EigenCloud (@eigenlayer): AI has a trust problem. Verifiability is the fix.

AI has a trust problem. Verifiability is the fix. Our GM of AI @nima_vaziri sat down with @a16z’s @alive_eth and @danboneh of @Stanford to map the deepest fault lines in AI today. ☁️ Models we can’t trust. ☁️ Centralized providers can censor, shut down, or shift rules overnight. Outsourced training hides backdoors. Even “open” weights don’t prove what’s actually running. Trust. Backdoors. Black boxes. The path forward is clear: 🔥 Zero-knowledge proofs for verifiable inference 🔥 TEEs for hardware-backed integrity 🔥 Optimistic verification from blockchains 🔥 Decentralized infra beyond single points of control From “trust us” to “verify yourself.” That’s the shift. That’s the unlock. The frontier is here. The builders decide what comes next. Timestamps: 00:00:00 Introduction: AI & Crypto Intersection Overview 00:01:58 Four Major AI-Crypto Trends 00:02:44 AI Agents Need Financial Infrastructure 00:04:03 - Proof of Humanity: Fighting AI-Generated Content 00:04:17 Decentralizing AI Infrastructure Networks 00:04:44 Synthetic Life: Autonomous AI Agents 00:06:20 Verifiable AI: Trusting What You Can’t See 00:10:16 Current Performance Numbers for AI Proofs 00:13:18 The Era of Experience in AI Learning 00:14:56 AI Agents Having Life of its Own 00:18:21 Algorithmic Fairness & Verifiable Models 00:23:18 Privacy in AI: Trusted Execution Environments 00:25:47 Economic Incentive for Open Weight Models 00:31:39 Attribution Problem: Who Gets Paid for AI Training? 00:35:52 Content Provenance & Authentication (C2PA) 00:48:03 AI Security: Finding Exploits & Vulnerabilities 00:54:53 Educational Applications: LLMs as Learning Partner 00:58:29 Reliance on LLMs and Cognitive Abilities 01:03:57 Content Providers’ Fear of LLM Training