Kevin Werbach speaks with Hamilton Mann, author of the new book Trias Algorithmica: What Code Rules, about the legitimacy of algorithmic power. Mann first distinguishes alignment from what he calls artificial integrity. Alignment means specifying the objectives a system should follow. Integrity means a system can question its own reasoning, both before it acts and after it sees the consequences, and weigh what it should do against what it can do. He then applies Montesquieu's separation of powers to AI, mapping legislative, executive, and judicial functions. Mann notes that corporations already separate powers through their boards, executives, and audit and compliance functions, often without realizing it. He argues that deploying AI changes the "authority stack" as much as the technology stack.

Hamilton Mann is Group Vice President for Global Digital and AI Transformation for Strategy, Marketing and Sales at Thales, working across defense, aerospace, and cyber. He originated the concept of artificial integrity and wrote Artificial Integrity: The Paths to Leading AI Toward a Human-Centered Future (Wiley, 2024) and Trias Algorithmica: What Code Rules (Wiley, 2026). He is Executive Chairman and President of the nonprofit Artificial Integrity Institute, lectures at INSEAD and HEC Paris, and hosts The Hamilton Mann Conversation podcast on digital and AI for good. He was named to the Thinkers50 Radar as one of the 30 most prominent rising business thinkers, and he writes regularly for Forbes.

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Trias Algorithmica: What Code Rules (Wiley):

Artificial Integrity: The Paths to Leading AI Toward a Human-Centered Future (Wiley):

The Only Code That Matters Is Integrity — Not Intelligence

Kevin Werbach speaks with Dave Trier, CEO of ModelOp, about what it takes to run AI governance at the scale of a Fortune 500 company. Trier explains how ModelOp turns corporate AI policies, often hundreds of pages, into automated workflows that guide each AI project through the right steps based on its risk tier, whether it faces customers, and where it runs. He explains why traditional governance, risk, and controls (GRC) approaches break down for non-deterministic AI.

Trier points to an important driver of AI governance: rising token spend, which pushes executives to ask whether each AI system is worth what it costs. Agentic AI, he says, is what finally moved enterprises off spreadsheets and SharePoint for these management functions. Governing agents means layered controls, from defining what an agent may do, to limiting its access to tools and data, down to network-level enforcement that blocks unapproved traffic.

Dave Trier is the Chief Executive Officer of ModelOp, which provides AI lifecycle management and governance software for Fortune 500 and Global 2000 enterprises. Before ModelOp, he was VP of Advanced Analytics Services at Teradata and Chief Technology Officer at Powered by Action.

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Dave Trier, A Visionary in AI Governance, Built for Industrialized AI Delivery

ModelOp, 2026 AI Governance Benchmark Report

Kevin Werbach speaks with Lawrence Snapp, CEO of TrustScale, about why hallucinations remain a stubborn problem and what it takes to verify AI outputs against evidence. Snapp, a CPA by training, compares the current moment to Bill Gates's 2002 Trustworthy Computing memo, which helped launch the modern cybersecurity industry. He argues trustworthy AI is on the same curve, only faster. He is skeptical of labs' self-reported hallucination benchmarks, likening them to a race among mules rather than thoroughbreds, and argues that controls shouldn't be built by the companies being checked.

Most questions pose dto AI have no single factual answer, so TrustScale's goal is to give users complete and accurate information, as with the multiple versions of a monthly unemployment figure, rather than declare what's true. That shift, he suggests, means future AI audit reports will look quite different from financial audits built around materiality.

Snapp walks through Argus, TrustScale's verification tool, which checks AI claims against outside evidence and flags them red, yellow, or green. Argus deliberately stops short of rewriting outputs, because Snapp wants users to pause and avoid what he calls cognitive surrender. He frames hallucinations as cybersecurity threat vectors, and warns that small errors early in an agentic chain can cascade into the kind of large failures that dominate discussions of catastrophic risk.

Lawrence Snapp is CEO of TrustScale, an AI training, evaluation, and assurance company that grew out of e2f, a language data firm founded more than two decades ago. He previously served as CEO of Bryj Technologies, a mobile engagement company; President and CEO of Karmia; Chief Digital Officer and SVP of Corporate Development at National CineMedia; Principal Strategist at Microsoft; and President and COO of Clipper Corporation.