Ritual in the Crypto × AI Landscape
Understanding Ritual’s approach to Crypto × AI
Ritual incorporates novel architecture and cutting-edge research, while maintaining familiar interfaces for users and developers. Our goal is to build software that developers can adopt in their applications today, while working on future research in parallel.
Here’s how Ritual fits into the broader Crypto × AI landscape:
Model training networks
Protocols focusing on distributed model training and ownership through pooled GPU compute resources.
Ritual begins by focusing on AI inference, the first consumer use-case of AI to achieve product market fit, while also supporting fine-tuning capabilities.
Ritual’s architecture naturally extends to support training workloads through its optimized infrastructure for heterogeneous compute and long-running stateful compute execution.
Web2 inference networks
Platforms that aim to create decentralized alternatives to traditional Web2 AI Inference APIs.
Ritual is an orchestration layer for all compute, including AI Inference. Naturally, via Web2 adapters, Ritual is a slot-in backend for Web2 AI Inference APIs with stronger guarantees around execution privacy and computational integrity. Other Web2 inference networks can also easily tap into Ritual as an additional venue for underlying AI Inference compute. Further, some other networks leverage sampling based consensus, which has degraded safety, liveness, and game theoretic implications in the context of Web3—Ritual improves upon this.
Agent frameworks
Frameworks and Protocols focusing on enabling AI agent development and deployment.
Ritual provides comprehensive primitives for building AI agents and supports integration with existing developer frameworks. Our architecture handles pricing, orchestration, and execution complexities, allowing developers to focus on their applications.
Through native Agent support and features like AI Inference, TEE Code Execution, and Scheduled Transactions, Ritual enables robust on-chain agent capabilities.
IP & Model provenance platforms
Protocols focusing on building tooling to monetize AI models.
Ritual enables native model monetization and model marketplaces with complete on-chain provenance and integrity, with cutting-edge research (vTune) backing our innovations. Further, the corresponding graph precompiles for operating across the model graphs is scaled via Symphony and priced via Resonance, neither of which the above platforms possess.
TEE infrastructure networks
Protocols focusing on building compute networks and coprocessors backed by Trusted Execution Environments (TEEs).
Ritual is proof-system agnostic subsuming TEE code execution as just one of many heterogeneous compute offerings. Nodes across these TEE infrastructure networks benefit from symbiotically offering their TEE compute on Ritual. Ritual enshrines the TEE natively into our chain as sidecars to guarantee the same level of safety and liveness that the rest of our sidecars have, other networks may not do to the same extent.
Inference Networks
Inference protocols that build economic networks to incentivize compute providers, and programmably validate execution.
Ritual is hyperfocused on enabling net-new user applications, starting by extending the Ethereum Virtual Machine (EVM) that tens of thousands of developers are already familiar with today. Ritual borrows many of the ideas that have worked in other economic protocols and applies them to a Turing-complete execution environment to enable infinite developer flexibility. Ritual and other inference networks are similar in their approach to generalizing compute (Ritual does this through homogeneous execution), with symbiotic opportunity for other inference networks to leverage Ritual architecture. That said, other inference networks lack the fee mechanisms and scaling solutions that are native to Ritual via Resonance and Symphony.
DePIN networks
Protocols focusing on building decentralized physical infrastructure networks (DePIN), bringing together distributed node sets, many with dedicated GPU hardware and homogeneous resources.
Ritual is the ideal network for nodes across these DePIN platforms to broadcast and offer their execution services. Node operators on Ritual can specialize in their compute offerings, earning rewards for offering their homogeneous compute. Ritual has partnered with many of other networks to bootstrap initial compute capabilities.
Proof systems for verifiable inference
Protocols building proof systems optimized for verifiable AI inference.
Ritual is proof-system agnostic and incorporates many of other offerings as drop-in options to users depending on their specific application needs. Other proof system libraries have mechanisms through which computational integrity can be achieved, and Ritual is the fabric through which they can seamlessly interop with applications and protocols running on-chain.
Bring-your-own-compute networks
Protocols focusing on building edge infrastructure where users bring their own hardware to power AI inference.
Ritual does not focus on edge inference, instead believing that a majority of users will tend to use simple, hosted architecture. Still, other networks can easily tap into Ritual for execution of larger, more performant models and nodes in these networks can symbiotically offer their execution capabilities to the Ritual network.
For developers that wish to bring-their-own-compute to power their AI applications, Infernet remains the easiest framework to orchestrate AI compute or bring off-chain compute on-chain.
Data monetization networks
Protocols focusing on building data monetization networks where users can be paid for their data used in training AI models.
While Ritual does not primarily focus on data monetization, it enables many of the underlying primitives like native model monetization and model marketplaces that power other networks. It is symbiotic for networks to tap into and build upon Ritual.
On-chain inference networks
Protocols focusing on building on-chain inference networks which enable AI inference consumed in smart contracts.
Ritual architecture efficiently prices, orchestrates, and exposes all types of compute including AI, ZK, or TEE execution. Most other platforms are broadly overfit to a restricted set of AI computation with levels of safety, liveness, and computational efficiency to the end user lower than those of Ritual.
Privacy AI
Projects privacy-preserving AI solutions using advanced cryptographic techniques, such as Fully Homomorphic Encryption (FHE) or Multi-Party Computation (MPC) to do privacy perserving inference.
Other solutions can be integrated into existing applications and protocols as libraries or toolkits, enabling privacy-preserving AI capabilities without requiring significant architectural changes.
Ritual’s modular architecture is designed to seamlessly integrate with any other privacy-preserving solutions, whether at the chain level or within Infernet. We are also partnered with a variety of other protocols. This flexibility allows developers to choose the privacy solution that best fits their needs. Additionally, we are developing our own innovative privacy solution, Cascade, which will soon be announced and will provide native privacy capabilities within the Ritual ecosystem.
Generic chain infrastructure
Protocols building generic chain infrastructure enhanced by GPUs.
Ritual is focused on enabling net-new user functionality through homogeneous compute. Ritual does not innovate on generic, heterogeneous execution performance—we simply adopt the best-in-class solutions as they evolve.
Legacy chain rebrand
Blockchains like NEAR and Internet Computer have rebranded their existing sovereign L1 theses to focus on AI capabilities. NEAR has shifted from being a smart contract platform to “The Blockchain for AI”, while Internet Computer (ICP) has evolved from a distributed computing platform to emphasizing AI model hosting and inference capabilities.
While Ritual can serve as a general-purpose blockchain, it was purpose-built from the ground up for AI and compute-intensive workloads. Rather than retrofitting AI capabilities onto existing infrastructure, Ritual’s architecture natively incorporates all the functionality discussed above— from AI inference and agent frameworks to TEE execution and cross-chain interoperability—in a cohesive, optimized system.