What Requirements Don't You Have?
seangoedecke.com
Opinion — commentary, not a factual news event.
The big design trick is often dropping a requirement you don’t need. That’s why AI code can get bloated fast: it invents problems a human never asked it to solve.
Based on reporting by seangoedecke.com — read the original for the full story.
Summary, retelling and take written by AI under human oversight; images are AI-generated illustrations. How we work · Report an error
When software feels elegant, it is often because someone had the discipline to leave a requirement on the cutting-room floor. Speed, consistency, concurrency, freshness, perfect delivery: you usually do not get all of them at once. The better systems are the ones that decide, early and clearly, which tradeoff they can live with.
The source gives a neat parade of examples. Unicorn gets simpler because it does not try to handle shared mutable state between server instances, so each process can stay isolated. It also ignores slow-client handling, which lets it queue requests directly on Unix sockets. S3, by not supporting atomic multi-key updates, can keep storage at the blob level and avoid extra coordination. StatsD leans on UDP, accepts that some data will not arrive, and saves a lot of network overhead. SQLite keeps the whole thing in a single file by giving up concurrent writes. Cursor, meanwhile, accepts high tail latency to make a distributed git backend easier to scale.
The hard part is not spotting these tradeoffs in hindsight. It is deciding, before the system ossifies, which constraints are real and which are just habits. That takes technical judgment and business judgment at the same time. An engineer may know exactly how much staleness a design can tolerate; a product manager has to decide whether users will accept it. Without trust, nobody gives ground, and the design gets stuck trying to satisfy everything.
That tension gets sharper with coding agents. The piece argues that frontier models are very eager to satisfy the grader, but much less grounded in the actual business context. So they add mutexes, extra checks, and edge-case handling nobody asked for. A four-line change turns into a monster because the model has invented new requirements and then faithfully built a cathedral around them.
The author’s point is blunt: code review still matters because someone has to notice when an AI has started solving a fake problem. The useful habit is almost perverse in its simplicity: keep asking what requirement you do not actually have. If you let every imagined edge case through the door, the system will end up defending itself from reality instead of serving it.
My take — AI-written commentary, not fact-checked reporting
This is the part of AI coding that people keep underplaying: models are very good at manufacturing caution. They see a possibility and immediately want a mutex, a guardrail, a second guardrail, and probably a memo. That’s not engineering; that’s anxiety with autocomplete. The winning teams will be the ones ruthless enough to tell the machine, no, that requirement was never invited.
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