Fake project data is frequently seen in the crypto world. These days, trusting code is far more reliable than trusting hype. The waters in the AI circle are also deep; models with outrageously high scores on leaderboards often perform worse in practice than a cat knocking over a water cup at home. Data pollution during training, test questions being tailored, evaluation processes operating as black boxes—scores are severely disconnected from actual capabilities, making it a classic case of “showing off” versus “real ability” in AI. This “Emperor’s New Clothes” approach harms users, misleads investors, and even skews regulation. If this continues, the trust foundation of the entire industry will be eroded. At this moment, @inference_labs’ Subnet 2 stands out as a breath of fresh air. Using hardcore zero-knowledge proof operations, it generates a verifiable, tamper-proof cryptographic ID for each model inference, making cheating impossible. From now on, AI performance is no longer self-promoted by platforms; anyone can verify authenticity with cryptographic “truth-seeing mirrors.” For users, this finally means clearer eyes when choosing models; for the industry, it’s a crucial pillar for rebuilding trust. After all, AI has long permeated all aspects of life, and verifiable real performance is far more tangible than inflated scores—who wants to constantly battle the “Schrödinger’s AI”? How does Subnet 2’s zero-knowledge proof generate cryptographic IDs for models? What are other applications of zero-knowledge proof technology? Besides zero-knowledge proofs, what other technologies can ensure the credibility of AI models?