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THE FUTURE IS AI-CENTRIC, AND BLOCKCHAINS NEED TO BE AS WELL


FOR SUCCESSFUL AI-BLOCKCHAIN INTEGRATIONS TO BECOME A REALITY, THE
INFRASTRUCTURE UNDERPINNING THEM NEEDS A COMPLETE OVERHAUL.

By David Pinger|Edited by Nelson Wang
Dec 20, 2024, 5:57 p.m. UTC


Every few decades, a new technology emerges that changes everything: the
personal computer in the 1980s, the internet in the 1990s, the smartphone in the
2000s. And as AI agents ride a wave of excitement into 2025, and the tech world
isn’t asking whether AI agents will similarly reshape our lives — it’s asking
how soon.



But for all the excitement, the promise of decentralized agents remains
unfulfilled. Most so-called agents today are little more than glorified chatbots
or copilots, incapable of true autonomy and complex task-handling — not the
autopilots real AI agents should be. So, what’s holding back this revolution,
and how do we move from theory to reality?

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THE CURRENT REALITY: TRUE DECENTRALIZED AGENTS DON’T EXIST YET

Let’s start with what’s out there today. If you’ve been scrolling through
X/Twitter, you’ve likely seen a lot of buzz around bots like Truth Terminal and
Freysa. They’re clever, highly engaging thought experiments — but they’re not
decentralized agents. Not even close. What they really are are semi-scripted
bots wrapped in mystique, incapable of autonomous decision-making and task
execution. As a result they can’t learn, adapt or execute dynamically, at scale
or otherwise.



Even more serious players in the AI-blockchain space have struggled to deliver
on the promise of truly decentralized agents. Because traditional blockchains
have no “natural” way of processing AI, many projects end up taking shortcuts.
Some narrowly focus on verification, ensuring AI outputs are credible but
failing to provide any meaningful utility once those outputs are brought
on-chain.



Others emphasize execution but skip the critical step of decentralizing the AI
inference process itself. Often, these solutions operate without validators or
consensus mechanisms for AI outputs, effectively sidestepping the core
principles of blockchain. These stopgap solutions might create flashy headlines
with a strong narrative and sleek Minimum Viable Product (MVP), but they
ultimately lack the substance needed for real-world utility.



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

For decentralized agents to become a reality, the infrastructure underpinning
them needs a complete overhaul. The first and most fundamental challenge is
enabling blockchain and AI to “talk” to each other seamlessly. AI generates
probabilistic outputs and relies on real-time processing, while blockchains
demand deterministic results and are constrained by transaction finality and
throughput limitations. Bridging this divide necessitates custom-built
infrastructure, which I'll discuss further in the next section.



The next step is scalability. Most traditional blockchains are prohibitively
slow. Sure, they work fine for human-driven transactions, but agents operate at
machine speed. Processing thousands — or millions — of interactions in real
time? No chance. Therefore, a reimagined infrastructure must offer
programmability for intricate multi-chain tasks and scalability to process
millions of agent interactions without throttling the network.



Then there’s programmability. Today’s blockchains rely on rigid,
if-this-then-that smart contracts, which are great for straightforward tasks but
inadequate for the complex, multi-step workflows AI agents require. Think of an
agent managing a DeFi trading strategy. It can’t just execute a buy or sell
order — it needs to analyze data, validate its model, execute trades across
chains and adjust based on real-time conditions. This is far beyond the
capabilities of traditional blockchain programming.



Finally, there’s reliability. AI agents will eventually be tasked with
high-stakes operations, and mistakes will be inconvenient at best, and
devastating at worst. Current systems are prone to errors, especially when
integrating outputs from large language models (LLMs). One wrong prediction, and
an agent could wreak havoc, whether that’s draining a DeFi pool or executing a
flawed financial strategy. To avoid this, the infrastructure needs to include
automated guardrails, real-time validation and error correction baked into the
system itself.



All this should be combined into a robust developer platform with durable
primitives and on-chain infrastructure, so developers can build new products and
experiences more efficiently and cost-effectively. Without this, AI will remain
stuck in 2024 — relegated to copilots and playthings that hardly scratch the
surface of what’s possible.




A FULL-STACK APPROACH TO A COMPLEX CHALLENGE

So what does this agent-centric infrastructure look like? Given the technical
complexity of integrating AI with blockchain, the best solution is to take a
custom, full-stack approach, where every layer of the infrastructure — from
consensus mechanisms to developer tools — is optimized for the specific demands
of autonomous agents.



In addition to being able to orchestrate real-time, multi-step workflows,
AI-first chains must include a proving system capable of handling a diverse
range of machine learning models, from simple algorithms to advanced AIs. This
level of fluidity demands an omnichain infrastructure that prioritizes speed,
composability and scalability to allow agents to navigate and operate within a
fragmented blockchain ecosystem without any specialized adaptations.



AI-first chains must also address the unique risks posed by integrating LLMs and
other AI systems. To mitigate this, AI-first chains should embed safeguards at
every layer, from validating inferences to ensuring alignment with user-defined
goals. Priority capabilities include real-time error detection, decision
validation and mechanisms to prevent agents from acting on faulty or malicious
data.




FROM STORYTELLING TO SOLUTION-BUILDING

2024 saw a lot of early hype around AI agents, and 2025 is when the Web3
industry will actually earn it. This all begins with a radical reimagining of
traditional blockchains where every layer — from on-chain execution to the
application layer — is designed with AI agents in mind. Only then will AI agents
be able to evolve from entertaining bots to indispensable operators and
collaborators, redefining entire industries and upending the way we think about
work and play.

It is increasingly clear that businesses that prioritize genuine, powerful
AI-blockchain integrations will dominate the scene, providing valuable services
that would be impossible to deploy on a traditional chain or Web2 platform.
Within this competitive backdrop, the shift from human-centric systems to
agent-centric ones isn’t optional; it’s inevitable.



Note: The views expressed in this column are those of the author and do not
necessarily reflect those of CoinDesk, Inc. or its owners and affiliates.

OpinionAIWeb3

DAVID PINGER

David Pinger is the co-founder and CEO of Warden Protocol, a company that
focuses on bringing safe AI to Web3. Before co-founding Warden, he led research
and development at Qredo Labs, driving web3 innovations such as stateless
chains, webassembly, and zero-knowledge proofs. Before Qredo, he held roles in
product, data analytics, and operations at both Uber and Binance. David began
his career as a financial analyst in venture capital and private equity, funding
high-growth internet startups. He holds an MBA from Pantheon Sorbonne
University.




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