Why Traditional Methods Fall Short with AI

The Fundamental Mismatch

Traditional methodologies assume human limitations:

• Developers write ~100 lines/day
• Context switching is expensive
• Knowledge transfer takes time

AI breaks these assumptions:

• Generates thousands of lines/minute
• No context switching cost
• Instant “knowledge” of any framework

Where Waterfall Breaks

Problem 1: Upfront Design Becomes Guesswork

• AI can prototype five architectures while you’re writing requirements
• Detailed specs become prison bars, not guidelines
• By implementation phase, better patterns have emerged

Problem 2: Phase Gates Block Learning

• AI reveals design flaws immediately through code
• Waiting for “testing phase” wastes AI’s rapid feedback
• Sequential phases ignore AI’s iterative nature

Where Agile Stumbles

Problem 1: Sprint Velocity Becomes Meaningless

• AI completes “8 story points” in 10 minutes
• Planning poker is absurd when AI codes at conversation speed
• Team velocity metrics don’t capture AI amplification

Problem 2: Ceremonies Become Bottlenecks

• Daily standups slower than AI implementation
• Sprint reviews can’t keep pace with AI output
• Retrospectives happen after AI has moved on

The Core Issues

1. Trust vs Verification Gap

Traditional: Trust developers to write correct code Reality: AI needs constant verification, not trust

2. Planning vs Discovery

Traditional: Plan then execute Reality: AI enables discovery through execution

3. Documentation Timing

Traditional: Document after stability Reality: Documentation drives AI behavior

4. Error Philosophy

Traditional: Prevent errors through process Reality: Fix errors faster than preventing them

The New Reality

With AI, you’re not managing code creation - you’re managing code generation. This requires:

• Real-time steering, not upfront planning
• Continuous verification, not phase gates
• Living documentation, not post-facto specs

Traditional methods optimize for human constraints that no longer exist.