What is a Token Economy? An Introduction to Token Economy (Tokenomics)

Kajetan Olas

01 Mar 2024
What is a Token Economy? An Introduction to Token Economy (Tokenomics)

Token Economy is often defined as the study of determining and evaluating the economic characteristics of a cryptographic token.

Today most blockchain projects fund their operations through the sale of tokens. For that reason, founders need to have a good understanding of the tokenomics design process. Surprisingly, studies show that in most cases tokenomics design is unsound and based on intuition. In this article, we approach the topic from a perspective that’s backed by empirical data and shown to work.

Key Tokenomics Considerations

Optimal Tokenomics depends on the specifics of the project. Part of them is deciding what behaviors you want to incentivize (disincentivize) in a way that aligns with the projects’ interests. That’s a tricky task since you need to identify every relevant user behavior and corresponding incentive. You also need to pick quantitative parameters that will ensure the right balance. In the case of DeFi protocols, systemic risk is especially high. Arbitrary parameters and assumptions often lead to death spirals and vulnerability to attacks. For that reason, all tokenomics systems should be stress-tested and validated before release.

Key Principles

There are many good practices that founders should follow when designing tokenomics for their project. We’ll briefly cover the most important ones.

Utility is The Key

This one seems obvious, but let me explain... What matters in the context of tokenomics is the utility of your token - that’s not the same as the utility of your product. Demand for blockchain products doesn’t automatically translate to demand for their native tokens! While users’ adoption of your product is important, it’s not enough. You need to tie the value of your token to the success of the product. This can be done through different Value Capture mechanisms e.g. redistributing profits among holders.

(PS. Governance Rights and Staking are most definitely not enough!)

Look at comparable projects

When designing tokenomics it's good to look for projects similar to the one you’re creating. The more similar, the better. Read their whitepapers, study their tokenomics, and look at key metrics. Then ask yourself - what are the things they did well, and what are their mistakes? You’re guaranteed to find some inspiration. A key metric you can use when deciding upon the initial valuation of your project is Total Value Locked/market capitalization

Minimize Volatility

As a founder the key metric that will determine whether people call you a genius or a fraud is the price of your token. For that reason, many teams optimize tokenomics for high token price above everything else. This is often done by offering unsustainable APY in exchange for stacking tokens. Other common choices include burning, or buyback programs funded by anything other than revenue. While these mechanisms may be able to drive the hype and price up, they don’t increase the value of the protocol itself. The result is high volatility and a lack of resilience to malicious attacks and adverse market conditions. Ironically, optimizing for high prices usually results in the opposite effect. What you should do instead is focus on minimizing volatility, as it fosters sustainable growth.

Overview of Supply-Side

Supply-side tokenomics relates to all the mechanisms that affect the number of tokens in circulation and its allocation structure.

While supply is important for tokenomics design it’s not as significant as people think. Mechanisms like staking or burning should be designed to support the use of products and aren’t utilities on their own.

Capped or Uncapped Supply

Founders put a lot of attention into choosing between a capped or uncapped supply of the token. It’s a common belief that capping supply at some maximum level increases the value of currently circulating tokens. Research shows that it doesn’t really matter, but tokens without capped supply, statistically perform slightly better.

Inflation Rate

Projects should aim for low, stable inflation. Unless the annualized inflation rate is above 100%, there’s very little correlation between the rate of supply changes and the price of token. For that reason, it’s recommended to adjust token emissions in a way that fosters activity on the network. A reasonable inflation rate that won't affect the price is between 1-5% monthly.

https://tokenomics-guide.notion.site/2-5-Supply-Policy-ff3f8ab217b143278c3e8fd0c03ac137#1c29b133f3ff48a9b8efd2d25e908f5c

Allocation:

The industry standards are more or less like the following.

https://lstephanian.mirror.xyz/_next/image?url=https%3A%2F%2Fimages.mirror-media.xyz%2Fpublication-images%2FDnzLtQ1Nc9IIObEpyJTTG.png&w=3840&q=75
https://www.liquifi.finance/post/token-vesting-and-allocation-benchmarks

Overview of Demand-Side

Demand-side concerns people’s subjective willingness to buy the tokens. Reasons can be different. It may be due to the utility of your tokens, speculation, or economic incentives provided by your protocol. Sometimes people act irrationally, so token demand has to be considered in the context of behavioral economics.

Role of incentives

The primary incentive that drives the demand for your token should be its utility. Utility is its real-world application or a way in which it captures value generated by the use of your product. Staking, liquidity providing, deflationary policy, and other supply-control mechanisms may support tokens’ value accrual. A common way to do that is through revenue-funded buybacks. Projects may use collected fees to buy their tokens on DEXs. Then burn them, or put them into a treasury fund.

How to design incentives?

  • Make them tangible. If you want to promote desired behaviors within the ecosystem you need to provide real rewards. People don’t care about governance rights, because these rights don’t translate to any monetary value. On the other hand, they care about staking rewards, which can be sold for profits.
  • Make them easy to understand. If you want to incentivize or disincentivize user behavior, then you should make it clear how the mechanism works. Users often have no time to dive into your whitepaper. If they don’t understand how your product works then they won’t use it.
  • Test, test, test. If you don’t test how different incentives balance the tokenomy of your product, then you’re setting yourself up for a terra-luna style collapse.

Conclusion

Proper design of projects’ tokenomics is not easy. Even though it may seem like choosing different parameters and incentives intuitively will work, it’s a reason why the value of projects’ tokens often goes to 0. There are however sound and tested design practices. Stick with us, and get to know them!

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

What is a token economy?

  • A token economy refers to the study and analysis of a cryptographic token's economic characteristics, crucial for blockchain projects' funding and success.

What are the key principles of designing a token economy?

  • Essential principles include ensuring the utility of tokens, analyzing comparable projects, and aiming to minimize volatility to foster sustainable growth.

How does supply and demand affect token economy?

  • The supply side involves mechanisms like capping token supply or adjusting inflation rates, while the demand side focuses on creating genuine utility and incentives for token holders.

What are the common pitfalls in designing a token economy?

  • The most common problems occur when token’s main function is the transfer of value, rather than supporting the creation of value. (e.g. you can stake useless token to get more of that useless token)

How can token economies be tested and validated before launch?

  • Tokenomics can be tested by constructing a mathematical model and running a large number of randomized simulations.

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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