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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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