alt Hacker NewsThere some real game theory mechanics at play when it comes to LLM usage in corporations. Devs are cranking out superficially superior code and documentation by just aiming the Claude Code fire hose at everything they can. Leadership encourages this because from what they can tell, there is no downside.
It's hard to say if this code is structurally better or worse than before, but it's certainly voluminous and as far as leadership could ever tell, with their flawed metrics, that is all that matters. It will be years before we figure out if this is a good idea and worth the cognitive atrophy.
Anyone not using LLMs all day is just not going to be as prolific. I can't imagine that the same factors aren't at play in the scientific research community where it's all about how much you can publish.
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> It's hard to say if this code is structurally better or worse than before, but it's certainly voluminous and as far as leadership could ever tell, with their flawed metrics, that is all that matters.
Hah. I've been working with ChatGPT-5.6 Sol and a lot of guidance to port a particular geometry from 2800 lines of Python-scripted Rhino3D (basically a custom parametric CAD kernel in there, plus use of it) to Python-scripted OCCT/FreeCAD (an existing CAD kernel) and it's up to 36,000 lines and only about half way there. And I've been setting goals and targets for duplication minimization, code size reduction, etc. The results are fine for my purposes, but if I put a positive value on "voluminous" I'd be super impressed; with my actual negative value assignment on lines of code, this is more "absolute crap but still useful to me."
I think "absolute crap but still useful to me" is a pretty high value and not worth neglecting, but I'm definitely scared by leadership who sees the toilet overflowng and assumes that means that value has been maximized.
> It's hard to say if this code is structurally better or worse than before,
Huh?
But that's easy to tell?
It's better then incompetents code, worse then a motivated average dev... But good enough hence the real question is value aka time& money invested/quality.
That's much harder to tell, and I currently think opus/fable generated code is decent enough to be safely in favor - at least on subscription
> Leadership encourages this because from what they can tell, there is no downside.
This was my big fear before we saw price increases. Now I'm pinning all my hopes on AI being too expensive to justify further big corporate pushes. (Sigh.) I love having new tools, but I hate being pushed to use ______ tool to meet some managerial metric.
IMO, cognitive athrophy is the point. If humans become dependent on AI, those corporations will get enough leverage to change laws in their favor. For example, people don't want a huge data center in the middle of their city, but the corporations will say that the same people can't do anything without AI powered by this data center. That's why I think that making humans deficient and dependent on AI is the entire point.
> "superficially superior code"
Can you unpack that a bit? It produces measurably, meaningfully inferior code everywhere I see it in use.
> "leadership encourages this because from what they can tell, there is no downside"
As said 'leadership' I find this a bit puzzling. I'm seeing strong, quantifiable evidence of increasing churn, increasing incident count, and length of downtime from the date of our biggest push into GenAI, and I'm organizing efforts on my teams to mitigate those issues and actively reduce GenAI adoption.
If you mean my c-suite, you're mostly correct although they are already rumbling about seeing zero or negative ROI on GenAI investments.
> Anyone not using LLMs all day is just not going to be as prolific
Agreed, but prolific != productive.
>I can't imagine that the same factors aren't at play in the scientific research community
I can't speak for the entire scientific research community, but I can say that for basic biomedical research (not clinical research), LLMs are mostly ignored. They simply don't have the ability to parse most raw wet lab data.
This obviously has not stopped the creation of a legion of startups, new Asst Profs, etc, claiming that they are using "AI" to crack new frontiers. In practice, the most effective of these use machine learning, rather than LLMs.