This is honestly one of the clearest explanations of why AI coding workflows fail even when the models themselves are good.
Most teams are still treating AI like “generate code faster” instead of building proper product-to-implementation systems around it.
The point about agents inheriting product context instead of guessing it from prompts is huge. Without boundaries, wedges, exclusions, and journey-level validation, AI tends to default toward generic SaaS bloat surprisingly fast.
Also really liked the focus on testing user value instead of just testing mechanics. A passing test suite means very little if the user still cannot reach first value smoothly.
Honestly feels like the future AI-native teams will look less like “prompt engineers” and more like strong product architects with structured workflows.
This is also very aligned with how foundersbar.com thinks about early-stage product building: validation first, clear product bets, lean roadmap slices, and building only what actually supports the wedge.