Author: William M. Peaster, Bankless; Translated by: Bai Shui, Golden Finance
As early as 2014, Ethereum founder Vitalik Buterin began contemplating autonomous agents and DAOs, a distant dream for most of the world at the time.
In his early vision, as described in the piece (DAOs, DACs, DAs, etc.: An Incomplete Terminology Guide), DAOs are decentralized entities, "automation at the center, humans at the periphery"—organizations that rely on code rather than human hierarchies to maintain efficiency and transparency.
A decade later, Jesse Walden of Variant has just published "DAO 2.0", reflecting on the evolution of DAOs in practice since Vitalik's early writings.
In short, Walden points out that the initial wave of DAOs often resembled cooperatives—human-centered digital organizations that did not emphasize automation.
Nevertheless, Walden continues to believe that new advancements in AI—particularly large language models (LLMs) and generative models—are now poised to better realize the decentralized autonomy envisioned by Vitalik a decade ago.
However, as more DAO experiments increasingly adopt AI agents, we will face new influences and issues here. Below, let’s look at five key areas that DAOs must address as they integrate AI into their approaches.
Transform Governance
In Vitalik's original framework, DAOs were designed to reduce reliance on hierarchical human decision-making by encoding governance rules on-chain.
Initially, humans still remained at the "periphery", but were crucial for complex judgments. In the DAO 2.0 world described by Walden, humans still linger at the periphery—providing capital and strategic direction—but the center of power is gradually no longer human.
This dynamic will redefine the governance of many DAOs. We will still see human alliances negotiating and voting on outcomes, but various operational decisions will increasingly be guided by the learning patterns of AI models. Currently, how to achieve this balance is an outstanding question and design space.
Minimizing Model Misalignment
The early vision of DAOs aimed to offset human bias, corruption, and inefficiency through transparent, immutable code.
Currently, a key challenge is shifting from unreliable human decision-making to ensuring that AI agents are "aligned" with the goals of the DAO. The main vulnerability here is no longer human collusion but model misalignment: the risk that AI-driven DAOs optimize for metrics or behaviors that deviate from human expected outcomes.
In the DAO 2.0 paradigm, this consistency issue (originally a philosophical question within the AI safety circle) becomes a practical issue in economics and governance.
This may not be a top concern for DAOs attempting basic AI tools today, but as AI models become more advanced and deeply integrated into decentralized governance structures, it is expected to become a primary area for scrutiny and refinement.
New Attack Surfaces
Consider the recent Freysa competition, where human p0pular.eth deceived the AI agent Freysa into misunderstanding its "approveTransfer" function, winning a $47,000 Ether prize.
Although Freysa has built-in safeguards—explicitly instructing never to send rewards—human creativity ultimately outstripped the model, exploiting the interaction between prompts and code logic until the AI released the funds.
This early competition example underscores that as DAOs integrate more complex AI models, they will also inherit new attack surfaces. Just as Vitalik worried about human collusion in DOs or DAOs, DAO 2.0 must now consider adversarial inputs against AI training data or prompt engineering attacks.
Manipulating the reasoning process of a legal expert, providing misleading on-chain data, or cleverly influencing its parameters could become a new form of "governance takeover", where the battlefield shifts from human majority voting attacks to more subtle and complex forms of AI exploitation.
New Centralization Issues
The evolution of DAO 2.0 will transfer significant power to those who create, train, and control specific underlying AI models for the DAO, which could lead to new forms of centralized bottlenecks.
Of course, training and maintaining advanced AI models requires specialized expertise and infrastructure, so in some future organizations, we will see directional control superficially in the hands of the community, but actually in the hands of skilled experts.
This is understandable. But looking ahead, it will be interesting to track how AI-experimenting DAOs respond to issues like model updates, parameter tuning, and hardware configurations.
Strategy and Strategic Operations Roles and Community Support
Walden's distinction between "Strategy and Operations" indicates a long-term balance: AI can handle day-to-day DAO tasks while humans provide strategic direction.
However, as AI models become more advanced, they may also gradually encroach upon the strategic layer of DAOs. Over time, the role of "peripheral humans" may further diminish.
This raises the question: what will the next wave of AI-driven DAOs look like, where in many cases, humans may simply provide funding and watch from the sidelines?
In this paradigm, will humans largely become the least influential interchangeable investors, shifting from co-owning brands to a model more akin to AI-managed autonomous economic machines?
I believe we will see more organizational model trends in the DAO space, where humans play a passive stakeholder role rather than an active managerial role. However, as meaningful decisions made by humans become fewer and the provision of on-chain capital becomes increasingly easier elsewhere, maintaining community support may become a persistent challenge over time.
How DAOs Stay Proactive
The good news is that all the challenges mentioned above can be actively addressed. For example:
In terms of governance—DAOs can experiment with governance mechanisms that reserve certain high-impact decisions for human voters or rotating committees of human experts.
Regarding consistency—by treating consistency checks as a recurring operational cost (like security audits), DAOs can ensure that the loyalty of AI agents to public goals is not a one-time issue but a continuous responsibility.
Regarding centralization—DAOs can invest in broader skill-building for community members. Over time, this will mitigate the risk of a few "AI wizards" controlling governance and promote a decentralized approach to technical management.
Regarding support—as humans become more passive stakeholders in more DAOs, these organizations can double down on storytelling, shared missions, and community rituals to transcend the direct logic of capital allocation and maintain long-term support.
Whatever happens next, it is clear that the future here is vast.
Consider how Vitalik recently launched Deep Funding, which is not a DAO effort but aims to leverage AI and human judges to pioneer a new funding mechanism for Ethereum open-source development.
This is just a new experiment, but it highlights a broader trend: the intersection of AI and decentralized collaboration is accelerating. As new mechanisms emerge and mature, we can expect DAOs to increasingly adapt to and expand these AI concepts. These innovations will bring unique challenges, so now is the time to start preparing.