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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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

AI-Driven Frontend Automation: Elevating Developer Productivity to New Heights

Gracjan Prusik

11 Mar 2025
AI-Driven Frontend Automation: Elevating Developer Productivity to New Heights

AI Revolution in the Frontend Developer's Workshop

In today's world, programming without AI support means giving up a powerful tool that radically increases a developer's productivity and efficiency. For the modern developer, AI in frontend automation is not just a curiosity, but a key tool that enhances productivity. From automatically generating components, to refactoring, and testing – AI tools are fundamentally changing our daily work, allowing us to focus on the creative aspects of programming instead of the tedious task of writing repetitive code. In this article, I will show how these tools are most commonly used to work faster, smarter, and with greater satisfaction.

This post kicks off a series dedicated to the use of AI in frontend automation, where we will analyze and discuss specific tools, techniques, and practical use cases of AI that help developers in their everyday tasks.

AI in Frontend Automation – How It Helps with Code Refactoring

One of the most common uses of AI is improving code quality and finding errors. These tools can analyze code and suggest optimizations. As a result, we will be able to write code much faster and significantly reduce the risk of human error.

How AI Saves Us from Frustrating Bugs

Imagine this situation: you spend hours debugging an application, not understanding why data isn't being fetched. Everything seems correct, the syntax is fine, yet something isn't working. Often, the problem lies in small details that are hard to catch when reviewing the code.

Let’s take a look at an example:

function fetchData() {
    fetch("htts://jsonplaceholder.typicode.com/posts")
      .then((response) => response.json())
      .then((data) => console.log(data))
      .catch((error) => console.error(error));
}

At first glance, the code looks correct. However, upon running it, no data is retrieved. Why? There’s a typo in the URL – "htts" instead of "https." This is a classic example of an error that could cost a developer hours of frustrating debugging.

When we ask AI to refactor this code, not only will we receive a more readable version using newer patterns (async/await), but also – and most importantly – AI will automatically detect and fix the typo in the URL:

async function fetchPosts() {
    try {
      const response = await fetch(
        "https://jsonplaceholder.typicode.com/posts"
      );
      const data = await response.json();
      console.log(data);
    } catch (error) {
      console.error(error);
    }
}

How AI in Frontend Automation Speeds Up UI Creation

One of the most obvious applications of AI in frontend development is generating UI components. Tools like GitHub Copilot, ChatGPT, or Claude can generate component code based on a short description or an image provided to them.

With these tools, we can create complex user interfaces in just a few seconds. Generating a complete, functional UI component often takes less than a minute. Furthermore, the generated code is typically error-free, includes appropriate animations, and is fully responsive, adapting to different screen sizes. It is important to describe exactly what we expect.

Here’s a view generated by Claude after entering the request: “Based on the loaded data, display posts. The page should be responsive. The main colors are: #CCFF89, #151515, and #E4E4E4.”

Generated posts view

AI in Code Analysis and Understanding

AI can analyze existing code and help understand it, which is particularly useful in large, complex projects or code written by someone else.

Example: Generating a summary of a function's behavior

Let’s assume we have a function for processing user data, the workings of which we don’t understand at first glance. AI can analyze the code and generate a readable explanation:

function processUserData(users) {
  return users
    .filter(user => user.isActive) // Checks the `isActive` value for each user and keeps only the objects where `isActive` is true
    .map(user => ({ 
      id: user.id, // Retrieves the `id` value from each user object
      name: `${user.firstName} ${user.lastName}`, // Creates a new string by combining `firstName` and `lastName`
      email: user.email.toLowerCase(), // Converts the email address to lowercase
    }));
}

In this case, AI not only summarizes the code's functionality but also breaks down individual operations into easier-to-understand segments.

AI in Frontend Automation – Translations and Error Detection

Every frontend developer knows that programming isn’t just about creatively building interfaces—it also involves many repetitive, tedious tasks. One of these is implementing translations for multilingual applications (i18n). Adding translations for each key in JSON files and then verifying them can be time-consuming and error-prone.

However, AI can significantly speed up this process. Using ChatGPT, DeepSeek, or Claude allows for automatic generation of translations for the user interface, as well as detecting linguistic and stylistic errors.

Example:

We have a translation file in JSON format:

{
  "welcome_message": "Welcome to our application!",
  "logout_button": "Log out",
  "error_message": "Something went wrong. Please try again later."
}

AI can automatically generate its Polish version:

{
  "welcome_message": "Witaj w naszej aplikacji!",
  "logout_button": "Wyloguj się",
  "error_message": "Coś poszło nie tak. Spróbuj ponownie później."
}

Moreover, AI can detect spelling errors or inconsistencies in translations. For example, if one part of the application uses "Log out" and another says "Exit," AI can suggest unifying the terminology.

This type of automation not only saves time but also minimizes the risk of human errors. And this is just one example – AI also assists in generating documentation, writing tests, and optimizing performance, which we will discuss in upcoming articles.

Summary

Artificial intelligence is transforming the way frontend developers work daily. From generating components and refactoring code to detecting errors, automating testing, and documentation—AI significantly accelerates and streamlines the development process. Without these tools, we would lose a lot of valuable time, which we certainly want to avoid.

In the next parts of this series, we will cover topics such as:

Stay tuned to keep up with the latest insights!