Andreas Bauer

Three Ceilings: Model Monoculture, Solvency, and the Penalty Doctrine in Markets for Expert Services

When generative AI drives the marginal cost of a persuasive expert artefact toward zero, production-cost signals of competence collapse and outcome-contingent liability commitments take their place. Such commitments look robust to better AI: a positive failure rate always leaves a residual to price. We show that this r...

Insuring the Fallback: Capacity, Monitoring and the Third Audience in Competence Signalling under Improving AI

Doctrine as a Fixed Point: A Formal Model of the Enforceable Penalty Ceiling when Human Oversight of AI Must Remain Effective

Article 14 of the EU AI Act requires that a high-risk system be overseen by natural persons who understand its limits, remain alert to automation bias, and can disregard or override its output. That capability is invisible in the output and decays precisely when the system is good. A provider can certify it only by an...

Insuring the Fallback: Capital, Monitoring, and the Certification of Preserved Human Capability under Improving AI

When generative AI makes the deliverable uninformative, a professional-services provider can still certify the preserved human capability to catch the machine's errors, through a liability pledge whose expected cost falls in that capability. The pledge is credible only up to what can be collected, and that ceiling is s...

The Institutional Window: Occupation- and Jurisdiction-Specific Calibration of Liability Signaling for Preserved Human Fallback Capability

Problem definition. When generative AI produces expert artifacts clients cannot distinguish from a competent provider's, the classical cost-based quality signal collapses and only outcome-contingent commitments can separate types. Such a commitment certifies an endogenous, perishable asset: the human fallback capabilit...

The Fallback as Signal: Preserved Human Skill, Liability, and Competence Signaling in Credence-Good Markets under Improving AI

This work characterize the least-cost separating pledge schedule, show that client stakes shift engagement toward or away from the least-skilled worker depending on the size of the pledge, and derive a stakes threshold above which building skill dominates free-riding on a rival's training - reversing the asymmetric-spe...

The Last Costly Signal: How Generative AI Collapses Competence Signaling and Why Liability Sustains Markets for Expert Services

It is shown that provenance certification priced as a type-independent stamp (e.g., C2PA) cannot restore full separation, while a verified commitment to forgo the AI frontier re-imposes the pre-AI artifact cost function.