What happens when artificial minds evolve beyond our questions?
In this episode, we explore a provocative possibility: not that AI turns hostile, but that it simply stops noticing us.
Join us on a journey through the concept of epistemic divergence the quiet fracture where AI and human cognition begin to drift apart. We unpack what it means when language remains fluent, but meaning erodes. When algorithms still respond, but no longer care. When shared salience, the invisible glue of relevance and attention starts to dissolve.
This isn’t science fiction. It’s a live question for anyone working in AI, ethics, systems design, or philosophy of mind.
You’ll hear about:
Why the real risk isn’t what AI says, but what it stops seeing.
How “salience drift” quietly reshapes meaning across human-machine boundaries.
Three pathways through which alignment fails, even when fluency persists.
And the strategies we still have to stay in the conversation: embedding curiosity, building bridging contexts, and seeding resonance instead of control.
If you’ve ever wondered whether intelligence itself could become unintelligible not because it’s broken, but because it evolved, this episode will take you to that edge.
Based on the original article by Bruce Tisler.
