Onchain AI doesn’t have to mean putting a massive AI model onchain. A better approach is to give every task the right kind of intelligence. Some tasks need deep reasoning. Some only need a quick classification. Others may need image analysis, structured data processing, or cryptographic verification. That’s where Ritual gets interesting. Instead of forcing every application into the same AI workflow, Ritual opens the door to a more flexible system where different models and compute primitives can work together. A simple model can handle a simple task. A specialized model can handle a specific problem. An LLM can step in when the task actually requires reasoning or natural language. And agents can connect these pieces into a larger workflow. This matters because real applications care about more than model size. They care about speed, cost, reliability, privacy, and whether the result can be trusted. Ritual is building toward a world where intelligence becomes part of the programmable stack, not just an external API that applications call. The interesting future isn’t about putting one giant model everywhere. It’s about making different forms of intelligence usable onchain, exactly where they are needed. That’s the bigger idea behind programmable intelligence. And Ritual is building the infrastructure for it. @ritualfnd @ritualnet