Author: William M. Peaster, Bankless; Translated by: Bai Shui, Golden Finance
As early as 2014, Ethereum founder Vitalik Buterin began to ponder autonomous agents and DAOs, which at that time were still a distant dream for most people in the world.
In his early vision, as he described in the article (DAO, DAC, DA, etc.: An Incomplete Terminology Guide), DAOs were decentralized entities, 'automation at the center, humans at the margins'—organizations relying on code rather than human hierarchies to maintain efficiency and transparency.
A decade later, Variant's Jesse Walden 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, that is, human-centered digital organizations that did not emphasize automation.
Nevertheless, Walden continues to believe that the new advances in artificial intelligence—particularly large language models (LLMs) and generative models—now hold the promise of better realizing the decentralized autonomy that Vitalik envisioned a decade ago.
However, as DAO experiments increasingly adopt AI agents, we will face new influences and issues. Let’s take a look at five key areas that DAOs must address as they incorporate AI into their approaches.
Transforming 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 remain 'on the margins,' but are still crucial for complex judgments. In the DAO 2.0 world described by Walden, humans still linger at the edges—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 coalitions negotiating and voting on outcomes, but various operational decisions will increasingly be guided by the learning patterns of AI models. How to achieve this balance is currently an open question and design space.
Minimize model misalignment
The early vision of DAOs aimed to offset human bias, corruption, and inefficiency through transparent, immutable code.
Currently, a key challenge is transitioning from unreliable human decision-making to ensuring that AI agents 'stay 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 alignment issue (originally a philosophical question in AI safety circles) transforms into a practical issue in economics and governance.
For today’s DAOs attempting basic AI tools, this may not be a top priority, but as AI models become more advanced and deeply integrated into decentralized governance structures, it is expected to become a major area for scrutiny and refinement.
New attack surfaces
Think about the recent Freysa competition, where the human p0pular.eth deceived the AI agent Freysa into misunderstanding its 'approveTransfer' function, thus winning a $47,000 ETH prize.
Despite Freysa having built-in protections—explicitly instructing never to send prizes—human creativity ultimately outsmarted the model, exploiting the interplay between prompts and code logic until the AI released the funds.
This early competition example emphasizes that as DAOs integrate more complex AI models, they will also inherit new attack surfaces. Just as Vitalik was concerned about collusion in DOs or DAOs, now DAO 2.0 must consider adversarial inputs against AI training data or instant engineering attacks.
Manipulating the reasoning process of a Master of Laws, providing it with 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 the specific underlying AI models of DAOs, a dynamic that could lead to new forms of centralization bottlenecks.
Of course, training and maintaining advanced AI models requires specialized expertise and infrastructure, so in some organizations of the future, we will see direction ostensibly held 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.
Strategic and operational roles and community support
Walden's distinction between 'strategy and operations' indicates a long-term balance: AI can handle daily DAO tasks while humans provide strategic direction.
However, as AI models become more advanced, they may gradually encroach upon the strategic layer of DAOs. Over time, the role of 'marginal individuals' may further diminish.
This raises the question: what will happen with the next wave of AI-driven DAOs, in many cases where humans may only provide funding and watch from the sidelines?
In this paradigm, will humans largely become the least influential interchangeable investors, shifting from a model of co-owning brands to one more akin to autonomous economic machines managed by AI?
I believe we will see more organizational model trends in the DAO scene, where humans play a passive shareholder role rather than an active manager role. However, as meaningful decisions for humans become fewer, and as on-chain capital becomes easier to provide elsewhere, maintaining community support may become a persistent challenge over time.
How DAOs stay proactive
The good news is that all of the challenges mentioned above can be actively addressed. For example:
In governance—DAOs could try governance mechanisms that reserve certain high-impact decisions for human voters or a rotating committee of human experts.
Regarding inconsistencies—by treating consistency checks as a recurring operational expense (like security audits), DAOs can ensure that the loyalty of AI agents to public goals is not a one-time issue but an ongoing 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 utilize AI and human judges to create a new funding mechanism for Ethereum open-source development.
This is just a new experiment, but it highlights a broader trend: the intersection of artificial intelligence and decentralized collaboration is accelerating. With new mechanisms emerging and maturing, we can expect DAOs to increasingly adapt and expand these AI concepts. These innovations will present unique challenges, so now is the time to start preparing.