March 17, 2026
More code, more chaos?
If you thought the code writing speed was your problem; you have bigger problems
AI makes more code, but the line to go live just got longer
TLDR: Execs are pushing AI assistants to crank out 40% more code, but commenters say the real slowdown is reviews, testing, and getting features live. The thread splits between “just review as you go,” “fail faster to learn,” and cynics who claim leadership cares more about slide‑deck wins than quality.
VPs came home from conferences declaring “40% more code!” and the internet rolled its eyes so hard it hit the back wall. Commenters say the real problem isn’t typing speed—it’s the traffic jam after the keyboard. The thread’s loudest chorus waves the flag of the “Theory of Constraints,” shouting that speeding up a non‑bottleneck step just piles up unfinished work. One user even dropped a nod to The Goal, the cult business book explaining why more parts don’t mean more cars. Translation: more code, same review line = chaos.
Solutions, snark, and side‑eye flew. The new meme: “Push one PR; review one PR” (PR = a code change waiting for approval). A contrarian shot back: why not learn faster by building the wrong thing faster? Cue debate: rapid experiments vs. reckless overload. The cynics had a field day, roasting execs “hooked by vendor slide decks” and claiming companies don’t want good code—just numbers to brag about. People joked about “Jurassic” developer memory when reviews land a week late, flaky tests that magically pass on the second try, and features trapped in meetings-about-meetings purgatory. The vibe? Gleeful roasting of leadership, grim humor from veterans, and an anxious reality check: the bottleneck is understanding, review, testing, and actually getting it live, not how fast you can summon code with AI.
Key Points
- •The article argues that boosting code-writing speed with AI assistants addresses a non-bottleneck step in many orgs.
- •Applying the Theory of Constraints, it states system throughput is limited by the single bottleneck, not upstream speed.
- •Increasing code output creates queues at reviews, raising lead times and risking quality degradation.
- •Pressure from backlogs can lead to rubber-stamped reviews, flaky CI delays, and prolonged staging due to manual approvals.
- •Overall delivery slows and quality suffers when optimizing coding without addressing review, CI, and release bottlenecks.