Module 6: Vibe Coding — AI Strategy for Business Leaders

What this module covers

Apply a risk checklist to any AI-built prototype before shipping it, recognise the patterns where AI coding assistance genuinely earns trust, and track how your own judgement should change as you gain experience.

Lessons in this module

6a — Wrong-environment assumptions
Instructions assume a standard IDE when the actual AI/no-code tool works differently.
6b — Security risks AI won't flag
AI answers what's asked but won't volunteer that a shared credential unlocks far more than the builder realises.
6c — Fragile multi-step processes
Long dependency chains break at any single step, leaving a non-technical builder stranded mid-sequence.
6d — Complexity normalised, not questioned
AI explains why something is complex rather than finding a simpler path.
6e — Silent architectural debt
AI answers each question well while letting inconsistencies accumulate across sessions unaudited.
6f — What AI coding assistance actually gets right
Three genuine strengths, and a three-year judgement arc from taking AI output at face value to building checks into an automated pipeline.

Deliverable

Try this: A “vibe-coding risk checklist” you apply before shipping your own AI-built prototype.

Check your understanding

1What is the core lesson of the “won't proactively flag security risk” failure pattern?

AI never gives correct security advice
AI answers what's asked but often won't volunteer risks the builder didn't know to ask about
Security is not a real concern in AI-built products
Only enterprise tools have this problem

2What does it mean that multi-step AI-assisted processes are “fragile”?

The steps take too long to read
A single failure anywhere in a long dependency chain can break the entire process, often leaving the builder without context to recover
AI cannot generate multi-step instructions at all
This only applies to mobile apps

3What is one example of AI coding assistance catching something valuable?

It refused to write any code without human approval
It flagged a hardcoded model-tier configuration that would have caused unnecessary cost at scale if left unchecked
It automatically fixed every bug without being asked
It replaced the need for any testing

4According to the three-year judgement arc, what changed most between the early and later stages?

Nothing — judgement about AI output stayed constant throughout
Checks moved from reactive (fixing things after they broke) to systemic (built into an automated pipeline run on every change)
The builder stopped using AI assistance entirely
AI-generated code stopped requiring any review