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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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