February 2, 2026
Devs vs. bots: pass the popcorn
Coding assistants are solving the wrong problem
AI code helpers are fixing commas, not problems — and devs are roasting
TLDR: The article says AI code assistants pump out more tasks but add bugs and confusion, arguing we need tools that improve discussion instead. Comments split between “this is marketing” and “AI helps if you’re skilled,” with extra heat over mid-level jobs getting squeezed while seniors post big wins.
Another day, another flame war: a new post claims coding bots boost output but bloat chaos, citing stats like teams doing 21% more tasks with no real gains, pros being 19% slower while thinking they’re faster, and nearly half of AI-written code flagged as insecure. The twist? The comments are the main event.
One camp lit the torches. verdverm called it “tl;dr content marketing,” grumbling that it leans on old numbers while cooler ideas like agent swarms and airline-style checks are getting ignored. zkmon piled on with a snarky “why is this on the front page?” and a “greenish glow” zinger that the thread turned into a running joke. Translation: some devs think this is a sales pitch dressed as a think piece.
But the pro-AI crowd didn’t stay quiet. Quothling dropped a humblebrag: they had Claude upgrade a gnarly HubSpot integration “with basically no human interaction” besides review. The catch? They warn these tools fall apart when the real problem (bad or changing requirements) only shows up mid-build. Meanwhile, veteran micw framed AI as an enabler—great if you already know the craft, dangerous if you don’t.
The bigger mood: AI might be squeezing mid-level jobs (as monero-xmr put it), helping seniors fly while juniors get blamed when bots ship bugs. Drama, memes, and a very online question: are bots writing code, or just writing checks devs have to cash?
Key Points
- •Index.dev (2025) found teams using AI completed 21% more tasks without improving company-wide delivery metrics.
- •METR (2025) reported experienced developers were 19% slower with AI assistants, despite believing they were faster.
- •Apiiro (2024) reported 48% of AI-generated code contained security vulnerabilities, contributing to downstream remediation.
- •The article argues AI assistants require precise requirements; unresolved gaps often get buried in large code changes, causing breakages and maintainability issues.
- •Seasoned engineers report strong outcomes (e.g., Google principal engineer, Boris Cherny), but such benefits depend on senior expertise and organizational autonomy; junior/mid-level engineers face constraints and rising expectations.