Economics of Ethereum – Analysis

Kajetan Olas

05 Mar 2024
Economics of Ethereum – Analysis

Ethereum Economics is a comprehensive term that encapsulates the economic principles and mechanisms guiding the Ethereum network, touching on everything from its currency, Ether, to its role in facilitating decentralized applications and smart contracts. 

Understanding Ethereum Economics

Ethereum's economic framework is designed to balance scalability, security, and decentralization, often referred to as the blockchain trilemma. The network's native cryptocurrency, ETH, is not just a medium of exchange but also a vital component of Ethereum's security protocol, especially with the transition to proof of stake (PoS) in Ethereum 2.0. Ethereum Economics also encompasses the platform's approach to transaction fees (gas fees), its monetary policy, and the mechanisms for issuing new ETH into the system, which together influence the network's liquidity, usability, and overall value.

Supply and Demand Dynamics

The supply and demand dynamics within Ethereum Economics are crucial for understanding its market value and the incentives for participation in the network.

Supply Aspects:

  • Ether Issuance: The total supply of ETH is influenced by the network's issuance rate, which has evolved over time. Unlike Bitcoin, Ethereum does not have a hard cap on the total supply of ETH, but various upgrades (notably the London Hard Fork and EIP-1559) have introduced mechanisms to moderate the growth of the supply.
  • Ethereum 2.0 and Staking: The transition to Ethereum 2.0 introduces staking, where validators lock up ETH as a security deposit to participate in network consensus. This shift from proof of work (PoW) to proof of stake (PoS) not only changes the security model but also influences the circulating supply of ETH by encouraging the locking up of Ether in staking contracts.

Demand Aspects

  • Smart Contracts and dApps: Ethereum's utility as a platform for deploying smart contracts and dApps creates a fundamental demand for ETH, as it is required to pay for transaction fees and computational services.
  • Decentralized Finance (DeFi) and NFTs: The explosive growth of DeFi and the NFT market on the Ethereum platform has significantly increased the demand for ETH. These applications require ETH for transaction fees, collateral, and sometimes as a medium of exchange within their ecosystems.

The interplay between supply and demand is further nuanced by factors such as network upgrades, changes in transaction fees, and the overall adoption rate of Ethereum-based applications. As Ethereum continues to evolve, particularly with the full implementation of Ethereum 2.0, these dynamics are likely to shift, presenting new economic considerations and opportunities for users, investors, and developers alike.

Ethereum's Monetary Policy

Ethereum's monetary policy is a critical aspect of its economic model. It focuses on managing the supply of Ether (ETH) to ensure network security and incentivize balanced participation. Unlike traditional monetary systems or even other cryptocurrencies with a fixed supply cap, Ethereum adopts a more dynamic approach.

Transaction Fees

EIP-1559, implemented as part of the London Hard Fork in 2021, significantly altered Ethereum's monetary policy. It introduced new mechanisms in regard to charging transaction fees. Specifically it divided fee into 2 parts. The Base Fee and Priority Fee. 

Base Fee

Base Fee is dynamically determined based on network congestion and burned. It increases are based on two factors: how congested the network is, and for how long it’s been congested. This means that the base fee will keep increasing until activity on the network goes back to targeted average level. Because that base fee is burned it also means that ETH is deflationary when the network’s activity is high.

Priority Fee

Priority Fee is an additional fee which serves as a tip for validators. It’s set by a user, to encourage validators to process his transaction. Higher fee means that transaction will be validated before other transactions. Priority fee should be above 0, because validators must have some incentive to include transaction in a block.

Transaction fees over time, accesed 05.03.2024, https://bitinfocharts.com/comparison/ethereum-transactionfees.html#3y

Staking and Ethereum 2.0 

The transition to Ethereum 2.0 and its proof of stake (PoS) consensus mechanism introduces staking rewards as a new form of issuance. Validators stake a minimum of 32 ETH to participate in the network's security and consensus mechanism. High entry barrier contributes to a fact that only 26% of total supply is staked. This is lower value than in some other blockchains like Cardano (64% staked).

https://www.coinbase.com/earn/staking/ethereum

Penalties

Ethereum ensures that validators act in the best interest of the network by introducing a set of penalties.

Slashing

Slashing is a penalty for violating protocol’s rules and engaging in dishonest behavior. Portion of validator’s ETH is taken away if he tries to

  • Propose two different blocks
  • Attesting conflicting versions of the blockchain (attesting means voting in favor of validator’s view of the chain.

Inactivity Leaks

If validator is inactive and doesn’t participate in e.g. block attestations, he gradually loses some of his ETH. This ensures that stakers actively participate in securing the network. This prevent a situation when a number of validators would regularly ignore their duties, and contribute to destabilizing the nework.

Conclusion

The exploration of Ethereum Economics makes one appreciate it’s complexity. It’s impressive how precise Ethereum developers had to be. They found parameters that balance its ecosystem very well (e.g. how did they choose that staking precisely 32ETH is better than 28ETH). Besides quantitative parameters the logic behind the fees structure, penalties, and others is interesting as well. All this, makes Ethereum Economics an excellent case study for token engineers and blockchain developers. 

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

What role does Ether (ETH) play in Ethereum's economy?

  • ETH is not only a medium of exchange but it's also crucial for the network's security. In PoS model what prevents validators from harming the network is the fact that they might lose their ETH stake.

How did EIP-1559 change Ethereum's fee market?

  • EIP-1559 introduced a two-part fee structure with a base fee and priority fee. Base fee gets burned, contributing to Ethereum being potentially deflationary under high activity. Priority fee incentivizes miners to include transactions in a block.

What is Ethereum's strategy for scaling and addressing network congestion?

  • Ethereum’s strategy is to make use of Layer 2 Solutions, with Ethereum blockchain serving as a secure settlement layer.

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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!