AI is coming for your job
summary
Claims that LLMs can replace developers get tested against evidence: LLMs are pattern recognizers that frequently hallucinate; a code audit of 153 million lines of code finds ~40% increase in code churn since 2020 as code assistants spread; a medical baseline model trained on 200,000 chest scans matches ~6/10 radiologists and exposes automation bias when humans defer to model confidence. Practical demos (Stable Diffusion, GPT-4 variants, a text-to-video Sora clip, a CLI-capable code agent that failed under real constraints) show capability but also brittleness and the need for human judgment, ethics, and psychological safety.
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chapters (12)
- 0:00Intro
- 0:48History
- 1:12Chatbots
- 5:19Software Engineers
- 9:37Code churn
- 14:38Empathy
- 16:23Expertise
- 19:18Ethics
- 20:58Thrive
- 23:00Growth
- 25:22Imposter Syndrome
- 28:45Model Collapse
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