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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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!