Web3 and AI: Enhancing Security, Efficiency, and User Experience

Paulina Lewandowska

12 Jan 2023
Web3 and AI: Enhancing Security, Efficiency, and User Experience

Introduction: Web3 and AI - A Match Made in the Digital World

Through machine learning and intelligent automation, artificial intelligence (AI) has the potential to revolutionize a variety of industries and applications. AI has the potential to improve the features and functionality of blockchain-based goods and services in the world of web3(third generation of the World Wide Web) providing users with even more value.

The term "Web3" describes the Internet's decentralized, peer-to-peer functionality made possible by blockchain technology. It enables the development of programs and services that run on a decentralized network of computers rather than being managed by a single organization. The addition of AI to web3 has the potential to greatly improve the functionality and capabilities of these goods and services while also enhancing user experience, security, and effectiveness.

This article will explore the usefulness of AI in web3, looking at the various applications of AI in blockchain-based goods and services. We will examine the advantages of AI integration in web3 as well as how AI will affect web3 and blockchain technology in the future. Come along as we explore the intriguing potential of AI in the web3 world.

AI 101: A Beginner's Guide to Artificial Intelligence in Web3/Blockchain

But what exactly is artificial intelligence, and how can it be applied to products for the web, blockchain, and cryptocurrency?

Artificial intelligence (AI) is the capacity of machines to carry out tasks that normally require human intelligence, such as problem-solving, learning, and judgment. This is accomplished with the aid of machine learning algorithms, which give computers the ability to examine data, spot trends, and then predict the future or make decisions based on those trends.

In order to improve the functionality and capabilities of web3/blockchain products, AI can be applied in a number of different ways. Several instances include:

  • AI can be used to enable chat bots or smart contracts that can comprehend and respond to human language, making them more user-friendly and effective. This is known as natural language processing.
  • Predictive analytics: AI can analyze market trends or asset pricing data to make future predictions that may be useful to traders or investors.
  • Artificial intelligence (AI) can be used to spot unusual patterns or behaviors that might be signs of fraud, enhancing the security of blockchain-based systems.
  • Supply chain management: AI can be used to improve efficiency and cut waste by optimizing the flow of resources and goods through a supply chain.

AI in Action: Real-World Examples of Artificial Intelligence in Web3/Blockchain Products

Let's examine some specific applications of AI in the market now that we have a better understanding of what AI is and how it can be applied to web3/blockchain products.

  1. AI-powered chatbots are a useful tool for customer service or support because they can comprehend and respond to human language. Natural language processing can also be used to improve the usability and comprehension of smart contracts, which are self-executing contracts with the terms of the agreement written in code.
  2. Another application of AI in web3/blockchain products is predictive analytics. AI can analyze market trends or asset pricing data to make future predictions, which can be useful for traders or investors. For instance, an AI-powered trading platform built on blockchain technology could analyze market data and suggest trades to users.
  3. Fraud detection in web3/blockchain products can also be done using AI. Data analysis using machine learning algorithms can spot unusual patterns or behaviors that could be signs of fraud, enhancing system security. An AI-based payment platform, for instance, could be used to identify and stop fraudulent transactions.
  4. Another application of AI in web3/blockchain products is predictive analytics. AI can analyze market trends or asset pricing data to make future predictions, which can be useful for traders or investors. For instance, an AI-powered trading platform built on blockchain technology could analyze market data and suggest trades to users.

Benefits of using AI in web3/blockchain products

So far, we've seen how AI can be applied to web3/blockchain products in a number of ways to improve their capabilities and functionality. But what advantages do these products' use of AI offer?

BenefitDescription
Improved user experienceNatural language processing and predictive analytics are made possible by AI, which makes web and blockchain products more approachable and simple to use.
Enhanced security and reliabilityAI can identify and correct errors in code or data, as well as detect and stop fraudulent activity, increasing the overall security and dependability of web3/blockchain products.
Increased efficiency and automationAI can automate or improve supply chain management, freeing up time and resources for more difficult tasks..

All things considered, the incorporation of AI in web3/blockchain products has the potential to significantly improve user experience, security, and efficiency. Future applications and advantages of AI technology are likely to be even more creative as the field develops.

Web3/Blockchain's Future with AI: A Look at the Potential of Artificial Intelligence in Decentralized Technologies

As we've seen, AI has the potential to significantly improve the functionality and capabilities of web3/blockchain products, adding value for users and advancing a number of technical aspects. But what part will AI play in the development of blockchain and web3 technology?

  • Integration of AI into web3/blockchain products and services could improve decentralized finance (DeFi) platforms, allowing for more transparent and efficient financial transactions. Decentralized applications (DApps) could benefit from improved performance and user experience, and blockchain networks could benefit from increased security and dependability.
  • New blockchain protocols and standards could be created and put into use by using AI to analyze data and find patterns that could be used to create more secure or efficient blockchain systems. It could also be used to evaluate new protocols and help roll out updates and improvements to current systems.
  • Resolving issues with web3 and blockchain technology: AI could be used to improve the efficiency of blockchain networks or to create new applications and use cases that promote technology adoption. Additionally, it could be utilized to increase the scalability of blockchain systems or to help create new governance or regulatory frameworks..

Conclusion: Is AI an integral part of the web3 ecosystem?

As a result, it is evident that AI has the potential to significantly improve the functionality and capabilities of web3/blockchain products, enhancing user experience and advancing various facets of technology. The use of AI in web3/blockchain products spans a wide range of areas, including supply chain optimization, fraud detection, and predictive analytics.

But does the web3 ecosystem include AI in its entirety? Although it is unquestionably a potent tool that can benefit web3/blockchain products and services greatly, it is important to understand that AI is only one part of the ecosystem. Decentralized networks, blockchain technology, and other innovations all contribute to the web3 ecosystem and its future development.

The use and integration of AI with other technologies will ultimately determine its place in the web3 ecosystem. As AI technologies develop, it's critical to think about the ethical, legal, and social ramifications of their use and to make sure they're applied responsibly and advantageously.

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Token Engineering Process

Kajetan Olas

13 Apr 2024
Token Engineering Process

Token Engineering is an emerging field that addresses the systematic design and engineering of blockchain-based tokens. It applies rigorous mathematical methods from the Complex Systems Engineering discipline to tokenomics design.

In this article, we will walk through the Token Engineering Process and break it down into three key stages. Discovery Phase, Design Phase, and Deployment Phase.

Discovery Phase of Token Engineering Process

The first stage of the token engineering process is the Discovery Phase. It focuses on constructing high-level business plans, defining objectives, and identifying problems to be solved. That phase is also the time when token engineers first define key stakeholders in the project.

Defining the Problem

This may seem counterintuitive. Why would we start with the problem when designing tokenomics? Shouldn’t we start with more down-to-earth matters like token supply? The answer is No. Tokens are a medium for creating and exchanging value within a project’s ecosystem. Since crypto projects draw their value from solving problems that can’t be solved through TradFi mechanisms, their tokenomics should reflect that. 

The industry standard, developed by McKinsey & Co. and adapted to token engineering purposes by Outlier Ventures, is structuring the problem through a logic tree, following MECE.
MECE stands for Mutually Exclusive, Collectively Exhaustive. Mutually Exclusive means that problems in the tree should not overlap. Collectively Exhaustive means that the tree should cover all issues.

In practice, the “Problem” should be replaced by a whole problem statement worksheet. The same will hold for some of the boxes.
A commonly used tool for designing these kinds of diagrams is the Miro whiteboard.

Identifying Stakeholders and Value Flows in Token Engineering

This part is about identifying all relevant actors in the ecosystem and how value flows between them. To illustrate what we mean let’s consider an example of NFT marketplace. In its case, relevant actors might be sellers, buyers, NFT creators, and a marketplace owner. Possible value flow when conducting a transaction might be: buyer gets rid of his tokens, seller gets some of them, marketplace owner gets some of them as fees, and NFT creators get some of them as royalties.

Incentive Mechanisms Canvas

The last part of what we consider to be in the Discovery Phase is filling the Incentive Mechanisms Canvas. After successfully identifying value flows in the previous stage, token engineers search for frictions to desired behaviors and point out the undesired behaviors. For example, friction to activity on an NFT marketplace might be respecting royalty fees by marketplace owners since it reduces value flowing to the seller.

source: https://www.canva.com/design/DAFDTNKsIJs/8Ky9EoJJI7p98qKLIu2XNw/view#7

Design Phase of Token Engineering Process

The second stage of the Token Engineering Process is the Design Phase in which you make use of high-level descriptions from the previous step to come up with a specific design of the project. This will include everything that can be usually found in crypto whitepapers (e.g. governance mechanisms, incentive mechanisms, token supply, etc). After finishing the design, token engineers should represent the whole value flow and transactional logic on detailed visual diagrams. These diagrams will be a basis for creating mathematical models in the Deployment Phase. 

Token Engineering Artonomous Design Diagram
Artonomous design diagram, source: Artonomous GitHub

Objective Function

Every crypto project has some objective. The objective can consist of many goals, such as decentralization or token price. The objective function is a mathematical function assigning weights to different factors that influence the main objective in the order of their importance. This function will be a reference for machine learning algorithms in the next steps. They will try to find quantitative parameters (e.g. network fees) that maximize the output of this function.
Modified Metcalfe’s Law can serve as an inspiration during that step. It’s a framework for valuing crypto projects, but we believe that after adjustments it can also be used in this context.

Deployment Phase of Token Engineering Process

The Deployment Phase is final, but also the most demanding step in the process. It involves the implementation of machine learning algorithms that test our assumptions and optimize quantitative parameters. Token Engineering draws from Nassim Taleb’s concept of Antifragility and extensively uses feedback loops to make a system that gains from arising shocks.

Agent-based Modelling 

In agent-based modeling, we describe a set of behaviors and goals displayed by each agent participating in the system (this is why previous steps focused so much on describing stakeholders). Each agent is controlled by an autonomous AI and continuously optimizes his strategy. He learns from his experience and can mimic the behavior of other agents if he finds it effective (Reinforced Learning). This approach allows for mimicking real users, who adapt their strategies with time. An example adaptive agent would be a cryptocurrency trader, who changes his trading strategy in response to experiencing a loss of money.

Monte Carlo Simulations

Token Engineers use the Monte Carlo method to simulate the consequences of various possible interactions while taking into account the probability of their occurrence. By running a large number of simulations it’s possible to stress-test the project in multiple scenarios and identify emergent risks.

Testnet Deployment

If possible, it's highly beneficial for projects to extend the testing phase even further by letting real users use the network. Idea is the same as in agent-based testing - continuous optimization based on provided metrics. Furthermore, in case the project considers airdropping its tokens, giving them to early users is a great strategy. Even though part of the activity will be disingenuine and airdrop-oriented, such strategy still works better than most.

Time Duration

Token engineering process may take from as little as 2 weeks to as much as 5 months. It depends on the project category (Layer 1 protocol will require more time, than a simple DApp), and security requirements. For example, a bank issuing its digital token will have a very low risk tolerance.

Required Skills for Token Engineering

Token engineering is a multidisciplinary field and requires a great amount of specialized knowledge. Key knowledge areas are:

  • Systems Engineering
  • Machine Learning
  • Market Research
  • Capital Markets
  • Current trends in Web3
  • Blockchain Engineering
  • Statistics

Summary

The token engineering process consists of 3 steps: Discovery Phase, Design Phase, and Deployment Phase. It’s utilized mostly by established blockchain projects, and financial institutions like the International Monetary Fund. Even though it’s a very resource-consuming process, we believe it’s worth it. Projects that went through scrupulous design and testing before launch are much more likely to receive VC funding and be in the 10% of crypto projects that survive the bear market. Going through that process also has a symbolic meaning - it shows that the project is long-term oriented.

If you're looking to create a robust tokenomics model and go through institutional-grade testing please reach out to contact@nextrope.com. Our team is ready to help you with the token engineering process and ensure your project’s resilience in the long term.

FAQ

What does token engineering process look like?

  • Token engineering process is conducted in a 3-step methodical fashion. This includes Discovery Phase, Design Phase, and Deployment Phase. Each of these stages should be tailored to the specific needs of a project.

Is token engineering meant only for big projects?

  • We recommend that even small projects go through a simplified design and optimization process. This increases community's trust and makes sure that the tokenomics doesn't have any obvious flaws.

How long does the token engineering process take?

  • It depends on the project and may range from 2 weeks to 5 months.

What is Berachain? 🐻 ⛓️ + Proof-of-Liquidity Explained

Karolina

18 Mar 2024
What is Berachain? 🐻 ⛓️ + Proof-of-Liquidity Explained

Enter Berachain: a high-performance, EVM-compatible blockchain that is set to redefine the landscape of decentralized applications (dApps) and blockchain services. Built on the innovative Proof-of-Liquidity consensus and leveraging the robust Polaris framework alongside the CometBFT consensus engine, Berachain is poised to offer an unprecedented blend of efficiency, security, and user-centric benefits. Let's dive into what makes it a groundbreaking development in the blockchain ecosystem.

What is Berachain?

Overview

Berachain is an EVM-compatible Layer 1 (L1) blockchain that stands out through its adoption of the Proof-of-Liquidity (PoL) consensus mechanism. Designed to address the critical challenges faced by decentralized networks. It introduces a cutting-edge approach to blockchain governance and operations.

Key Features

  • High-performance Capabilities. Berachain is engineered for speed and scalability, catering to the growing demand for efficient blockchain solutions.
  • EVM Compatibility. It supports all Ethereum tooling, operations, and smart contract languages, making it a seamless transition for developers and projects from the Ethereum ecosystem.
  • Proof-of-Liquidity.This novel consensus mechanism focuses on building liquidity, decentralizing stake, and aligning the interests of validators and protocol developers.

MUST READ: Docs

EVM-Compatible vs EVM-Equivalent

EVM-Compatible

EVM compatibility means a blockchain can interact with Ethereum's ecosystem to some extent. It can interact supporting its smart contracts and tools but not replicating the entire EVM environment.

EVM-Equivalent

An EVM-equivalent blockchain, on the other hand, aims to fully replicate Ethereum's environment. It ensures complete compatibility and a smooth transition for developers and users alike.

Berachain's Position

Berachain can be considered an "EVM-equivalent-plus" blockchain. It supports all Ethereum operations, tooling, and additional functionalities that optimize for its unique Proof-of-Liquidity and abstracted use cases.

Berachain Modular First Approach

At the heart of Berachain's development philosophy is the Polaris EVM framework. It's a testament to the blockchain's commitment to modularity and flexibility. This approach allows for the easy separation of the EVM runtime layer, ensuring that Berachain can adapt and evolve without compromising on performance or security.

Proof Of Liquidity Overview

High-Level Model Objectives

  • Systemically Build Liquidity. By enhancing trading efficiency, price stability, and network growth, Berachain aims to foster a thriving ecosystem of decentralized applications.
  • Solve Stake Centralization. The PoL consensus works to distribute stake more evenly across the network, preventing monopolization and ensuring a decentralized, secure blockchain.
  • Align Protocols and Validators. Berachain encourages a symbiotic relationship between validators and the broader protocol ecosystem.

Proof-of-Liquidity vs Proof-of-Stake

Unlike traditional Proof of Stake (PoS), which often leads to stake centralization and reduced liquidity, Proof of Liquidity (PoL) introduces mechanisms to incentivize liquidity provision and ensure a fairer, more decentralized network. Berachain separates the governance token (BGT) from the chain's gas token (BERA) and incentives liquidity through BEX pools. Berachain's PoL aims to overcome the limitations of PoS, fostering a more secure and user-centric blockchain.

Berachain EVM and Modular Approach

Polaris EVM

Polaris EVM is the cornerstone of Berachain's EVM compatibility, offering developers an enhanced environment for smart contract execution that includes stateful precompiles and custom modules. This framework ensures that Berachain not only meets but exceeds the capabilities of the traditional Ethereum Virtual Machine.

CometBFT

The CometBFT consensus engine underpins Berachain's network, providing a secure and efficient mechanism for transaction verification and block production. By leveraging the principles of Byzantine fault tolerance (BFT), CometBFT ensures the integrity and resilience of the Berachain blockchain.

Conclusion

Berachain represents a significant leap forward in blockchain technology, combining the best of Ethereum's ecosystem with innovative consensus mechanisms and a modular development approach. As the blockchain landscape continues to evolve, Berachain stands out as a promising platform for developers, users, and validators alike, offering a scalable, efficient, and inclusive environment for decentralized applications and services.

Resources

For those interested in exploring further, a wealth of resources is available, including the Berachain documentation, GitHub repository, and community forums. It offers a compelling vision for the future of blockchain technology, marked by efficiency, security, and community-driven innovation.

FAQ

How is Berachain different?

  • It integrates Proof-of-Liquidity to address stake centralization and enhance liquidity, setting it apart from other blockchains.

Is Berachain EVM-compatible?

  • Yes, it supports Ethereum's tooling and smart contract languages, facilitating easy migration of dApps.

Can it handle high transaction volumes?

  • Yes, thanks to the Polaris framework and CometBFT consensus engine, it's built for scalability and high throughput.