{"slug": "ai-can-write-code-faster-than-i-can-responsibly-review-it", "title": "AI Can Write Code Faster Than I Can Responsibly Review It", "summary": "A developer using Claude Code while building Zenovay reports that AI can produce code faster than they can responsibly review it, creating 'review debt' where understanding is deferred. The engineer now separates thinking from writing, requires the rule to come from them, and keeps changes small enough to review without scrolling through an entire afternoon of generated code.", "body_md": "I use Claude Code almost every day while building Zenovay.\n\nIt can scaffold a feature, trace a bug across several files, write tests, refactor an old module, and explain an unfamiliar part of the codebase before I have finished my coffee.\n\nThat sounds like pure leverage.\n\nBut it created a problem I did not expect:\n\n**AI can produce code much faster than I can build a reliable mental model of it.**\n\nThe bottleneck is no longer writing code.\n\nIt is human attention.\n\nThere is something satisfying about watching an agent work through a task.\n\nFiles change. Tests appear. Type errors disappear. The terminal keeps moving.\n\nTwenty minutes later, there is a 700-line diff and everything is green.\n\nIt feels like a productive session.\n\nThen I open the diff and realize I cannot confidently explain every decision it made.\n\nWhy was this abstraction added?\n\nDoes this retry create duplicate events?\n\nIs the permission check happening at the API boundary, or only in the interface?\n\nWhat happens when the webhook arrives twice?\n\nDid the code preserve the privacy rule I described three prompts ago?\n\nThe dangerous output is rarely code that obviously fails.\n\nIt is code that compiles, passes the happy-path test, and quietly implements the wrong assumption.\n\nBefore coding agents, the effort required to write a feature naturally limited its size.\n\nYou had time to think while typing. You noticed awkward interfaces because you had to use them repeatedly. You remembered why a branch existed because you wrote it ten minutes earlier.\n\nAI removes much of that friction.\n\nThat is useful, but the friction was also doing hidden work.\n\nIt slowed the amount of new code entering the system.\n\nNow a solo developer can generate changes at something close to team velocity. The review capacity is still one person.\n\nThat creates a kind of review debt.\n\nEvery accepted line that I do not fully understand becomes a small future obligation. Maybe it is harmless. Maybe it becomes the function nobody wants to touch six months later because nobody remembers why it works.\n\nThe code was generated quickly.\n\nThe understanding was deferred.\n\nWhile building an analytics product, small technical decisions can carry real consequences.\n\nA mistake in attribution can show a customer the wrong acquisition channel.\n\nA mistake in session replay can capture something that should have been masked.\n\nA mistake in billing logic can affect real money.\n\nA mistake in authorization can expose one customer's data to another.\n\nI still use AI around all of these areas, but I do not let it silently decide the important boundaries.\n\nThere are a few things I now treat differently:\n\nThe agent can help implement the rule.\n\nThe rule itself has to come from me.\n\nI want to know exactly what enters the system, where it is transformed, how long it stays, and what must never be stored.\n\nA plausible-looking permission check is not enough.\n\nFor auth, subscriptions, invoices, refunds, and webhooks, I review the full path instead of only the changed function.\n\nAI is very good at generating a migration that looks correct.\n\nIt is less aware of the strange data already living in production, the old client still sending a deprecated field, or the rollback you will need at 2 AM.\n\nAn agent will happily turn an unclear requirement into working code.\n\nThat does not make the requirement correct.\n\nWhen the model fills in a missing product decision, it often chooses the most conventional answer. Conventional is not always what your product needs.\n\nI used to prompt for an implementation and review the result.\n\nNow I separate thinking from writing.\n\nFirst, I ask for the proposed approach, affected files, assumptions, failure cases, and the parts of the task that are still ambiguous.\n\nOnly then do I let it edit.\n\nI also try to keep each change small enough that I can review it without scrolling through an entire afternoon of generated code.\n\nFor critical paths, I ask the agent to attack its own solution:\n\nThis catches useful issues, but it does not transfer responsibility to the model.\n\nTests do not do that either.\n\nA test suite proves that the cases we thought about behave as expected. It says nothing about the cases nobody considered.\n\nAt the end, I ask myself one simple question:\n\n**Could I explain this change to another developer without mentioning the AI?**\n\nIf the answer is no, I am not ready to merge it.\n\nCoding agents are probably the biggest productivity improvement in my workflow.\n\nI am not going back.\n\nBut I no longer measure a good AI session by the number of files changed or how quickly the task reached a green checkmark.\n\nThe useful metric is how much verified progress entered the codebase.\n\nThat is usually less impressive than the generated diff.\n\nIt is also much more valuable.\n\nAI can help write the code.\n\nIt can suggest tests, point out risks, and challenge an implementation.\n\nBut once that code reaches production, the model is not maintaining the promise behind it.\n\nI am.\n\nHow are you handling this in your own workflow?\n\nDo you review every line of AI-generated code, or are there areas where you deliberately trust it more?\n\nDisclosure: I use AI heavily while building Zenovay and used it to help structure and edit this post. The opinions, examples, and final decisions are my own.", "url": "https://wpnews.pro/news/ai-can-write-code-faster-than-i-can-responsibly-review-it", "canonical_source": "https://dev.to/zenovay/ai-can-write-code-faster-than-i-can-responsibly-review-it-4ig4", "published_at": "2026-07-22 17:30:30+00:00", "updated_at": "2026-07-22 18:01:04.249314+00:00", "lang": "en", "topics": ["artificial-intelligence", "developer-tools", "ai-agents", "ai-safety", "ai-ethics"], "entities": ["Claude Code", "Zenovay"], "alternates": {"html": "https://wpnews.pro/news/ai-can-write-code-faster-than-i-can-responsibly-review-it", "markdown": "https://wpnews.pro/news/ai-can-write-code-faster-than-i-can-responsibly-review-it.md", "text": "https://wpnews.pro/news/ai-can-write-code-faster-than-i-can-responsibly-review-it.txt", "jsonld": "https://wpnews.pro/news/ai-can-write-code-faster-than-i-can-responsibly-review-it.jsonld"}}