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description: Developer surveys show near-universal AI coding tool adoption alongside widespread distrust of the output it produces: Stack Overflow data finds over 84% using...
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# Everyone Codes With AI Now. Almost Nobody Trusts It. Here’s the Skill That Fills the Gap.

**[Medium](https://daily.dev/sources/medium_js)** · 10 min read · 4 upvotes · 0 comments

## Summary

Developer surveys show near-universal AI coding tool adoption alongside widespread distrust of the output it produces: Stack Overflow data finds over 84% using or planning to use AI tools while 46% distrust its accuracy, and a SonarSource survey of 1,100+ developers found 96% don't fully trust AI code correctness yet only 48% always verify before committing. A METR randomized trial found experienced developers were actually 19% slower with AI despite predicting and perceiving a speedup, though METR now considers that result outdated for current tools. The piece argues the key skill for beginners is verification rather than typing, and proposes a five-step routine: predict outputs before running code, test normal cases, attack edge cases (zero, empty, negative, duplicates), explain the code aloud, and convert findings into permanent tests - illustrated with a Python off-by-zero slicing bug.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://medium.com/data-science-collective/everyone-codes-with-ai-now-almost-nobody-trusts-it-heres-the-skill-that-fills-the-gap-5eab1e75999b>

## Questions this post answers

### Does AI actually make experienced developers faster at coding tasks?

Not necessarily. A randomized controlled trial by METR had 16 experienced open-source developers work on 246 real issues in their own repositories, with AI tools randomly allowed or disallowed. Developers predicted a 24% speedup and believed afterward it had saved 20% of their time, but allowing AI actually increased completion time by 19%. METR has since called this result historical, noting it tested only early-2025 tools.

_Developers deciding how much to trust AI coding gains can track real speedup data alongside discussions like this on daily.dev._

### Why does a Python function like items[-n:] return the whole list when n is 0 instead of an empty list?

Because in Python, negative zero equals zero, so items[-0:] is interpreted as items[0:], which starts slicing from the beginning and returns the entire list rather than an empty one. This is a classic edge-case bug that passes normal testing with typical inputs like 2 or 5 but silently breaks when n equals zero, often surfacing only in production.

_Developers debugging subtle slicing edge cases can compare notes on Python gotchas like this through daily.dev._

### How many developers fully verify AI-generated code before committing it?

Only 48% always verify AI-generated code before committing, according to a 2026 SonarSource survey of more than 1,100 developers, even though 96% said they do not fully trust that AI-generated code is functionally correct. Separately, 38% of developers reported that reviewing AI-generated code takes more effort than reviewing code written by humans.

_Teams building code review habits around AI output can track verification practices and surveys like this on daily.dev._

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---

Tags: [#python](https://daily.dev/tags/python), [#testing](https://daily.dev/tags/testing), [#ai-coding](https://daily.dev/tags/ai-coding), [#code-review](https://daily.dev/tags/code-review)

[View this post on daily.dev](https://daily.dev/posts/everyone-codes-with-ai-now-almost-nobody-trusts-it-here-s-the-skill-that-fills-the-gap--1rcfejrwa)

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