Eliza Labs’ Auto.fun is making waves in the tech world. It has a bold vision of democratizing AI so that anyone—coder or not—can curate and deploy powerful AI agents on Web3. Sounds utopian, doesn't it? Before we pop the champagne, let’s take a look at a less reported story. While the vision of AI-powered DeFi and automated social media management is alluring, I see a potential storm brewing on the governance front. A storm that would threaten the very principles that Web3 purports to protect.

Democratization Or Centralized Control?

The allure of no-code is undeniable. It democratizes access, making it easier for anyone to build onto and contribute to this next big thing in tech. This ‘democratization’ might unintentionally create the conditions for centralization. Think about it: who controls the underlying Auto.fun platform? Eliza Labs, of course. ElizaOS may be open-source, but the vast majority of users won’t be working directly with the code. They certainly won’t understand the nuances of what goes into generating the AI algorithms. Users are going to expect that the platform works as intended. At the end of the day, they’re inherently trusting a single, centralized party, despite existing in a trustless environment.

Asking that question without first reading the code is akin to giving everyone access to a printing press while only a select few know how to create the typefaces. Well, of course, anybody can just throw something in print. Maybe the most important creators of that message are the type designers at Eliza Labs.

This isn’t FUD, just a sober look at the dangers that lie ahead. We witnessed this dynamic unfold in the early days of social media. They claimed to offer real connection and empowerment. They ended up concentrating that power in the hands of a few tech giants who controlled the algorithms and the data. Are we fated to go through this cycle again with AI and Web3?

Who Governs the AI Agents?

Here’s where it starts to get really interesting – and really creepy. Auto.fun’s AI agents are meant to automate user tasks all over DeFi, social media, and other blockchain services. But what happens when these agents misbehave? What if an agent, programmed with flawed logic or malicious intent, starts manipulating markets, spreading misinformation, or violating smart contract rules?

  • Scenario 1: An AI agent designed for yield farming accidentally triggers a cascading liquidation event in a DeFi protocol. Who is responsible? The user who deployed the agent? Eliza Labs? The developers of the underlying smart contract?
  • Scenario 2: A social media agent starts spreading biased or misleading information, influencing public opinion on a decentralized governance proposal. How do you hold it accountable? How do you even detect that it's happening in a decentralized environment?

These aren't hypothetical scenarios. They're real possibilities. The kinds of challenges emerging in Web3 today, governance or otherwise, are ones that existing models are ill-suited to address. DAOs, to take one of many possible examples, are incredibly slow and wasteful at resolving disputes. Now try to picture that in a DAO attempting to debug a rogue AI agent live. It would be akin to trying to turn the Titanic using a rowboat paddle.

The ‘more fair than fair’ token launch model based on bonding curves is not the solution either. It's a step in the right direction, but it doesn't address the fundamental governance challenges posed by AI agents operating autonomously on Web3.

Security Audits Aren't Enough

Auto.fun’s open-source architecture and focus on transparency is a great example to follow. Transparency alone isn't enough. We need verifiability and accountability. That takes more than security audits alone. That will take a major rethinking of AI governance in decentralized spaces.

Security audits are more akin to a seasonal inspection of the locks on your digital front door. These are key elements. They will not protect you from someone who has a key, or someone who can exploit a defect in the door’s design. With no-code AI, the attack surface is arguably exponentially greater. You are no longer auditing the code, but the potential for users to create malicious/flawed agents.

This is where I think the risk of regulatory overreach stacks up. If Web3 can’t figure out how to govern the use of AI within this space, governments will intervene and create regulation. But those rules aren’t necessarily hospitable to decentralization or innovation. We’ve already witnessed this kind of action with centralized exchanges, and the threat of a similar crackdown on AI-powered Web3 applications is very real.

The longterm success of Auto.fun will depend on our ability to resolve these governance challenges. The shape of AI on Web3 will depend on how proactively we address these concerns. Democratizing access to AI is not enough; we need to democratize the responsibility for its use as well. Otherwise, we would be building the governance hellscape that threatens the very premise of the decentralized web. That would be a tragedy. A calamity that would have been entirely preventable with smarter design and vision.

We need a multi-faceted approach:

  • Formal Verification: Rigorous mathematical proofs to ensure that AI agents behave as intended.
  • AI Ethics Frameworks: Development and adoption of ethical guidelines for AI development and deployment on Web3.
  • Decentralized Dispute Resolution: Innovative mechanisms for resolving disputes involving AI agents, leveraging on-chain data and community consensus.
  • Education, Education, Education: Educating users about the risks and responsibilities of deploying AI agents on Web3.

Ultimately, the success of Auto.fun – and the future of AI on Web3 – depends on our ability to address these governance challenges proactively. It's not enough to democratize access to AI; we must also democratize the responsibility for its use. Otherwise, we risk creating a governance nightmare that undermines the very foundations of the decentralized web. And that would be a tragedy. A tragedy that could have been averted with careful planning and foresight.