposes.

The possibilities for AI in Web3 are vast, and its potential impact on this emerging technology is significant. By leveraging AI’s capabilities, Web3 can further enhance its functionality, offering new and innovative ways to interact with the web.

How smart is the AI we see today?

Pretty much all AI that exists today is what’s known as Artificial Narrow (or weak) Intelligence. While it can mimic human-like behavior to some extent, it can still only do what it’s programmed to.

Since the creation of the first AI program, a checkers program, in 1951, there have been significant advancements in AI technologies such as ChatGPT. However, none of these programs can replicate human “thinking.” They can only operate within the constraints defined by their programmers. Thankfully, we still have a long way to go before we see the likes of HAL 9000.

The next phase in AI’s evolution is the development of Artificial General Intelligence, which will enable machines to understand and learn any task a human can. Perhaps one day, we may even witness the emergence of Artificial Superintelligence, where machines surpass even our brightest minds. But for now, let’s focus on what AI can currently offer and its current limitations.

Subcategories of AI

There are many categories within AI, and they’re combined in different ways to build the AI tools we see today. The six main categories of AI are as follows:

Machine Learning (ML)

This uses algorithms that allow machines to learn from data and improve their performance over time. It is designed to imitate human learning by improving accuracy over time.

Netflix uses this to offer more tailored and accurate recommendations as your watching history grows. (For those of us who binge-watch, this can happen very quickly.)

Neural Networks

A subset of ML and inspired by the structure of the human brain, neural networks use interconnected nodes to simulate complex patterns and solve problems.

Your phone’s facial recognition uses this to improve accuracy and recognize your face from different angles and expressions.

Deep Learning

Deep learning uses huge neural networks to analyze and handle large complex data sets. These neural networks have added layers to their complexity, which is what the word “deep” refers to. It often involves less human input than a small neural set and can be scaled to handle more complex data. It’s also known as “scalable machine learning.”

Tesla and other self-driving cars use deep learning to recognize different objects on the road and learn new objects. It allows them to differentiate between a stop sign and a yield sign.

Cognitive Computing

Cognitive computing aims to augment our own intelligence rather than make independent decisions. It combines different elements of AI to provide insights and deep analysis of data.

Cognitive computing is employed to help determine the risk level of investments. It analyzes relevant data to offer insights and predictions, but people make the final decision.

Natural Language Processing (NLP)

These are training programs designed to help computers comprehend human language in a natural way by using a combination of artificial intelligence, linguistics, and social sciences.

NLP enables virtual assistants such as Siri and Alexa to interpret our various requests and commands.

Computer Vision

Computer vision or visual recognition is the process of teaching machine learning programs to derive meaningful information from videos.

Google Lens utilizes this technique to identify objects or search for products like clothing on the internet based on an image.

These AI categories handle a range of tasks, from simple automation to complex problem-solving and human-ike communication. Many AI tools combine multiple categories of AI.

Use cases of AI in Web3

So what are the use cases for AI within Web3?

One possible application is to simplify the Web3 experience for both users and developers. By employing natural language processing (NLP), we could design tools that can create smart contracts for non-coders or chatbots that can guide users through decentralized exchanges and protocols. By doing so, AI can play a critical role in removing the complexity of Web3 and encouraging more widespread adoption.

Additionally, AI is used to enhance security and identify fraudulent activities by detecting suspicious behavior and enhancing consensus mechanisms. For instance, blockchain projects and blockchain-as-a-service companies could leverage AI to develop customizable blockchains and layer protocols. Moreover, personal AIs are being used to add to the utility of Decentralized Autonomous Organizations (DAOs) and non-fungible tokens (NFTs). With so many possibilities, we’ll look at several examples that highlight how AI and Web3 are a perfect match.

Where Web3 is embracing AI

NFTs

When we think of AI in the NFT space, the most common example is using tools like MidJourney and Stable Diffusion to generate unique image collections. Nonetheless, AI has even more to offer to the world of digital assets.

CharacterGPT is an AI-based NFT project where holders can transform text into interactive characters with unique personalities. According to the project, these characters will include an inbuilt personal AI that they can imbue with a distinct personality. Holders of these NFTs can train the AI, tailor the personality, and deploy it in dApps within the Alethea ecosystem. This has the potential to bring additional functionality to the NFT sector.

Security

With $3.9 billion lost in Web3 due to hacks and fraud, the sector is seeking tools to protect its assets. AI can assist in safeguarding both on-chain and off-chain activities by minimizing the likelihood of human error and monitoring suspicious actions. Additionally, AI can analyze massive datasets and train models to detect fraudulent behavior, 51% attacks, and money laundering.

Several security companies are developing AI-powered tools to decrease the number of hacks and fraud in the crypto ecosystem. Chainalysis utilizes AI to analyze vast quantities of transaction data to flag suspicious activities and identify trends and patterns in the crypto market.

Meanwhile, AnChain.ai evaluates transactions, smart contracts, and risk profiles and has recently inked a deal with the SEC to help supervise and regulate the DeFi (Decentralized Finance) industry in the United States. AnChain.AI uses a predictive model to recognize unknown users and suspicious transactions as a “preventative” measure rather than only responding to incidents after they occur.

Metaverse

The metaverse has received criticism for lacking a truly immersive experience. The clunky, basic graphics and non-intuitive technology used to interact with it can’t come close to the deeply intuitive experience of human life. AI may be the solution to this problem, as it could create that perfect balance between complexity and simplicity to enhance the human experience.

In fact, Meta—a strong advocate for the metaverse—is betting on AI to improve the user experience. They recently unveiled they are developing an “AI Supercomputer” called the “AI Research SuperCluster” (RSC), which will be the world’s fastest. This technology will be used to create better AI models and training models in NLP, deep learning, and computer vision. Meta envisions RSC being able to provide augmented reality tools, real-time translations, content moderation at scale, and much more to make the metaverse immersive and secure.

One example of the tools they are developing is BuilderBot, which is a metaverse experience that allows users to create objects and backgrounds using natural voice commands. Although it is still in its early stages, with basic graphics, the AI tools behind it demonstrate impressive capabilities and potential. Meta is playing the long game, and time will tell whether their investment in AI will pay off.

Gaming

AI has the potential to revolutionize the gaming industry by creating more immersive experiences. It can enable gamers to have unscripted conversations with NPCs that can respond intelligently and create “endless” landscapes that generate as you travel. This can lead to more detailed cause-and-effect decision-making in the gaming world. AI can also drive innovative monetization schemes that tailor experiences based on complex data mining and player behavior.

In Web3, AI-powered games with NFT characters are emerging. Delysium is an example of a play-to-earn MMO game that features a generated open-world format, customizable characters, and “AI Metabeings.” These AI Metabeings are NPCs, powered by unique AI tools, that interact with the game similarly to humans and can even earn tokens. Projects like this and CharacterGPT demonstrate the potential for AI to enhance gaming experiences in the Web3 space.

Web3 and AI—the future of the internet?

The convergence of Web3 and AI represents a significant technological revolution that is impacting our lives on a daily basis. Web3 has transformed our perception of finance, centralization, and tech monopolies, while AI has become integrated into our daily routines through facial recognition, self-driving cars, investment tools, Siri, Alexa, ChatGPT, MidJourney, and many other applications. By leveraging each other’s strengths, these two technologies can further enhance our digital experiences.

The full potential of the internet is yet to be unlocked, but Web3 and AI have the power to make it a reality. As with any tool, their potential benefits and drawbacks depend on how they are used. The early pioneers of the World Wide Web envisioned a decentralized, open-source, and free information system that could benefit all. By using these technologies wisely, we can work towards achieving that vision. However, if we misuse them, we may only exacerbate existing inequalities. One thing is for sure; it’s going to be a wild ride.