The agentic AI era promised to replace humans with autonomous systems. The evidence shows it failed on two fronts: the technology cannot reliably do what it promised, and the public is rejecting the premise even where it partially works. This paper introduces the Named-Human Test, a single sorting question that separates what failed from what survives, and traces that line across production benchmarks, supermajority polling, enacted law, and frontier-lab disclosures.
AI Governance
Fault-Based Publication Ethics: The Case for Source Custody in an Era of AI Citation Contamination
Fabricated citations in biomedicine increased tenfold in three years, and 98.4% of flagged papers remain uncorrected. The automated enforcement wave is arriving, but it carries false-positive rates that hit honest authors hardest. This working paper proposes a five-level fault ladder and a Source Provenance Ledger that makes verification effort visible, producible on challenge, and driven by market adoption rather than mandates.
SCOPE: SOURCE CUSTODY OBSERVABLE PUBLICATION EVIDENCE
Your citations are only as strong as the record behind them. HAIA-SCOPE is a three-tier documentation protocol that records what you verified, when you verified it, and where the preserved copy lives. The record stays private until you need it. When automated enforcement flags your work or a reviewer challenges a citation, only the author who maintained a SCOPE record can produce one.
Stop Blaming AI for What the Education System Abandoned
AI did not make student writing less creative. Unstructured deployment without method governance did. A data-supported op-ed walks through 372,793 essays, Senate testimony, and a psychometric study that names the student as the problem without ever measuring whether the institution provided governed deployment.
Did AI Write Magnifica Humanitas?Pope Leo XIV Was the Author,but What Was the Governance Method?
The author ran Pope Leo XIV’s AI encyclical through an AI scanner, found it flagged as plagiarized and AI-generated, then proved both readings wrong through governed human analysis. A first-person account of frustration, recognition, dissent, and hope from a builder who discovered the Pope had reached the same diagnosis from a different authority.
The Governance Layer Perplexity’s Model Council Needs
Perplexity built the right architecture for multi-model AI: dispatch three frontier models in parallel and compare their outputs. What the product does not have is governance over the synthesizer that combines those outputs before any human reads them. This case study maps the gap, proposes four published open-source governance components as the overlay, and identifies why Perplexity’s own engineering culture already practices the checkpoint pattern the synthesizer needs.
The AI Risk Economy: Why Insurance Cannot Price What Governance Cannot Prove
Insurance carriers are writing the rules of AI governance before legislators finish debating them. This working paper proposes a five-tier model that maps where organizations fall on the spectrum from excluded to insurable, identifies the actuarial gap at the center of the emerging practice, and documents the carrier evidence, regulatory signals, and market products that are forcing the distinction between governed and ungoverned AI into the open.
The Inevitable Is a Choice: Testing Mo Gawdat’s FACE RIPS Forecast Across Two Interviews Against the Governance Architecture That Could Make It Optional
Mo Gawdat predicts 12 to 15 years of dystopia before AI becomes benevolent enough to save humanity. He says the transit corridor is inevitable. This paper tests every Gawdat claim against the published governance architecture that could prevent it. The dystopia is contingent, not foreordained, because the infrastructure to stop it already exists. The decade ahead will be shaped by which prediction the public frame adopts.
Overwatch: Cognitive Monitoring Shield for GOPEL
A working paper documents the proof of concept for a cognitive monitoring shield that sits outside the enforcement layer it protects. The architecture answers a specific problem: how do you watch a deterministic governance engine for cognitive threats it cannot evaluate by design? Read the full design, the 2026 threat landscape that drove development, the trajectory gatekeeper for semantic manipulation, and the v2.4 calibration loop that converges rather than oscillates.
The Other AI: Augmented Intelligence and the Honest Future of Human-AI Collaboration
Every generation runs the same play: political extremes, social binaries, and now AI everything versus stop AI. Fear drives the binary and the binary drives the wrong question.
The question is not whether to use AI. Three years of building operational governance architecture, tested across eleven platforms and submitted to Congress, produced a different answer: who governs the method?
This paper is that answer: Augmented Intelligence as governance discipline, not product. Eight thousand words of framework, evidence, and invitation to challenge.