AI: It AI-n't what you think!
summary
Claims that large models AI hallucinates facts — examples include a false death report and bogus customer-service refunds — and that generative systems are accelerated inference not intelligence. Practical wins: feeding legacy functions yields pinpoint bug diagnoses, IDE suggestions and bug fixes that often work. Warnings: generated code is inconsistent, unreliable for accountability, and needs human verification. Advice: use models for ideas and code inspection, treat data and requirements as the real asset, and reskill to focus on problem framing, judgment and business agility rather than line-by-line typing.
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chapters (12)
- 0:00Welcome
- 3:58Fascination and fear
- 8:15Embrace discomfort
- 12:16Evolutionary or revolutionary
- 18:07Generational tech change
- 25:12AI as alternate info
- 33:40When AI excels and where it fails
- 41:58AI for ideas not solutions
- 48:42AI and bad code cycle
- 51:19AI as black swan
- 56:39Adapting to change
- 59:11Skill up don't fear
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