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An AI answer can be polished, plausible, and completely wrong, and that’s where the real risk shows up. We sit down with Glenn Broder, founder of Learn AI Like a Pro and creator of Human Judgment in the Age of AI, to unpack a problem most AI adoption plans ignore: capability debt. You can gain speed and output while quietly losing the human skill to question, verify, and handle exceptions when the model fails.

We get concrete about what responsible AI governance looks like inside real teams. Glenn argues that plug-and-play tools often force employees to conform to the system, turning AI into a cognitive wheelchair. The alternative is designing a cognitive jungle gym where AI drafts the work but humans stay accountable for judgment. We talk about building deterministic layers, requiring proof and citations, and setting up workflows where people must read, edit, and own what gets sent.

Glenn also shares a practical five-stage approach to safer AI workflow automation: capture, validate, baseline, delegate, and retest. We explore how to retest human capability using real artifacts like email drafts and meeting follow-ups, why “confident hallucinations” are so hard to spot, and how structured debate style reasoning can train better critical thinking for the next generation of AI-augmented workers.

If you care about AI policy, workforce development, and keeping real expertise alive while using LLMs, this conversation will sharpen how you deploy AI without abdicating responsibility. Subscribe, share this with a leader on your team, and leave a review, what’s one AI output you would never send without double-checking?

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