Over the past couple of years, we’ve seen a surge in Crypto x AI projects , but let’s be honest: most of them are either riding hype waves or still rely heavily on web2 infrastructure behind the scenes.

@ritualnet  sits right at that intersection, but it’s clearly trying something more foundational than just “bringing AI to crypto.” It starts by acknowledging a simple but overlooked truth: the way most blockchains work today doesn’t match what AI workloads actually need.

The Core Problem

Blockchains replicate everything — every node runs the same code. That works fine for things like swaps or NFT mints, but AI doesn’t work that way. Different models require different hardware. Running a simple classifier on an H100 is overkill; trying to run a massive LLM on a CPU is pointless.

That mismatch explains why most chains struggle to meaningfully integrate AI.

Ritual’s insight is: you can’t just plug AI into a blockchain , you need to redesign the chain around it.

So What Actually Sets Ritual Apart?

There’s quite a lot that Ritual does differently , if you dive into their docs, you’ll find an entire stack rebuilt around expressive compute. And I’ll touch on more of those innovations later.

But for now, let’s look at just three key things that already show how fundamentally this chain is thinking differently: 

⚪️Node Specialization: Instead of every node doing the same thing, Ritual lets them specialize based on their hardware capabilities. Feels obvious ( we already see this in cloud infra ) but it’s rare in crypto. It opens the door for GPU-heavy tasks to coexist with lighter workloads in the same network.

⚪️Sidecars: This is one of the biggest technical unlocks. Sidecars handle compute-heavy tasks  like model inference, ZK proving, or TEE execution  off the main execution client, but still tightly integrated. It keeps the chain lean while enabling much more powerful operations.

⚪️Resonance Fee Market: Resonance, their fee market, is interesting because it lets compute supply and demand find equilibrium across different job types and hardware. That’s a much more granular model than the typical gas-based approach.

Could This Actually Work?
Too early to say  but what’s clear is that Ritual isn’t just slapping “AI” on a token and calling it innovation. It’s addressing the real friction points that make AI + crypto integrations hard today.
For example, most AI x Crypto projects right now either:

-Run inference off-chain and just write results on-chain (basically glorified API calls), or

-Only deal with toy models because of chain limitations.

Ritual tries to bridge that gap: real inference, real models, real attribution , all happening in a decentralized and verifiable way. Their focus on verifiable provenance (being able to prove who trained a model, how it was updated, etc.) is particularly relevant in a world of LLM copycats and black box APIs.

Still Unanswered Questions
The hardware side is still unclear. Node specialization sounds great, but who’s really going to run high-end GPUs for this? What’s the incentive model? How is slashing or reliability handled?
The agent story is promising but early. It’s a cool vision  onchain agents that can operate indefinitely  but most examples are still theoretical. 

EVM++ adoption will depend on tooling and developer experience. New features are great, but only if they’re easy to build with.

So what I think :
Ritual doesn’t feel like another AI narrative play. It feels like a genuine rethink of what an AI-compatible blockchain would look like  and more importantly, why the current stack can’t support that vision.

Is it ready to take over the world? No. But it does feel like the first step in the right direction. If the future of AI is going to be more open, verifiable, and decentralized, we’re going to need infrastructure that actually supports that  not just more wrappers and APIs.
And Ritual might just be that.