AI can rapidly produce 70% of a solution, but that final 30% - edge cases, security, production integration - remains as challenging as ever.
🔸 TLDR
▪️ AI boosts speed to “something working”, but engineering skill is what makes it “safe + shippable”.
🔸 THE QUOTE
“AI can rapidly produce 70% of a solution, but that final 30% – edge cases, security, production integration – remains as challenging as ever.” — Markus Eisele
Markus (a Java Champions member) has a point that hits hard in real projects: AI accelerates the draft, not the delivery.
🔸 WHY THE “LAST 30%” HURTS SO MUCH
▪️ Edge cases: weird inputs, nulls, timezones, retries, partial failures 😵💫
▪️ Security: authz gaps, injection paths, secrets, dependency risks 🔒
▪️ Production integration: CI/CD, configs, rollout strategy, backward compatibility 🚀
▪️ Observability: logs/metrics/traces that actually help at 3AM 📉
▪️ Performance & cost: load behavior, memory, cold starts, scaling 💸
🔸 HOW TO USE AI WITHOUT SHIPPING REGRET
▪️ Ask AI for options + tradeoffs, not “the final answer” ⚖️
▪️ Make it write tests first (unit + integration + contract) ✅
▪️ Add a security checklist: input validation, authZ, secrets, deps 🔍
▪️ Demand production readiness: config, health checks, timeouts, retries 🧰
▪️ Keep humans on the hook for review + ownership (esp. the “last 30%”) 🧑💻
🔸 TAKEAWAYS
▪️ AI is great for scaffolding and exploration 🏗️
▪️ The “last 30%” is where senior thinking lives (security, reliability, integration) 🧠
▪️ Treat AI as an accelerator, not an autopilot ✈️
▪️ Your real leverage: tests, reviews, and production discipline 🛡️
💬 Question: what’s the hardest part of your “last 30%” lately—edge cases, security, or prod integration?
#Java #SoftwareEngineering #AI #LLM #DevSecOps #Security #Testing #CodeReview #Production #PlatformEngineering #DeveloperExperience
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