AI code reviewed by AI can look correct, tests pass, style is fine and reviews approve, but still miss real requirements. The issue is not ability, it is circular validation. Both systems rely on the same patterns. Without a human-checked spec as the source of truth, correct can still be wrong.
AI coding is crazy fast, but without structure, things fall apart quickly. Templates turn one-off wins into something your whole team can actually rely on.
AI adoption is nearly universal, but the 2025 DORA Report shows that faster coding doesn’t always mean increased productivity.
AI is a developer productivity force multiplier, but poor documentation, unclear ownership and fragmented tooling don’t magically disappear with AI.
Systemic engineering bottlenecks won’t disappear by just throwing AI at them. You’ve got to fix the roadblocks to realize the full value of AI tools.