VC vs STO – tokenization the future of fundraising?

Maciej Zieliński

28 Jan 2021
VC vs STO – tokenization the future of fundraising?

Tokenization is becoming a better alternative to solutions that have been present in the financial world for decades. Why might STO be a better choice than the traditional venture capital model? 

Finding a fund is one of the main challenges facing growth-hungry entrepreneurs. Over the years, the financial world has developed solutions that effectively help them do this.

One of them is venture capital (risk capital). VC is financing provided by investors to small companies that they believe have long-term growth potential. It usually comes from wealthy individuals or institutional investors such as banks. VC does not have to take the form of money. It can also take the form of technology or business advice. During the transaction, parts of the ownership of a company are sold to several investors through venture capital funds. 

VC has functioned for several decades as a source of obtaining funding for enterprises. However, it is important to be aware of its limitations. On the Nextrope blog we have taken a closer look at them, while trying to answer the question in what respects STO may be a better choice. 

VC vs STO - key differences

Control

It is common practice for a member of the Venture Capital management team to have a direct influence on the activities in the financed company, e.g. by joining the board. This means that by signing an agreement with the fund, the owners of the company lose full control over the management of their business.  From that moment on, the owners must inform the fund about every key decision, which the fund usually has the right to overrule. 

Of course, an experienced VC fund in this way is able to contribute to improving the management of the company and have a positive impact on its development. However, their possible lack of familiarity with the realities of a particular industry may result in blocking decisions that the owners consider to be the most appropriate.

By opting for an STO, they leave themselves the option to run their company in the way they think is best. It is up to the owners of the company to decide which decisions require a vote of the token holders and which they will make entirely independently. And if a vote is indeed necessary on a particular issue, investors will be able to take part in it through their account from anywhere in the world, which will significantly speed up the whole process.

Cost and time-consuming

The process of organising VS funding is relatively complex and involves many, often costly, intermediaries. In addition, it is extremely time-consuming. The first stages alone usually take between 12 and 18 months. This would not be such a big problem if it were not for the necessity to participate in numerous travelling marketing actions and negotiations with potential investors, which often distract owners from the development of their companies for several months.

In addition, VC always carries the risk of delays in funding. As venture capital involves the exchange of a large amount of funds, the investor may not be willing to submit them all at once. Often, a company will have to meet certain milestones in order to receive the entire amount requested.

On the other hand, a well executed tokenisation in some cases can result in funding being raised in as little as a few weeks. There are also no payment delays involved, as all funds go to the company as soon as tokenisation is completed. The process itself is also much simpler and involves far fewer intermediaries (READ HOW IT WORKS STEP BY STEP HERE).

VC vs STO: liquidity and entry barriers

Venture capital is demanding not only for the companies seeking funding, but also for the investors themselves. Usually, in order to join an investment round, they need to have relatively large capital at their disposal. Therefore, most of them are institutions or wealthy private individuals. It is the high entry barrier that significantly narrows the group of potential investors.

Added to this is the issue of high illiquidity. If someone is considering investing their money in venture capital, they must be prepared to freeze it for a very long time - about 7-12 years. A premature withdrawal of funds is associated with significant losses and cannot be carried out without management approval. Because of this lack of liquidity, investment in venture capital often scares off even those with sufficient capital.

STO, above all, allows the minimum investment amount to be set quite freely. This significantly broadens the group of investors - there can be thousands of them, they just need to be accredited. Moreover, it solves the problem of lack of liquidity. The tokens issued represent traditional ownership and revenue rights, while providing investors with the ability to freely trade them on secondary markets. As a result, they are able to liquidate their investment at essentially any time. 

STOs and Venture Capital - what's next? 

The growing popularity of STOs is just one manifestation of the digitalisation trend that is gaining momentum in financial markets and may soon lead to the emergence of completely new capital management mechanisms. Blockchain-based smart contracts and distributed ledgers will significantly speed up the process of not only raising and circulating capital, but also, for example, preparing an audit.

However, it is worth remembering that there are no universal solutions and STO will not be the most optimal choice in every case.  If you would like to find out how STO would work for your company, our team will be happy to answer all your questions. 

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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!