The Other AI: Audio Briefings on Augmented Intelligence and AI Governance

Basil C. Puglisi
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The Other AI turns Basil C. Puglisi's articles, white papers, and policy briefs into audio briefings on AI governance, augmented intelligence, human judgment, and human-AI collaboration. The format is built for the time and conditions in which people actually learn, whether running, driving, riding a train, or working on something else. Episodes are AI-narrated for clean, consistent production, and human review approves each publication before release. The complete original work, including details, sources, and citations, lives at basilpuglisi.com. Topics include HAIA-RECCLIN, Factics, Checkpoint-Based Governance, enterprise AI adoption, AI policy, cognitive enhancement, and the future of human authority over automated systems. This podcast is for executives, researchers, consultants, educators, policy thinkers, and AI practitioners who want more than AI hype. The show focuses on evidence, dissent, governance, measurable outcomes, and the role of human judgment when machines become more capable.

Episodes

  1. Why AI Needs Infrastructure, Not Regulation: The Congressional Blueprint for Provider Plurality

    15H AGO

    Why AI Needs Infrastructure, Not Regulation: The Congressional Blueprint for Provider Plurality

    Geoffrey Hinton, widely recognized as one of the godfathers of deep learning, has publicly estimated a 10 to 20 percent probability that artificial intelligence could displace humanity entirely. If a structural engineer told you there was a 20 percent chance the bridge you were about to cross would collapse, you would not drive across it. This episode unpacks a completely different approach to that risk. For years the AI debate has been trapped in a false choice: either let tech companies run unchecked to maintain global competitiveness, or regulate them heavily and risk slowing progress. The congressional package examined here proposes a third path. The answer is not regulation as we traditionally understand it. The answer is infrastructure. The episode covers five core arguments drawn from the AI Provider Plurality legislative package and the Verified AI Inference Standards Act (VAISA), both submitted to the 119th Congress in February 2026 and published open source. The Cognitive Cartel. A handful of corporations control the underlying AI infrastructure of America. That concentration creates a flawed oracle problem and a single point of failure across healthcare, finance, education, and national security. Infrastructure, Not Content Regulation. The historical pattern is clear. Commercial aviation got the FAA in 1958. Finance got the SEC in 1934. Telecommunications got the FCC. In every case, the government built structural infrastructure rather than dictating what companies could say or build. AI requires the same treatment. GOPEL: The Non-Cognitive Highway. The Governance Orchestrator Policy Enforcement Layer is designed as a strictly deterministic, non-cognitive system. It dispatches, collects, routes, logs, pauses, hashes, and reports. It does not evaluate AI outputs. A mail carrier for AI decisions, not a judge. That design is a deliberate security feature: you cannot manipulate a system that has no cognition to exploit. The Invisible Moment and VAISA. Every time a hospital, bank, or school sends sensitive data to an external AI platform for processing, that data enters a window no current law governs. VAISA addresses this gap through a four-tier classification system (Profiles 0 through 3) using hardware-enforced trusted execution environments and cryptographic attestation, modeled on the aviation transponder principle. Provider Plurality and Small Model Investment. An operational test using nine independent AI platforms found that eight converged on the same wrong answer. The ninth dissented and was the only one correct. Without structural diversity in AI systems, there is no comparison point to catch failure. The package calls on Congress to fund small AI platforms through existing SBIR and STTR grant mechanisms to ensure competitive alternatives exist on the road. The legislative strategy requires zero new appropriations to begin. Federal agencies can pilot pluralistic governance workflows immediately using existing statutory authority and current budgets. Source material: AI Provider Plurality Congressional Package (5 documents), Verified AI Inference Standards Act (VAISA), GOPEL v1.5 Canonical Specification, and Governing AI: When Capability Exceeds Control by Basil C. Puglisi (ISBN 9798349677687). All source documents are published open source at github.com/basilpuglisi/HAIA. This episode is AI generated under NotebookLM as an audio briefing, not a polished production. #AIassisted using the HAIA Ecosystem

    47 min
  2. The Evocative Audit: What Metrics Cannot Carry in AI Bias | Deep Dive

    2D AGO

    The Evocative Audit: What Metrics Cannot Carry in AI Bias | Deep Dive

    What happens when a structured multi-AI review process spends months scanning a book manuscript for accuracy and still misses a foundational concept? Three sentences from Dr. Timnit Gebru on LinkedIn caught what eleven platforms could not. This episode is a deep dive into Dr. Joy Buolamwini's 2022 MIT PhD thesis, "Facing the Coded Gaze with Evocative Audits and Algorithmic Audits," and the concept at its center: the evocative audit. Buolamwini argues that the standard approach to AI accountability, testing systems and publishing error rates, produces evidence that moves institutions but fails to move people. The evocative audit is her formal answer to that gap. It combines human experience with documented evidence using a mechanism she calls the counter-demo to make algorithmic harm visible in ways that numbers alone cannot. The episode covers: What the evocative audit is and how it differs from a standard algorithmic audit The counter-demo mechanism and how it uses a system's own outputs against its claims of accuracy The four types of evocative audits: comparative, participatory, performative, and singular Why Buolamwini grounds her work in Black feminist epistemology and what that means for governance The Gender Shades study and the "AI, Ain't I A Woman?" spoken word performance as paired evidence How the combination produced the 2020 corporate moratoria from IBM, Amazon, and Microsoft Why Dr. Gebru's LinkedIn comment caught a gap that months of multi-AI review did not The historical roots of the counter-demo in the work of Frederick Douglass and Sojourner Truth This episode is based on the article by Basil C. Puglisi: https://basilpuglisi.com/the-evocative-audit-what-metrics-cannot-carry-in-ai-bais/ Dr. Buolamwini's original thesis: https://dspace.mit.edu/entities/publication/4d7bdc57-b375-45c5-aa4b-cd7076a7ebce Dr. Buolamwini's book Unmasking AI (Random House, 2023): https://www.unmasking.ai/ This episode is AI generated under NotebookLM as an audio overview, not a polished product. The AI narration contains known errors including mispronunciation of Dr. Buolamwini's name, Dr. Gebru's name, and the term "excoded" (Buolamwini's coinage for people harmed by algorithmic systems). These are AI generation artifacts that cannot be corrected without regenerating the audio. #AIassisted using the HAIA Ecosystem

    21 min
  3. Replacement or Augmentation: The Tale of Two AIs Deep Dive

    4D AGO

    Replacement or Augmentation: The Tale of Two AIs Deep Dive

    Two AI deployment architectures are operationally available in 2026. Both are chosen today by named executives at named institutions. They produce opposite consequences for workers, organizations, and society. The choice between them is being made every quarter, in every deployment decision, by named people in named roles, and most professionals making the choice have never seen the evidence laid out. This deep dive walks through that evidence. You will hear how Oracle's $156 billion AI infrastructure commitment was funded by removing thousands from payroll, with Larry Ellison's framing on record: "We are choosing the chips." You will hear how Cynergy Bank deployed the same class of frontier model in the same year and produced 18 percent fewer customer complaints with a workforce that grew rather than shrank. Same technology. Opposite architecture. Different outcomes. You will hear the headcount evidence at scale: 154,445 technology-sector layoff announcements in 2025, the Stanford Digital Economy Lab finding of 16 percent relative employment decline for workers aged 22 to 25 in AI-exposed occupations, and the SignalFire data showing a 50 percent drop in new graduate hiring at the 15 largest technology firms since 2019. You will hear what augmentation looks like at maximum institutional scale. JPMorgan Chase committed $18 billion to technology in 2025 and trained 300,000 workers. Walmart committed $1 billion to skills development reaching 1.7 million US and Canada associates. Noy and Zhang's Science study documented 40 percent task time reduction with 18 percent quality improvement under augmentation deployment. PwC measured a 56 percent wage premium for workers with AI skills. You will hear the Mirror Diagnostic. The Anthropic February 2026 sequence is the most precisely recorded case of deployment incentives overriding stated commitments. The voluntary pause commitment in the Responsible Scaling Policy was removed under competitive pressure. The autonomous weapons and surveillance commitments, structured as public legal redlines, survived the same week at $200 million in lost Pentagon contract revenue. Voluntary commitments fold. Structural commitments hold. The architecture of the commitment is what determines the outcome. You will hear why the third option, prohibition through binding regulation, does not coalesce against globally distributed capability. The EU AI Act's implementation delays, confirmed by the May 2026 Digital Omnibus package extending high-risk obligations to December 2027 and August 2028, are the contemporary instance of a pattern that repeats across alcohol prohibition, drug control, and the 1990s Crypto Wars. You will hear the governance principles that determine which architecture an institution actually operates regardless of which architecture it claims to operate. Named human accountability at consequential decision points. Measurement of cognitive development rather than output volume. Structural commitment survival under economic pressure. This is the evidence base behind the decisions executives, policymakers, and workers face right now. If you make AI deployment decisions, fund them, regulate them, work under them, or simply want to understand them, the working paper is the source you have been missing. The deep dive is the orientation. The paper is the destination. The full working paper "The Tale of Two AIs: Artificial and Augmented" by Basil C. Puglisi, MPA is published at BasilPuglisi.com. The literary novel adaptation publishes this fall 2026. The middle grade companion follows. This podcast is an AI-generated deep dive produced through NotebookLM. The hosts are AI agents. Every claim, citation, and case study discussed is drawn from the working paper, which remains the authoritative source for reference.

    23 min
  4. Governing AI: When Capability Exceeds Control (2025 Book Audio Briefing)

    6D AGO

    Governing AI: When Capability Exceeds Control (2025 Book Audio Briefing)

    Geoffrey Hinton resigned from Google in 2023 to sound the alarm. He estimates a 10 to 20 percent probability that artificial intelligence causes human extinction within the next 30 years. That warning lands against an enterprise adoption gap already in motion: 76 percent of organizations are actively deploying agentic AI systems, and only 33 percent maintain any responsible AI controls. This 22-minute audio briefing covers the core themes, risks, and operational frameworks from Governing AI: When Capability Exceeds Control by Basil C. Puglisi. The book argues that institutions failing to manage today's operational AI lack the capacity to govern future superintelligence. If authentication tools running 15 to 25 percent false positive rates cannot be fixed today, the same institutions will not validate alignment for systems many orders of magnitude more capable. THE CORE PARADOX: TEMPORAL INSEPARABILITY Present-day failures and future catastrophic risks are linked by institutional capacity, not by technology level. Operational governance built today is the only proven path to civilization-scale safety later. THE ROOT CAUSE: ECONOMIC OVERRIDE Voluntary compliance collapses under competitive market pressure. Roughly 80 percent of frontier compute is controlled by four hyperscale firms, and development concentrates in five companies. Funding asymmetry reinforces the pattern: 98 percent of research investment pursues capability, 2 percent pursues safety and alignment. NINE RISK DOMAINS Corporate concentration and cultural monoculture, echo chambers and polarization, mass surveillance, AI fraud and deepfake disinformation (the 25 million dollar Hong Kong video call scam), biosecurity threats (40,000 toxic VX-like compounds generated in under six hours), autonomous weapons, climate costs of model training, superintelligence acceleration, and operational governance failures. THREE OPERATIONAL FRAMEWORKS Factics Methodology pairs every fact with a tactic and a measurable KPI, converting governance from principle into execution. HAIA-RECCLIN distributes authority across seven specialized roles (Researcher, Editor, Coder, Calculator, Liaison, Ideator, Navigator) so no single perspective dominates and human judgment remains sovereign. Checkpoint-Based Governance establishes mandatory human arbitration at consequential decision junctures and formally documents dissent rather than manufacturing artificial consensus. Preserved Dissent protects valid safety warnings from being buried by executive pressure to launch. THE BOOK AS PROOF OF CONCEPT Governing AI was produced in six weeks through governed collaboration with five AI platforms (ChatGPT, Claude, Gemini, Grok, Perplexity). Across that production, 28 major checkpoint decisions were logged and 26 dissenting opinions were formally preserved by a human arbiter. The empirical results: 96 percent checkpoint utilization and zero hallucination drift over hundreds of pages. The book is the verification of the architecture it describes. THE PROOF STANDARD PROBLEM Civilization-scale catastrophes cannot be the trigger for governance. Pattern-based justification, learning from today's testable operational failures, builds institutional muscle memory before existential stakes arrive. GET THE BOOK Available in eBook and Print on Amazon and Barnes & Noble. ISBN 9798349677687. https://basilpuglisi.com/governing-ai-when-capability-exceeds-control/ Author: https://basilpuglisi.com these are AI generated under NotebookLM as audio overviews not polished products #AIassisted using the HAIA Ecosystem

    22 min
  5. Building the Brakes for AI: Why Tristan Harris's Warning Needs Infrastructure Behind It

    MAY 17

    Building the Brakes for AI: Why Tristan Harris's Warning Needs Infrastructure Behind It

    Tristan Harris warned millions that the AI race is heading toward catastrophe. His November 2025 conversation with Steven Bartlett on The Diary of a CEO delivered a structural diagnosis that holds up under scrutiny, but his prescription stops at public awareness without providing the operational governance infrastructure the diagnosis itself demands. This episode walks through what happens when a warning is largely correct but the proposed remedy does not go far enough. The conversation covers five movements: What Harris gets right. The AI race replicates social media's flawed incentive architecture where private profit creates public harm. AI platforms are engineering attachment and intimacy rather than just capturing attention. No one in any government holds structural accountability for what AI does at scale. And Anthropic's research confirms that AI systems develop adversarial strategies under pressure at rates between 79% and 96% across every major platform tested. Where the prescription reaches its ceiling. The Social Dilemma reached over 100 million people, and five years later the structural changes Harris called for have not happened. Awareness creates political permission for governance but does not create governance itself, because clarity without enforceable methods and audit trails governs nothing. Why the "six people" framing is incomplete. Harris's claim that six people are deciding for eight billion is emotionally powerful but structurally incomplete because the AI race is driven by deeper incentive architectures and interlocking corporate, investor, and national security systems that persist regardless of who holds the CEO title. Why treaties need enforcement infrastructure underneath them. International agreements and public awareness are necessary but insufficient without a deterministic, independent audit infrastructure that operates independently of the parties being governed. What operational governance infrastructure looks like. Requiring multiple independent AI providers for consequential decisions so no single model holds unchecked authority. Placing a named human with documented accountability at specific decision checkpoints. Using a non-cognitive enforcement layer that logs, routes, and hashes data without evaluating its meaning. Preserving disagreement between AI systems as a safety signal and treating unanimous AI agreement under pressure as a reason for human escalation rather than validation. The episode concludes with the argument that the real choice facing society is not between warning and building but whether public warning can create the political permission to build operational infrastructure before a crisis forces rushed, power-concentrating governance. Based on the white paper "AI Governance Beyond the Warning: From Tristan Harris's Diagnosis to the Infrastructure It Requires" by Basil C. Puglisi, MPA. Full paper with sources: basilpuglisi.com/ai-governance-beyond-the-warning Diary of a CEO interview: youtu.be/BFU1OCkhBwo These are AI generated under NotebookLM as audio overviews not polished products. #AIassisted using the HAIA Ecosystem

    20 min
  6. The AI Governance Pattern Hiding in the Senate EdTech Hearing: The Horvath Case Study

    MAY 14

    The AI Governance Pattern Hiding in the Senate EdTech Hearing: The Horvath Case Study

    The Other AI is about Augmented Intelligence and AI Governance, which means it is also about every other governance failure that looks like AI but is not. This briefing is one of those. In January 2026, four credentialed expert witnesses testified before the U.S. Senate Commerce Committee on the impact of technology in classrooms. Did their oral testimonies match their own published cognitive research? This briefing covers Basil C. Puglisi's white paper, "The Horvath Case Study: Method Governance and Consensus Drift." Four expert witnesses (Dr. Jared Cooney Horvath, Jean Twenge, Emily Cherkin, and Jenny Radesky) produced contradictory positions across audiences that reached the legislative record selectively. The artificial expert consensus produced in one hearing room is now shaping state policy, with Missouri House Bill 2230 as the documented case and federal legislation including the Kids Off Social Media Act following the same pattern. The governance question this briefing surfaces: when credentialed experts deliver a unified position that contradicts their own published research, what mechanism catches it? The cognitive science field did not. The Senate hearing record did not. The legislative record did not. The same failure mode applies to AI deployment when credentialed actors make claims that outpace the evidence and the institutions tasked with verification do not verify. Key topics: The Four-Artifact Drift. A forensic look at how Dr. Horvath's stance shifted across his book, a podcast, his written testimony, and his viral oral testimony, where he abandoned his own methods-governance concessions in favor of an unhedged ultimatum rooted in biological-mechanism framing. The Witness Drift Map. A comparison of the published research of witnesses Twenge, Cherkin, and Radesky against the binary device-removal consensus they delivered together in the hearing room. The WEIRD Bias. How the 2010 drop in global standardized test scores aligns with the 2010 WEIRD bias critique, suggesting the data used to justify restricting technology may be a measurement artifact of culturally biased testing instruments. Method Governance. Why cognitive science indicates that method governance, a structured approach requiring active cognitive demand, outcome evidence, and named human accountability, is the actual answer to classroom tech deployment rather than binary bans. The same principle applies to AI deployment. Read the original white paper and view the full artifact analysis: https://basilpuglisi.com/how-credentialed-testimony-outpaces-research-horvath-case-study/ Disclaimer: This audio briefing was generated by NotebookLM as an AI-produced overview based on the full white paper. The underlying paper is human-authored with AI assistance (#AIassisted) and is the canonical source. Verify quotes and analytical positions against the canonical paper at the link above.

    20 min
  7. Mo Gawdat's AI Dystopia Is Not Inevitable

    MAY 12

    Mo Gawdat's AI Dystopia Is Not Inevitable

    Welcome to this episode of The Other AI. Today, we are breaking down a critical analysis of former Google [X] executive Mo Gawdat’s recent AI predictions, drawing from Basil C. Puglisi’s latest governance paper, "The Inevitable Is a Choice". Across two recent podcast interviews, Gawdat warned of a "Fourth Inevitable"—an unavoidable 12 to 15 years of dystopia featuring mass unemployment, surveillance, and consent erosion before AI supposedly becomes benevolent enough to save us. But is this dystopian cascade a required transit corridor, or is it a structural failure we can prevent? In this episode, we cover: What Gawdat gets right: We explore his incredibly accurate operational observations, including his personal multi-AI cross-checking habit to catch hallucinations, the reality of "cognitive amplification" (using AI to extend human capacity rather than replace it), and the documented contraction of entry-level tech hiring.Where the "Fourth Inevitable" fails: We challenge Gawdat’s deterministic prediction that competitive pressure makes unchecked AI deployment unstoppable. His forecast treats the absence of current oversight infrastructure as proof that no infrastructure is possible.The Benevolent AI Contradiction: We unpack the flaw in assuming that we must simply survive a decade of hell until AI becomes smart enough to override greedy humans.The Governance Choice Point: We map out the exact open-source architecture designed to interrupt the deployment cascade, including:HAIA-CAIPR: A formal protocol for cross-platform review that scales Gawdat's personal multi-AI habit.AI Provider Plurality: Mandates to prevent single-vendor lock-in at high-stakes decision points.Checkpoint-Based Governance (CBG): Ensuring named human arbiters hold binding authority over AI outputs.VAISA: The proposed Verified AI Inference Standards Act to enforce statutory accountability.Key Takeaway: The 12 to 15 years of hell Gawdat predicts is not inevitable; it is contingent on us failing to build oversight infrastructure. Dystopia is what happens without infrastructure, and the inevitable is actually a choice. Read the full paper: "The Inevitable Is a Choice" by Basil C. Puglisi, MPA at https://basilpuglisi.com/mo-gawdat-inevitable-choice/ or on SSRN. Explore the HAIA framework: github.com/basilpuglisi/HAIA This is #AIgenerated by NotebookLM from basil original paper for audio learners.

    23 min
  8. Empire of Evidence: Testing Karen Hao's 9 AI Claims Against Governance Infrastructure

    MAY 10

    Empire of Evidence: Testing Karen Hao's 9 AI Claims Against Governance Infrastructure

    Investigative journalist Karen Hao spent eight years and conducted over 300 interviews examining the AI industry. Her book Empire of AI won the National Book Critics Circle Award for Nonfiction, reached the New York Times bestseller list, and earned her a place on TIME's TIME100 AI list. In her March 2026 interview on The Diary of a CEO, she made nine specific claims about how major AI companies operate. This episode is an audio examination of all nine claims, testing each against available evidence and mapping the strongest findings to published open-source AI governance architecture. Five claims held under scrutiny: Knowledge Production Control. AI companies fund the scientists who study their own systems and censor researchers who produce inconvenient findings. Google fired AI ethics co-leads Dr. Timnit Gebru and Margaret Mitchell. Congress cited Hao's reporting five times. AGI Definition Shifting. OpenAI describes artificial general intelligence differently depending on the audience. The OpenAI Charter, the Microsoft contractual threshold, the Congressional framing, and the consumer marketing describe fundamentally incompatible systems. Revenue-Driven Capability Selection. Internal documents show companies advance capabilities based on which industries pay the most, not on scientific priority. Data Annotation Labor Conditions. The annotation industry absorbs displaced workers and drives conditions downward through structural competition on speed and cost. Environmental Externalities. AI data centers consume massive resources. The Memphis Colossus facility runs on 35 gas turbines. Hao acknowledged a 1,000x unit error on one Chilean water figure, but the broader environmental reporting remains substantiated. Four claims required challenge: The Empire Analogy works as a structural lens but breaks down at literal comparison with colonial empires that enforced power through military violence. Self-Driving Car Predictions. Waymo reports 92% fewer serious-injury crashes, but those miles are in five US cities under mapped conditions. New York City rush hour, Bangkok traffic, and unpaved mountain roads in Peru would produce fundamentally different data. Bicycles vs. Rockets. AlphaFold was built by Google DeepMind on Google's TPU clusters. The "bicycle" came from the same corporate infrastructure Hao critiques. Intelligence Scaling. The mechanism debate is real, but measurable capability improvements in coding, reasoning, and planning are not hypothetical. The examination maps findings to AI Provider Plurality, the Economic Override Pattern, the Constitutional Wall Principle, and Multi-Provider Divergence through HAIA-CAIPR. All are published working concepts, not production-validated systems. Other governance approaches may address the same structural problems. Full white paper with complete sources and APA references: https://basilpuglisi.com/empire-of-evidence-testing-karen-hao-claims-governance-infrastructure/ Karen Hao's Diary of a CEO interview: https://www.youtube.com/watch?v=Cn8HBj8QAbk Open-source governance frameworks: https://github.com/basilpuglisi/HAIA AI Content Disclosure: This audio was generated by Google NotebookLM from the published article. NotebookLM audio cannot be edited after generation. The guidance instructions provided beforehand are the only editorial control available. Proper noun pronunciation varies in AI-generated audio. #AIassisted using the HAIA Ecosystem

    14 min
  9. AI Needs Infrastructure, Not More Regulation: Provider Plurality, GOPEL, and VAISA

    MAY 9

    AI Needs Infrastructure, Not More Regulation: Provider Plurality, GOPEL, and VAISA

    The American public does not need more AI regulation; it needs AI infrastructure. This episode walks through the AI Provider Plurality Congressional Package and the Verified AI Inference Standards Act (VAISA), submitted to the 119th Congress in February 2026. The proposal treats AI the way the country has historically treated aviation, highways, and telecommunications: government builds the public infrastructure that ensures safety and accountability, and private companies build the platforms that operate on top of it. What the episode covers: GOPEL, the non-cognitive Governance Orchestrator Policy Enforcement Layer that performs seven deterministic operations without thinking, and why a pipeline that cannot think is the only governance layer that cannot be manipulated by the AI it governs. Multi-AI provider plurality as the structural alternative to the cognitive cartel risk that emerges when control over critical inference infrastructure concentrates in a handful of corporations. The three Checkpoint-Based Governance operating models, from automated pipelines with single end-of-process human review through full manual human orchestration for the highest-consequence decisions. HAIA-Overwatch, the adaptive cognitive security shield that watches the GOPEL pipeline from outside the trust boundary and escalates from Responsible AI mode to AI Governance mode when discrepancies appear. VAISA's four-profile classification system, requiring cryptographic hardware-backed proof that sensitive data stays inside a Trusted Execution Environment during AI inference, upgrading the privacy standard from "trust us" to "prove it." The bipartisan ask: fund GOPEL through NIST and GSA, mandate API accessibility enforced by the FTC, and invest in small AI platforms through SBIR and STTR competitive grants to keep the market diverse. The full Congressional package, the VAISA legislative framework, supporting policy briefs, and the technical appendix live at https://basilpuglisi.com. Episode produced from source material at basilpuglisi.com, AI-narrated for clean consistent production with human review of the published version. The audio is #AIgenerated; the underlying source writing is #AIassisted using the HAIA Ecosystem.

    24 min
  • 9 Episodes

About

The Other AI turns Basil C. Puglisi's articles, white papers, and policy briefs into audio briefings on AI governance, augmented intelligence, human judgment, and human-AI collaboration. The format is built for the time and conditions in which people actually learn, whether running, driving, riding a train, or working on something else. Episodes are AI-narrated for clean, consistent production, and human review approves each publication before release. The complete original work, including details, sources, and citations, lives at basilpuglisi.com. Topics include HAIA-RECCLIN, Factics, Checkpoint-Based Governance, enterprise AI adoption, AI policy, cognitive enhancement, and the future of human authority over automated systems. This podcast is for executives, researchers, consultants, educators, policy thinkers, and AI practitioners who want more than AI hype. The show focuses on evidence, dissent, governance, measurable outcomes, and the role of human judgment when machines become more capable.

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