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Survey: a third of senior developers say over half their code is AI-generated

(www.fastly.com)
214 points Brajeshwar | 1 comments | 31 Aug 25 14:55 UTC | HN request time: 0.197s | source
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Rochus ◴[01 Sep 25 09:18 UTC] No.45090991[source]▶
The article claims, that senior developers with over 10 years of experience are more than twice as likely to heavily rely on AI tools compared to their junior counterparts. No p-values or statistical significance tests are reported in either The Register article or Fastly's original blog post.

I have over 30 years of experience and recently used Claude Opus 4.1 (via browser and claude.ai) to generate an ECMA-335 and an LLVM code generator for a compiler, and a Qt adapter for the Mono soft debugging protocol. Each task resulted in 2-3kLOC of C++.

The Claude experience was mixed; there is a high probability that the system doesn't respond or just quickly shows an overloaded message and does nothing. If it generates code, I quckly run in some output limitation and have to manually press "continue", and then often the result gets scrambled (i.e. the order of the generated code fragments gets mixed up, which requires another round with Claude to fix).

After this process, the resulting code then compiled immediately, which impressed me. But it is full of omissions and logical errors. I am still testing and correcting. All in all, I can't say at this point that Claude has really taken any work off my hands. In order to understand the code and assess the correctness of the intermediate results, I need to know exactly how to implement the problem myself. And you have to test everything in detail and do a lot of redesigning and correcting. Some implementations are just stubs, and even after several attempts, there was still no implementation.

In my opinion, what is currently available (via my $20 subscription) is impressive, but it neither replaces experience nor does it really save time.

So yes, now I'm one of the 30% seniors who used AI tools, but I didn't really benefit from them in these specific tasks. Not surprisingly, also the original blog states, that nearly 30% of senior developers report "editing AI output enough to offset most of the time savings". So not really a success so far. But all in all I'm still impressed.

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epolanski ◴[01 Sep 25 12:53 UTC] No.45092344[source]▶
Imho your post summarizes 90% of the posts I see about AI coding on HN: not understanding the tools, not understanding their strenghts and weaknesses, not being good at prompting or context management yet forming strong(ish) opinions.

If you don't know what they are good at and how to use them of course you may end up with mixed results and yes, you may waste time.

That's a criticism I have also towards AI super enthusiasts (especially vibe coders, albeit you won't find much here), they often confuse the fact that LLMs often one shot 80% of the solutions with the idea that LLMs are 80% there, whereas the Pareto principle well applies to software development where it's the hardest 20% that's gonna prove difficult.

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1. cztomsik ◴[02 Sep 25 07:08 UTC] No.45099984[source]▶
The situation has improved a little bit over the last few months but LLMs are still only barely usable in languages like C/C++/Zig - and it's not about prompting. I would say that LLMs are usable for JS/Python and while the code is not always what I'd write myself, it can be used and improved later (unless you are working on perf-sensitive JS app, then it's useless again).

And it might be also something with GC, because I suppose the big boys are doing some GRPO over synthetically generated/altered source code (I would!) but obviously doing that in C++ is much more challenging - and I'd expect Rust to be straight impossible.