How to Design a Sustainable Tokenomics Model in a Defi Project?

Karolina

26 Feb 2024
How to Design a Sustainable Tokenomics Model in a Defi Project?

Investors look for projects that not only develop innovative products but also do it sustainably. In a way that allows for long-term growth, and resistance to uneasy conditions in the crypto market. Projects can achieve this through a studious design of their tokenomics model.

Understanding Tokenomics

Tokenomics, short for token economics, refers to the study and design of economic systems within blockchain networks and crypto projects. At its core, tokenomics encompasses the distribution, circulation, and utilization of tokens to incentivize various stakeholders and drive desired behaviors within the ecosystem.

Key Components of Sustainable Tokenomics

Token Allocation

Defining clear purposes and rules for the treasury fund to align the interests is essential. A well-defined allocation strategy ensures that tokens are distributed in a good manner. That it promotes decentralization, fosters community participation, and supports the long-term growth of the ecosystem.

Token Allocation
Source: https://messari.io/article/power-and-wealth-in-cryptoeconomies

Maintaining a balanced token allocation to achieve decentralized governance and organic project growth is critical. By distributing tokens equitably among stakeholders, projects can mitigate the risk of centralization, and foster a diverse and engaged community. While it may be tempting to allocate most tokens for the founding team and institutional investors, projects should remember that the value of their tokens is in large part determined by how decentralized the ownership structure is.

Token Vesting Schedule

Token Vesting Schedule
For example: Thetan Arena token vesting schedule: the square marks out the period September 2021 to March 2022. (https://doc.thetanarena.com/ economy/theta-gem)

Implementing a structured vesting schedule for team members, investors, and advisors is crucial to ensure the alignment of incentives and commitment. A vesting schedule gradually releases tokens over a specified period, incentivizing continued participation and discouraging short-term speculation.

Maximum Inflation

Managing inflation is a delicate balancing act for crypto projects, as excessive inflation can erode the purchasing power of tokens. Insufficient inflation may hinder growth and adoption. An important metric in that regard is Maximum Inflation, which refers to the total supply increase over time. It is calculated through dividing maximum supply by the initial supply.

Projects must carefully calibrate their inflationary policies to maintain a healthy balance between supply and demand. While also incentivizing long-term holding and participation. By adjusting maximum inflation rates in response to project needs, crypto projects can optimize tokenomics for sustainable growth and stability.

Bitcoin inflation vs. time
Bitcoin inflation vs. time. Source: Research Gate

Value Accrual

Ensuring that tokens accrue tangible value to holders is essential for fostering long-term engagement and participation within the ecosystem. Value accrual mechanisms may include utility features, governance rights, revenue-sharing mechanisms, or other incentives that incentivize holding and active participation in the project.

Strategies for Designing Sustainable Tokenomics Models

Designing Sustainable Tokenomic Models

Defining Clear Objectives

Establishing clear objectives and goals for the project's economic model is fundamental to its success. By articulating a compelling vision and roadmap, projects can attract stakeholders, align incentives, and rally support for their long-term mission. Clear objectives also provide a framework for decision-making and resource allocation, guiding the project towards sustainable growth.

Incorporating Governance Mechanisms

Implementing robust governance mechanisms is essential for ensuring democratic decision-making and community involvement in protocol upgrades and changes. By empowering token holders to vote on proposals, participate in governance discussions, and shape the future direction of the project, projects can foster a sense of ownership and accountability within the community.

Ensuring Transparency and Accountability

Promoting transparency and accountability in tokenomics design and fund management is critical for building trust and confidence among stakeholders. By providing regular updates, financial reports, and disclosures, projects can demonstrate their commitment to integrity

Case Studies: Examining Sustainable Tokenomics Models

Ethereum (ETH)

Ethereum, often regarded as the pioneer of smart contract platforms, boasts a robust tokenomics model that underpins its vibrant ecosystem. ETH serves as the native currency of the Ethereum network, facilitating transactions, powering decentralized applications (dApps), and serving as collateral for various DeFi protocols. With a clear distribution schedule, Ethereum incentivizes miners, validators, developers, and users to contribute to the network's security, scalability, and innovation.

Cardano (ADA)

Cardano ADA Allocation
Source: Coin Gecko

Cardano, one of the most prominent Layer 1 platforms, attributes much of its success to a tokenomics model focused on long-term growth. The platform itself states in its whitepaper: “The overall focus beyond a particular set of innovations is to provide a more balanced and sustainable ecosystem that better accounts for the needs of its users as well as other systems seeking integration”. Cardano tokenomics model supports sustainable development goals through research-based approach, decentralized governance structure, and well-thought treasury system. Unfortunately, commitment to sustainable growth came with a cost. Cardano Blockchain is much slower than many of its competitors, which reflects the famous blockchain trilemma (hypothesis that blockchain can’t be secure, scalable, and decentralized at the same time).

Challenges and Future Directions

Tokenomics has emerged as a powerful tool for incentivizing and coordinating decentralized networks. It also presents various challenges and areas for improvement. Addressing issues such as governance effectiveness, economic sustainability, and regulatory compliance will be crucial for advancing crypto projects, in the face of progressing regulatory scrutiny.

MUST READ: "Tokenization Regulations"

Conclusion

Tokenomics represents a foundational aspect of crypto and Web3 projects, providing the economic infrastructure needed to incentivize participation, coordinate activity, and drive value creation within decentralized networks. By designing sustainable economic models that align incentives, foster community engagement, and promote long-term growth, projects can get the best out of blockchain technology

If you're looking to design a sustainable tokenomics model for your DeFi project, please reach out to contact@nextrope.com. Our team is ready to help you create a tokenomics structure that aligns with your project's long-term growth and market resilience.

FAQ

Why is token allocation important?

  • Proper token allocation promotes decentralization and community engagement, vital for a project's success.

What's a token vesting schedule?

  • A schedule ensuring stakeholders remain committed by gradually releasing their tokens over time.

How can DeFi tokens retain value?

  • By implementing supply control mechanisms and expanding utility within the ecosystem.

What are key challenges in tokenomics design?

  • Balancing incentives, managing inflation, and navigating regulatory landscapes are significant challenges.

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Master UI Component Creation with AI: The Ultimate Guide for Developers

Gracjan Prusik

24 Mar 2025
Master UI Component Creation with AI: The Ultimate Guide for Developers

Introduction

Modern frontend development is evolving rapidly, and creating UI components with AI tools is helping developers save time while enhancing interface quality. With AI, we can not only speed up the creation of UI components but also improve their quality, optimize styles, and ensure better accessibility.

This article explores how creating UI components with AI is transforming frontend development by saving time and improving workflows. Specifically, we will discuss:

  • Generating components from images,
  • AI for style analysis and optimization,
  • Automatic style conversion and code migration,
  • AI in generating UI animations.

Creating UI Components with AI from Images

One of the interesting applications of AI in frontend development is the ability to generate components from an image. AI can recognize the structure of the interface and generate HTML/CSS or JSX code that matches the provided image. One of the most popular tools for UI creation is Lovable.

For testing, let's see how the tool performs in creating a simple contact form from the page you are currently on, which is Nextrope.

Query:

"Recreate the image I've sent you in Next.js using CSS."

Sample image used in the query:

The result received:

Tworzenie komponentów UI - Wygenerowany widok

Lovable did an excellent job transforming the image into code. The view is fully responsive. It’s important to remember that the more precise the request, the better the AI will replicate the expected view. However, even with a simple command, the tool performed surprisingly well.

Of course, AI has its limitations. It still makes quite a few mistakes. The generated code still requires review and fixes from the developer, but in some cases, the entire process is significantly sped up—correcting the code often takes less time than creating the component from scratch.

Optimizing UI Components with AI Tools for Style Improvements

This problem of inaccurate code reproduction by AI can be partially addressed by using it to analyze and improve styles. Tools like ChatGPT, DeepSeek and Claude are capable of not only generating code but also diagnosing CSS errors and suggesting why a particular style might not be working as expected.

Simple example: Why is the div not centered?

Query: "Why is this div not centered?"

AI analyzes the code and provides the following response:

Problem: The parent container does not have a defined width or display: flex.
Solution: Add the appropriate styles to the parent container.

.parent {
  display: flex;
  justify-content: center;
  align-items: center;
  width: 100vw;
  height: 100vh;
}

AI for Automatic Style Conversion and Code Migration in UI Components

AI can assist with style conversion between different technologies, such as transferring code from traditional CSS to Styled Components or Tailwind CSS.

Let's assume we have a style written in traditional CSS:

.button {
  background-color: blue;
  color: white;
  padding: 10px 20px;
  border-radius: 5px;
  transition: background-color 0.3s ease;
}

.button:hover {
  background-color: darkblue;
}

We can use AI for automatic conversion to Styled Components:

import styled from "styled-components";

const Button = styled.button`
  background-color: blue;
  color: white;
  padding: 10px 20px;
  border-radius: 5px;
  transition: background-color 0.3s ease;

  &:hover {
    background-color: darkblue;
  }
`;

export default Button;

AI can also assist in migrating code between frameworks, such as from React to Vue or from CSS to Tailwind.

This makes style migration easier and faster.

How AI Enhances UI Animation Creation

Animations are crucial for enhancing user experience in interfaces, but they are not always provided in the project specification. In such cases, developers have to come up with how the animations should look, which can be time-consuming and require significant creativity. AI, in this context, becomes helpful because it can automatically generate CSS animations or animations using libraries like Framer Motion, saving both time and effort.

Example: Automatically Generated Button Animation

Suppose we need to add a subtle scaling animation to a button but don't have a ready-made animation design. Instead of creating it from scratch, AI can generate the code that meets our needs.

Code generated by AI:

import { motion } from "framer-motion";

const AnimatedButton = () => (
  <motion.button
    whileHover={{ scale: 1.1 }}
    whileTap={{ scale: 0.9 }}
    className="bg-blue-500 text-white px-4 py-2 rounded-lg"
  >
    Press me
  </motion.button>
);

In this way, AI accelerates the animation creation process, providing developers with a simple and quick option to achieve the desired effect without the need to manually design animations from scratch.

Summary

AI significantly accelerates the creation of UI components. We can generate ready-made components from images, optimize styles, transform code between technologies, and create animations in just a few seconds. Tools like ChatGPT, DeepSeek, Claude and Lovable are a huge help for frontend developers, enabling faster and more efficient work.

In the next part of the series, we will take a look at:

If you want to learn more about how AI is impacting the entire automation of frontend processes and changing the role of developers, check out our blog article: AI in Frontend Automation – How It's Changing the Developer's Job?

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AI in Real Estate: How Does It Support the Housing Market?

Miłosz Mach

18 Mar 2025
AI in Real Estate: How Does It Support the Housing Market?

The digital transformation is reshaping numerous sectors of the economy, and real estate is no exception. By 2025, AI will no longer be a mere gadget but a powerful tool that facilitates customer interactions, streamlines decision-making processes, and optimizes sales operations. Simultaneously, blockchain technology ensures security, transparency, and scalability in transactions. With this article, we launch a series of publications exploring AI in business, focusing today on the application of artificial intelligence within the real estate industry.

AI vs. Tradition: Key Implementations of AI in Real Estate

Designing, selling, and managing properties—traditional methods are increasingly giving way to data-driven decision-making.

Breakthroughs in Customer Service

AI-powered chatbots and virtual assistants are revolutionizing how companies interact with their customers. These tools handle hundreds of inquiries simultaneously, personalize offers, and guide clients through the purchasing process. Implementing AI agents can lead to higher-quality leads for developers and automate responses to most standard customer queries. However, technical challenges in deploying such systems include:

  • Integration with existing real estate databases: Chatbots must have access to up-to-date listings, prices, and availability.
  • Personalization of communication: Systems must adapt their interactions to individual customer needs.
  • Management of industry-specific knowledge: Chatbots require specialized expertise about local real estate markets.

Advanced Data Analysis

Cognitive AI systems utilize deep learning to analyze complex relationships within the real estate market, such as macroeconomic trends, local zoning plans, and user behavior on social media platforms. Deploying such solutions necessitates:

  • Collecting high-quality historical data.
  • Building infrastructure for real-time data processing.
  • Developing appropriate machine learning models.
  • Continuously monitoring and updating models based on new data.

Intelligent Design

Generative artificial intelligence is revolutionizing architectural design. These advanced algorithms can produce dozens of building design variants that account for site constraints, legal requirements, energy efficiency considerations, and aesthetic preferences.

Optimizing Building Energy Efficiency

Smart building management systems (BMS) leverage AI to optimize energy consumption while maintaining resident comfort. Reinforcement learning algorithms analyze data from temperature, humidity, and air quality sensors to adjust heating, cooling, and ventilation parameters effectively.

Integration of AI with Blockchain in Real Estate

The convergence of AI with blockchain technology opens up new possibilities for the real estate sector. Blockchain is a distributed database where information is stored in immutable "blocks." It ensures transaction security and data transparency while AI analyzes these data points to derive actionable insights. In practice, this means that ownership histories, all transactions, and property modifications are recorded in an unalterable format, with AI aiding in interpreting these records and informing decision-making processes.

AI has the potential to bring significant value to the real estate sector—estimated between $110 billion and $180 billion by experts at McKinsey & Company.

Key development directions over the coming years include:

  • Autonomous negotiation systems: AI agents equipped with game theory strategies capable of conducting complex negotiations.
  • AI in urban planning: Algorithms designed to plan city development and optimize spatial allocation.
  • Property tokenization: Leveraging blockchain technology to divide properties into digital tokens that enable fractional investment opportunities.

Conclusion

For companies today, the question is no longer "if" but "how" to implement AI to maximize benefits and enhance competitiveness. A strategic approach begins with identifying specific business challenges followed by selecting appropriate technologies.

What values could AI potentially bring to your organization?
  • Reduction of operational costs through automation
  • Enhanced customer experience and shorter transaction times
  • Increased accuracy in forecasts and valuations, minimizing business risks
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Want to implement AI in your real estate business?

Nextrope specializes in implementing AI and blockchain solutions tailored to specific business needs. Our expertise allows us to:

  • Create intelligent chatbots that serve customers 24/7
  • Implement analytical systems for property valuation
  • Build secure blockchain solutions for real estate transactions
Schedule a free consultation

Or check out other articles from the "AI in Business" series