Since 2024, AI has dramatically advanced, yet trust in its reliability remains low among users and companies, particularly in finance, healthcare, and personal data. This skepticism stems from AI’s core vulnerabilities, including hallucinations and privacy issues, which hinder widespread adoption. Even with significant investment and advancements, many professionals express distrust; a Forrester survey indicated that trust is viewed as AI’s greatest obstacle. Particularly in healthcare, where sharing electronic health records with AI presents legal and ethical risks, the need for robust privacy protection is paramount. Decentralized, privacy-preserving technologies, such as zero-knowledge proofs (ZK-SNARKs), present solutions by allowing verification of AI decisions without compromising personal data. Such technologies can facilitate a new class of decentralized AI systems where user privacy and institutional risk are safeguarded. As enterprises seek accountability and resilience from AI systems, the integration of decentralized cryptography could reshape trust in AI, ultimately determining its adoption trajectory and unlocking its projected $15.7 trillion economic potential by 2030.

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