Supply and Demand in Crypto Markets

Kajetan Olas

01 Mar 2024
Supply and Demand in Crypto Markets

From the creators' perspective, we steer supply and demand in crypto markets to incentivize (disincentivize) certain behaviors in a way that benefits the project. 

Often, a project’s best interest is seen as equivalent to a high token price. For that reason, tokenomics often incentivizes participating in pyramid schemes that give an illusion of growth and value appreciation.  Here we explore how to design sustainable tokenomics that will help your project thrive in the long run.

Price Swing Effects

As an entrepreneur, the valuation of your digital asset often determines if you're seen as a visionary or an impostor. Consequently, many teams prioritize strategies aimed at boosting their token's value, frequently through methods like offering exorbitantly high annual percentage yields for token staking. Other tactics include token destruction or repurchase schemes, financed by means other than actual earnings. While these strategies may temporarily elevate excitement and price, they fail to enhance the intrinsic worth of the platform. This leads to significant price instability and diminishes the platform's ability to withstand hostile actions or negative market trends. Paradoxically, the pursuit of elevated prices typically backfires. Instead, the focus should be on reducing price volatility, which supports steady and long-term development.

Price per Token

The Initial price of a token unit should reflect the utility it provides. That price depends on the total value of the project divided by quantity of tokens in circulation. Theoretically, the nominal value of tokens shouldn’t matter. 100$ worth of tokens corresponds to the same share in market cap, regardless of whether we have 100 tokens worth 1$ each, or 1 token worth 100$. But just like in traditional markets - human psychology plays a big role. Market participants show a preference for tokens priced between 10$ and 100$. Such tokens statistically perform slightly better on the market. For this reason, we suggest choosing a supply quantity, that will cause the price per token to oscillate in the 10$-100$ range.

On the opposite end - tokens with prices below 0.01 are shown to underperform and be more volatile.

Supply

Supply-side of 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. In 99% cases, project’s value relies mostly on demand. This means product adoption by users and the ability to generate and capture value.

Initial and maximum supply

How many tokens do we want to initially distribute, and what’s the maximum number of tokens? This relates to the maximum inflation rate - the total dilution of tokens' value over the lifespan of a project. The maximum inflation rate can be calculated through dividing maximum supply by initial supply.

It doesn’t matter if the circulating supply makes 20% or 80% of the maximum supply. In fact, you can be successful even without a capped maximum supply. Many of the 100 projects with the largest capitalization have no capped supply, with Ethereum being the prime example. 

Interestingly supply increases don’t matter that much in the short term. On a month-month basis correlation between token emissions rate and price is less than 5%. For that reason, you shouldn’t worry too much about the dilution of value. As long as the annualized inflation rate is below 100% your project will be stable. 

Allocation:

A typical allocation structure that’s often considered to be industry’s best practice is oscillating in the following ranges:

  • Team: 10% - 20%
  • Venture Capital: 10% - 20%
  • Advisors: 3% - 5%
  • Treasury: 15% - 30%
  • Protocol emissions (e.g. staking reward): 30% - 50%
  • Airdrops (optional): 3% - 7%

Vesting

Vesting relates to the process of locking a portion of tokens for a chosen amount of time and gradually releasing them. It’s a concept taken from the world of startups. Traditionally these companies would vest equity allocated to founders so that they can’t abandon the project early. That’s because if these entrepreneurs would be able to sell their equity in the early stages then they might lose motivation to keep working on the project. In DeFi, on top of aligning incentives, vesting reduces volatility and big price dumps in the early stages.

Vesting usually applies to institutional investors, advisors, and founders. Industry standard is setting its length between 2 and 5 years.

https://www.liquifi.finance/post/token-vesting-and-allocation-benchmarks

Demand

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.

Utility

Your product should provide real value to the customer, and be able to capture some of it. If the price of your token increases for any reason not related to its utility, then it’s due to speculation on utility in the future.

Expected Utility

If you’re looking to fund your project before developing an MVP then you base on investors’  trust in your ability to deliver utility in the future.  A key way to increase this trust, and be more successful with an ICO, is through having a strong founding team, and an innovative idea. You should show people, that you’re likely to deliver something that will have a lot of value to a lot of users.

Hype

There are also cases when demand comes from pure hype. While this euphoria may be pleasant in the short-term, it's worth remembering that in the long term, a crash will follow.

Conclusion

Supply and Demand are key concepts in the crypto space just like in real economy. Though the equilibrium is after all set by the market forces, we can influence it by various adaptive mechanisms. It’s key to remember, they can only work if your product provides actual value to customers. That’s because customer-driven demand is the only sustainable way of increasing project’s value.

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

How to know what portion of demand can be attributed to speculation?

  • Fear and Greed Index is often used to measure market sentiments in that regard.

Can supply and demand mechanisms be manipulated in crypto markets?

  • Yes, it’s not uncommon for big investors to engage in speculative attacks.

How does supply affect the tokenomics of a project?

  • There are many ways in which supply affects tokenomics. Key things to consider are emissions rate and allocation.

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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