Every word of this seems objectively false. AI is more than capable of handling the details. I have generated countless tools for myself without needing to know or care about the details.
The pro argument is always quantity based, yet we never get to see the actual gain in quality. It's always "I'm doing SO MUCH more". Never "I'm doing better, with less effort."
It's also what we see in the wild. There are so many things going on, so many news about "AI". Are things getting better in any meaningful way? Why are we getting so much from "AI" yet things keep getting worse? The only real, objective and verifiable gain has been the stock price of a handful of companies, most of them heavily into infrastructure and manufacturing hardware.
Seems like a pretty clear pattern. There's so much output, but everything is worse. And the only argument we hear for why this is actually a good thing is just how much more output there is. Hmmm
I mostly work on UI (for an internal, science-focused app) and it used be that features would take a sprint or two to grind out, and would always be an MVP to meet just the most essential user requirements, because that's all we could afford, time-wise, to do. And if I figured out while implementing there could be a nicer UI approach to something, it was often too late to change the approach.
Now, I can get to that MVP in under a day. And I can experiment with ten radically different approaches, or change my mind about something significant at any stage in the implementation. I can afford to add refinements and user-pleasing extras that I could never have done before. I'm not delivering features at 10x the speed. There's a limit to the amount of code I can (and should) be submitting for review. But the features I'm building are _better_, and the effort is less. I'm not wasting mental bandwidth typing out a dozen React components full of the same-old state management, form processing code, etc. I'm not trawling through charting library docs, looking for the precise combination of settings to make my chart's x-axis ticks labels rotate 45 degrees. My mental energy is focussed on figuring out what solution is actually _right_, not the minutiae of how to persuade the computer to do it.
> I'm not delivering features at 10x the speed. There's a limit to the amount of code I can (and should) be submitting for review.
My point exactly
> But the features I'm building are _better_, and the effort is less.
Can we cash that check or is it gonna trade heavily discounted like all grand claims on AI since all software all around us together with the economy and the Earth's life supporting systems keep degrading every year? We keep getting promised the better, but we only get to see the more.
No it wasn't. You said the claims were only based on quantity. I'm saying the opposite. I deliver the same quantity, with higher quality.
> Can we cash that check or is it gonna trade heavily discounted like all grand claims on AI since all software all around us together with the economy and the Earth's life supporting systems keep degrading every year?
I'm not sure what evidence I could offer you that would ever convince you. We've had strong coding models for less than a year, and you've already appear to have closed your mind about them and wrapped them up into your depression.
Someone that describes programming like that is strongly suspect IMO.
> I'm not wasting mental bandwidth typing out a dozen React components full of the same-old state management, form processing code, etc.
Why haven't you abstracted that away already? Like building a UI library.
> I'm not trawling through charting library docs, looking for the precise combination of settings to make my chart's x-axis ticks labels rotate 45 degrees.
There's a method to actually read library docs to find what you need. This seems like someone complaining about the effort to do research with a book, but forgot to use the table of contents and the index and instead starts on page 1.
> My mental energy is focussed on figuring out what solution is actually _right_, not the minutiae of how to persuade the computer to do it.
You don't persuade a computer, you just translate the solution that is right. That's what coding is, merely translating. And to do that well, you need to know both languages: The domain and the computer platform. If you don't know the computer platform, you can't translate an idea. It would be like trying to speak French without knowing French. Yes you can try with Google Translate, but machine translated texts can be quite horrible to the native speaker. Same with using AI to generate code.
Saying "nuh uh" after I pointed out something that is plain to see isn't much of an argument. Show us better than? Where is the unequivocally better software, or anything really, "AI" enabled you to do?
Or is it just claims that amount to "trust me" or "look at all these repos/web apps"?
> The pro argument is always quantity based, yet we never get to see the actual gain in quality. It's always "I'm doing SO MUCH more". Never "I'm doing better, with less effort."
For me, it's "I can do this now; yesterday I could not."
I am not a developer - I am a hack with some experience in a smattering of languages who hits very real obstacles very quickly. AI has allowed me to overcome those obstacles and build things that literally I could not do before.
Yesterday I could not differentiate quake 3 matches within a single demo file.
Today I can split them into separate files and have the server stream them to me so I can watch them on demand.
It’s basically the same thing as AI generated art, whichever side of that you fall on.
I do IT, I can’t make any kind of art to save my own life. The art the AI generates is neat to me because I get to feel in control of making something beyond my own skills.
The flip side is equally true, though. The things I make are derivative, and not particularly neat to other people (and actively irritate actual artists).
AI software is a lot like that. I’m happy people who aren’t software engineers can make their own thing and be totally in control of that. I’ve also yet to see much AI software that blows existing stuff out of the water. Most of is derivative of something that already exists, if not an outright clone with a slightly different emphasis (think Jira but for a very specific workflow).
I'm not going to show you but I'll tell you, because I'm hoping you're asking in good faith and not just being snarky.
With AI, I now have tools to analyze recorded demos from quake 3 and pull metadata from them, match them against recorded match statistics, and most importantly, split a large multi-match demo file into multiple separate matches/files which are all self-contained.
Doing this required the LLM to review the source for q3 to determine how to re-create gamestate and correct various aspects that were non-trivial due to huffman encoding.
I also patched the server to be able to stream demos to attached clients, so multiple people can watch a demo simultaneously. It can now seamlessly switch between demo-playing and game-serving. I've had multiple PRs to the main repo merged for this.
All these are far, far beyond my ability to do by myself, and none of these features/functions were really available in 2026. There are some long-abandoned demo tools out there, but they either don't work, don't compile, or otherwise are windows-only. My demo tools are python, and of course the q3 server code is c++.
These aren't great new things. They're not ground-breaking. They're merely new things I made for me and my friends. And I think it's great.
As long as these tools remain for your personal use there is nothing wrong with that. The moment other people rely on them however, it would become reckless oversight to operate in that way.
Everyone feels like they are now the ceo of their own empire of ai flunkies who do the actual work guided by their above-all-that vision, and they luuuuuuv that.
But useless ceos generate bad output just like useless direct coders.
It's like everyone is adopting the once only-for-the-rich mindset where artisan is actually a derogatory term. Where Micheal Angelo is the same as the cow stall poop shoveler, because they actually do something directly themselves.
Tools? Yes. Reliable foundations? Eh, mostly not. Maybe I'm holding it wrong, but I'm not impressed. Not in the least because in order to get a good system you must have a good image of the system in your head. And if AI builds the system, how will you ever get that system in your head?
Owns? What a strange word to use. I doubt they own it in the sense that the profits of it go to their bank account. Use a better word and things will be clearer.
Yes, owns. Ownership as in accountability, stewardship and care. But I think you already got this from my post and if we're fighting about semantics, there's no constructive discussion to be had.
Accountability - yes, you can fire a human. But you can also fire the AI, by switching to a different model or switching back to manual coding. The rest just depends on having a strong enough model.
It doesn't handle anything, he reproduce statistical patterns. If it seems it cares about the details it's because he's been trained to regurgitate stuff. Another thing is being expert in something and being able to prompt the clanker, and those who are, usually underestimate how much their knowledge is steering the machine in the right direction
This. I don't particularly like the LLM writing style, but we've read a ton of very poorly written texts over the year with no complaints. LLMs are average writers with an annoying style, but not bad writers. If the content is good, I don't care if a LLM wrote it.
The writing style is this staccato LLM-like style that is difficult to read as it has zero flow and meaningless sentences.
Like what does the second sentence even mean? Is it even a sentence? "The roofline math, the prompt-processing catch, the NPU red herring, and the owner-measured speeds."
We need to stomp out this immediately: yes the style and patterns of your writing matter, not just the content. That would be true even if we weren't drowning in a flood of low effort crap, which we are. Writing is not just a set of facts devoid of any context in a vacuum. If your writing style sucks that absolutely takes away from any point you might wish to make.
But the specific problem with LLMs is that they waste your time: they appear to have substance and effort put in in a way that a bullshitting human could simply never accomplish because it would take too much effort to do so, defeating the point of not just putting in effort. For example, using an LLM to triage a production issue, it chugs through logs and stacktraces and outputs a completely wrong explanation, which gets copied and pasted into an issue. It's got everything that would indicate effort was put in: an explanation of exactly what is happening and why, with plenty of supporting information. The only problem is, it's made up and full of assertions that are false. Claude Fable just told me moments ago that a problem I was debugging was due to virtio-GPU giving back bad timestamps, confidently with an explanation of why. It wasn't and it isn't known to. Fine: I knew what I was getting. If someone copies and pastes an LLM explanation without context, I don't know what I'm getting, and LLM writing tells are the only way I can avoid shortening my lifespan spending time on things I should have been more skeptical about but my human senses failed to flag as suspicious.
When someone posts an article or github issue or PR and it's undisclosed LLM slop, then we have a problem. Again. These PRs, issues, articles look completely legit. Like this one:
No bad intentions involved: the person just simply couldn't tell when the LLM was bullshitting it, and his PR passed the sniff test just enough to get merged and cause regressions.
So if something outright smells like LLM slop from the writing style, that's a bad sign. The author has probably not written most of the sentences as they are presented, which is hard to distinguish from them not having written them at all. If they had proofread the article, they would have hopefully also noticed the repetitive, annoying LLM writing style and fixed it. When they don't, it tells me one of two things:
1. They didn't really put that much effort in, OR
2. They seriously lack taste.
Neither option is really super good.
It's not good that we're allowing people to think this isn't an issue. It definitely is an issue. It will become a worse issue once someone figures out how to fix the LLM slop writing style in post training, because then we will no longer have any good signal that human effort was put in to any prose at all.
I'll leave my opinion about this specific article out of it because it's really not specifically about this article. I can only think of one reason for people to make these bad faith arguments in favor of ignoring the glowing red "I DID NOT PUT ANY EFFORT INTO THIS" signs LLMs currently leave all over your work, and that is hoping that the pathway stays open for yourself to use.
We comprehend, but these "scaling laws" have been in effect for less than a decade (and they're more historical observations over a very short period than actual laws), and while some technologies progress exponentially for some amount of time, the complexity of some computational problems grows exponentially forever. For example, if computational resources double every year, it may still be five centuries before some computational problems can become practically computable. It is mathematically proven that no amount of intelligence can compensate for resources, and even if exponential growth could be sustained for a long time, and that's a big if, an exponential curve still grows slowly at the beginning.
For example, suppose AI helps us figure out a way to exploit much more of the sun's energy. Accomplishing that necessary preliminary can, on its own, take many decades, and if we split our efforts among multiple approaches, it may take longer. Intelligence can't break the actual laws of mathematics or of physics. And that's before we consider things like how resources will be allocated when AI tells people that man-made climate change and transgenderism are real.
This generation of AI hasn't even hit its first crisis. Assuming there will be none is like settlers assuming their town will never suffer a major earthquake because they haven't had one in five years.
Of course, improvements in problems that may not grow exponentially also matter a great deal, but there are too many unknowns (look at how many unknowns there are around quantum computing).
GPT literally generates perfect code for me in languages that do not exist anywhere in its training set, so I’m not sure how you’ve achieved this level of failure.
Try working in anything domain specific outside of common CRUD patterns. E.g. scientific software development where you describe a problem + give data. I have yet to see a single example of feeding in a problem in natural language involving a specific scientific domain that wasn't pretty catastrophically incorrect.
But yeah, if you want to feed it math and get code, it's reasonably okay with that. All LLMs I've used seem bad at understanding things that don't look like broad human knowledge. I've seen this same general issue across many different models. (And to be fair, geology, geophysics, and remote sensing are what I'm testing, and their semi-rare niches.)
It's also quite dangerous because it's not obvious that what it's doing is complete hallucinations unless you actually are a domain expert. Things _sound_ reasonable. E.g. "this is likely feature X" which _does_ exist, but is absolutely _not_ relevant to the problem or present in the input dataset.
But my current employer is pushing this exact thing (human language + scientific data + LLM -> advanced analysis of scientific data by LLM -> business decisions) and it _really_ worries me. It often gives the rough equivalent of "Start the procedure by severing the patient's aorta. Once they stop moving, you can deal with the hangnail". Just in very reasonable sounding language. And a lot of people don't know any better, because most users aren't domain experts.
Stuff it's not directly trained on is going to be flaky and sucky. It was like that with programming at first too and it still is sometimes. It's hard to imagine this won't improve with better more focused training. They focus on improving "CRUD" for obvious reasons. The specialization era hasn't begun yet.
Your domain, while I'm sure it is very interesting and complex, if it proves economically interesting will be cracked as well.
Just for some context, the domain we're talking about is oil and gas and mineral exploration. E.g. At my previous job, I used to personally manage a >$400 million per year budget and that wasn't even considered significant. We had multiple >$10 billion per year projects ongoing. That was 10 years ago. The amounts are larger now.
The issue isn't a lack of economic interest.
It might be a lack of training data in addition to inherent complexity, but it's certainly not a lack of economic interest.
I have no idea how and why GenAI would be useful in your profession. I'm sure a lot of money is moved there (not sure about the profits though), but it's not clear to me how software itself is budging that needle. I suppose better algorithms and better understanding of geology will do it, but software itself seems just subservient to that goal.
I guess what I'm saying is that "domain knowledge" is taking software development for a ride here. The software is just the vehicle, the science is the engine here and I can see why companies like OpenAI start going for the low-hanging fruits first instead.
Your specific company might be profitable, but does automating "mineral exploration" give you leverage over quite literally all other domains? My guess is not. For "CRUD" it is a resounding yes, it provides gigantic leverage. Once you automate basic software development you enter a new world. 10 billion, 10 trillion, all bets are off. You automate the creation of the next iteration of automation and on we go. Let's hope it takes a while for this take off. I can't see ourselves being ready for it.
My guess is it'll take a decade or so for real AI science to start taking off though - if that soon - so you're probably fine for now.
Yes. My point was that LLMs aren't currently good for everything. The original commenter literally said they were good at everything and I offered a counterpoint of something they're not good at: Most science.
(And yes, a lot of science is software. Analysis is software.)
Disagree, someone like the other commenter who points out LLMs don't even understand the domain concepts correctly versus someone who uses it anyways for corporate proprietary results have very different standards for what is acceptable. If you wrangle an LLM with harnesses and clever prompts you could use it to get some amazing results but that has more to do with trial and error and creativity, not some kind of fundamental skill of using LLMs.
It definitely understands the concepts well enough if you give it the right context. I'm not the only one saying this either. Like I said, it's a skill issue.
That's the Clever Hans argument, and the fact that you confidently use this unfalsifiable tactic ("Give it just the right context and it understands stuff!! It works!!" (Well, until the next iteration and then the next until the system paints itself into a corner)) tells me you are engaging in broscience / pseudoscience. Like I say, anti-scientific attitudes like yours are part of the problem, fanning the hype. It's bad faith to attribute people's criticisms of LLMs as some kind of lack of skill. People on here, many who are actual scientists and professional programmers, are very intelligent and highly trained, if they wanted to play around with LLMs they very likely capable of getting impressive one-time results, but proper, sustained use in a non-"vibe-coding" manner, such as with guarantees for validity, consistency, replicability, extensibility, and so forth is a completely open problem. Therefore it is out of proportion to reduce that to human skill. It's analogous to framing a bad design pattern as user error--disingenuous and bad faith. Ironically, with an intellectual standard like that, it then becomes easy to become overconfident about LLMs.
Ever since the first Davinci model of GPT-3 ive literally been using LLMs daily. It was an indispensable tool for me from the very beginning and despite 10,000+ hours of usage and research, I still feel like ive barely cracked the surface of whats possible with current genai tech.
