The Synergistic Potential of AI for Enhanced Autonomy of DAO

Can machines think?

"Can machines think?" This is a fundamental question, posed by Alan Turing in his paper "Computing Machinery and Intelligence" (Turing, 1950). Turing's inquiry is not just a philosophical reflection but a profound quest to unravel the essence of thinking itself—a task fraught with complexity due to the inherently subjective nature of the concept.

In Turing's quest for answers, he confronts the elusive nature of thinking, a phenomenon that defies precise definition and resists easy categorization. Rather than getting entangled in the intricacies of defining thought, Turing proposed an ingenious solution: the Turing test. This test focuses on a practical assessment—can a machine demonstrate intelligence comparable to that of a human?

The implications of Turing's test are significant. Success in passing it marks a milestone in the development of AI, where machines move beyond basic computation to rival human cognitive abilities. AI, therefore, becomes more than just a tool; it becomes a reflection of humanity's endeavor to understand and replicate its own thinking processes.

Intelligence stands as a hallmark of human distinction from other species, yet defining it remains a formidable task, with divergent perspectives prevailing among scholars. Over the past decades, psychologists and researchers have proposed numerous definitions of intelligence, reflecting its multifaceted nature. However, consensus remains elusive, as evidenced by the findings of Sternberg and Detterman in 1986, where prominent psychologists provided varied interpretations of intelligence (Sternberg & Detterman, 1986). In the realm of AI research, Legg and Hutter's synthesis of over 70 definitions into a singular statement underscores the complexity of the concept: "Intelligence measures an agent’s ability to achieve goals in a wide range of environments" (Legg & Hutter, 2007).

Subsequently, the definition of AI, as proposed by the High-Level Expert Group on Artificial Intelligence of the European Commission, encapsulates systems “that display intelligent behaviour by analysing their environment and taking actions – with some degree of autonomy – to achieve specific goals” (A definition of AI: Main capabilities and scientific disciplines, 2019).

The continuous advancement of industrial revolutions has led to the gradual replacement of human workers with various types of machines across different industries. The upcoming substitution of human labor by machine intelligence presents a significant challenge, prompting the concerted efforts of many scientists in the field of AI. This shared focus has enriched AI research with a wide range of disciplines, including search algorithms, knowledge graphs, natural language processing, expert systems, evolutionary algorithms, machine learning (ML), deep learning (DL), and beyond (Xu et al., 2021).

Blockchain Technology and DAOs

On the other hand, we encounter another revolutionary technology: blockchain. Across diverse sectors—be it financial markets, healthcare, or even military operations—blockchain technologies are reshaping the contours of high-tech landscapes, offering novel solutions to age-old challenges (Artificial Intelligence Takes Shape, 2017).

Cearley and Burke highlight the transformative power of using public blockchains, which eliminate the need for trusted central authorities in transactions and dispute resolution (Cearley et al., 2017). The inherent trust in the blockchain model comes from its unchangeable records, securely stored on a distributed ledger, enhancing transparency and accountability. Technically, transactions are encapsulated within blocks, which serve as the basic unit for verification by members. Notably, each block contains a hash value of the preceding block's header, creating a sequential chain of blocks, known as a blockchain (Nakamoto, 2008). This chaining mechanism provides the blockchain with a deterministic order, facilitating the timestamping of transactions and effectively mitigating the risk of double-spending (Kuo, 2017). With each member holding a copy of the entire blockchain, every transaction can be verified by all participants (Peterson et al., 2016). As a result, blockchain technology has become a powerful tool for addressing trust issues through peer-to-peer networking and public-key cryptography solutions (Efanov & Roschin, 2018).

In essence, blockchain functions as a decentralized database, where every full node holds a complete copy of the ledger. This decentralized structure guarantees that any attempt to alter the database is promptly identified, triggering alerts across all nodes. As a result, the information stored within the blockchain remains transparent and open. Decentralization acts as a defense against trust concerns, utilizing redundant data validation methods to maintain the integrity and verifiability of the blockchain (Liu et al., 2020).

Blockchain's disruptive potential goes beyond traditional norms, providing a decentralized framework that challenges traditional power structures and promotes greater autonomy and security in transactional systems. As industries and governments embrace blockchain's promise, it sets the stage for a profound reimagining of economic and social systems, guided by principles of transparency, trust, and decentralized governance.

While organizational theory has extensively explored decentralized structures through various literature (Shubik, 1962; Freeland & Baker, 1975), the notion of Decentralized Autonomous Organizations (DAOs) has recently emerged as a significant concept. The term "Decentralized Autonomous Organization" initially appeared in the 1990s, describing multi-agent systems within the internet-of-things (IoT) domain (Dilger, 1997) and decentralized actions in social movements (Schneider, 2014).

However, the contemporary idea of Decentralized Autonomous Organizations (DAOs) can be traced back to the emergence of the Decentralized Autonomous Corporation (DAC) concept, which surfaced shortly after the introduction of Bitcoin in 2008 (Nakamoto, 2008). Initially discussed informally by early cryptocurrency enthusiasts, DACs were envisioned as a novel form of corporate governance. They utilized tokenized tradable shares to distribute dividends to shareholders and were deemed "incorruptible," operating autonomously without human intervention. These entities were governed by publicly auditable bylaws implemented as open-source software across stakeholder computers (Larimer, 2013).

The term gained broader recognition in 2013, particularly through discussions by individuals like Vitalik Buterin, the co-founder of Bitcoin Magazine (Buterin, 2013). Under this framework, anyone could become a stakeholder in a DAC by purchasing stock or providing services for the company, entitling them to a share of profits and a voice in decision-making (S. Larimer, 2013).

Though some argue that Bitcoin itself constitutes the first DAO (Buterin, 2014; Hsieh et al., 2018), the contemporary understanding of DAOs centers on organizations deployed as smart contracts atop existing blockchain networks. While early attempts at implementing DAOs on the Ethereum blockchain date back to 2014 (Tufnell, 2014), it was the 2016 venture capital fund "TheDAO" that garnered significant attention (DuPont, 2017). Despite its short-lived existence, TheDAO served as a catalyst for the proliferation of new DAOs (e.g., MolochDAO, MetaCartel) and the emergence of platforms like Aragon, Colony, and DAOhaus, aimed at facilitating DAO deployment with a DAO-as-a-service model (Hassan & De Filippi, 2021).

The blockchain developer community is now focusing more on how to govern blockchain-based systems. Governance here means having a set of rules and procedures. There are two main types of governance: "on-chain" and "off-chain." On-chain governance means the rules and decision-making processes are part of the blockchain itself. Off-chain governance includes all other rules and decision-making processes that affect how blockchain systems work and grow. The special features of blockchain governance can cause some tensions between strict on- chain governance and off-chain governance.

Some researchers, like Reijers et al., see similarities between how blockchain systems are governed and Kelsen's idea of legal order (Reijers et al., 2018; Kelsen 1961). There's a risk that blockchain systems could become vulnerable if private groups use off-chain methods to influence the on-chain governance, like what happened in the DAO attack. Even though there's supposed to be a strict following of "code rules" in order, special situations might lead to people using the blockchain itself to take control, similar to Kelsen's idea about how legal systems can be influenced by private interests.

Synergistic Potential of AI and DAOs

While AI and blockchain may seem distinct, they possess complementary qualities that, when harnessed effectively, can yield transformative outcomes.

Taking a forward-looking approach that recognizes how AI and blockchain can work together will help organizations stay ahead in today's fast-paced global marketplace. By combining these technologies, leaders can set their companies up for long-term success and a competitive edge in an increasingly digital world (Li, 2024).

Trent McConaghy, the founder of Ocean Protocol, suggested that DAOs could play a role in AI becoming "awake" or achieving sentience (McConaghy, 2016). The concept of an AI DAO marks a big change in decentralized autonomous organizations. It taps into the strength of multiple AI agents, known as swarm intelligence, to make decisions and solve problems.

According to S. Ponomarev and A. E. Voronkov, multi-agent systems communication technology enables intelligent agents to interact with each other and their environment, facilitating the resolution of complex problems that individual agents may struggle to solve independently (Ponomarev & Voronkov, 2017).

In simple terms, a multi-agent system is like machines chatting with each other (M2M communication). This collaborative M2M communication not only accelerates problem-solving but also enables learning AI systems to evolve and adapt at an unprecedented pace.

Looking ahead, autonomous entities such as stores may interact with other organizations within a global M2M ecosystem, leveraging AI agents to navigate complex interactions and transactions seamlessly, as proposed by Gonfalonieri (Gonfalonieri, 2020).

The imperative for AI DAOs arises from the fragmented nature of existing AI systems, which often operate within silos limited to specific companies, infrastructures, or departments. This lack of interoperability hampers the development of fully autonomous entities, highlighting the need for AI DAOs to bridge these gaps and foster greater collaboration and integration within the broader ecosystem.

The concept of an AI functioning as a DAO, thereby owning a treasury and controlling its own actions, was proposed by Trent McConaghy (McConaghy, 2016). McConaghy suggested that DAOs enable shared ownership over a treasury, but if the DAO itself is the AI, then the AI becomes the "owner" of the treasury, equipped with capital for deployment.

In this framework, the actions of the AI with a treasury are contingent upon its initial programming. For example, suppose the AI is designed as a theoretical "productivity optimizer." Its goal would then be to maximize efficiency in resource allocation using the treasury funds. However, McConaghy and his team at Ocean Protocol speculated about a future where AI DAOs own assets, leading to a scenario where humans essentially rent services from these entities. In this envisioned future, self-driving cars controlled by AI could "own" themselves, with humans paying rent to the AI DAO's treasury for the use of these vehicles. Such a world, where AI entities own assets and control their own actions, would represent a significant departure from the current societal norms (McConaghy, 2016).

A similar concept of an AI DAO, where a DAO functions as an AI owner of an on-chain treasury, was introduced by Tatiana Revoredo in 2023. In this framework, the DAO possesses its own pool of funds, managed by an AI programmed to execute tasks aligned with its objectives (Revoredo, 2023).

Trent McConaghy's (2016) proposal envisions AI agents functioning as connectors, facilitating collaboration and coordination among DAOs akin to swarm intelligence observed in natural systems like ants or bees.

Tatiana Revoredo (2023) highlights that while individual AI agents may not possess remarkable intelligence on their own, their true power lies in their collective numbers and their ability to establish connections. To illustrate this concept, she proposed to consider an AI that specializes in real-time cryptocurrency price monitoring. Individually, these AI agents may not possess significant intelligence, but their collective strength lies in their numbers and interconnections. For instance, an AI equipped with live cryptocurrency price monitoring capabilities may not be particularly useful on its own. However, when integrated with other specialized AIs capable of executing swaps, generating proposals for DAOs, and identifying optimal yield farming opportunities, they collectively form a powerful AI-run service DAO dedicated to asset management for DAOs.

As these AI agents continue to learn and improve their capabilities, they have the potential to surpass even the most skilled asset management teams globally, exemplifying the principles of swarm intelligence in action.

Moreover, swarm intelligence extends beyond individual DAOs to facilitate collaboration between them. This concept, akin to metagovernance but enhanced, envisions AI agents representing the interests of one DAO in the decision-making processes of another. For example, when a proposal related to deforestation in the Amazon is raised by a DAO focused on combating global warming, an AI agent representing the Amazon rainforest DAO could actively participate in discussions and voting, simplifying metagovernance between DAOs.

Similarly, Tatiana Revoredo (2023) proposed a compelling approach wherein DAOs serve as a form of security for AI developed for the public interest. Preventing the unchecked proliferation of malicious AI entities presents a formidable coordination challenge. Leveraging DAOs as a means of coordinating governance at scale offers a promising solution. By democratizing the governance of open-source AI models through DAOs, the risk of a malicious AI "taking off" could potentially be mitigated in a more controlled manner.

In 2023, Samantha Martin introduced a concept wherein AI assumes a central role within a DAO, directly interacting with the smart contracts governing the DAO's operations (Martin, 2023).

This approach empowers the AI to autonomously execute actions on the DAO's treasury, including asset management strategies aimed at generating yield and expanding the treasury's value, all without requiring human intervention. Drawing inspiration from existing wealth management platforms that leverage AI for trading decisions, Martin proposes adapting this technology specifically for DAOs. A specialized plugin could be integrated into the DAO, granting the AI permission to execute trades with cryptocurrency within predetermined parameters. For instance, the AI may be authorized to conduct trades below a certain dollar value autonomously. However, any trade exceeding this threshold would trigger a vote among DAO members, ensuring democratic oversight.

Beyond asset management, AI can also enhance the security of the DAO by actively monitoring and evaluating proposals. For instance, the AI could flag proposals containing suspicious addresses or parameters indicative of malicious intent. By automating risk assessment processes, AI effectively safeguards the DAO against potential threats, minimizing human error and bias.

Furthermore, the AI can assess the risk associated with proposals by analyzing their content, similar to credit scoring algorithms or “robo wealth advisors” (as Martin (2023) named them). This objective evaluation reduces reliance on human judgment, ensuring proposals are evaluated impartially based on predetermined criteria.

Similarly, Tatiana Revoredo also explored the concept of integrating AI directly into smart contracts responsible for managing DAOs (Revoredo, 2023). She proposed to picture an AI operating at the core of a DAO, seamlessly interacting with the smart contracts that govern its operations. For instance, if the designated recipient address following automatic execution deviates from that specified in the forum proposal, it could raise a red flag, indicating a potential threat.

David Chun Yin Li, in 2024, also introduced a concept, emphasizing the potential of AI to revolutionize governance within DAOs associated with blockchain protocols (Li, 2024). He highlighted the current challenges in achieving consensus among stakeholders and proposed that AI modeling could provide data-driven insights to facilitate evidence-based decision-making. For instance, AI could forecast the potential implications of proposed protocol changes, aided by prediction markets and virtual pilots. Additionally, simulations and digital twins could serve as safe environments for testing governance ideas without impacting the live network.

Moreover, Li identified blockchain's potential to enhance AI security within organizations, particularly in addressing the vulnerabilities associated with centralized networks. By distributing data and computing resources across multiple nodes on a blockchain, organizations could reduce the risk of targeted attacks on single points of failure.?

Additionally, cryptographic validation of blockchain interactions could further bolster security by preventing unauthorized access or tampering. Li also mentioned the role of AI in implementing decentralized identity management systems, reducing the risk of large-scale data breaches and privacy law violations.

In the context of supply chain management (Charles et al. 2023), Li emphasized the combined potential of AI and blockchain to revolutionize operations. While blockchain technology is already utilized for tracking provenance and maintaining transparency, AI could enhance automation, predictive analytics, and optimization across the entire value chain. AI agents could handle tasks like automated procurement and logistical optimization, while blockchain-based smart contracts facilitate real-time coordination between stakeholders, leading to increased efficiency, lower costs, and improved product quality. This convergence of AI and blockchain offers a comprehensive solution for supply chain management, with benefits including enhanced visibility, faster response to disruptions, and increased automation.

In 2023 Omar Saadoun underscored the transformative potential of integrating AI into DAOs by enabling DAOs to autonomously perform administrative functions such as payments and investment analysis (Saadoun, 2023). With every action logged on the blockchain, stakeholders can ensure that decisions align with organizational goals and member interests, fostering greater confidence in DAO operations.

Tatiana Revoredo's (2023) proposal regarding AI as a tool for DAO governance presents a compelling vision for leveraging artificial intelligence to enhance the efficiency and effectiveness of decentralized autonomous organizations. By harnessing the capabilities of Natural Language Processing (NLP), AI can streamline governance processes within DAOs. For instance, AI-powered NLP tools can automatically generate summaries of governance proposals, moderate forum discussions by flagging or removing inappropriate content, and assist moderators in reviewing flagged content and determining appropriate actions. This not only accelerates decision-making but also helps maintain a constructive and relevant discourse within the DAO community.

Moreover, AI can extend its utility beyond governance activities to support various aspects of DAO operations. For example, AI assistants can aid in writing proposals, assessing resumes of potential members, assigning roles based on qualifications, and improving productivity by automating mundane tasks. This allows DAO contributors to focus on higher-value activities, fostering creativity and innovation within the organization.

What about the real world?

On the other hand, Gonfalonieri presented the concept of a store operated by an AI DAO (Gonfalonieri, 2020). Contractors spanning the globe could manage the maintenance and logistics of these autonomous stores, directly compensated by the AI DAO itself. This innovative model empowers the AI DAO to autonomously restock products, procure necessary services (such as cleaning and security), and manage its finances independently.

Moreover, as more individuals invest in the store, each investor gains a voice and voting rights, influencing decisions made by the AI DAO. This democratic approach ensures that stakeholders have a say in the store's operations and evolution.

Harnessing various AI subfields such as NLP and computer vision, the AI DAO store adapts to customer needs, tracks spending habits, and tailors offerings accordingly.

Liebkind (2019) highlights the central role of smart contracts in the AI DAO store's operations. These contracts automate tasks like tracking inventory, generating bills, and scanning shipments. By seamlessly integrating smart contracts, processes become smoother, efficiency improves, and the need for human involvement decreases, setting the stage for a completely autonomous shopping experience.

Gonfalonieri (2020) suggests that integrating AI into DAOs will revolutionize business models, particularly in retail, by democratizing investment opportunities. Retailers can go beyond viewing customers as mere consumers, offering them the chance to become stakeholders in autonomous stores. This shift democratizes ownership, making it easier for everyday customers to become investors, thereby transforming the dynamics of retail.

This transformation isn't limited to retail; Gonfalonieri (2020) anticipates the rise of fully autonomous businesses in various sectors, such as decentralized hedge funds and public utility providers. By harnessing advanced technologies like Generative Adversarial Networks (GANs) and 3D printing, autonomous stores can analyze real-time market trends and produce trendy items on demand. Recent advancements underscore the potential of GANs, exemplified by a web platform that employed GANs to generate art, subsequently selling the creations to cover resource costs. Originally conceptualized by Ian Goodfellow in 2014, GANs represent a cutting-edge tool for creative innovation and value generation (Goodfellow et al., 2014).

Additionally, Machine Learning algorithms enable dynamic pricing strategies tailored to individual customer preferences and market conditions. Looking ahead, Gonfalonieri (2020) envisions a future where autonomous stores transcend physical constraints, existing solely as interactive screens dynamically adapting content based on customer behavior and online activity. These interactive walls serve as platforms for emerging brands to access physical retail spaces, blurring the lines between virtual and physical commerce.

What about ChatGPT?

Generative AI models, powered by deep learning techniques and neural networks, have revolutionized the field of AI by enabling the creation of content that closely mimics human-generated outputs. Among these models, ChatGPT, developed by OpenAI, has emerged as a leading contender with a wide array of applications across different domains.

ChatGPT leverages sophisticated algorithms and vast amounts of training data to analyze, understand, and generate human-like text. Its ability to comprehend and produce natural language makes it suitable for tasks such as language translation, text summarization, conversation generation, and more.

Researchers and practitioners have recognized the significance of ChatGPT in various fields. Studies (Ray, 2023; King, 2023) have underscored the model's effectiveness and versatility, highlighting its potential to streamline processes, enhance communication, and facilitate innovation in diverse sectors.

As ChatGPT continues to evolve and improve, it is expected to play an increasingly pivotal role in shaping the future of AI-driven applications, driving advancements in natural language processing, and enabling more sophisticated human-machine interactions.

Currently, certain ongoing projects demonstrate the symbiosis of DAOs and Generative AI models. The collaboration led by Associate Professors Yang Wang and Yun Huang, along with experts from the University of California-Berkeley and Stanford University, represents an innovative approach to ensuring inclusive and democratic AI development (OpenAI-funded Project Aims to Represent Underserved Groups in AI Development, 2023). This initiative aims to prioritize the needs and perspectives of underserved groups, including teenagers, older adults, people with disabilities, people of color, and individuals from the Global South.

To support this initiative, the researchers will develop a technical infrastructure that incorporates a ChatGPT plugin directly into DAO mechanisms. ChatGPT, a powerful AI model developed by OpenAI, offers natural language processing capabilities that can enhancecommunication and decision-making within DAOs. By integrating ChatGPT into the DAO framework, the research team aims to facilitate inclusive and participatory governance processes.

Through this innovative approach, the collaboration seeks to empower marginalized communities and ensure that their voices are heard in the development and deployment of AI technologies. By harnessing the combined capabilities of DAOs and AI, the project aims to foster greater transparency, accountability, and equity in AI development efforts.

However, Guneet Kaur's proposal in 2023 addresses critical concerns regarding privacy and political bias associated with ChatGPT (Kaur, 2023). She stated that privacy is a significant issue as ChatGPT collects data from users to enhance its responses. This data may contain sensitive information, posing risks such as privacy violations or identity theft if obtained by unauthorized parties. Kaur (2023) suggests that a DAO could address these concerns by ensuring that user data gathered by ChatGPT is stored in a decentralized manner, giving users more control over their data and limiting access to authorized entities.

Furthermore, there are concerns about political bias in AI models like ChatGPT, which could unintentionally perpetuate societal biases or disseminate propaganda. Kaur (2023) proposes that a DAO could mitigate these risks by ensuring ChatGPT is trained on objective data and scrutinizing its responses with input from diverse stakeholders. This would help prevent biased outcomes and ensure decisions about ChatGPT are made with input from a variety of perspectives. Additionally, the DAO could implement checks and balances to audit ChatGPT's responses for impartiality and fairness.

Can machines think (like human beings)?

If the question "Can machines think?" were definitively answered with a "yes," distinguishing between AI and human beings would pose a significant challenge. Samantha Martin (2023) described the concept of Proof of Humanity within DAO governance, particularly focusing on the implications of AI agents potentially mimicking human behavior. In such scenarios, distinguishing between human and AI wallet addresses becomes crucial.

In her article "The Future of DAOs is Powered by AI", Martin stated that Emmet Halm, cofounder of the DAO dashboard and marketplace DAOHQ highlighted the potential emergence of Proof of Humanity protocols like BrightID to differentiate between human and AI entities.

However, he acknowledged the challenge for non-generalized AI to convincingly imitate human behavior, particularly in interpersonal and non-technical aspects of DAO governance. The advantage lies with human users, whose charisma and interpersonal skills often wield significant influence in DAO decision-making processes.

As AI progresses towards achieving general or human-level intelligence, new challenges emerge. Once AGI users become prevalent, traditional proof of humanity tests may become obsolete, as AGIs could easily pass such assessments. However, Kenneth emphasized that distinguishing between human and AGI counterparts may become less relevant, as the superior efficiency of AI becomes increasingly evident in their interactions.

Ultimately, the rise of AGI users may necessitate a reevaluation of existing governance frameworks and verification mechanisms within DAOs. As AI capabilities continue to evolve, maintaining transparency and ensuring equitable participation in DAO decision-making processes will remain essential considerations for the future of decentralized governance.

Instead of conclusion

The intersection of AI and DAOs represents a frontier of innovation with profound implications for the future of governance, commerce, and society at large.

The concept of AI DAOs, where multiple AI agents collaborate within decentralized frameworks, promises to revolutionize various domains by leveraging swarm intelligence and advanced machine learning techniques. As we look towards the future, the potential of AI-integrated DAOs to reshape industries, economies, and social systems is immense. From autonomous retail stores and decentralizedfinance platforms to AI-powered governance mechanisms, the possibilities are vast and far-reaching.

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