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Disclaimer: I am not a programmer, though I keep myself broadly aware of trends. I've only used LLMs for coding for toy problems or personal tooling (AutoHotkey macros, Rimworld mods, a mortar calculator, automating discharge paperwork at my dad's hospital)*. I've noted that they've been excellent at explaining production code to even a dilettante like me, which is by itself immensely useful. And for everything else, I'm so used to the utility they provide me personally that I can't imagine going back.
They being said, I am not in a position to judge the economic utility a professional programmer derives from using it for their day job, though it's abundantly clear that the demand for tokens is enormous, and that the capability of SOTA LLMs is a moving target, getting better every day on both benchmarks and actual projects. And look, I understand there's a position where you say "sure, but these things still aren't actually good" - but if you're claiming they haven't gotten better, then I'm going to gently suggest you might want to check yourself for early-onset dementia. The jump from GPT-3 barely coding a working React toy-example to current models is the kind of improvement curve that should at minimum make you sit up and notice.
In other words, even if you think they're not good enough today, you should at the very least notice that a large and ever-increasing fraction of US GDP is being invested in making them better, with consistent improvements.
However, here's a tweet from Andrej Karpathy which I will reproduce in full:
*Sadly they can't make the Rimworld mods I want. This is a combination of a skill-issue on my part (people have successfully made rather large and performant mods with AI), and because I wanted something niche as hell, in the form of compatibility with a very large overhaul mod called Combat Extended. Hey, at least Nano Banana Pro made the art assets with minimal human input, if you think my coding skills are trash, wait till you see my art.
I find it peculiar that Karpathy doesn't see a relationship between those two things. I've noticed a trend where the most glowing reviews of AI capabilities seem to be for people who are using it in areas where they, themselves, do not have enough skill to confidently perform the task. At its worst, it's a sort of tool-assisted Dunning Kruger effect that's actually breathtaking if you can decouple enough to look at it in the abstract.
"I clearly couldn't do this thing but I can clearly tell that Grok/Claude/ChatGPT/Gemini did it right" is a hell of a thing. It's already causing real stress on public software security databases. There's a continuous trend that looks something like this:
"I ran $LIBRARY through Claude and it says there was a potential denial of service attack. I asked Claude for a mitigation and it provided this code."
"Can you explain this piece of the code?"
"I asked Claude to explain this piece of code and it said the following."
Other than feeling like a scene from Office Space, it's effectively acting as a denial of service attack on its own. The amount of time necessary to submit something like that without a deep understanding of the problem is considerably lower than the time necessary for a genuine SME to comb through it and judge it on merits.
Hell, if I were a malicious actor I'd probably try to exploit that by shit-flooding the system to buy myself more time.
Hmm? That's not my takeaway from the tweet (xeet?). He's not denying a connection between AI capabilities and code quality decline, he's making a more subtle point about skill distribution.
The basic model goes like this: AI tools multiply your output at every skill level. Give a novice programmer access to ChatGPT, Claude or Copilot (maybe not Copilot, lol) , and they go from "can't write working code" to "can produce something that technically runs." Give an expert like Karpathy the same tools, and he goes from "excellent" to "somewhat more excellent." The multiplicative factor might even be similar! But that's the rub, there are way more novices than experts.
So you get a flood of new code. Most of it is mediocre-to-bad, because most people are mediocre-to-bad at programming, and multiplying "bad" by even a generous factor still gives you "not great." The experts are also producing more, and their output is better, but nobody writes news articles about the twentieth high-quality library that quietly does its job. We only notice when things break.
This maps onto basically every domain. Take medicine as a test case (yay, the one domain where I'm a quasi-expert) Any random person can feed their lab results into ChatGPT and get something interpretable back. This is genuinely useful! Going from "incomprehensible numbers" to "your kidneys are probably fine but your cholesterol needs work" is a huge upgrade for the average patient. They might miss nuances or accept hallucinated explanations, but they're still better off than before.
Meanwhile, as someone who actually understands medicine, I can extract even more value. I can write better prompts, catch inconsistencies, verify citations, and integrate the AI's suggestions into a broader clinical picture. The AI makes me more productive, but I was already productive, so the absolute gains are smaller relative to my baseline. And critically, I'm less likely to get fooled by confident-sounding nonsense (it's rare but happens at above negligible rates).
This is where I tentatively endorse a "skill issue" framing, where everyone's output getting multiplied, but bad times a multiplier is still usually bad, and there are simply more bad actors (in the neutral sense) than good ones. The denominator in "slop per good output" has gotten larger, but so has the numerator, and the numerator was already bigger to start with. From inside the system, if you're Andrej Karpathy, you mostly notice that you're faster. From outside, you notice that GitHub is full of garbage and the latest Windows update broke your system.
This isn't even a new pattern. Every productivity tool follows similar dynamics. When word processors became common, suddenly everyone could produce professional-looking documents. Did the average quality of written work improve? Well, the floor certainly rose (less illegible handwriting, if I continue to accurately insult my colleagues), but we also got an explosion of mediocre memos and reports that previously wouldn't have been written at all. The ceiling barely budged because good writers were already good. I get more use out of an LLM for writing advice than, say, Scott.
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