How NOT to Create a DAO: Common Pitfalls You Should Avoid

Kajetan Olas

27 Dec 2024
How NOT to Create a DAO: Common Pitfalls You Should Avoid

Decentralized Autonomous Organizations (DAOs) represent a fundamental shift in how communities, companies, and initiatives can coordinate efforts, funds, and decisions on the blockchain. By leveraging transparent smart contracts and on-chain governance mechanisms, DAOs aim to distribute authority, reduce overhead, and foster a more democratic decision-making process. However, building a successful DAO isn’t just about cutting-edge tech or grand ideas—it also requires a clear vision, well-crafted governance rules, and a strategically engaged community.

In this article, we’ll take a counterintuitive approach by highlighting how not to create a DAO. By focusing on common pitfalls—from legal oversights to governance missteps—we can better understand what truly contributes to a thriving, sustainable DAO. This perspective aligns with the importance of recognizing cognitive biases, such as insensitivity to base rates and the conjunction fallacy, which often lead enthusiastic founders to overlook real-world data and complexity. Avoiding these traps can be the difference between launching a resilient DAO and watching an ambitious project crumble under misaligned structures or unmet expectations.

2. Missing the Governance Threshold Mark

Governance Thresholds Gone Wrong

Governance thresholds dictate how many votes or what percentage of voting power is needed to pass a proposal within a DAO. Striking the right balance here is crucial. Thresholds that are set too high can stifle progress by making it nearly impossible for proposals to succeed, effectively discouraging member participation. On the other hand, thresholds that are too low can lead to frivolous proposals or constant voting spam, making governance more of a burden than a benefit.

When designing your DAO’s thresholds, consider:

  • Community size and engagement levels: Larger communities might handle higher thresholds more comfortably, while smaller groups may benefit from lower requirements to encourage active participation.
  • Type of proposals: Operational decisions may need a lower threshold, whereas critical changes (such as tokenomics or treasury management) often require more consensus.
  • Voter fatigue: The more frequently members are asked to vote—and if it’s too easy to put forward proposals—the greater the risk of apathy or disengagement.

Over-Complex vs. Over-Simplified Governance

It’s tempting to either pile on complicated governance rules or oversimplify them to keep decision-making quick. However, both extremes can be problematic. Simplicity in governance is key to enhancing clarity and participation. Overly complex smart contracts and procedural layers can dissuade newcomers from getting involved, while an oversimplified model might fail to address potential conflicts or security vulnerabilities.

Some issues to watch out for:

  • Complex Smart Contracts: More code means more potential bugs and greater difficulty in auditing or updating governance logic.
  • Opaque Voting Processes: If members can’t easily understand how votes are tallied or how proposals are introduced, engagement drops.
  • Excessive Centralization in “Simple” Models: In trying to streamline governance, some DAOs inadvertently concentrate power in the hands of a few decision-makers.

Ultimately, aiming for a balanced governance framework—one that is easy enough for members to participate in but comprehensive enough to protect the DAO from abuse—is central to avoiding the pitfalls of governance threshold mismanagement.

3. Underestimating Legal and Regulatory Aspects

Legal Wrappers and Compliance

Building a DAO without considering legal and regulatory frameworks is a common recipe for disaster. While decentralization is a powerful concept, it doesn’t absolve projects from potential liabilities and compliance obligations. Assigning your DAO a formal legal wrapper—whether it’s a foundation, a cooperative, an LLC, or another entity type—can help mitigate personal risks for contributors and align your organization with existing regulatory regimes.

Failing to think through these details often leads to:

  • Personal Liability for Founders: Without a proper legal entity, core contributors might become personally responsible for any legal disputes or financial mishaps involving the DAO.
  • Regulatory Crackdowns: Governing bodies worldwide are actively monitoring DAOs for compliance with securities laws, anti-money laundering (AML) regulations, and tax obligations. Ignoring these can lead to penalties, fines, or forced shutdowns.

Non-Existent or Inadequate Documentation

Equally problematic is the lack of clear documentation outlining the DAO’s legal structure and operational protocols. From voting procedures to treasury management, every aspect of the DAO’s lifecycle should be properly documented to reduce ambiguity and help new members understand their responsibilities. Inadequate documentation or outright neglect can create:

  • Confusion Over Roles and Responsibilities: Without explicit definitions, it’s easy for tasks to fall through the cracks or for disagreements to escalate.
  • Inability to Enforce Rules: DAOs rely on both smart contracts and social consensus. Formalizing rules in documentation helps ensure consistent enforcement and prevents unwelcome surprises.

In short, underestimating the legal dimension of DAO creation can derail even the most innovative projects. By proactively addressing legal and regulatory considerations—and maintaining thorough documentation—you not only protect core contributors but also fortify trust within your community and with external stakeholders.

Overlooking Community Building

The Importance of Community Engagement

A DAO, at its core, is nothing without an active and supportive community. Driving grassroots enthusiasm and participation is often the deciding factor between a thriving DAO and one that fizzles out. Yet, it’s surprisingly easy to underestimate just how vital it is to nurture community trust and engagement—especially during the early stages.

Some common pitfalls include:

  • Treating Community Members as Passive Observers
    Instead of viewing your community as a dynamic force, you might slip into a one-way communication style. This discourages members from taking initiative or contributing fresh ideas.
  • Lack of Clear Roles and Channels
    Without well-defined roles and open communication channels—like forums, Discord servers, or governance platforms—members can feel confused about where to participate or how to add value.
  • Ignoring Early Feedback
    In a DAO, the “wisdom of the crowd” can be a powerful asset. Overlooking or trivializing user feedback can lead to missed opportunities for innovation and improvement.

Failing to Incentivize Properly

Well-structured incentives lie at the heart of any successful DAO. Whether you’re offering governance tokens, staking rewards, or recognition badges, these incentives must be aligned with the DAO’s long-term goals. Misalignment often causes short-sighted behavior, where participants chase quick rewards rather than contributing meaningfully.

  • Overemphasis on Token Speculation
    If the primary draw for community members is the promise of quick token price gains, you risk attracting speculators instead of builders. This can lead to fleeting participation and sell-offs at the first sign of trouble.
  • Neglecting Non-Monetary Rewards
    Recognition, social standing, and meaningful collaboration can be just as powerful as financial incentives. When a DAO fails to provide pathways for skill development or leadership, member engagement wanes.
  • Cognitive Bias Traps
    Biases such as the conjunction fallacy can mislead founders into believing that if multiple positive outcomes are possible (e.g., rising token prices, active participation, mainstream adoption), then all those outcomes will inevitably happen together. This wishful thinking can blind DAOs to the need for thoughtful, data-driven incentive models.

To avoid these pitfalls, DAO creators must actively foster a culture of transparency, collaboration, and mutual respect. By setting clear expectations, leveraging diverse incentive structures, and consistently involving community feedback, you ensure members are motivated to contribute more than just their votes—they become co-creators in the DAO’s shared vision.

5. Ignoring Technical Considerations

Token Standards and Governance Frameworks

A solid technical foundation is essential when you create a DAO, particularly if it involves on-chain governance. Selecting the appropriate token standards and governance frameworks can significantly impact your DAO’s security, efficiency, and scalability.

Some pitfalls to watch out for include:

  • Choosing Incompatible Token Standards
    If your DAO relies on a token that isn’t easily integrated with governance contracts or lacks upgradeability, you might face roadblocks when implementing new features or patching vulnerabilities.
  • Underestimating Smart Contract Complexity
    Even “simple” governance tokens can hide complex logic behind the scenes. Overlooking these complexities may result in bugs, lockouts, or exploits that harm the DAO’s reputation and finances.
  • Ignoring Off-Chain vs. On-Chain Dynamics
    Governance strategies often combine on-chain decisions with off-chain discussions (e.g., using platforms like Discord or forums). Failing to synchronize these two spheres can fracture community engagement and hamper decision-making.

Poor Architecture and Security

Robust security isn’t just about preventing hacks—it's about building an architecture that can adapt to evolving threats and changing community needs.

Key oversights include:

  • Inadequate Auditing
    Smart contracts require thorough reviews, both automated and manual. Rushing to mainnet deployment without proper audits can lead to major losses—financial, reputational, or both.
  • No Contingency Plans
    If a vulnerability is discovered, how will you respond? Lacking emergency procedures or fallback governance mechanisms can leave a DAO paralyzed when critical decisions must be made quickly.
  • Over-Engineered Solutions
    While security is paramount, over-complicating the DAO’s architecture can create unintended vulnerabilities. Keeping your setup as simple as possible reduces attack surfaces and makes it easier for community members to understand and trust the system.

In short, technical considerations form the bedrock of a functional DAO. Choosing appropriate token standards, thoroughly auditing contracts, and designing for both present-day and future needs are non-negotiable steps in avoiding costly pitfalls.

Best Practices and Lessons

When studying successful DAOs, certain themes emerge time and again. According to Aragon the most robust DAOs share a commitment to simplicity, iteration, and transparent governance. Instead of rolling out overly sophisticated models from day one, they evolve and adapt based on community feedback and real-world performance.

Here are a few best practices worth emulating:

  • Iterative Approach to Governance
    Start small and build up. Launch a Minimal Viable DAO (MVD) to test voting processes, incentive mechanisms, and proposal management. Gather community feedback and refine before taking bigger steps.
  • Simple, Transparent Rules and Processes
    Ensure proposals are easy to understand and that the voting process is accessible to all token holders. Overly complicated frameworks can dissuade new members from participating.
  • Clear Roles and Shared Responsibilities
    Define contributor and community member roles early on. Whether you rely on working groups, committees, or elected leaders, clarity prevents power vacuums and fosters collaboration.
  • Open Communication and Education
    From Discord channels to public documentation, keep conversation and learning at the heart of your DAO. Encourage members to ask questions, propose improvements, and take leadership roles.

Academic Perspectives

Beyond practical experience, a growing body of research offers theoretical insights that can strengthen DAO governance. The discusses emerging patterns in DAOs, including how incentives and on-chain rules interact with off-chain social dynamics. By examining these findings, DAO creators can better anticipate challenges—like voter apathy, whale influence, or collusion—and integrate solutions from the outset.

Incorporating academic perspectives can help:

  • Validate Governance Assumptions
    Empirical data and rigorous analyses can confirm or challenge the assumptions behind your DAO’s architecture, preventing costly mistakes.
  • Stay Ahead of Regulatory and Social Shifts
    Academics often explore how upcoming policies or societal trends might impact DAOs, offering a forward-looking lens that day-to-day builders might miss.
  • Establish Credibility
    Aligning your DAO’s structure and operations with recognized research signals professionalism and thoroughness, potentially attracting more serious contributors, partners, and investors.

Conclusion

As you can see, creating a DAO involves more than just deploying a smart contract and distributing tokens. By examining these common pitfalls—from poor governance thresholds to inadequate legal structures, from neglecting community engagement to disregarding technical complexities—you gain a clearer picture of what not to do when you set out to create a DAO. Failing to address these areas often leads to compromised security, stalled decision-making, regulatory headaches, or outright community collapse

At Nextrope, we specialize in tailored blockchain and cryptocurrency solutions, including DAO creation and tokenomics design. If you’re looking to avoid these common pitfalls and build a thriving DAO that stands the test of time, feel free to contact us or explore more resources on our blog.

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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!