Network Effects in Crypto Projects: Fueling Adoption and Value

Kajetan Olas

04 Mar 2024
Network Effects in Crypto Projects: Fueling Adoption and Value

The term "network effects" frequently surfaces as a factor underpinning the exponential growth of cryptocurrencies. But what exactly are network effects in crypto projects, and why are they so important? At its core, a network effect occurs when a product or service becomes more valuable as more people use it. This phenomenon is not exclusive to the digital age; it has influenced the adoption and success of technologies ranging from the telephone to the internet. However, in the context of cryptocurrency, network effects not only fuel adoption but are also directly correlated with market capitalization. This article delves into the mechanics of network effects in crypto and discusses just how much adoption influences projects’ value.

Measuring Network Effects in Crypto

One of the most widely recognized methods for measuring these effects is Metcalfe’s Law, which posits that the value of a network is proportional to the square of the number of its users. Studies have found that this law applies to blockchain networks particularly well. In practice, we measure user base (N) as the number of nodes or active addresses.

Case Studies: Bitcoin and Ethereum

To illustrate the practical application of Metcalfe’s Law in cryptocurrencies, let's examine Bitcoin and Ethereum, two of the most prominent blockchain networks.

  • Bitcoin: As the first cryptocurrency, Bitcoin has demonstrated a remarkable correlation between its network size and value. Historical data shows that periods of rapid growth in the number of active wallets are closely followed by increases in Bitcoin's market price. Pearson’s correlation coefficient between BTC price and a squared number of nodes is approximately 0.9. In the case of squared number of active addresses, it’s approximately 0.95. This pattern underscores just how important network effects are.
  • Ethereum: Ethereum's utility extends beyond mere financial transactions, encompassing smart contracts and decentralized applications (DApps). This added functionality attracts a diverse user base, further amplifying its network effects. Pearson’s correlation coefficient takes similar values as in BTC case. 

Network Effects and Cryptocurrency Adoption

The adoption of cryptocurrency is significantly influenced by network effects, which not only enhance the value of the digital currency but also contribute to its widespread acceptance and use. As the network of users grows, the cryptocurrency becomes more useful and desirable, creating a virtuous cycle that attracts even more users. This is particularly evident in the context of payments and remittances, where the value of a cryptocurrency network increases with the number of individuals and institutions willing to accept and transact in the currency.

Enhancing Security and Trust

One of the critical ways network effects contribute to cryptocurrency adoption is by enhancing the security and trustworthiness of the network. Blockchain technology, the foundation of most cryptocurrencies, becomes more secure as more participants join the network. The decentralized nature of blockchain makes it increasingly difficult for malicious actors to compromise the network's integrity, thereby bolstering user confidence in the system. This enhanced security is a direct consequence of the network effects, as a larger network provides greater resistance against attacks. 

Impact on Liquidity and Market Depth

A larger user base means more transactions and, consequently, greater liquidity, making it easier for users to buy and sell without causing significant price fluctuations. This increased market depth attracts investors and traders, further fueling cryptocurrency adoption. That’s especially important for Dapps which release native tokens traded on DEXs like Uniswap. It’s common for teams to provide initial liquidity that fosters trading, but it’s much better when that liquidity is provided by users.

Fostering network effects techniques

Very strong correlation between the value of blockchain projects and their user base makes it clear that adoption is very important. So, how should projects foster the growth of their user base?

Airdrops

Airdrops are a very common (and relatively cheap) way to grow user base of a network in its beginnings. The way they work is the following: projects allocate a certain number of tokens (e.g. 1% of total supply) to people who engage with the project. For example, a project may announce that people who retweet and like their posts on X a certain number of times will get some tokens. Another type of engagement might be participating in testnet and providing feedback to founders. Airdrops are effective because even if only 5% of these attracted users will stay for long-term then it’s still a great return on investment.

Subsidized incentives

When the project is just starting it may be a good idea to allocate some capital towards higher incentives for early users. An example might be providing higher APY for stakers (like 10%, instead of 5%) for the first 6 months. While such subsidized incentives are good in the beginning they must end at some point. That’s because they’re unsustainable in the long term. An example of what can happen if unsustainable incentives last for a little too long is the Anchor Protocol’s case. Anchor hoped to attract a lot of users by providing 15% APY. In that sense it achieved success, but because it didn’t end the program in time, the protocol became unsolvable and crashed. Though while it lasted the network’s growth was truly exponential.

Vision Oriented Project

Probably the most sustainable and organic way to grow your user base is by showing users an inspiring vision associated with your project. This is about creating a protocol that in some way promises to change the world for the better. An example may be Cardano which acquired an enormous fanbase oriented around its mission statement. That was despite poor user experience in their beginnings. 

Challenges and Limitations

Despite the positive impact of network effects on cryptocurrency adoption and value, some notable challenges and limitations must be acknowledged.

Scalability Concerns

A primary challenge posed by network effects is scalability. As the network grows, the underlying blockchain technology must be able to handle an increasing number of transactions quickly and efficiently. However, many cryptocurrencies, including Bitcoin and Ethereum, have encountered difficulties scaling their networks to meet demand without compromising security or decentralization. This is the most important factor hindering adoption, since many users prefer to use a centralized payment system, just because it’s more efficient. For example, VISA can process 24k TPS, while BTC can process 7 TPS.

https://www.okx.com/learn/blockchain-trilemma-guide

Conclusion

While network effects are a powerful driver of cryptocurrency adoption and value, they also present significant scalability challenges that must be addressed. However, in the case of emerging projects, benefits coming from increased adoption outweigh the costs and it’s shown that the greatest factor influencing the value of these projects is their user base.

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

What are network effects?

  • Network effects is a term describing a situation when the value of a network grows more than proportionally relative to the number of users.

How do network effects influence projects' value?

  • There is a very strong correlation between a squared number of active addresses/nodes and the market capitalization of projects.

How to foster adoption and occurrence of network effects within my project?

  • By building a community. This can be achieved through orienting protocol around an inspiring vision, airdrops, and subsidized incentives for users.

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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