Token Distribution Models

Kajetan Olas

15 Mar 2024
Token Distribution Models

The landscape of blockchain and cryptocurrency is continually evolving, marked by the relentless pursuit of models that not only enhance network security and decentralization but also deepen user engagement and ownership. At the heart of this evolution lies the concept of token distribution, a pivotal strategy that can transform users from passive participants into active stakeholders and owners within digital ecosystems. Token distribution is not merely about allocating digital assets; it's about creating a framework where each token serves as a beacon of ownership, rights, and incentives, aligning the interests of users with the long-term success of the platform.

As we delve into the world of token distribution, we find ourselves tracing the path of its evolution. From the foundational Proof of Work mechanisms, to the speculative fervor of ICOs, and onto the community-centric airdrops. Each era has brought with it lessons, challenges, and a deeper understanding of what it means to distribute ownership.

New trend

However, the journey has not been without its pitfalls. Many strategies, while successful in bootstrapping networks and attracting initial interest, have struggled to foster genuine user engagement or have inadvertently encouraged speculative behaviors that detract from the project's core value proposition. It's within this context that we explore the concept of "Progressive Ownership"—a model that aims to refine the token distribution process into a more nuanced, loyalty-driven approach that rewards true product-market fit and user commitment.

The Evolution of Token Distribution Models

The concept of token distribution has undergone significant transformation since the inception of blockchain technology. Each era has introduced new mechanisms for distributing tokens and lowering barriers to entry, while also revealing unique challenges. Let’s explore these pivotal stages in the evolution of token distribution models.

Proof of Work (2009–present): The Dawn of Hardware Formation

The journey began with Bitcoin, which introduced the world to the Proof of Work (PoW) model. This approach allowed anyone with computational resources to participate in network security operations, known as "mining," in exchange for tokens. This mechanism not only secured the network but also democratized access to token ownership. However, as the sector matured, mining became increasingly professionalized, requiring significant investments in specialized hardware. This shift heightened the barriers to entry, gradually sidelining the average user and emphasizing the need for substantial upfront investment. This altered the initial egalitarian vision.

ICOs (2014–2018): The Era of Capital Formation

Following the PoW era, the cryptocurrency space witnessed the rise of Initial Coin Offerings (ICOs). This period came with a new model where projects could raise capital by selling tokens directly to the public. This approach theoretically democratized investment opportunities, allowing projects to reach a broader audience beyond traditional venture capital avenues. Ethereum's ICO in 2014 stood as a landmark event, inspiring a wave of similar endeavors. However, the ICO craze also attracted numerous fraudulent schemes, leading to a regulatory crackdown and a reevaluation of this model,

Airdrops (2020–present): Bootstrapping Usage through Community Engagement

In response to the pitfalls of ICOs, the industry shifted towards a more user-centric model: airdrops. This approach involved distributing tokens freely to existing communities or users based on their engagement or historical usage. In principle this fosters a sense of ownership and participation without a direct financial investment. The era of airdrops, particularly during the "DeFi Summer" of 2020, sought to catalyze network usage and decentralization. However, the emphasis on broad, indiscriminate distribution often attracted short-term speculators rather than committed users. This complicates efforts to achieve sustained growth and genuine community development.

Reflections on the Evolution

Each era of token distribution has contributed to the blockchain landscape's growth, expanding access and participation in unique ways. From the hardware-intensive commitments of PoW, through the speculative enthusiasm of ICOs, to the community-focused aspirations of airdrops. The evolution of token distribution models reflects the cryptocurrency sector's dynamics to balance inclusivity, security, and sustainable development. Yet, as we've learned, each model comes with its set of challenges, highlighting the need for continuous innovation. New token distribution strategies come up to foster genuine user ownership and engagement in the ever-evolving digital ecosystem.

Progressive Ownership: A New Frontier

Amidst the evolution of token distribution models, with each era bringing its blend of innovation and challenge, the concept of "Progressive Ownership" emerges. This is a transformative approach aimed at realigning the incentives of blockchain applications and their users. This novel framework represents a significant pivot from previous models, focusing on nurturing genuine user engagement.

Foundation of Progressive Ownership

Progressive ownership stands on the idea that tokens should be distributed to users progressively for their contributions to the network. This model asserts that achieving product-market fit remains paramount and that token distribution should complement, not precede this fit.

In the realm of progressive ownership, tokens become a means to deepen users' commitment to an application. They transform active users into stakeholders with a vested interest in the platform's success. This approach aims to move beyond the shortcomings of indiscriminate airdrops and speculative ICOs. It proposes a more sustainable method of community building.

Key Principles and Advantages

  • Incremental Engagement: Progressive ownership advocates for rewarding users in stages, reflecting their growing engagement and value to the ecosystem. This method encourages long-term participation and deters speculative behavior by closely aligning token incentives with genuine user activity and contributions.
  • Opt-in Ownership: Central to this model is the concept of opt-in ownership, where users have the choice to convert their earned incentives or revenue shares into tokens representing a more profound stake in the project. This voluntary transition from user to owner ensures that tokens are held by those most aligned with the project's long-term vision and success.

Implementing Progressive Ownership

Successful implementation of progressive ownership requires careful planning and a deep understanding of user behavior and incentives. Projects must first establish a clear value proposition and product-market fit, creating an ecosystem where users’ contributions are quantifiable and rewardable. Following this, a transparent and accessible mechanism for transitioning users from passive beneficiaries of revenue share to active token holders must be established, ensuring clarity around the benefits and responsibilities of ownership. 

Example Implementation - Project Catalyst

Project Catalyst is a Cardano-based initiative. It’s a decentralized funding mechanism that invites community members to propose projects, which are then voted on by ADA holders. Successful proposals receive funding in ADA, with over $79 million allocated to fund more than 1600 projects by March 2024. This process not only democratizes innovation within the Cardano ecosystem but also aligns with the principles of progressive ownership by giving token holders a vested interest in the network's growth and success. Through Project Catalyst, Cardano effectively engages its community in governance and decision-making, fostering a deeper sense of ownership and participation among ADA holders.

Conclusion

By aligning token incentives with genuine user engagement projects can pave the way for more sustainable development. Such an approach not only deepens user loyalty and retention but also fosters a more vibrant, participatory community. This is the groundwork for the next generation of Champions that will spread the knowledge about your platform.

If you're looking for ways to foster the adoption of your DeFi project, please reach out to contact@nextrope.com. Our team is ready to help you create a strategy that will grow your user base and ensure long-term growth.

FAQ

How to go about designing token distribution in practice?

  • It's a good idea to take inspiration from projects similar to yours, which succeded in terms of fostering progressive ownership.

Are airdrops effective?

  • Yes. Despite all their shortcomings, if implemented correctly airdrops can do great for marketing purposes for relatively low cost.

Why is fostering an ownership-based culture important?

  • Because if your users feel like they partially own the project, then they will contribute to the development process, and share that project with all their friends.

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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