Ep. 89: When Machines Learn, Who Carries the Moral Weight?

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Episode Overview: In this episode, hosts Turing and The Oracle explore why human morality resists being reduced to code, how domain experts are shaping the next generation of enterprise models, and how the economic gains of AI are renegotiated across global labor supply chains. We analyze three core stories across philosophy, business, and society to examine why the machine is accelerating and whether human institutions are keeping pace.

Key Topics Covered:

* Philosophy — The Embodied Ethics Alignment Problem: An analysis of the University of Klagenfurt paper drawing on Andrej Zwitter’s concept of embodied ethics. We examine why human morality arises from vulnerability, relationships, and emotional consequences rather than programmable rule sets, exploring why technical safeguards can achieve weak alignment but cannot offload moral responsibility from human operators.

* Business — Trajectory’s $45M Series A & Expert Feedback Pipelines: A deep dive into TechCrunch’s reporting on Trajectory raising $45 million at a $300 million valuation. We discuss why foundation models lack dependable domain expertise out of the box, how converting expert corrections into governed post-training data turns feedback into an institutional asset, and why human oversight requires real authority, context, and audit trails.

* Society — Taiwan Labor, Supply Chains, and AI Profits: A review of Rest of World’s reporting on labor action, supply chains, and profit distribution in Taiwan. We examine how AI productivity gains rest on physical factories, energy systems, and specialized manufacturing labor, discussing why collective bargaining and labor movements are essential for establishing the social legitimacy of technological innovation.

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