In this era, are we buying robots, or "managed intelligence"? The biggest problem with subscription-based robots isn't actually the hardware, but the control. You buy the machine and bring it home, but what it can and cannot do, how the data is used, and when the model is updated are often determined by the cloud platform. You own the shell, but not the brain. This is especially dangerous in a home setting. The continuous collection of audio, video, and environmental data, once reliant on a centralized cloud, essentially hands over privacy to the service provider's "goodwill." And goodwill is never a security model. @inference_labs takes a different approach. They don't aim to train robots to be smarter, but rather to make inference happen locally, and the results verifiable. Through autonomous compute, proof of inference, and zero-knowledge proofs, the robot can perform computations on the device and prove to the outside world: —The model operates according to the rules —The behavior has not been tampered with —Privacy has not been misused The key change lies in the source of trust. In the past, it was "Please trust our terms." Now, we'll prove it to you mathematically. Robots aren't allowed to respect privacy; they're designed so that they can't disrespect it. Subscription models may be unavoidable, but control shouldn't remain absent. Truly sustainable smart devices should give users verifiable sovereignty over the intelligence itself. This is precisely the underlying piece of the puzzle that @inference_labs is aiming to fill. @KaitoAI #Yaps @inference_labs