ABOUT THIS EPISODE
Most people use AI as a basic chatbot. Others use it to automate entire parts of their job or do cutting edge research. Eric and John unpack why the gap is so wide, and what to do on either side.
SummaryAndrej Karpathy recently argued that the gap in how people experience AI is getting wider, fast. Eric reads Karpathy’s post, and he and John dig into why: cost, access, interfaces that hide what's possible, and how quickly the tools keep changing.
Then they get practical. If AI has let you down before, it might be time to try again. Eric shares how rebuilding their podcast editing and organizing a music library with AI went from frustrating to genuinely useful. And if you're already deep into AI, John has a reminder: it's still a tool, and the results are the point.
In this episode00:56: What is the great disparity?
02:47: Karpathy's AI capability funnel
07:41: FOMO and the isolation of advanced AI use
14:34: Why the gap exists: access and cost
19:24: The Alexa problem: interfaces that hide what AI can do
26:35: If AI let you down, try it again
28:08: Rebuilding podcast editing and a music library with AI
34:07: Advice for power users: the results are the point
MentionedEric reads Andrej Karpathy's post on the widening gap in how people experience AI. Karpathy calls his numbers napkin math.
Eric's podcast-editing experiment runs on DaVinci Resolve and Model Context Protocol (MCP).
Spotify's Web API rate limits come up in the music-library story.
