Author: Davide Crapis Source: notion Translation: Shan Ouba, Golden Finance
The origins of decentralized autonomous organizations (DAOs) can be traced back to key ideas before the birth of Ethereum. In 1996, Nick Szabo proposed the concept of "Smart Contracts", self-executing agreements embedded in software that automatically execute and enforce terms without intermediaries. By envisioning a system where contractual obligations are managed by computational logic, Szabo laid the theoretical foundation for the creation of entities that can operate autonomously. These early theoretical frameworks paved the way for the emergence of DAOs, defining their potential as self-governing organizations.
Decentralized Autonomous Corporation (DAC)
In 2013, Vitalik Buterin published a series of articles in Bitcoin Magazine, proposing the concept of a decentralized autonomous corporation. In his article, Buterin defined a DAC as a blockchain-based entity and summarized the following three core principles:
1. Autonomy: Operations are guided by rules encoded in smart contracts without constant human intervention.
2. Decentralization: Power and decision-making are distributed and managed among participants rather than concentrated in a single entity.
3. Transparency: All processes and activities are recorded on a public blockchain, ensuring accountability and visibility to all stakeholders.
Buterin’s early research suggested that DACs could be implemented on the Bitcoin blockchain. However, Bitcoin’s scripting language lacked the ability to handle such complex logic, a limitation that suggested the need for an advanced blockchain capable of executing more complex programs.
This was a technical gap in Bitcoin that made possible the development of Ethereum, a platform with a Turing-complete programming language. Ethereum’s advanced capabilities made the concept of DACs more practical and flexible, and eventually evolved into the DAOs we know today.
From DACs to DAOs
The transition from DACs to DAOs was more than a rebranding, it was an extension of the concept. While DACs were initially framed as blockchain companies with predefined operating rules, DAOs expanded the idea into a more flexible governance model.
The main features of DAOs include:
• Programmable governance: DAOs operate according to rules encoded in smart contracts, ensuring that decisions are automatically executed when conditions are met.
• Tokenized voting: Members use governance tokens to make proposals and vote, enabling decentralized decision-making.
• Transparency: Every action and decision is recorded on-chain, ensuring accountability and verifiability.
• Global accessibility: DAOs are open to global participants, promoting a diverse and inclusive membership structure.
• Modular and adaptable design: DAOs can evolve over time, introducing new features or governance mechanisms to meet the changing needs of the community.
These features enable communities to create organizations tailored to their specific needs, ranging from venture capital to community resource management. By combining programmable governance with decision-making, DAOs on Ethereum overcome many of the limitations of the original DAC concept. For example, Aragon provides design tools for modular governance systems, while MolochDAO demonstrates a simplified trust-minimized funding mechanism.
The evolution from DACs to DAOs reflects a broader shift in blockchain innovation—from static, rigid frameworks to dynamic systems that can adapt to a variety of use cases. It also shows how Ethereum’s design addresses the limitations of Bitcoin’s infrastructure and paves the way for decentralized governance.
The DAO and Ethereum’s Early Experiments
In 2016, Ethereum launched its first high-profile DAO, simply named “The DAO.” The DAO was designed as a decentralized venture capital fund that allowed token holders to propose and vote on projects to be funded. The DAO raised more than $150 million in ETH in a short period of time, a figure that at the time became one of the largest crowdfunding campaigns in history.
However, The DAO exposed critical vulnerabilities in early DAO implementations. A **reentrancy bug** in its code was maliciously exploited, resulting in the theft of approximately $60 million in ETH. This incident sparked a heated debate within the Ethereum community:
• Hard fork supporters advocated for recovering the stolen funds by rolling back the blockchain, emphasizing the importance of community trust.
• Opponents who insist that "code is law" warn that modifying the blockchain will undermine its immutability and censorship resistance.
Ultimately, the Ethereum community implemented a hard fork that split Ethereum into two chains: Ethereum (ETH), which recovered the stolen funds; and Ethereum Classic (ETC), which retained the unmodified history. This decision highlights the challenge of balancing immutability and practical governance in a decentralized system.
The Evolution of the DAO
After the collapse of The DAO, the Ethereum ecosystem focused on improving the security and functionality of the DAO. Some important advances during this phase include:
1. Multi-signature wallets and Gnosis Safe:
Multi-signature wallets became a fundamental tool for improving the security of DAOs. This approach greatly reduced the vulnerabilities caused by single points of failure. For example, Gnosis Safe provides a user-friendly multi-signature wallet management platform that allows DAOs to implement hierarchical approvals in key decisions such as fund allocation or contract upgrades. These safeguards ensure that a single malicious act or error cannot jeopardize the entire organization.
2. Aragon and MolochDAO:
• Aragon has developed a comprehensive framework for designing and deploying DAOs, providing modular governance tools that allow communities to customize the decision-making process as needed. Its features include on-chain voting and role-based permission management, allowing DAOs to adapt to a variety of use cases.
• MolochDAO takes a minimalist approach, focusing on financing public goods within the Ethereum ecosystem. One of its core innovations is the "ragequit" mechanism, which allows members to exit and withdraw their fair share of funds when they disagree with collective decisions. This mechanism enhances trust and reduces disputes.
DAOs in Diverse Applications
By 2020, DAOs have evolved to meet a wide variety of use cases:
Protocol DAOs: Enable token holders to participate in protocol updates and resource allocation, for example:
MakerDAO governs the Maker Protocol, which supports the DAI stablecoin. Token holders can vote on key parameters such as collateral types and risk models, ensuring that the protocol remains stable and effective.
Uniswap DAO governs the Uniswap decentralized exchange, allowing token holders to propose and vote on protocol upgrades, liquidity mining programs, and fee adjustments. Uniswap’s success proves that DAOs can manage large financial infrastructure.
Social DAOs: Organizations like Friends With Benefits (FWB) work to foster communities around shared values and resources, Seed Club helps creators and communities launch tokenized communities, and Cabin DAO creates decentralized co-living spaces and supports digital nomads.
Investment DAOs: Organizations such as The LAO allow members to pool capital for venture capital while retaining collective governance rights. Other examples include MetaCartel Ventures, which focuses on funding early-stage decentralized applications (dApps) and blockchain-based projects. Another notable example is FlamingoDAO, which focuses on acquiring and curating artworks as part of its investment strategy.
Recent Innovations and Challenges in DAO Design
By 2024, the DAO ecosystem has matured significantly, with new tools and ideas driving its development. Key trends include:
1. AI-Enhanced Governance:
Some organizations (such as ai16z) have proposed integrating artificial intelligence (AI) into DAO decision-making processes to improve adaptability and efficiency while maintaining transparency.
2. Public Goods DAOs:
Projects such as Gitcoin and Optimism’s “Retroactive Public Goods Funding” focus on supporting open source infrastructure and community-driven initiatives.
3. DAO-as-a-Service Platforms:
Services like daos.fun simplify the creation and management of DAOs, making decentralized governance more accessible to a wider audience.
While DAOs show great potential, they still face several ongoing challenges:
• Scalability: Current on-chain voting systems are often slow and costly, limiting their usability in large-scale organizations.
• Coordination Complexity: As DAOs scale, it becomes more difficult to reach consensus among different participants.
• Regulatory Uncertainty: The legal status of DAOs and their participants is unclear, which poses potential risks to members.
Solving these challenges will require continued innovation in governance models, legal frameworks, and technological infrastructure. From Szabo’s vision of smart contracts to Ethereum’s experiments and broader practice, DAOs are evolving into a transformative collective decision-making and organizational tool. As they continue to mature, DAOs are poised to redefine how individuals and communities collaborate in the digital age.
Human-AI DAOs
The concept of DAOs is evolving into hybrid models that encompass collaboration between humans and artificial intelligence (AI). These organizations are poised to address some of the limitations of traditional DAOs while opening up new areas of governance and innovation.
Structure of Hybrid DAOs
Hybrid DAOs, which combine human actors and AI agents, are a cutting-edge evolution in decentralized governance. These organizations aim to harness the complementary strengths of human creativity and AI efficiency to build structures that are adaptable, scalable, and resilient. By integrating human intuition with computational precision, hybrid DAOs can not only solve existing problems but also explore new possibilities for decentralized collaboration.
Hybrid DAOs can be structured in the following ways:
• Hierarchical decision-making: Human participants are responsible for high-level strategic and governance decisions, while AI agents perform operational tasks, analyze data, and provide recommendations.
• AI-focused roles: AI models can act as advisors or mediators, process complex data sets to guide collective decision-making, or perform automated tasks such as compliance monitoring and optimal resource allocation.
• Consensus protocols: Hybrid DAOs can leverage AI to facilitate consensus, reducing information overload for participants by analyzing proposals and presenting concise arguments for or against.
Hybrid DAOs can address key challenges faced by traditional DAOs. In particular:
Scalability: AI systems can automate repetitive tasks, reduce bottlenecks caused by human-only decision-making processes, and speed up operations. For example, in a DAO managing a global grant program, AI can automatically categorize and prioritize grant proposals based on predefined criteria. By presenting a ranked list of proposals with key insights, AI enables the DAO to handle a higher number of submissions while maintaining efficiency and fairness.
Coordination: AI can serve as an impartial coordinator to resolve disputes, align member interests, and ensure fair voting outcomes. For example, in a DAO managing a community-driven project, AI can analyze historical voting patterns and project outcomes to recommend strategies that minimize groupthink and ensure diverse perspectives are represented.
Security: AI-enhanced smart contracts can detect and mitigate potential vulnerabilities in real time, increasing resilience to malicious attacks. For example, an AI system integrated into a DAO that manages a decentralized finance (DeFi) protocol can monitor transaction data for unusual patterns that indicate possible vulnerabilities. The AI can then trigger automated protective measures, such as freezing specific contracts or notifying administrators, to prevent further damage while enabling rapid response to threats.
Applications of AI Governance and Alignment
Hybrid DAOs offer a promising framework for managing the governance and alignment of advanced AI systems. For example:
Ethical Oversight: Hybrid DAOs can enforce ethical AI use through transparent, community-driven governance, ensuring that AI evolves in line with societal values.
Funding Alignment Programs: DAOs can pool resources to support AI alignment research, incentivizing open collaboration and accountability among developers.
Adaptive Governance Models: By incorporating AI agents that can learn and feedback in real time, hybrid DAOs can dynamically adapt governance rules to emerging challenges in AI ethics and safety.
Decision-making Experiments: Hybrid DAOs can conduct experiments where AI competes with human decision-makers in tasks, such as funding allocation, enabling data-driven comparisons to assess their effectiveness in achieving desired outcomes (e.g., retroactive funding success).
By combining the benefits of decentralized governance with the power of AI, hybrid DAOs can play a key role in shaping a future where technology and humans coexist in harmony.
Human-AI DAOs
The concept of DAOs is evolving to include hybrid models where humans and AI collaborate within a decentralized framework. These organizations can address some of the current limitations of DAOs while opening new areas for innovation and governance.
Building Hybrid DAOs
Hybrid DAOs combine human actors and AI agents and represent a cutting-edge development in decentralized governance. These organizations aim to leverage the complementary strengths of human creativity and AI efficiency to build structures that are adaptable, scalable, and resilient. By leveraging human intuition and computational precision, hybrid DAOs can address existing limitations while exploring new possibilities for decentralized collaboration. They can be structured in a variety of ways:
Hierarchical decision-making: Human participants are responsible for high-level strategic and governance decisions, while AI agents perform operational tasks, analyze data, and provide recommendations.
Specialized roles for AI: AI models can act as advisors or mediators, process complex data sets to guide collective decision-making, or perform automated tasks such as monitoring compliance and optimizing resource allocation.
Consensus protocols: Hybrid DAOs can leverage AI to facilitate consensus by analyzing proposals and summarizing arguments for and against, thereby reducing information overload for participants.
Challenges hybrid DAOs can solve
Hybrid DAOs can solve key challenges faced by traditional DAOs. In particular:
Scalability: AI systems can automate repetitive tasks, reduce bottlenecks caused by human-only decision-making processes, and speed up operations. For example, in a DAO managing a global grant program, AI can automatically categorize and prioritize grant proposals based on predefined criteria. By presenting a ranked list of proposals with key insights, AI enables the DAO to handle a higher volume of submissions while maintaining efficiency and fairness.
Coordination: AI can serve as an impartial coordinator to resolve disputes, align member interests, and ensure fair voting outcomes. For example, in a DAO managing a community-driven project, AI can analyze historical voting patterns and project outcomes to recommend strategies that minimize groupthink and ensure diverse perspectives are represented.
Security: Smart contracts enhanced by AI can detect and mitigate potential vulnerabilities in real time, increasing resilience to malicious attacks. For example, an AI system integrated into a DAO governing a decentralized finance (DeFi) protocol can monitor transaction data for unusual patterns that indicate a possible vulnerability. AI can then trigger automated protective measures, such as freezing a specific contract or notifying administrators, to prevent further damage while enabling rapid response to threats.
Participation: Participation in governance remains a significant challenge for DAOs, with low participation and a lack of incentives for thoughtful decision-making. AI can help address this by analyzing voter behavior and recommending incentive structures, such as rewarding delegates and voters who contribute to successful outcomes. AI can also help members make more informed decisions by providing data-driven insights, synthesizing complex strategic, short-term, and long-term considerations.
Transparency: Transparency about power distribution, voting history, and governance trends is critical to the success of a DAO. Tools such as governance dashboards can be enhanced with AI to act as a neutral observer, analyzing and visualizing decision-making patterns. AI can also help establish mechanisms that reward good decisions and enable voters to share in the benefits of the network's success, not just token value.
These are just examples of the potential of hybrid DAOs. As AI technology continues to advance and DAO governance frameworks continue to develop, we can expect to see innovation and evolution in these organizations in the years to come.
Alex
Kikyo
Hui Xin
Brian
Joy