Token Utility: Balancing Supply, Demand, and Velocity

Kajetan Olas

14 Mar 2024
Token Utility: Balancing Supply, Demand, and Velocity

This article embarks on a journey through the multifaceted landscape of token utility. It dissects the influence of supply, demand, and velocity. We include practical information that can be utilized during the process of crypto assets valuation.

The Supply Side: Scarcity and Abundance

The concept of scarcity as a value driver is deeply rooted in human psychology; it's what transforms simple metals into precious commodities. In the realm of cryptocurrencies, this principle has been adopted with fervor, giving rise to the "digital gold" narrative. Let's delve into how supply shapes the world of token utility and the broader implications it holds.

Fixed Supply and Deflationary Models

  • The Allure of Limitation: Crypto-architects often imbue their tokens with a fixed supply, aiming to mimic the scarcity of resources like gold. This artificial scarcity is designed to create a deflationary environment. It holds the premise that as demand escalates, so too will the value for the lucky holders.
  • Scarcity and Value: The perceived value of supply-limited tokens is based on a simple market principle. When an item is rare, and demand outstrips supply, prices should theoretically rise. This underpins the investment cases for many cryptocurrencies.

Token Sinks and Their Limitations

  • Combatting Inflation: Token sinks, such as burning and staking, are strategic maneuvers employed to curtail supply actively. The logic is straightforward: decrease supply to bolster value.
  • A Closer Inspection: While reducing the token count through sinks may initially drive up value, it's not a panacea. Such tactics provide only temporary relief in the face of absent or low token utility.
  • Sustainable Scarcity: Tokens should have intrinsic value. There must be a reason for users to hold it beyond the mere expectation of appreciation due to scarcity. Without real-world utility or a compelling use case, scarcity alone may not sustain the long-term value of a token.

In the still-maturing world of blockchain economies, the supply of tokens is but one piece of the puzzle. In the subsequent sections, we'll explore how demand and velocity interact to form the complete picture of tokenomics.

Creating and Sustaining Demand

The true test for any cryptocurrency is not just its scarcity but the demand for it in the market. Demand is the engine that drives the token's acceptance and value.

Utility-Driven Demand

  • Beyond Speculation: While speculative trading can inflate prices temporarily, lasting demand is fueled by a token's utility. This includes its use in transactions, its role in governance, or its ability to grant access to certain services or rewards within its ecosystem.
  • Real-World Applications: The more a token can be used for real-world purposes, the more essential it becomes. Tokens that solve genuine problems or enhance the user experience in noticeable ways tend to cultivate more sustainable demand.

Understanding Velocity in the Token Economy

The concept of velocity in token economics often remains underappreciated, yet it is a vital factor in the valuation and stability of a cryptocurrency.

The Relationship Between Velocity and Value

  • Defining Velocity: Velocity is the rate at which a token changes hands in an economy. A high velocity means the token is frequently traded, which can suggest a thriving economy or, conversely, indicate a lack of incentive to hold the token long-term.
  • Impact on Token Value: While traditional economies aim for a balanced velocity of money, the crypto economy faces unique challenges that can make high velocity a sign of instability.

The Misapplication of MV = PQ in Crypto Valuations

  • Classical Economics Meets Crypto: The MV = PQ equation, while a mainstay in traditional economics, doesn't neatly apply to the decentralized and often speculative nature of cryptocurrencies.

When velocity is too high, it indicates that tokens are not held onto, which can suggest a lack of compelling reasons to keep the token for long-term value. On the other hand, extremely low velocity can signal a stagnant market or hoarding behavior, where tokens are not being used for their intended purpose, thus hindering the ecosystem's activity and growth.

To manage velocity effectively, developers can introduce features like time-locked contracts to reward longer holds or implement burn-and-mint mechanisms that adjust the token supply relative to its use in transactions. Also, by aligning token utility with the platform's growth—such as through loyalty programs or tiered services—users are encouraged to engage with the token economically, increasing its velocity in a controlled and beneficial manner.

Balancing the Three Pillars

Creating a successful token requires more than just technical prowess; it necessitates a nuanced understanding of economic principles and market behavior. Achieving an equilibrium between supply, demand, and velocity is akin to finding the perfect pitch in a complex symphony.

Designing for Sustainability

Strategic planning is the bedrock of sustainable token design. It involves implementing adaptive supply mechanisms that respond to the natural ebb and flow of the market. These mechanisms must discourage harmful practices like hoarding during bull runs or panic selling during downturns, which can create swings in token velocity. A great example of a blockchain with adaptive governance is Ethereum.

Utility must be at the forefront of a token's design — it must offer something of real value. Whether it's through enabling seamless transactions, allowing holders to participate in governance, or providing access to exclusive services, utility ensures that a token remains in demand.

A supportive and engaged community is also a hallmark of a balanced token economy. When token holders feel they are part of the project's journey, they're more likely to use the token as intended rather than speculate on its price. This sentiment can help stabilize the velocity, as the community-driven use of the token creates a steady stream of transactions.

Conclusion

Token utility is not just a buzzword; it's the heartbeat of any cryptocurrency's value proposition. It stands as the crucial element in balancing the triad of supply, demand, and velocity. In this exploration, we've uncovered that while scarcity can bolster value, it is the token's real-world use and the economic incentives it creates that sustain demand and manage velocity. 

Cryptocurrency is not a static field; it's an ever-shifting landscape that requires agility and foresight. As we continue to push the boundaries of what blockchain can achieve, we also continue to learn, adapt, and grow. Token utility, when deeply integrated and thoughtfully executed, remains the linchpin of this vibrant and burgeoning economy.

FAQ

How to manage token supply?

  • Employ scarcity tactics like burning tokens to reduce supply and staking mechanisms to lock tokens temporarily, balancing the supply to increase value.

How to address velocity's impact?

  • Mitigate high velocity's potential to decrease value by incentivizing longer holding periods or creating utilities that encourage users to circulate tokens more slowly.

What's the most important pillar?

  • All are important, but in our opinion, the demand has the biggest influence over the project's success.

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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!