Business Use Case: Enhancing DAO Governance with AI through the DNA Layer
In the evolving landscape of decentralized governance, Decentralized Autonomous Organizations (DAOs) challenge traditional hierarchical structures by enabling a flat, democratic decision-making process, where governance is executed through collective member consensus and smart contracts. This model allows for a high degree of transparency and community involvement. However, the novelty of DAOs brings with it inherent complexities.
The sheer volume of governance decisions, ranging from financial transactions to proposal vetting, can be overwhelming, and ensuring that these decisions are made in compliance with both the internal rules of the DAO and external legal requirements is a formidable task, often laden with nuances that are difficult for individuals to consistently interpret and enforce.
One of the most challenging structural issues of DAOs is that the decentralized and often anonymous nature of the vehicle can lead to problems in maintaining accountability and ethical standards. Without a centralized authority, it becomes crucial to have a robust, impartial mechanism for governance oversight.
The lack of such a mechanism can result in inefficiencies, disputes, and a potential loss of trust among members, which are detrimental to the longevity and success of a DAO. We believe at DNA Layer that this is the reason so many DAOs fall apart.
The Objective and Solution
The primary objective of this business case is to integrate an AI model (particularly an LLM) into a customer’s DAO to revolutionize governance through the automation of its decision-making process. The goal would be to create a system where governance proposals, financial transactions, and operational decisions are automatically reviewed and assessed for compliance with both the internal rules of the DAO and applicable legal standards. The requirements for a solution would be to address the challenges of scalability, accountability, and regulatory compliance in DAO operations.
The solution on DNA Layer would be to upload an LLM that has been trained offline to understand and interpret local laws, regulations, and the legal framework and internal rules governing the DAO, and integrate it into its decentralized inference framework to analyze, interpret, and provide insights on governance-related content within the DAO codex. Through its modular network design, DNA Layer would also provide the computational resources and secure environment necessary for their operation.
On DNA Layer, an LLM inference request would be triggered periodically to automatically review proposals submitted within a DAO, assessing for coherence, relevance, and adherence to the DAO’s predefined rules and ethical guidelines. The model would flag proposals that are inconsistent, irrelevant, or violate the DAO’s standards to prevent unnecessary or harmful proposals from proceeding to a vote.
Concurrently, the LLM would monitor and analyze transactions from the DAO’s treasury on-chain address to ensure that each transaction aligns with the DAO’s financial and operational rules, and does not compromise the interests of the DAO members.
Each query transaction, after validation is performed, would roll up to DNA Layer's settlement layer at which point it would be added to the ledger to ensure that the results of all LLM operations for governance are transparent, verifiable, and even revertible by DAO members.
The Outcome
By leveraging the computational heuristics of an LLM within the constructs of a DAO, governance propositions could be autonomously analyzed with a lens prioritizing compliance and underlying jurisprudential guidance. An LLM operationalized within DNA Layer's AIVM would mitigate the risks associated with decentralized adjudication while allowing the DAO to conform to the canonical statutes that the DAO codex is built upon.
This would foster an equilibrium between autonomy and structured regulatory fidelity, resulting in a self-regulated machine of compliance, ethics, accountability, and decentralized governance.
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