From DAO to Hybrid DAO: How can humans and AI govern together?

Author: Davide Crapis Source: notion Translation: Shan Oppa, Golden Finance

The origins of Decentralized Autonomous Organizations (DAOs) can be traced back to key ideas that preceded the birth of Ethereum. In 1996, Nick Szabo proposed the concept of "smart contracts", which are self-executing agreements embedded in software that can automatically execute and enforce terms without an intermediary. By imagining a system in which computational logic governs contractual obligations , Szabo laid the theoretical foundation for creating entities capable of autonomous functioning. 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 decentralized autonomous companies . In his article, Buterin defined DAC as a blockchain-based entity and summarized the following three core principles:

1. Autonomy: Operations are guided by rules encoded in smart contracts, eliminating the need for ongoing human intervention.

2. Decentralization: Power and decision-making are distributed and managed by 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 DAC could be implemented on the Bitcoin blockchain. However, Bitcoin’s scripting language lacks the ability to handle such complex logic, a limitation that points to 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. The advanced features of Ethereum made the concept of DAC more practical and flexible, and eventually developed into the DAO we know today.

From DAC to DAO

The transition from DAC to DAO is not just a rebranding, but an extension of the concept. While DAC was initially framed as a blockchain company with predefined operating rules, DAO expands this idea into a more flexible governance model .

The main features of DAO include:

• Programmable Governance: The DAO operates 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 suggestions and vote, achieving decentralized decision-making.

• Transparency: Every action and decision is recorded on-chain, ensuring accountability and verifiability.

• Global accessibility: The DAO is open to global participants, promoting a diverse and inclusive membership structure.

• Modular and adaptive design: DAO 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 financing mechanism.

The evolution from DAC to DAO reflects a broader shift in blockchain innovation—from static, rigid frameworks to dynamic systems that can adapt to multiple use cases. It also shows how Ethereum’s design addresses the limitations of Bitcoin’s infrastructure, paving 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 is designed as a decentralized venture capital fund that allows token holders to propose and vote on projects to fund. The DAO raised over $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 earlier DAO implementations. The 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:

• The party supporting the hard fork advocates rolling back the blockchain to recover stolen funds, emphasizing the importance of community trust.

• Opponents of “code is law” warn that modifying the blockchain would destroy its immutability and censorship resistance.

Eventually, the Ethereum community implemented a hard fork that split Ethereum into two chains: Ethereum (ETH) , which restored the stolen funds; and Ethereum Classic (ETC) , which preserved its unmodified history. This decision highlights the challenge of balancing immutability and practical governance in decentralized systems.

The evolution of DAO

After The DAO collapsed, the Ethereum ecosystem focused on improving the security and functionality of the DAO. At this stage, some important advances include:

1. Multi-signature wallet and Gnosis Safe:

Multi-signature wallet has become a basic tool to improve DAO security. This approach greatly reduces vulnerabilities due to 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 security measures ensure that a single malicious act or mistake does not compromise the entire organization.

2. Aragon and MolochDAO:

• Aragon has developed a comprehensive framework for designing and deploying DAOs, providing modular governance tools that allow the community to customize the decision-making process as needed. Its features include on-chain voting and role-based permission management, allowing the DAO to adapt to a variety of use cases.

• MolochDAO takes a minimalist approach and focuses 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 if they disagree with collective decisions. This mechanism enhances trust and reduces disputes.

Diverse applications of DAO

By 2020, DAOs have evolved to serve a wide variety of use cases:

Protocol DAO : Enables token holders to participate in protocol updates and resource allocation, such as:

• MakerDAO manages the Maker protocol, which supports the DAI stablecoin. Token holders can vote on key parameters such as collateral types and risk models, ensuring the protocol remains stable and effective.

• Uniswap DAO manages the Uniswap decentralized exchange, allowing token holders to propose and vote on protocol upgrades, liquidity mining plans, and fee adjustments. The success of Uniswap proves that DAO can manage large-scale financial infrastructure.

Social DAOs : Organizations like Friends With Benefits (FWB) work to cultivate 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.

Invest in DAOs : Organizations such as The LAO allow members to pool capital for venture capital investments while retaining collective governance rights. Other examples include MetaCartel Ventures, an organization focused on funding early-stage decentralized applications (dApps) and blockchain-based projects. Another famous example is FlamingoDAO, which focuses on acquiring and curating art as part of its investment strategy.

The latest 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 the DAO decision-making process to improve adaptability and efficiency while maintaining transparency.

2. Public goods DAO:

Projects like Gitcoin and Optimism's "Retrospective Public Goods Funding" focus on supporting open source infrastructure and community-driven initiatives.

3. DAO-as-a-Service platform:

Services like daos.fun simplify the creation and management of DAOs, making decentralized governance more accessible to a wider audience.

Although DAOs show great potential, they still face some ongoing challenges:

• Scalability : Current on-chain voting systems are often slow and costly, limiting their usability in large-scale organizations.

• Collaboration Complexity : As a DAO scales, reaching consensus among different participants becomes more difficult.

• Regulatory uncertainty : The legal status of the DAO and its participants is unclear, creating potential risks for members.

Addressing these challenges requires continued innovation in governance models, legal frameworks and technology infrastructure. From Szabo's vision of smart contracts to Ethereum's experiments to broader practice, DAO is gradually evolving into a transformative collective decision-making and organizational tool. As they continue to mature, DAOs have the potential to redefine how individuals and communities collaborate in the digital age.

DAO for humans and AI

The concept of DAO is evolving into a hybrid model that encompasses human and artificial intelligence (AI) collaboration. These organizations are expected to address some of the limitations of traditional DAOs while opening up new areas of governance and innovation.

The structure of hybrid DAO

Hybrid DAO combines human participants and AI agents and is the cutting-edge evolution of decentralized governance. These organizations aim to leverage the complementary strengths of human creativity and AI efficiency to build adaptable, scalable and resilient structures. By integrating human intuition and 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 actors are responsible for high- level strategic and governance decisions, while AI agents perform operational tasks, analyze data, and provide recommendations.

• AI-specific 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 allocation of resources.

• Consensus protocol : Hybrid DAO can leverage AI to promote consensus, reducing information overload for participants by analyzing proposals and presenting brief arguments for or against them.

Hybrid DAOs can solve key challenges faced by traditional DAOs. in particular:

• Scalability: AI systems can automate repetitive tasks, reduce bottlenecks caused by relying solely on human decision-making processes, and speed up operations. For example, in a DAO that manages global funding programs, AI can automatically classify and prioritize funding 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 mediator to resolve disputes, coordinate member interests and ensure fair voting results. For example, in a DAO managing a community-driven project, AI could analyze historical voting patterns and project results to recommend strategies that minimize groupthink and ensure diverse viewpoints are represented.

• Security: Smart contracts enhanced by AI can detect and mitigate potential vulnerabilities in real time, improving resistance to malicious attacks. For example, AI systems integrated into DAOs that govern decentralized finance (DeFi) protocols can monitor transaction data for unusual patterns to 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.

Artificial Intelligence Governance and Alignment Applications

Hybrid DAOs provide a promising framework for managing the governance and alignment of advanced artificial intelligence systems. For example:

• Ethical oversight: Hybrid DAOs can enforce ethical AI use through transparent, community-driven governance, ensuring that the evolution of AI is consistent with social values.

• Funding Alignment Initiatives: DAOs can pool resources to support AI alignment research, incentivizing open collaboration and accountability among developers.

• Adaptive governance model: By integrating artificial intelligence agents capable of real-time learning and feedback, hybrid DAOs can dynamically adjust governance rules to adapt to emerging challenges in the field of AI ethics and safety.

• Decision-making experiments: Hybrid DAOs can run experiments that pit AI against human decision-makers in tasks such as funding allocation, enabling data-driven comparisons to assess how effective they are in achieving desired outcomes (such as retroactive funding success) sex.

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

DAO between humans and AI

The concept of DAO is evolving to include hybrid models of humans and AI collaborating within a decentralized framework. These organizations could address some of the current limitations of DAOs while opening up new frontiers for innovation and governance.

Building a hybrid DAO

Hybrid DAOs combine human participants and AI agents and represent the cutting-edge development of decentralized governance. These organizations aim to leverage the complementary strengths of human creativity and AI efficiency to build adaptable, scalable and resilient structures. 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 actors 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 protocol: Hybrid DAOs can leverage AI to promote consensus by analyzing proposals and summarizing arguments for and against them, thereby reducing information overload for participants.

Challenges that 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 relying solely on human decision-making processes, and speed up operations. For example, in a DAO that manages global funding programs, AI can automatically classify and prioritize funding 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 mediator to resolve disputes, coordinate member interests, and ensure fair voting results. For example, in a DAO managing a community-driven project, AI could analyze historical voting patterns and project results to recommend strategies that minimize groupthink and ensure diverse viewpoints are represented.

• Security: Smart contracts enhanced by AI can detect and mitigate potential vulnerabilities in real time, improving resistance to malicious attacks. For example, AI systems integrated into DAOs that govern decentralized finance (DeFi) protocols can monitor transaction data for unusual patterns to 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.

• 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 solve this problem by analyzing voter behavior and recommending incentive structures, such as rewarding representatives and voters who contribute to successful outcomes. AI can also help members make more informed decisions by providing data-driven insights that synthesize complex strategic, short-term and long-term considerations.

• Transparency: Transparency regarding 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 create mechanisms that reward good decisions and enable voters to share in the benefits of the network’s success, beyond 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 evolve, we can expect to see innovation and evolution in these organizations in the coming years.