Every AI PM course right now is teaching the wrong skill. They're all focused on prompt engineering, LLM integration, building with ChatGPT. Meanwhile, the AI PMs earning $900K at Netflix, Amazon, and Meta are being paid for a completely different competency: knowing which AI technique to use and, more... importantly, which ones to skip. Jyothi breaks it into three buckets. Traditional ML for structured data: fraud detection, churn prediction, anything where the problem fits in a spreadsheet with clear inputs and a predicted output. A regression model solves this in a week at a fraction of LLM costs. Deep learning for perception tasks: images, video, audio. If humans do it easily but you can't write explicit rules for it, that's neural network territory. Gen AI for natural language interfaces, content generation, and reasoning across unstructured information. The diagnostic she gives PMs is worth memorizing. Can I put this problem in a spreadsheet? Start with ML. Is this a perception problem? Deep learning. Does it require reading, writing, or conversational interaction? Gen AI. Here's what the clip doesn't say but the career math makes obvious. At $900K comp, these companies expect you to own the P&L impact of every AI decision. Choosing an LLM when XGBoost handles the job means 10x the compute cost, longer time to ship, and harder maintenance. The PM who picks the right technique is saving millions in infrastructure. That's what justifies the package. Most PM candidates walk into AI interviews ready to talk about ChatGPT. The $900K candidates walk in asking what type of problem they're solving first. That question is the whole interview.show more

Aakash Gupta

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