AI-driven code generation speeds output but worsens delivery risk; the article argues that Extreme Programming's intentional constraints, like pair programming, are more needed than ever to keep software human-centric.
The explosion of AI tooling lets teams spin up whole products, features and infrastructure in hours. Yet the classic delivery failures persist: projects miss budgets, timelines, and user needs. The article points out that speed alone isn't the problem; the bottleneck has shifted to validation, alignment and learning.
Extreme Programming, created in the late 1990s, deliberately inserts friction-pair programming, small batches, continuous testing-to force teams to pause, share context and catch mistakes early. Pair programming halves raw output on paper but doubles shared understanding, surfacing assumptions and building trust. Those constraints become critical when AI can generate code faster than humans can review it.
AI-augmented development amplifies a known risk: large language models degrade in accuracy toward the middle of their context windows, leading to brittle, tangled code that spirals into technical debt. Without XP-style constraints, autonomous agents can pile unvalidated logic on top of each other, creating entropy that no tool can clean up. The piece argues that re-introducing XP practices restores the human feedback loops needed to keep software reliable.
Finally, the article reminds leaders that software remains a human discipline. Values like simplicity, communication, feedback, respect and courage shape collaboration as much as code. By aligning product strategy, operating rhythms and engineering practices around people-not just platforms-teams can achieve sustainable delivery even as AI accelerates output.
Check out the full stdlib collection for more frameworks, templates, and guides to accelerate your technical leadership journey.