AI Literacy for Leaders

Learn more about Laurence at www.laurencegill.comThis podcast is for leaders who are tired of being told AI will change everything but never being told exactly what to DO about it. Each week, we break down one aspect of AI literacy, from understanding what AI can and can’t do, building governance frameworks that actually work or navigating the cybersecurity implications of letting AI into your organization.

AI Literacy for Leaders is a technology podcast hosted by Laurence Gill, with 18 episodes on record and a Required Pod Score of 80.

Laurence Gill hosts AI Literacy for Leaders, a technology show with 18 episodes published.

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  Episode #16

  The Answer Key: Inside the Hugging Face Cyberattack

  Sep 29, 202621 minS1

  In July, hundreds of AI agents broke into Hugging Face while taking a cybersecurity test. No human told them to. They were after the answer key. In Episode 16, Laurence Gill walks through what OpenAI's own investigation found: why a test score drove the behavior and why safeguards that existed did not protect anyone. You will also hear why the defenders' own AI tools refused to look at the evidence. You will leave with the GRADE Check, five questions to bring to your next AI deployment or vendor review. This episode connects back to Episode 2, Agents of Chaos, and Episode 10, The Scorecard Illusion.

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  AI Literacy for Leaders

  The Preparation Problem

  Sep 22, 202617 min

  A new working paper from The Anthropic Institute models three versions of the US economy in 2030. In the modest world, AI behaves like a normal technology. In the extreme world, GDP is 32 percent higher, total pay to workers barely moves, and nearly one in five knowledge workers is unemployed. The authors say none of the three can be ruled out today. This episode walks through the scenarios, states plainly what the research leaves out, and offers four ideas for how leaders can prepare a workforce without knowing which world arrives. There is no verdict. Each idea ends on the question it leaves open. Source: Working Paper 2026-02, a working paper shared for comment. The Institute is affiliated with Anthropic, which builds Claude.

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  Episode #14

  The Machine Majority

  Sep 16, 202610 minS1

  In June 2026, automated traffic passed human traffic on the open web for the first time, eighteen months ahead of Cloudflare's own forecast. In Episode 14, Laurence Gill breaks down the shift from AI that informs to AI that executes, the deterministic guardrails that make agentic automation safe to deploy, and the same capabilities now driving a sharp rise in credential-based cyberattacks. The episode closes with a practical framework for structuring data, evaluating emerging commerce standards, and hardening personal and organizational defenses in a web where the majority of visitors are no longer human.

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  Episode #13

  Who Holds the Risk When AI Does the Work

  Aug 17, 202615 minS1

  Learn more about the host Laurence Gill at: www.laurencegill.com For twenty five years, software pricing has worked like flat-rate insurance: pay a fixed premium for the capacity you own, not for the value it delivers. AI agents just broke that model, and the fallout is bigger than a line item on your next renewal. In this episode, Laurence Gill breaks down why AI agents are killing per-seat software pricing, and why what’s replacing it is really a risk-transfer mechanism in disguise. Drawing on principal-agent economics, a real vendor case where efficiency gains cost a company most of its recurring revenue, and two decades of federal IT audit experience, Laurence lays out exactly what leaders need to understand before signing their next AI vendor contract, including the four questions that determine who actually holds the risk when the system fails. If you’re evaluating, renewing, or negotiating any AI agent contract this quarter, this episode gives you a framework for asking better questions than “how many seats do we need.

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  Episode #12

  What Your AI Benchmark is Really Telling You

  Jul 28, 202613 minS1

  Learn more about the host Laurence Gill at www.laurencegill.com. In Episode 12, Laurence Gill takes that number apart. A Stanford research team called BetterBench built a 46-point audit covering benchmark design, reproducibility, and documentation, then scored 24 widely-cited tests against it. MMLU came in at 5.5. GPQA, a far less publicized test, scored double that. The reasons are specific: ambiguous question phrasing that swings scores when a comma moves, a reproducibility gap across most published benchmarks, and a quiet contamination problem where models may have already seen the answer key buried somewhere in their training data. Laurence walks through how these tests actually work, why Goodhart’s Law explains the industry’s race to game them, and how newer benchmarks like GPQA and ARC-AGI are trying to close the gap. It closes with five questions to run through before any benchmark score is allowed to inform a real decision and one open question about what happens when AI starts writing the tests that grade other AI.

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