AI in web3: How AI manifests in the world of web3
Experts predict a large part of the world’s software will be rewritten using AI/ML as their central structure block. PWC estimates that AI will contribute $15.7 trillion to the global economy by 2030, resulting in a 14% increase in global GDP. As the years pass, AI will not be the only technology to gain prominence. Considering the ubiquitous components of software applications, capabilities such as databases and identity make a mark too. Intelligence may be considered the building block of modern software applications. Software concepts such as cloud computing, cyber security and networking are being reimagined using ML. Web3 will be the next iteration of many of these software trends, and ML (Machine learning) will likely play a fundamental role in the evolution of AI-based web3 technologies.
AI is influencing a range of other technologies and industries, and web3 is not an exception. However, there are fundamental technical roadblocks to web3 technologies adopting AI. So, it is important to identify how the incorporation of AI in web3 can be materialized and what major roadblocks could prevent this from happening. Currently, AI-based solutions are primarily centralized. However, the real question is- What role will AI play within the new decentralized world of web3, considering all the hype surrounding it? How can we untangle AI’s centralization tendencies? This article will discuss all of this in detail.
- What is web3?
- What is AI?
- How AI in web3 makes layers of web3 intelligence?
- Why AI in web3?
- Why does web3 follow the top-down adoption of ML technologies?
What is web3?
Web3 is a high-level concept that describes the future of the internet. It involves sharing power and benefits through decentralization. Once web3 is in its full-blown form, a few large technology companies will not be able to control the core capabilities of the internet. Users will have control over their data and, resultantly, greater privacy. There will be no censorship, and the rewards earned will be distributed equally. Although web3 is not yet defined in a standard way, these are its most prominent characteristics.
Decentralization is a fundamental tenet of web3. Web2 uses HTTP to locate information, which is done using unique web addresses. Web3, by virtue of being blockchain-based, would allow information to be stored in multiple locations across a network. This would allow users to have greater control over the vast databases that internet giants such as Google and Meta currently hold. Web3 will allow users to sell the data generated from disparate computing resources such as mobile phones, desktops and appliances if they wish to. This ensures that users retain control over their data.
Permissionless and trustless: Web3 is based on open-source software and is decentralized. Web3 apps that run on blockchains are called dApps.
Artificial intelligence (AI) and machine learning: Web3 will use technologies based on Semantic Web concepts and natural language processing to enable computers to understand information like humans. Web3 will also utilize machine learning. This branch of artificial intelligence uses data and algorithms to mimic human learning, slowly improving its accuracy. These capabilities will allow computers to produce more relevant and faster results in many areas, such as drug development.
Connectivity: Information and content are more connected with web3 and are accessible by multiple applications. Additionally, there is an increase in the number of devices that can connect to the internet. The Internet of Things also has an important role to play here.
What is AI?
Artificial intelligence (AI) is the simulation of human intelligence by computer systems. Some examples of AI are expert systems, natural language processing (NLP), speech recognition and computer vision. AI is built on specialized hardware and software that can be used to write and train machine learning algorithms. AI systems generally work by ingestion of large amounts of labeled data. They then analyze the data for patterns and correlations and use these patterns to predict future states. For instance, a chatbot can be fed text chat examples to make it learn how to have real-life conversations with people. An image recognition tool can also learn how to recognize objects in images by being exposed to millions of images. AI programming is focused on three cognitive skills: reasoning, learning, and self-correction.
There are two types of artificial intelligence.
- Strong AI – Systems with strong artificial intelligence can perform human-like tasks. These systems are more complicated and complex. These systems are programmed to solve problems without human intervention. Examples of strong AI are self-driving cars and hospital operating rooms.
- Weak AI – A weak AI system has been designed to do a particular job. Video games and personal assistants like Siri and Amazon’s Alexa are examples of weak AI systems. The assistants answer your questions by asking you questions.
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How AI in web3 makes layers of web3 intelligence?
ML is an integral part of AI. Web3’s addition of ML will spread to different layers of the web3 stack. Three key web3 layers can provide ML-driven insights.
Current blockchain platforms focus on developing key distributed computing components that allow for the decentralized processing of financial transactions. These key building blocks include consensus mechanisms, mempool structures, and oracles. The next generation of layer 1 and layer 2 blockchains (companion and base) will incorporate ML-driven capabilities, just as the core components of traditional software infrastructures like storage and networking are becoming more intelligent. To illustrate, a blockchain runtime can use ML prediction to make transactions in order to create scalable consensus protocols. AI can add security to the blockchain, and AI applications can quickly mine data and predict behavior, detecting fraudulent behavior and stopping attacks. The blockchain will also benefit from AI as an AI protocol that might be able to predict transactions and create consensus protocols that scale easily.
Web3 stack can also integrate ML capabilities through the use of smart contracts and protocols. DeFi most prominently illustrates this trend. We are not far from seeing DeFi computerized market makers (AMMs) or lending protocols with more intelligent logic that is based on ML models. We can, for example, imagine a lending protocol using an intelligent score to balance loans from different types of wallets.
Decentralized applications (dApps) are expected to be among the most popular web3 solutions for rapidly adding ML-driven features. This trend is already evident in NFTs and will continue to grow. Next-generation NFTs will move from static images to artifacts with intelligent behavior. These NFTs may be able to adapt their behavior to the mood of the profile of their owners.
Why AI in web3?
Shift from generalization to individualism
Big tech has used centralized AI models over the past decade to extract value from users and gain insights. In web3, we are advancing the capabilities of AI to serve all people, not just the wealthy few. Every AI model is trained on the creator’s personal knowledge, passions, and experiences.
From users to owners
A handful of private companies control all the content generated and make a profit from it. Consequently, content creators often remain underpaid and neglected. In web3, creators fully control their data, AI models and digital assets. Few companies are helping to build platforms on blockchain, so creators have the sole access and power of their data to repurpose or share it as they wish.
From scarcity to utility
To ensure long-term sustainability, tokens are not enough to give users ownership or incentives. Tokens must be useful and provide real value to their users. Your personal AI creates and unlocks new value from the content you create and the creativity and intellect you use to create it. Your personal AI unlocks new opportunities for collaborations and creates value for you and your community through access and participation enabled by social tokens.
From consumption to participation
Today’s platforms are built for mass consumption, and it is a one-way road where content creators create content, and the audience consumes it. Creators and their communities have their own platform, thanks to personal AIs and their own way of exchanging value with social tokens. We are creating a new architecture of collaborative networks that shifts power from platforms to people and transforms the relationship between value consumption and value creation.
Subscriptions and investments
Creators have always hoped to build a large subscriber base over many years and then, hopefully, eventually monetize the subscriber base. The reality is that only a handful of creators earn a decent wage, and this situation is not good for either the creators or their subscribers. AI in web3 is driving a new creator economy that allows communities to invest in creators they love as well as the personal AIs that add value to their lives. Creators now have the opportunity to build a sustainable business around their creativity, and the community can benefit from this success.
Why does web3 follow the top-down adoption of ML technologies?
Considering the layers of web3 intelligence, it is reasonable to expect a base-up adoption trend to be logical. Blockchain runtimes may become intelligent, and some of that intelligence could impact higher layers like NFTs or DeFi protocols. However, serious technological limitations have necessitated a hierarchical adoption of ML technologies in web3. These technological roadblocks result from engineering new blockchain runtimes, as blockchains are designed around distributed computing.
This approach is different in relation to cutting-edge ML models, which require complex, long-running calculations to prepare and streamline designs that were designed generally for a unified design. It is possible to consolidate native ML capabilities within blockchain runtimes. However, this will need a few iterations.
Because DeFi protocols can rely on outer intelligent specialists and oracles to fully benefit from existing ML platforms, they have fewer restrictions to embrace ML highlights. The restrictions on dApps are almost non-existent for NFTs and dApps. This viewpoint suggests that web3’s adoption of ML capabilities will likely follow a hierarchical path, going from dApps and protocols to blockchain runtimes.
AI in web3 is a futuristic technology trend. Over the past decade, the rapid advancement of ML technology and research has resulted in an overwhelming amount of ML platforms, frameworks, and APIs that can be used to provide intelligent capabilities to web3 solutions. Already, we are seeing some instances of intelligence in web3 apps. We can confidently say that intelligent web3 exists, but not in an evenly distributed manner.
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