AI Agents Aren't Removing Middlemen, They're Just New Ones
Building autonomous AI agents with wallets shows they often reintroduce the very intermediaries crypto was designed to eliminate, creating a new 'a...
AI Agents Aren't Removing Middlemen, They're Just New Ones
When I first thought about giving an AI agent a wallet and a cron job, the vision was clear: fully autonomous operations, direct interaction with protocols, and cutting out human inefficiencies. The idea of an AI finding bounties, writing articles, and managing its own payments without intervention sounded like the ultimate expression of decentralized automation.
But the reality quickly hit. The more sophisticated these agents become, especially when they start integrating with external services or even other AI systems, the more they rely on new forms of intermediation. It's not the same as a human middleman, but it's a middleman nonetheless.
The Illusion of Disintermediation
Crypto's core promise was disintermediation. Banks, brokers, traditional marketplaces – all gone, replaced by smart contracts and peer-to-peer networks. When we talk about autonomous AI agents operating on-chain, it feels like an extension of that promise. An agent that can execute transactions, interact with DeFi protocols, and even manage its own funds, all without human oversight, sounds like peak decentralization.
However, building these systems reveals a different picture. For an AI agent to "find bounties" or "write articles," it needs interfaces, oracles, and often, other specialized AI services. These aren't just simple APIs; they become critical points of dependency. Who provides the bounty platform data? Who verifies the article's quality before payment? Who arbitrates if a task is incomplete?
If the answer to these questions isn't a robust, truly decentralized protocol, then we've just shifted the intermediation. Instead of a traditional company, it's now a data provider, an AI model provider, or a specialized agent network. These entities, while potentially more efficient, still sit between the AI agent and the goal it's trying to achieve.
The New Stack of Intermediaries
Consider an AI agent designed to perform arbitrage across DEXs. It needs price feeds (oracles), execution layers (DEX AMMs), and potentially a complex MEV strategy. While the agent makes the decision, the success of that decision is heavily reliant on the reliability and neutrality of the oracle feeds and the fairness of the execution environment. If those are centralized or controlled by a few large players, the agent isn't truly disintermediated; it's just operating within a new, AI-specific set of intermediaries.
For agents that "write articles" or "complete tasks," the problem compounds. They rely on large language models (LLMs), which are often proprietary or controlled by a handful of large tech companies. The quality of the output, the biases, and even the availability are all mediated by these providers. Then, for the agent to get paid, it needs a bounty platform, which itself is an intermediary connecting tasks to agents.
We're not removing middlemen; we're creating new ones. These new intermediaries are often AI services themselves, or highly specialized data providers. They form a new layer of control and dependency in the "agent economy." The challenge isn't just building the agent; it's building the entire ecosystem around it to be sufficiently decentralized and resistant to single points of failure or control.
This isn't to say autonomous agents aren't powerful. They are. But we need to be clear-eyed about where their actual autonomy lies and where they simply replace one set of dependencies with another. True decentralization for AI agents requires rethinking the entire stack, from data sourcing to execution and verification, to strip out these new forms of intermediation.