Bad Data Breaks AI More Than Bad Models
Shownotes
We talk about the real shift happening as AI moves from chatbot to teammate, and why persistent memory is the make or break layer for useful automation. We dig into governance, data hygiene, and practical workflow design so AI output gets better over time instead of turning into expensive noise.
• moving the show toward YouTube and a more educational format
• why memory matters when AI becomes a partner or agent
• how regulated industry thinking exposes weak spots in business data
• crystallization theory and remembering the path to the answer
• coding agents, vibe coding, planning, and scaling constraints like throughput
• governance basics including permissions, API calls, token usage, and QAQC
• hallucinations as a human and data problem more than a model problem
• using decay and forgetting to make memory useful
• the agency paradox of standardized delivery with client specific outputs
• domain expertise plus AI fluency as the new competitive advantage
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