For years, decentralized finance has promoted itself as a more open and programmable alternative to traditional financial systems. But as artificial intelligence becomes more deeply integrated into crypto trading, some of DeFi’s biggest structural limitations are becoming impossible to ignore.
AI agents can analyze markets faster than humans, execute strategies continuously, and adapt dynamically to changing conditions. What they cannot do efficiently is navigate fragmented user experiences, manage unpredictable transaction fees, or repeatedly wait for manual wallet approvals.
That friction may seem manageable for retail traders. For autonomous systems attempting to operate at scale, it becomes a serious infrastructure problem.
The rise of AI-driven trading is now forcing DeFi developers to confront an uncomfortable reality. Much of the ecosystem was built around assumptions that humans would always remain directly involved in execution.
As a result, decentralized trading infrastructure often lacks the seamless coordination autonomous systems require.
Gas management is one of the clearest examples. On many decentralized networks, traders must constantly maintain token balances to cover transaction costs across different chains and protocols. Human users can tolerate that inconvenience. AI systems operating around the clock cannot.
This has created growing interest in gasless DeFi trading tools designed to abstract away execution complexity and reduce operational overhead for automated systems.
Several infrastructure providers are now attempting to address the issue. Among them is Orbs, which recently launched SPOT, an AI-focused trading interface designed around machine-readable execution and gasless workflows.
Other companies are approaching similar challenges from different angles. Biconomy has focused heavily on account abstraction and gasless transaction infrastructure designed to reduce friction across decentralized applications. Meanwhile, Fetch.ai has spent years building autonomous economic agent infrastructure capable of coordinating transactions and interacting independently across decentralized networks.
Together, these projects reflect a growing realization across crypto infrastructure: DeFi may need to evolve beyond human-centric design entirely.
The platform supports advanced order functionality across decentralized exchanges while attempting to reduce the friction associated with autonomous trading. More importantly, it reflects a broader shift in how some crypto developers now view the future of financial infrastructure.
The debate is no longer simply about decentralization versus centralization. Increasingly, it is about whether DeFi can evolve quickly enough to support machine native participation.
That transition could reshape the architecture of decentralized markets entirely. AI systems require deterministic workflows, structured execution environments, and infrastructure reliability that many DeFi applications still struggle to provide consistently.
Some developers believe this pressure could ultimately improve the ecosystem. Competition to support autonomous agents may accelerate improvements around interoperability, execution quality, and user abstraction that benefit both humans and machines.
Others remain cautious. Autonomous financial systems introduce entirely new categories of risk, from unintended execution behavior to security vulnerabilities and governance concerns.
Still, one thing is becoming increasingly clear. AI agents are not adapting themselves to DeFi’s limitations. DeFi may have to adapt to them instead.
