What if today’s most powerful AI systems are closer to a free-floating hippocampus than to a thinking mind? We dive into the messy borderlands between neuroscience, semiotics, and political economy to ask what LLMs really do, why they feel authoritative, and where their limits begin. Along the way, we explore how humans negotiate meaning in real time while models operate in a frozen field of correlations, why that matters for education and writing, and how the surveillance stack turns our lives into tidy sequences for machines to memorize.

Together with our guest, we unpack grid cells, place cells, and the hippocampus as a vivid analogy for sequence modeling. Then we press on the big claims: can a next-token engine think, or does it merely interpolate? Why do these models stumble on math unless we bolt on tools? And how did the training corpus—heavy with ad copy, business speak, and now model-made text—nudge outputs toward a bland, consensus voice that can be tuned to institutional aims?

None of this unfolds in a vacuum. We follow the money to examine power costs, chip monopolies, and a rush to constant capital that favors server farms over genuine productivity gains. The result looks like a bubble stitched to state-capital priorities and fragile cloud infrastructure, not an inevitable march toward “superintelligence.” If planning is back on the table, we argue it needs new objectives: replace the one-size value function with interpretable quotas for health, learning, resilience, and ecological limits, and design cybernetic feedback that respects agency instead of erasing it.

Curious about a future where meaning stays alive and tools stay honest? Listen, share with a friend who’s wrestling with AI’s promises and pitfalls, and leave a review to tell us where you stand.

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Musis by Bitterlake, Used with Permission, all rights to Bitterlake

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Crew:
Host: C. Derick Varn
Intro and Outro Music by Bitter Lake.
Intro Video Design: Jason Myles
Art Design: Corn and C. Derick Varn

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