The Road to Accountable AI

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. Transcript 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.

Kevin Werbach speaks with Joshua Tan, CTO of Current AI, about the case for public AI: The idea that AI can and should be public infrastructure, like highways, libraries, or the internet. Tan traces his path from theoretical machine learning at Oxford to starting the reseaerch group Metagov. He explains what work on decentralized and collective organizations can offer AI governance. He also discusses autonomous organizations run by AI agents, the safety questions that arise when agents hold bank accounts and manage human employees, and why incentives shift once these experiments reach real scale. Tan positions public AI as a third way between closed proprietary AI and open-weight models, which are struggling to keep pace with frontier-scale capital costs. Current AI focuses on everything beyond the model, aiming to build a fully open source stack on top of fully open models, a Linux for AI. He describes this as a Honda Civic strategy: not at the frontier, but close enough, cheaper, and reliable. The conversation also covers the role of government funding, a new Swiss cooperative, a community-owned data center effort in Maine, and what it would take for Europe and other middle powers to compete. Joshua Tan is CTO at Current AI. He also leads product and strategy for the public AI movement and leads Stanford AO, an incubator and lab for autonomous organizations at Stanford OpenLab. He previously co-founded Metagov, a lab for engineering institutions, completed his PhD and postdoc in computer science at Oxford, and held fellowships at Stanford, Princeton, and MIT. He founded and led DAOstar. Transcript Joshua Tan research page Current AI Not just the model (Artifacts Interview)

Kevin Werbach speaks with Brett Levenson, co-founder and CEO of Moonbounce, about building real-time controls that make AI systems follow the policies companies write for them. Drawing on his years leading integrity work at Meta after the Cambridge Analytica scandal, Levenson explains why content moderation policies there could not be enforced consistently. His team shifted from a decision-first approach to what he calls info-first enforcement, breaking complex policies into simple yes-or-no questions organized in a decision tree. Reviewer consistency jumped dramatically. That atomized approach now underlies Moonbounce, which acts as a semantic firewall that evaluates every input, output, and action an AI takes. Levenson argues that AI made the application layer non-deterministic, breaking security models that assume predictable software. He describes the challenge of making decisions within a few hundred milliseconds, and a decision memory system that keeps policies adaptive by incorporating customer corrections without hidden state. He also explains Moonbounce's move beyond simple allow-or-block choices to escalating to humans and rewriting outputs. Werbach and Levenson discuss the legal tension around audit trails, payment providers acting as de facto regulators, and why Levenson believes the industry needs measurable, falsifiable standards. Brett Levenson is co-founder and CEO of Moonbounce. He left Apple in 2019 to lead business integrity at Facebook during the Cambridge Analytica fallout, and later headed Meta's Integrity unit. He co-founded Moonbounce with CTO Ash Bhardwaj, a former Apple engineering leader who built large-scale cloud and AI infrastructure. Moonbounce has raised $12 million in funding co-led by Amplify Partners and StepStone Group. Transcript The Facebook insider building content moderation for the AI era

Kevin Werbach speaks with AI journalist Sharon Goldman about her new venture, Ground Level AI. Goldman describes taking on the AI beat at VentureBeat in 2022, months before ChatGPT launched, and then learning to translate technical developments for Fortune's broad business audience. She explains why she left to launch her own newsletter and podcast, focused on infrastructure, security, governance, policy, and the communities absorbing AI's effects. Goldman shares her reporting from data center sites, and weighs how much of the data center backlash reflects local impacts versus broader anxieties about AI. The conversation turns to cybersecurity, where the speed and scale of AI-enabled attacks may outpace defenders. Goldman argues that the AI safety and cybersecurity communities, long divided by culture, must now work together. She closes on what she thinks is underappreciated: how quickly things are changing. Sharon Goldman is the founder of Ground Level AI. She spent four years covering the AI boom, most recently at Fortune and before that at VentureBeat, and has more than 15 years of experience covering technology and business for publications including Business Insider, Forbes, and CIO. She launched Ground Level AI in June 2026 as a newsletter and podcast on Substack. Transcript Ground Level AI Who gets a seat at the table to decide if frontier AI is safe? AI safety and cybersecurity have long been two different worlds. In the age of AI agents, they're colliding