The Future Is AI-Centric, and Blockchains Need to Be as Well

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As a seasoned crypto investor with a decade-long journey under my belt, I’ve witnessed the rise and fall of countless trends. From the meteoric rise of Bitcoin to the dotting of the Ethereum landscape, I’ve seen it all. Yet, nothing has piqued my curiosity quite like the promise of decentralized AI agents.

Approximately every three decades or so, a groundbreaking technology arises that transforms the way we live: the personal computer in the 1980s, the internet in the 1990s, and smartphones in the 2000s. By 2025, Artificial Intelligence (AI) agents are experiencing a surge of enthusiasm, prompting not whether they will redefine our existence but rather how quickly they’ll do so.

Despite the buzz surrounding decentralized agents, they have yet to live up to their potential. Many current “agents” are essentially advanced chatbots or assistants that lack true autonomy and the ability to handle complex tasks – a far cry from what real AI agents should be capable of. This leads us to question: What obstacles prevent this revolution, and how can we transition from theoretical concepts to practical implementation?

The current reality: true decentralized agents don’t exist yet

To kick things off, let’s discuss the AI tools that exist currently. You might have noticed a lot of chatter about entities like Truth Terminal and Freysa on platforms such as X or Twitter. These projects are intriguing, stimulating thought experiments indeed, but they lack one key characteristic – decentralization. In fact, they’re more like semi-programmed AI bots shrouded in mystery, unable to make independent decisions or carry out tasks autonomously. Since they can’t learn, adapt, or operate dynamically, they are limited in their ability to perform at scale or otherwise.

It’s been challenging for major players in the field of AI and blockchain to fully realize the concept of truly decentralized artificial intelligence agents. This is due to the fact that conventional blockchains lack a native methodology for processing AI, leading many projects to resort to shortcuts. Some of these projects primarily concentrate on verifying the authenticity of AI outputs, yet they fall short in providing any substantial utility once these verified outputs are integrated onto the blockchain.

In simpler terms, while some focus on implementing AI solutions, they overlook the crucial step of distributing the AI decision-making process. These solutions usually don’t have mechanisms to verify or agree on AI results, which goes against the fundamental principles of blockchain. These temporary solutions may attract attention with their compelling stories and impressive prototypes, but they lack the depth required for practical applications in the real world.

These challenges to integrating AI with blockchain come down to the fact that today’s internet is designed with human users in mind, not AI. This is especially true when it comes to Web3, since blockchain infrastructure, which is meant to operate silently in the background, is instead dragged to the front-end in the form of clunky user interfaces and manual cross-chain coordination requests. AI agents don’t adapt well to these chaotic data structures and UI patterns, and what the industry needs is a radical rethinking of how AI and blockchain systems are built to interact.

What AI agents need to succeed

To make decentralized agents a practical reality, the existing foundational structure requires significant upgrades. The primary and crucial hurdle is developing a means for blockchain and AI to communicate effortlessly. AI produces probabilistic outcomes and thrives on real-time processing, while blockchains require deterministic results and are limited by transaction finality and throughput constraints. Overcoming this discrepancy necessitates the construction of tailor-made infrastructure, which I’ll delve deeper into in the subsequent section.

The next phase involves improving the system’s ability to handle large volumes of data (scalability). Many existing blockchain networks struggle with this, as they are too slow for efficient machine-speed transactions. While they manage human-initiated transactions well, handling thousands or even millions of interactions in real time is out of the question. To address this issue, a revamped infrastructure needs to provide flexibility for complex multi-blockchain operations and the capacity to process vast amounts of agent interactions without causing network congestion.

In simpler terms, what today’s blockchains offer in terms of programmability is limited by their use of inflexible if-this-then-that smart contracts. These work well for simple tasks but fall short when it comes to the intricate, multi-step procedures that AI agents need. For instance, consider an AI agent handling a DeFi trading strategy. It doesn’t just execute buy or sell orders; it analyzes data, checks its model, executes trades across multiple chains, and adapts in real-time according to the current conditions. This level of complexity exceeds the capabilities of traditional blockchain programming.

Ultimately, it’s crucial to consider dependability. As AI agents are expected to handle critical tasks, even minor oversights could lead to inconvenience at best, and catastrophic outcomes at worst. Existing systems often have flaws, particularly when combining results from large language models (LLMs). A single incorrect prediction could cause chaos, such as draining a DeFi pool or implementing a faulty financial strategy. To prevent this, the system must incorporate automated safety measures, real-time validation, and built-in error correction mechanisms.

The idea is to unite all components into a strong development platform equipped with resilient building blocks and on-chain infrastructure. This will enable developers to create new products and innovative experiences in a more streamlined and budget-friendly manner. If we fail to do this, AI may continue to be limited to assistant roles and basic applications, barely tapping into its full potential beyond 2024.

A full-stack approach to a complex challenge

What does this agent-focused architecture entail? Given the intricacy involved in combining AI with blockchain, an optimal strategy would be to adopt a tailored, end-to-end methodology. In this approach, each level of the infrastructure – from consensus mechanisms to development tools – is meticulously designed to cater to the unique requirements of autonomous agents.

Besides managing complex, real-time workflows, AI-centric blockchains should incorporate a verification system that can accommodate various machine learning models, ranging from basic algorithms to sophisticated AIs. Such versatility necessitates an all-encompassing infrastructure that emphasizes speed, compatibility, and scalability. This way, agents can seamlessly traverse and function within a dispersed blockchain landscape without requiring any customized adjustments.

In incorporating Language Learning Models (LLMs) and other AI systems, AI-first chains need to be mindful of the distinct dangers these integrations may present. To minimize this risk, it’s crucial for AI-first chains to implement safeguards at every level, ranging from verifying inferences to maintaining compatibility with user-specified objectives. Key functionalities include immediate error detection, validation of decisions, and systems to prevent agents from operating on incorrect or malicious information.

From storytelling to solution-building

2024 marked an increase in anticipation for AI agents, while 2025 is expected to be the year when the Web3 sector truly validates this excitement. This transformation starts with a revolutionary concept of blockchains, where every level – from on-chain execution to the application layer – is engineered to accommodate AI agents. It’s only then that AI agents can progress from being merely entertaining chatbots to essential workers and partners, transforming industries and altering our perspectives on work and leisure.

As an analyst, I’m seeing a trend where businesses that invest in authentic and robust integrations of AI and blockchain technologies will undeniably thrive, offering services that would be unattainable on conventional chains or Web2 platforms. In this intensely competitive landscape, the transition from human-centric systems to agent-centric ones isn’t a choice; it’s an inevitable step towards progress.

In this article, it’s important to remember that the opinions presented belong solely to the writer and may not align with CoinDesk, Inc., its proprietors, or associated entities.

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2024-12-20 21:25