HeyJaredAI Literacy for Leaders
This podcast is for leaders who are tired of being told AI will change everything but never being told exactly what to DO about it.
- Indexed episodes, last 90 days
- 6
- Latest publication
- Sep 30, 2026
- Audience
- Checking…
- Earliest in this view
- Jul 6, 2026
Latest episodes
The Answer Key: Inside the Hugging Face Cyberattack (opens the original)
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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
The Preparation Problem (opens the original)
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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 workforc
The Machine Majority (opens the original)
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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,
Who Holds the Risk When AI Does the Work (opens the original)
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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
What Your AI Benchmark is Really Telling You (opens the original)
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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 pub
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