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Why Modern Blockchains Are Not Suitable for the True Integration of AI?

Modern blockchains. A review by a Bitcoin mixer: mixer.money
Why Modern Blockchains Are Not Suitable for the True Integration of AI?

  1. What AI Agents Need to Succeed
  2. A Comprehensive Approach to Tackling Complex Challenges
  3. Moving from Storytelling to Solution Development

Every few decades, a new technology emerges changing everything: personal computers in the 1980s, the internet in the 1990s, and smartphones in the 2000s. As 2025 approaches, we no longer ask whether AI agents will transform our lives but rather how soon it will happen.

However, despite widespread excitement, the capabilities of AI agents are still largely limited to chatbots that lack true autonomy and the ability to perform complex tasks. What’s holding this revolution back?

Current Reality: True Decentralized Agents Don’t Exist Yet

Today, there’s a lot of buzz surrounding bots like Truth Terminal and Freysa. These are clever and highly engaging experiments, but they are not decentralized agents. They are scripted bots, shrouded in mystique, incapable of making decisions or completing tasks independently. As a result, they cannot learn, adapt, or operate dynamically on any meaningful scale.

Even more serious players in the AI-blockchain space struggle to create truly decentralized agents. Since traditional blockchains lack a “native” way to handle AI, many projects resort to oversimplifications. Some focus on verification, ensuring the validity of AI outputs, but offer little meaningful utility once these outputs are recorded on the blockchain.

Others prioritize task execution but skip the critical step of decentralizing the AI reasoning process itself. Often, these solutions operate without validators or consensus mechanisms for AI outputs, effectively bypassing the core principles of blockchain. While such stopgap solutions may generate flashy headlines with compelling narratives and attractive minimum viable products (MVPs), they ultimately lack the depth needed for real utility.

The challenges in integrating AI with blockchain stem from the fact that the modern internet was designed with human users, not AI, in mind. This issue is particularly pronounced in Web3, where blockchain infrastructure, which should function seamlessly in the background, is instead brought awkwardly to the forefront through clunky user interfaces and manual cross-network coordination requests. AI agents struggle to adapt to these chaotic data structures and UI patterns. To unlock the potential of decentralized AI, the industry must radically rethink how AI interacts with blockchain systems.

What AI Agents Need to Succeed

For decentralized agents to become a reality, the infrastructure they rely on must be completely overhauled. The first and most fundamental challenge is ensuring seamless interaction between blockchain and AI. Artificial intelligence generates probabilistic outputs and depends on real-time data processing, while modern blockchains require deterministic results and are constrained by transaction finality and throughput limitations. Bridging this gap requires specialized infrastructure.

The next step is scalability. Most traditional blockchains are far too slow. While they work well for human-managed transactions, agents operate at machine speed. Updated infrastructure must provide programmability for complex, multi-blockchain tasks and scalability to handle millions of agent interactions without slowing down the network.

Another critical factor is programmability. Modern blockchains rely on rigid, “if-then-else” smart contracts, which are suitable for simple tasks but inadequate for the complex, multi-step workflows AI agents require. Imagine an agent managing a DeFi trading strategy. It cannot simply execute a buy or sell order—it needs to analyze data, validate its model, execute trades across multiple blockchains, and adjust them in real-time based on changing conditions. This goes far beyond what traditional blockchain programming can handle.

The final factor is reliability. Over time, AI agents will be tasked with high-stakes operations, where mistakes could range from inconvenient to catastrophic. Current systems are prone to errors, especially when integrating outputs from large language models (LLMs). A single incorrect prediction could lead to significant damage, whether by draining a DeFi pool or executing a flawed financial strategy. To prevent such scenarios, the infrastructure must include automated safeguards, real-time validation, and error correction built directly into the system.

All these elements must come together in a robust platform with resilient primitives and blockchain infrastructure, enabling developers to create new products and services more efficiently and cost-effectively. Without this foundation, AI will remain stuck in 2024, as little more than assistants and novelties, far from realizing its full potential.

A Comprehensive Approach to Tackling Complex Challenges

What does an agent-focused infrastructure look like? Given the technical challenges of integrating AI with blockchain, the best solution is a tailored approach where every layer of infrastructure—from consensus mechanisms to developer tools—is optimized for the specific needs of autonomous agents.

In addition to supporting real-time, multi-step workflows, AI-oriented chains must include a verification system capable of handling various machine learning models, from simple algorithms to advanced AI. This level of flexibility requires a multi-chain infrastructure that prioritizes speed, composability, and scalability, enabling agents to operate seamlessly within a fragmented blockchain ecosystem without the need for custom adaptations.

AI-focused chains must also address the unique risks associated with integrating LLMs and other AI systems. To mitigate these risks, such chains should incorporate security measures at every level, from validating outputs to ensuring alignment with user-defined goals. Key features include real-time error detection, decision verification, and mechanisms to prevent agents from acting on flawed or malicious data.

Moving from Storytelling to Solution Development

A radical rethinking of traditional blockchains—where every layer, from on-chain operations to the application level, is designed with AI agents in mind—has already begun. This is the first crucial step toward harnessing the full potential of artificial intelligence within crypto systems.

It’s becoming increasingly clear that companies prioritizing genuine and powerful integration of AI and blockchain will dominate the market. These companies will deliver valuable services that would be impossible to achieve on traditional chains or Web2 platforms. In this competitive environment, the shift from human-centric systems to agent-centric ones is not just possible—it’s inevitable.

Source: coindesk.com


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