Author: William M. Peaster, Bankless; Compiled by: White Water, Golden Finance

As early as 2014, Vitalik Buterin, the founder of Ethereum, began to consider autonomous agents and DAOs. Still a distant dream for most.

In his early vision, as he describes in "DAO, DAC, DA, etc.: An Incomplete Guide to Terminology," DAOs were decentralized entities with "automation at the center." , humans at the edge”—organizations that rely on a hierarchy of code rather than humans to maintain efficiency and transparency.

Ten years later, Variant's Jesse Walden has just published "DAO 2.0," reflecting on the evolution of DAOs in practice since Vitalik's early work.

In short, Walden noted that the initial wave of DAOs often resembled cooperatives, human-centered digital organizations that did not emphasize automation.

Nonetheless, Walden continues to believe that new advances in artificial intelligence—particularly large language models (LLMs) and generative models—now hold the promise of better enabling the decentralized autonomy Vitalik foresaw 10 years ago. .

However, as DAO experiments increasingly employ artificial intelligence agents, we will face new implications and problems here. Below, let’s take a look at five key areas that DAOs must address when incorporating artificial intelligence into their approach.

Transforming Governance

In Vitalik’s original framework, DAOs aimed to reduce reliance on hierarchical human decision-making by encoding governance rules on-chain.

Initially, humans are still on the "edge", but still crucial for complex judgments. In the DAO 2.0 world that Walden describes, humans still linger on the margins—providing capital and strategic direction—but the center of power is increasingly no longer humans.

This dynamic will redefine the governance of many DAOs. We will still see human alliances negotiating and voting on outcomes, but operational decisions of all kinds 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 the DAO aimed to counteract human bias, corruption, and inefficiency through transparent, immutable code.

Now, a key challenge is moving from unreliable human decisions to ensuring that AI agents are “aligned” with the goals of the DAO. The primary vulnerability here is no longer human collusion, but model misalignment: the risk of AI-powered DAOs optimizing for metrics or behaviors that deviate from human-intended outcomes.

In DAOIn the 2.0 paradigm, this consistency problem—originally a philosophical question in AI safety circles—becomes a practical problem of economics and governance.

This may not be a primary concern for today’s DAOs experimenting with basic AI tools, but as AI models become more advanced and deeply integrated into decentralized governance structures, expect it to become a subject of scrutiny and the main areas of improvement.

New attack surface

Consider the recent Freysa competition, where human p0pular.eth won $47,000 in ether by tricking the AI ​​agent Freysa into misinterpreting its "approveTransfer" function.

Although Freysa had built-in safeguards—explicit instructions to never send out prizes—human creativity eventually outpaced the model, exploiting the interplay between prompts and code logic until the AI ​​released the funds .

This early competition example highlights that as DAOs incorporate more complex AI models, they will also inherit new attack surfaces. Just as Vitalik worried about DO or DAO being colluded by humans, now DAO 2.0 must consider adversarial inputs to AI training data or just-in-time engineering attacks.

Manipulating an LL.M.’s reasoning process, feeding it misleading on-chain data, or subtly influencing its parameters could become a new form of “governance takeover,” in which the battlefield shifts from human majority voting attacks to more Subtle and complex forms of artificial intelligence exploitation.

New Centralization Issues

The evolution of DAO 2.0 shifts significant power to those who create, train, and control the AI ​​models underlying a specific DAO, a dynamic that may lead to new forms of centralized chokepoints .

Of course, training and maintaining advanced AI models requires specialized expertise and infrastructure, so in some organizations in the future we will see direction ostensibly in the hands of the community, but in reality In the hands of skilled experts.

This is understandable. But going forward, it will be interesting to track how DAOs for AI experiments respond to issues like model updates, parameter tuning, and hardware configuration.

Strategy vs. Strategic Operations Roles and Community Support

Walden’s “Strategy vs. Operations” distinction suggests a long-term balance: AI can handle day-to-day DAO tasks, while humans will provide strategic direction.

However, as artificial intelligence models become more advanced, they may also gradually invade the strategic layer of the DAO. Over time, the role of the “marginal” may shrink further.

This raises the question: What will happen with the next wave of AI-powered DAOs, where in many cases humans may just provide the funding and watch from the sidelines?

In this paradigm, will humans largely become the least influentialSwapping investors, moving away from co-owned brands to something more akin to autonomous economic machines managed by artificial intelligence?

I think we will see more of a trend towards organizational models in the DAO scenario, where humans just play the role of passive shareholders rather than active managers. However, as fewer decisions are made meaningful to humans and it becomes easier to provide on-chain capital elsewhere, maintaining community support may become an ongoing challenge over time.

How DAOs can stay proactive

The good news is that all of the above challenges can be proactively addressed. For example:

In terms of governance - DAOs could experiment with governance mechanisms that reserve certain high-impact decisions for human voters or a rotating committee of human experts.

About inconsistencies - By treating consistency checks as a recurring operating expense (like security audits), DAOs can ensure that the AI ​​agent's loyalty to public goals is not a one-time issue, but a Ongoing Responsibility.

About centralization – DAOs can invest in broader skill-building among community members. Over time, this will mitigate the risk of a handful of “AI wizards” controlling governance and promote a decentralized approach to technology management.

About support - As humans become passive stakeholders in more DAOs, these organizations can double down on storytelling, shared missions, and community rituals to transcend the immediate logic of capital allocation and maintain long-term support .

No matter what happens next, it's clear that the future here is bright.

Consider how Vitalik recently launched Deep Funding, which is not a DAO effort but aims to pioneer a new financing mechanism for Ethereum open source development using artificial intelligence and human judges.

This is just a new experiment, but it highlights a broader trend: the intersection of artificial intelligence and decentralized collaboration is accelerating. As new mechanisms arrive and mature, we can expect DAOs to increasingly adapt and expand on these AI ideas. These innovations will bring unique challenges, so now is the time to start preparing.