Forward the Original Title: A Brief History of DAOs

The origins of decentralized autonomous organizations (DAOs) trace back to foundational ideas developed long before Ethereum’s inception. In 1996, Nick Szabo introduced the concept of “smart contracts,” self-executing agreements embedded in software that could automate and enforce terms without intermediaries. By envisioning a system where computational logic governs contractual obligations, Szabo laid the groundwork for creating entities capable of operating autonomously. These early theoretical frameworks prefigured the emergence of DAOs as self-governing organizations.

Decentralized Autonomous Corporations

In 2013, Vitalik Buterin introduced the concept of decentralized autonomous corporations (DACs) in a series of articles published in Bitcoin Magazine. In his writings, Buterin envisioned DACs as blockchain-based entities characterized by three core principles:

1. Autonomy: Operations are guided by rules encoded in smart contracts, allowing the system to function without requiring ongoing human input.
2. Decentralization: Authority and decision-making are distributed among participants rather than centralized in a single entity.
3. Transparency: All processes and activities are recorded on a public blockchain, ensuring accountability and visibility for all stakeholders.

Buterin’s early work proposed that DACs could be implemented on Bitcoin’s blockchain. However, Bitcoin’s scripting language lacked the flexibility to handle the complex logic required for such entities. This limitation highlighted the need for a more advanced blockchain capable of executing sophisticated programs.

This was one of the gaps in Bitcoin that motivated the development of Ethereum—a platform designed with a Turing-complete programming language. Ethereum’s advanced capabilities enabled the realization of DACs in a more functional and versatile form, eventually evolving into what we now recognize as DAOs.

From DACs to DAOs

The transition from DACs to DAOs was not merely a rebranding but an expansion of the concept. While DACs were initially framed as blockchain-based corporations with predefined operational rules, DAOs expanded the idea to include more flexible governance models.

Key distinguishing features of DAOs include:

• Programmable Governance: DAOs operate based on rules encoded in smart contracts, ensuring decisions are automatically executed when conditions are met.
• Tokenized Voting: Members use governance tokens to propose and vote on changes, enabling decentralized decision-making.
• Transparency: Every action and decision is recorded on-chain, ensuring accountability and verifiability.
• Global Accessibility: DAOs are open to participants worldwide, fostering a diverse and inclusive membership.
• Modular and Adaptive Design: DAOs can evolve over time, incorporating new features or governance mechanisms to meet the changing needs of their communities.

These features allowed communities to create organizations tailored to their specific needs, ranging from venture funding to community resource management. By integrating programmable governance and decision-making, Ethereum’s DAOs addressed many of the limitations that had constrained the original DAC vision. For instance, platforms like Aragon emerged, enabling users to design DAOs with modular governance systems, and MolochDAO demonstrated simplified, trust-minimized funding mechanisms.

This evolution from DACs to DAOs reflects the broader shift in blockchain innovation—moving from static, rigid frameworks to dynamic systems capable of adapting to a wide variety of use cases. It also highlights how Ethereum’s design addressed the limitations of Bitcoin’s infrastructure, paving the way for decentralized governance.

The DAO and Ethereum’s Early Experimentation

In 2016, Ethereum introduced its first high-profile DAO, simply called “The DAO.” Designed as a decentralized venture capital fund, The DAO allowed token holders to propose and vote on funding initiatives. It raised over $150 million in ETH, marking one of the largest crowdfunding campaigns in history at the time.

However, The DAO revealed critical vulnerabilities in early DAO implementations. A reentrancy bug in its code was exploited, resulting in the theft of approximately $60 million worth of ETH. This event triggered a contentious debate within the Ethereum community:

• Supporters of a hard fork argued for rolling back the blockchain to recover the stolen funds, emphasizing the importance of community trust.
• Opponents adhering to “code-is-law” warned that altering the blockchain would undermine its immutability and censorship resistance.

Ultimately, the community implemented a hard fork, splitting Ethereum into Ethereum (ETH), which reversed the theft, and Ethereum Classic (ETC), which preserved the unaltered history. This decision underscored the challenges of balancing immutability with practical governance in decentralized systems.

The Evolution of DAOs

After The DAO’s collapse, the Ethereum ecosystem focused on improving DAO security and functionality. Key advancements during this period included:

1. Multisignature Wallets and Gnosis Safe: Multisig wallets became foundational tools for improving DAO security. This approach significantly reduced vulnerabilities caused by single points of failure. For example, Gnosis Safe provided a user-friendly platform for managing multisig wallets, allowing DAOs to implement layered approvals for critical decisions, such as fund disbursements or contract upgrades. These safeguards ensured that no single malicious actor or mistake could compromise an organization.
2. Aragon and MolochDAO: Aragon developed a comprehensive framework for designing and deploying DAOs, offering modular governance tools that allowed communities to customize decision-making processes. It enabled features such as on-chain voting and role-based permissions, making DAOs adaptable to various use cases. In contrast, MolochDAO focused on a minimalist approach aimed at funding public goods within the Ethereum ecosystem. One of its key innovations was the “ragequit” mechanism, which allowed members to withdraw their proportional share of funds if they disagreed with collective decisions. This feature fostered trust and minimized disputes.

Diverse Applications of DAOs

By 2020, DAOs had evolved to address a wide range of use cases:

• Protocol DAOs: Enabled token holders to participate in protocol updates and resource allocation, for example:
  • MakerDAO governed the Maker Protocol, which powers the DAI stablecoin. Token holders vote on critical parameters like collateral types and risk models, ensuring 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. The success of Uniswap exemplifies how DAOs can manage large-scale financial infrastructure.
• Social DAOs: Groups like Friends With Benefits (FWB) focused on fostering communities around shared values and resources, Seed Club helped creators and communities launch tokenized communities, Cabin DAO creates decentralized coliving spaces and supports digital nomads.
• Investment DAOs: Organizations such as The LAO allowed members to pool capital for venture investments while retaining collective governance. Other examples include MetaCartel Ventures, which focused on funding early-stage dApps and blockchain-based projects. Another prominent example is FlamingoDAO, which specializes in acquiring and curating art as part of its investment strategy.

Recent Innovations in DAO Design and Some Challenges

As of 2024, the DAO ecosystem has matured significantly, with new tools and concepts driving its growth. Key trends include:

1. AI-Enhanced Governance: Frameworks proposed by entities like ai16z integrate artificial intelligence into DAO decision-making processes, improving 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 broader audience.

Despite their promise, DAOs face persistent challenges:

• Scalability: Current on-chain voting systems are often slow and expensive, limiting their usability for large organizations.
• Coordination Complexities: Achieving consensus among diverse participants remains a significant hurdle as DAOs scale.
• Regulatory Uncertainty: The legal status of DAOs and their participants is unclear, creating potential risks for members.

Addressing these challenges will require continued innovation in governance models, legal frameworks, and technical infrastructure. From Szabo’s early vision of smart contracts to Ethereum’s experiments and beyond, DAOs have steadily evolved as transformative tools for collective decision-making and organization. As they continue to mature, DAOs hold the potential to redefine how individuals and communities collaborate in the digital age.

DAOs of Humans and AIs

The concept of DAOs is evolving to include hybrid models where humans and AIs collaborate within decentralized frameworks. These organizations could address some of the current limitations of DAOs while opening new frontiers for innovation and governance.

Structuring Hybrid DAOs

Hybrid DAOs, combining human participants and AI agents, represent a cutting-edge evolution in decentralized governance. These organizations aim to harness the complementary strengths of human creativity and AI efficiency to build adaptive, scalable, and resilient structures. By leveraging both human intuition and computational precision, hybrid DAOs can address existing limitations while exploring new possibilities for decentralized collaboration. They could be structured in several ways:

• Layered Decision-Making: Human participants handle high-level strategy and governance decisions, while AI agents execute operational tasks, analyze data, and provide recommendations.
• Specialized Roles for AI: AI models can act as advisors or mediators, processing complex datasets to guide collective decisions, or performing automated tasks such as monitoring compliance and optimizing resource allocation.
• Consensus Protocols: Hybrid DAOs could use AI to facilitate consensus by analyzing proposals and presenting summarized arguments for or against, reducing information overload for participants.

Hybrid DAOs could address key challenges faced by traditional DAOs. In particular:

• Scalability: AI systems can automate repetitive tasks, reducing the bottlenecks caused by human-only decision-making processes and speeding up operations.
• Coordination: AI can act as an impartial facilitator to resolve disputes, align member interests, and ensure fair voting outcomes.
• Security: Smart contracts augmented by AI can detect and mitigate potential exploits in real time, increasing resilience against malicious attacks.

Applications in 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 usage through transparent, community-driven governance, ensuring AI evolution aligns with societal values.
• Funding Alignment Initiatives: DAOs can pool resources to support AI alignment research, incentivizing open collaboration and accountability among developers.
• Adaptive Governance Models: By integrating AI agents capable of real-time learning and feedback, hybrid DAOs can dynamically evolve governance rules to adapt to emerging challenges in AI ethics and safety.

By combining the strengths of decentralized governance with the capabilities of artificial intelligence, hybrid DAOs could the key to a future where technology and humanity coexist harmoniously.

DAOs of Humans and AIs

The concept of DAOs is evolving to include hybrid models where humans and AIs collaborate within decentralized frameworks. These organizations could address some of the current limitations of DAOs while opening new frontiers for innovation and governance.

Structuring Hybrid DAOs

Hybrid DAOs, combining human participants and AI agents, represent a cutting-edge evolution in decentralized governance. These organizations aim to harness the complementary strengths of human creativity and AI efficiency to build adaptive, scalable, and resilient structures. By leveraging both human intuition and computational precision, hybrid DAOs can address existing limitations while exploring new possibilities for decentralized collaboration. They could be structured in several ways:

• Layered Decision-Making: Human participants handle high-level strategy and governance decisions, while AI agents execute operational tasks, analyze data, and provide recommendations.
• Specialized Roles for AI: AI models can act as advisors or mediators, processing complex datasets to guide collective decisions, or performing automated tasks such as monitoring compliance and optimizing resource allocation.
• Consensus Protocols: Hybrid DAOs could use AI to facilitate consensus by analyzing proposals and presenting summarized arguments for or against, reducing information overload for participants.

Challenges Hybrid DAOs Can Address

Hybrid DAOs could address key challenges faced by traditional DAOs. In particular:

• Scalability: AI systems can automate repetitive tasks, reducing the bottlenecks caused by human-only decision-making processes and speeding up operations. For example, in a DAO managing a global funding initiative, AI could automate the categorization and prioritization of funding proposals based on predefined criteria. By presenting a ranked list of proposals with key insights, AI enables the DAO to process a higher volume of submissions while maintaining efficiency and fairness.
• Coordination: AI can act as an impartial facilitator to resolve disputes, align member interests, and ensure fair voting outcomes. For instance, in a DAO managing community-driven projects, AI could analyze historical voting patterns and project outcomes to recommend strategies that minimize groupthink and ensure diverse perspectives are represented.
• Security: Smart contracts augmented by AI can detect and mitigate potential exploits in real time, increasing resilience against malicious attacks. For example, an AI system integrated into a DAO managing decentralized finance (DeFi) protocols could monitor for unusual patterns in transaction data that may indicate an exploit. The AI could then trigger automatic protective measures, such as freezing specific contracts or notifying administrators, to prevent further damage while enabling a rapid response to the threat.
• Participation: Participation in governance remains a significant challenge for DAOs, with low engagement and lack of incentives for thoughtful decision-making. AI could help address this by analyzing voter behavior and recommending incentive structures, such as rewarding delegates and voters who contribute to successful outcomes. AI could also assist in providing data-driven insights, helping members make more informed decisions by synthesizing complex strategic, short-term, and long-term considerations.
• Transparency: Transparency about power distribution, voter history, and governance trends is critical to DAO success. Tools like governance dashboards could be enhanced with AI to act as neutral observers, analyzing and visualizing decision-making patterns. AI could also help establish mechanisms to reward good decisions and enable voters to share in the network’s success beyond token value.

Applications in 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 usage through transparent, community-driven governance, ensuring AI evolution aligns with societal values.
• Funding Alignment Initiatives: DAOs can pool resources to support AI alignment research, incentivizing open collaboration and accountability among developers.
• Adaptive Governance Models: By integrating AI agents capable of real-time learning and feedback, hybrid DAOs can dynamically evolve governance rules to adapt to emerging challenges in AI ethics and safety.
• Experimentation in Decision-Making: Hybrid DAOs could experiment with AI competing against human decision-makers in tasks like fund allocation, enabling data-driven comparisons of their effectiveness in achieving desired outcomes, such as retroactive funding success.

By combining the strengths of decentralized governance with the capabilities of artificial intelligence, hybrid DAOs could play a pivotal role in shaping a future where technology and humanity coexist harmoniously.

Disclaimer:

1. This article is reprinted from [Davide Crapis]. Forward the Original Title: A Brief History of DAOs. All copyrights belong to the original author [Davide Crapis]. If there are objections to this reprint, please contact the Gate Learn team, and they will handle it promptly.
2. Liability Disclaimer: The views and opinions expressed in this article are solely those of the author and do not constitute any investment advice.
3. Translations of the article into other languages are done by the Gate Learn team. Unless mentioned, copying, distributing, or plagiarizing the translated articles is prohibited.