AI Writes Code 10× Faster—What’s Your Value? I Can Take the Blame, Can AI?

AI Can’t Fully Replace Programmers

A former Meta engineer shared that, after handing most coding work to Claude Code, she concluded that AI is hard to truly replace programmers.

Why Teams Still Need You When AI Is Faster

Interviewers may ask, “AI reads code, writes APIs, and adds tests faster than you—why do we still need you?” A popular short answer is “I can take the blame, can AI?” but the article warns that this alone is insufficient.

Hiring managers actually want to hear how you use AI to boost efficiency and your perspective on AI‑assisted programming.

How the Author Uses AI

Currently about 99% of coding tasks are delegated to a “Coding Agent.” The author spends the remaining time on requirement confirmation, result validation, and deployment decisions: confirming the scope, checking whether the agent’s conclusions are reasonable, ensuring changes can be released safely, and planning recovery if problems arise.

AI Suggestions Must Be Verified – A Redis SCAN Case

The author recounts using MiniMax M3 with Claude Code to troubleshoot a Redis failure. The agent quickly pointed to the SCAN loop and suggested data‑structure adjustments, degradation, monitoring, and tests.

However, the agent incorrectly claimed that SCAN caused server blocking. In reality, SCAN is designed for incremental iteration to avoid the long‑running blocking of KEYS. The real pressure stemmed from the application side’s continuous traversal, connection usage, and task scheduling.

Therefore, after an agent highlights a suspect, the author first validates whether that point can explain the observed symptoms using logs, metrics, and call‑chains, then checks upstream/downstream compatibility, data consistency, and resource impact.

Clarify Requirements Before Generating Code

In the Spring AI interview platform, the author uses a tool called grilling to ask the requirements round‑trip before letting the agent produce code.

Defining direction early is crucial; otherwise fast code generation may need to be discarded.

Lightweight Specification Before Long‑Term Work

For larger projects, the author writes a brief spec that pins down the goal, constraints, and acceptance criteria, e.g.:

Goal: Fix order export timeout.
Constraint: Keep API fields and permission checks unchanged; no new response format.
Acceptance: Add failure test cases, run related tests after implementation, and report command, result, and diff.

This spec guides the agent’s work and the subsequent review.

Human Review After Agent Reports Completion

When the agent says the issue is fixed, the author examines the actual git diff, test coverage, interface contracts, and whether existing capabilities were unintentionally duplicated.

Automated tools can catch null‑pointer or formatting bugs, but human time is reserved for business semantics, transaction safety, permission checks, compatibility, and avoiding duplicate effort.

Risk Management and Safety Hooks

Production risks such as configuration, secret, and certificate access must be manually confirmed. Dangerous commands are blocked with pre‑execution hooks (e.g., Claude Code’s PreToolUse Hook) and sandboxing, while tests and CI pipelines catch quality regressions.

Interview Answer Guidance

Instead of reciting a long script, pick a concrete project, explain what AI helped with, what manual review uncovered, and how you validated the result.

AI writes code faster, and in my project I let Claude Code handle reading code, task breakdown, implementation, test addition, and minor refactoring. This frees me to focus on requirement confirmation and technical optimization. However, I never merge AI‑generated code without review. In the Redis case, the agent suggested fixing SCAN , but I discovered its explanation of blocking was inaccurate, so I performed a manual verification before acceptance. Thus, code merging, release decisions, and rollback plans still require human judgment.