Chapter Six

Generation: Text,
Image, Video

Generative AI is the part of the stack that feels like magic — until you ship it. This chapter is the field manual for deciding when generation belongs in your product, how to productionize it without blowing up costs or credibility, and why most teams still get the economics wrong.

📖 ~12 min read Pages 41–47
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Meera's real cost $180 in API credits. $47K in legal scrub + QA + brand damage. Net negative.

Meera was buried in the marketing backlog at her Series B fintech. The CEO mandated 200 personalized landing-page variants — text, hero images, short video explainers. Her team of three had zero designers and a creative agency quote that would eat half the quarter's budget.

She spun up Midjourney for visuals, Claude for copy, Runway for clips. Forty-eight hours later she had the assets. They looked good. Until legal walked in.

“These images? Training data scraped from Shutterstock without licenses. And the video voiceover hallucinated a feature we don't even ship.” Meera's “2x faster” win became a three-week legal scrub. Output doubled. Trust dropped.

The rule Pure generation is flashy for demos. Transformation is what ships reliably in production.

Generative AI solves real problems — speed, personalization, ideation. But it hits the AI PM Trinity harder than anything else: messy training data (IP landmines), non-deterministic models (brand tone drift), and UX that swings between delight and disaster.

Chapter 6

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