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Source at https://www.linkedin.com/pulse/ai-liability-from-engineering-agi-governance-preparing-post-agi-icxcc

This episode delivers a comprehensive analysis of the escalating legal liability risks created by Generative AI, stressing that engineering choices are fundamentally legal decisions. We contrast two primary AI architectures, Fine-Tuning and Retrieval-Augmented Generation (RAG), arguing that one internalizes opaque, catastrophic risk (copyright, privacy) while the other externalizes traceable, operational risk (defamation, market substitution).

Learn about emerging legal doctrines, including the shift to classifying AI as a "product" subject to strict product liability and the existential threat of algorithmic disgorgement—an order forcing companies to destroy their core models.

Ultimately, we frame the current LLM governance debates as essential training for the future control of Artificial General Intelligence (AGI), concluding that the auditable RAG model represents a superior, provable path for future governance compared to the opaque fine-tuning approach.

⏱️ Timestamped Breakdown:

• [00:00] Introduction & The Escalating AI Liability Risk Landscape
• [02:15] Why Your Engineering Choices are Now Legal Decisions
• [05:40] Fine-Tuning vs. RAG: Contrasting Opaque vs. Traceable Risk in AI Architecture
  • Fine-Tuning: Internalized catastrophic risks (Copyright, Privacy).
  • RAG: Externalized operational risks (Defamation, Market Substitution).
• [11:00] Emerging Legal Doctrines: AI as a "Product" and Strict Product Liability
• [15:30] The Existential Threat: What is Algorithmic Disgorgement?
• [19:45] LLM Governance as Training for Post-AGI Legal Frameworks

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Keywords: AI liability, AGI governance, generative AI law, RAG, Fine-Tuning, product liability, algorithmic disgorgement, LLM governance, AI risk, legal framework, post-AGI

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