Easy for Humans, Hard for Machines: The Paradox Nobody Talks About

Why can AI crush law exams and chess grandmasters, yet still struggle with word games? In this episode, Kimberly and Jessica use Moravec's Paradox to unpack why machines and humans are "smart" in such different ways—and what that means for how we use AI at work and in daily life.

They start with a practical fact-check on agentic AI: what actually happens to your data when you let tools like ChatGPT or Gemini access your email, calendar, or billing systems, and which privacy toggles are worth changing. From there, they dive into why AI fails at the New York Times' Connections game, how sci-fi anticipated current concerns about AI psychology decades ago, and what brain-computer interfaces like Neuralink tell us about embodiment and intelligence.

Along the way: sycophantic bias, personality tests for language models, why edtech needs more friction, and a lighter "pit and peach" segment with unexpected life hacks.

Resources by Topic

Privacy & Security (ChatGPT)

OpenAI Memory & Controls (Official Guide)

OpenAI Data Controls & Privacy FAQ

OpenAI Blog: Using ChatGPT with Agents

Moravec's Paradox & Cognitive Science

Moravec's Paradox (Wikipedia)

"The Moravec Paradox" - Research Paper

Sycophancy & LLM Behavior

"Sycophancy in Large Language Models: Causes and Mitigations" (arxiv)

"Personality Testing of Large Language Models: Limited Temporal Stability, but Highlighted Prosociality"

Brain-Computer Interfaces & Embodied AI

Neuralink: "A Year of Telepathy" Update

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