
The AI Governance Pattern Hiding in the Senate EdTech Hearing: The Horvath Case Study
The Other AI: Audio Briefings on Augmented Intelligence and AI GovernanceShow Notes
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.
More from The Other AI: Audio Briefings on Augmented Intelligence and AI Governance
Every organization that puts AI to work makes the same choice, whether it names the choice or not. It can let agents carry the work with machines checking machines, or keep human agency in the work, with a named person holding authority at checkpoints and answering for what comes out. This Deep Dive on Basil C. Puglisi's article walks through Responsible AI as the factory and AI Governance as the handmade product, where each one belongs, and the rule each one carries. It covers why presence is not authority, the inspection mark a handmade product can carry, and cases that run from an airline chatbot to Australia's Robodebt scheme. It also weighs what research on disclosure and labels has found and what none of it has tested, along with the procurement question any officer can answer. It then covers the EU AI Act and how an SEC enforcement action, Colorado's new law, and New York's court rule reach the same line by other routes. https://basilpuglisi.com/agents-agency-transparency/ Podcast: The Other AI: Audio Briefings on Augmented Intelligence and AI Governance Spotify: https://open.spotify.com/show/033dvhzMIcWLdY7IUgsu7F Apple Podcasts: https://podcasts.apple.com/us/podcast/id1896506152 Amazon Music: https://music.amazon.com/podcasts/923d1a79-533f-4623-bae3-e2ba83453dfb YouTube playlist: https://www.youtube.com/playlist?list=PLchpU2bIYoEEBh2hdY-BVP9ckyTPiHOAQ Generated using NotebookLM. Content may have inaccuracies. For full detail refer to the original paper, document, or article. These are AI generated under NotebookLM as audio overviews not polished products. #AIassisted using the HAIA Ecosystem
In January 2026, one vocabulary covered both automation and human control, so a system that only watched itself could be sold as governed, and a person who only watched a screen could be counted as a governor. This episode follows the path from that definition problem to running code, from Checkpoint-Based Governance and the three layers of Ethical AI, Responsible AI, and AI Governance to the formal definition of who answers. The hosts walk the three HAIA Agents models. Agent Model 1, released in Alpha as the CBG Model 1 Runner, is Responsible AI written as code: a held-out Navigator, parallel review on three or more AI platforms, verification against pages the runner fetched, scoring on the author's own instruments, and a gate it cannot move, with a handoff to the human when a piece cannot pass. Agent Model 2, released as the CBG Gateway, is the hybrid that runs through to AI Governance at a signed human checkpoint after every role. The episode closes on Agent Model 3, Manual Human AI Governance, which built both releases, and on what the Alpha does not claim: neither program has been deployed, neither has called a live AI provider, and a signature establishes what was signed, not what was read. Read the full article: https://basilpuglisi.com/haia-agents-running-code/ Podcast: The Other AI: Audio Briefings on Augmented Intelligence and AI Governance Spotify: https://open.spotify.com/show/033dvhzMIcWLdY7IUgsu7F Apple Podcasts: https://podcasts.apple.com/us/podcast/id1896506152 Amazon Music: https://music.amazon.com/podcasts/923d1a79-533f-4623-bae3-e2ba83453dfb YouTube playlist: https://www.youtube.com/playlist?list=PLchpU2bIYoEEBh2hdY-BVP9ckyTPiHOAQ Generated using NotebookLM. Content may have inaccuracies. For full detail refer to the original paper, document, or article. These are AI generated under NotebookLM as audio overviews not polished products. #AIassisted using the HAIA Ecosystem
Tesla now calls itself a physical AI company, and its own record makes it the clearest live test of what autonomous really means. This deep dive follows the Optimus humanoid robot from the 2024 We, Robot demonstrations, where humans remotely operated much of the interaction, to Elon Musk's January 2026 statement that Optimus was not contributing to manufacturing work in any material way, and on to the new Fremont lines that feed an Optimus Academy for training data. It then follows the Robotaxi from its June 2025 launch with a passenger-seat safety monitor to the March 2026 letter in which Tesla told Senator Edward Markey how remote operators can redirect, command, and, as a last resort, control its vehicles. The episode covers NHTSA's audit of the Cybercab self-certification, the engineering analysis of Tesla's camera-based driving system, the capital behind the bet, and the investors and researchers who read the same record in opposite ways. The takeaway is that one word, autonomous, covers six operating modes, and every claim needs its mode, its date, its source, and a named human who decides. Read the full article: https://basilpuglisi.com/physical-ai-platform-shift/ Podcast: The Other AI: Audio Briefings on Augmented Intelligence and AI Governance Spotify: https://open.spotify.com/show/033dvhzMIcWLdY7IUgsu7F Apple Podcasts: https://podcasts.apple.com/us/podcast/id1896506152 Amazon Music: https://music.amazon.com/podcasts/923d1a79-533f-4623-bae3-e2ba83453dfb YouTube playlist: https://www.youtube.com/playlist?list=PLchpU2bIYoEEBh2hdY-BVP9ckyTPiHOAQ Generated using NotebookLM. Content may have inaccuracies. For full detail refer to the original paper, document, or article. These are AI generated under NotebookLM as audio overviews not polished products. #AIassisted using the HAIA Ecosystem
Chat made AI legible to every board. Robots that see, plan, and act now make it consequential in warehouses, labs, hospitals, and homes, where a wrong output moves mass instead of words. This episode works through the case that physical AI is the next platform shift, and through the reasons the deployment ledger still trails it. It covers the investor thesis that robot learning, autonomous science, and new human-machine interfaces are the next compounding domains beyond language and code. It weighs that thesis against independent economic evidence that robots can perform most physical work tasks in at least some settings yet are cost-competitive for only a small fraction of job tasks today. The hosts walk the model build path from NVIDIA Isaac GR00T, Google DeepMind's Gemini Robotics, and Physical Intelligence's π₀ through their 2026 successors, and why vendor results stay condition-specific. They work through the reliability arithmetic that decides whether a multi-step robot task survives production, and what a real deployment's hardware ledger reveals. They also cover why classical automation keeps the burden of proof on the learning system, and why every learned cell needs a tested fallback. The episode closes on governance. A weekly evidence cadence ends with a named person who decides to accept, modify, or reject, and who answers for the result. Model capability, deployment economics, and functional safety stay separate, and the checkpoint sits above engineered safety rather than replacing it. Read the full article: https://basilpuglisi.com/physical-ai-platform-shift/ Podcast: The Other AI: Audio Briefings on Augmented Intelligence and AI Governance Spotify: https://open.spotify.com/show/033dvhzMIcWLdY7IUgsu7F Apple Podcasts: https://podcasts.apple.com/us/podcast/id1896506152 Amazon Music: https://music.amazon.com/podcasts/923d1a79-533f-4623-bae3-e2ba83453dfb YouTube playlist: https://www.youtube.com/playlist?list=PLchpU2bIYoEEBh2hdY-BVP9ckyTPiHOAQ Generated using NotebookLM. Content may have inaccuracies. For full detail refer to the original paper, document, or article. These are AI generated under NotebookLM as audio overviews not polished products. #AIassisted using the HAIA Ecosystem
This episode is a story, told for entertainment. Teresa writes grant proposals for a food bank with a machine open on the second screen. Her brother-in-law Dale builds the air systems that keep hospitals breathing and has never typed a prompt. Over one backyard argument he asks the question every AI user hears, and her answer runs through Tron in 1982, Phil Tippett and the dinosaurs of Jurassic Park in 1993, and the two adaptations of The Shining, before it lands on the three old reasons people refuse a new tool. The history in the story is real and sourced. The people at the table are invented. Read the full story at https://basilpuglisi.com/intelligent-why-use-ai/ Podcast: The Other AI: Audio Briefings on Augmented Intelligence and AI Governance Spotify: https://open.spotify.com/show/033dvhzMIcWLdY7IUgsu7F Apple Podcasts: https://podcasts.apple.com/us/podcast/id1896506152 Amazon Music: https://music.amazon.com/podcasts/923d1a79-533f-4623-bae3-e2ba83453dfb YouTube playlist: https://www.youtube.com/playlist?list=PLchpU2bIYoEEBh2hdY-BVP9ckyTPiHOAQ Generated using NotebookLM. Content may have inaccuracies. For full detail refer to the original paper, document, or article. These are AI generated under NotebookLM as audio overviews not polished products. #AIassisted using the HAIA Ecosystem