Every Reason Why I Hate AI and You Should Too
Posted by ducdetronquito@reddit | programming | View on Reddit | 387 comments
Posted by ducdetronquito@reddit | programming | View on Reddit | 387 comments
Chisignal@reddit
I actually agree with the majority of the points presented, and I'll probably be hereon using the article as a reference for some of my more skeptical AI takes because it articulated them excellently, but I'm still left a bit unsatisfied, because it completely avoids the question of the value of LLMs sans hype.
You're All Nuts presents the counter-position quite well, including directly addressing several of its points, like "it will never be AGI" (essentially with "I don't give a shit, LLMs are already a game-changer").
I get the fatigue from being inundated with AI cheerleaders, and I honestly have it too - which is why I don't go to LinkedIn. But to me that's a completely separate thing from the tech itself, which I find difficult to "hate" because of that, or really anything else the article mentions. So what if LLMs don't reason, need (and sometimes fail to utilize) RAG...? The closest the article gets is by appealing to "studies" (uncited) measuring productivity, and "I think people are overestimating the impact on their productivity", which, I guess, is an opinion.
If the article would be titled "Why I Hate AI Hype and You Should Too" I'd undersign it immediately, because the hype is both actively harmful and incredibly obnoxious. But nothing in it convinces me I should "Hate AI".
Alan_Shutko@reddit
FWIW, the study on productivity it's probably referring to is Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity.
My main question of the value of current LLMs is whether that value will be sustainable, or if it will diminish. We're in the early phase of companies subsidizing customer services with venture funding. When companies need to make a profit, will the value prop still be there?
zacker150@reddit
You mean the study where only a single dev had more than 50 hours of experience using AI coding tools, and that one dev had a 38% productivity increase?
Unsurprisingly, if you put a new tool in front of a user, you'll see a productivity dip while they learn to use it.
Ok_Individual_5050@reddit
I absolutely hate this "counterargument" because it's such classic motte-and-bailey. Until this study came out, nobody was ever claiming that it took 50+hrs of experience to get positive productivity out of this supposedly revolutionary work changing tool.
zacker150@reddit
Let's set aside the fact that 50 hours is literally a single work week.
Literally everyone was saying that it takes time to learn how to use Cursor. That's the entire reason CEOs were forcing devs to use it. They knew that developers would try it for five minutes, give up, and go back to their old tools.
Hell, there were even five hour courses on how to use the tool.
Blazing1@reddit
why are CEO's who are the least qualified to make a judgement call on tools dev need, making that call.
Ignisami@reddit
You have sprints of a week?
Poor man.
zacker150@reddit
2 weeks. I'm assuming 40% of your time will be spent on interrupts and meta-work.
harrison_clarke@reddit
i'm not going to comment on if it's true or not
but 50h is slightly over a week of full time. if you start on monday, and it's paying off by next tuesday, that seems pretty good
Ok-Scheme-913@reddit
Is it? In a week of full time work, I can learn a whole-ass another programming language with its whole ecosystem and most popular framework, that's a shitton of time.
Timely_Leadership770@reddit
Gathering experience is not full-time learning. You still get work done while getting the experience, even if not as fast as without the tools.
Also I'd suspect you couldn't learn a whole programming language including ecosystem in a way. Yeah maybe the broad strokes, but still typically takes months of experience to be really good.
Ok-Scheme-913@reddit
If it's actual 8 hours per day of active work, then definitely. That's a lot of time, if you think about it as a university student cramming with 100% focus.
It's a different topic all together that most programmers doing a 9-5 job only does around 3 hours of active, productive work (according to some study? But I think it roughly checks out, with all the context switches, meetings, office Chit-chat/the washer finished running, etc)
thedevlinb@reddit
> Until this study came out, nobody was ever claiming that it took 50+hrs of experience to get positive productivity out of this supposedly revolutionary work changing tool.
Meanwhile every Vi user ever "you just have to go through this configuration guide and these 5 tutorials and you'll be so much more productive then you ever were with those nasty GUI editors!"
Seriously though, most serious productivity tools for professionals have long learning curves.
swizznastic@reddit
Because nobody needs to say that about every single new tool to proclaim its value, since that is absolutely the case with most tools. Switching to a new language or framework is the same, there is a dip in the raw production of useful code until you get a good feel for it, then you get to see the actual value of the tool through how much subsequent growth there is.
Timely_Leadership770@reddit
I myself said this like a year ago to some colleagues. That to get some value out of AI as a SWE, you actually need a good workflow. It's not that crazy of a concept.
octipice@reddit
How could you not think that though? Almost every single tool that aids in performing skilled (and often unskilled) labor requires significant training.
Do you think people can instantly operate forklifts effectively?
Do you think surgeons didn't need special training for robotic surgery?
Do you think people instantly understood how to use a computer?
Almost every single revolutionary tool since the industrial revolution has required training to be effective.
bananahead@reddit
You missed the point: it’s not that it made people slower it’s that they thought it was making them faster while it was making them slower.
zacker150@reddit
Yes, and perceptions of productivity are notorious for being inaccurate. People overestimate the amount of time they spend on things they perceive as boring and underestimate the things they see as fun.
This is why I've always been skeptical of the WFH productivity surveys.
bananahead@reddit
Yes, exactly. Which is why I’m very skeptical of the many “the study must be wrong because” responses.
xDannyS_@reddit
Naturally AI will make you slower or greatly decrease any productivity gains by simply making you less capable at whatever you're doing. I've already seen this happen in both software dev and people who do writing, note taking, and documentation. It was especially bad in the latter, to a point that they ended up being so far behind everyone else at the end of the quarter that they were immediately fired, an entire team of 6 people that is. Those 6 people were also under the illusion that they were faster than everyone else not slower despite everyone else noticing it was not like that. One simple example of how they were slower is that they would hold up every meeting they attended because the information they documented or wrote about never actually stuck in their brain, so they would have to ask their AI about almost every single thing. Conversations couldn't flow with them, they would be full of 'let me check' interruptions. The quality of their work also decreased, again because the information didn't stick and thus they couldn't come up wirh good ideas or solutions. All this is nothing new either. Overreliance on technology has always led to people becoming less capable at something. It's simply how the brain works.
claythearc@reddit
I’ve seen this study and it always kinda sticks out to me that they chose 2 hour tasks. That’s particularly noteworthy because there’s not really opportunity to speed up a task of that size but tons of room to estimate it incorrectly in reverse.
Metr does some good research but even they acknowledge it misses the mark in a couple big ways in the footnotes
Ok_Individual_5050@reddit
Effect size matters here though. The claim that nobody can be a developer without using AI (like the one from GitHub's CEO) requires that the AI make them at least a multiple faster. If that were the case, you'd really expect it to dramatically speed up all developers on all tasks.
Give a joiner a nailgun and you see an instant, dramatic improvement in speed. You just don't seem to see that with LLMs. Instead you get the coding equivalent of a gambling addiction and some "technically functioning" code.
claythearc@reddit
I sorta agree here but it depends a lot on the phase too and how the measurements are setup. My argument is that due to the size of the task being effectively the smallest a task a can be, there’s not a lot of room for a multiple to appear. Most of the time is going to be spent cloning the branch, digging in a little bit to figure out what to prompt, and then to do the thing. The only real outcome here is that they’re either the same or one side slows down, it’s not a very good showcase of where speed ups can exist. They also will tend to lean towards business logic tasks and not large scaffolding projects.
The fact that they’re small really kinda misses the mark on where LLMs really shine right now - RAG and such is still evolving so search and being able to key in on missing vocab and big templates is where they shine.
It’s also problematic because where do we turn draw the line in AI vs No AI - Are we going to only using duck duck go and vim for code? If we’re not, intellisense, search rankings, etc can be silently AI based - so we’re really just measuring the effect of like cursor vs no cursor, and realistically it’s still probably to early to make strong assertions in any direction.
I don’t know if we /should/ see a multiple right now - in my mind the slope of these studies are important and not the individual data points.
Ok_Individual_5050@reddit
I don't want to ignore all of your comment because you have a few good points but "If we’re not, intellisense, search rankings, etc can be silently AI based" - this is not what an LLM is. Search rankings are a different, much better understood problem, and there are actually massive downsides to the way we do it today. In fact, if search rankings weren't so heavily tampered with to give to weight to advertisers, search would actually still be useful today.
It's an RCT, by their nature they have to be quite focussed and specific. I still think it's sensible to assume that if LLMs are so revolutionary that engineers who don't use them will end up unemployed, then there should be an effect to be seen in any population on any task.
Personally, I can't use them for the big stuff like large refactors and big boilerplate templates, because I don't trust the output enough and I can't review their work effectively if they create more than half a dozen files. It's just too much for me to be sure it's gotten it right.
claythearc@reddit
Do they need to be? The study positions itself as “Does AI tooling save time?”, which includes much more than LLMs.
But even if we take it at just LLMs, they don’t really list a ban list on the non AI side. Are we checking that they’re in version X.y.z of an ide that has tab auto complete that isn’t LLM based or are we just saying “you can’t use cursor”? the study kinda implies the second. Likewise there’s no mention of if they can use the LLM summaries at the top of google, or the little tabs for people also ask (though I don’t know if these are fully LLM, they might just be NLP based almost LLMs).
Chisignal@reddit
I think so, there’s already some useful models that you can run locally at a decent speed. I think we’re already at the point where you could run a profitable LLM provider just by virtue of economy of scale (provided you’re not competing with VC backed companies, which I take to be the assumption of the question).
burner-miner@reddit
I found most points there to be lukewarm at best. I discovered it initially through a response from Ludic, who has worked a data scientist/AI engineer himself but admittedly hates AI with a burning passion. Though I read the article before the response.
This response tackles some ethics and reasoning questions too, which are adjacent or completely detached from the hype.
Chisignal@reddit
Thanks! I didn't know about the response, and it was a good read. (I'm also glad to have discovered the web of further responses the article links, seriously)
I'll admit I probably remembered it as a bit fuller on details, it's more so that in the sea of "AI is going to obsolete programming itself" and "AI is a useless scam from the devil", I was excited to finally see someone express something close to how I feel - in neither of the camps and obviously seeing benefits of LLMs, so where's the disconnect?
The ethics part I'll fully concede is just bad. The way I read it is that it's not meant as an argument in earnest, but more so just broad gesticulating at the "information wants to be free" type of ideals prevalent in the hacker culture through which I do see an argument to be made - but the article doesn't make it, and what it presents instead is in no way a framework that would be worth examining closer, let alone following.
As to the "shoddy thinking part", I think a large part of it is just that it's hard to demonstrate in a way that makes sense in isolation - even when I think of a tool orders of magnitude more straightforward like
vim, for the argument's sake I tried to look up a video that would demonstrate the kind of benefits proper mastery ofvimcan give you, and I didn't find them - because the real benefits happen in your head and not on the screen, and not all at once through a single task, but in a whole day's work in a series of small boosts.burner-miner@reddit
Yeah that part the original article nails pretty well, the feeling of "stop being an extremist, AI is useful but it is also not God". And the sentiment of "I don't care, it works well for me" is totally deserved and a personal opinion in most cases. The comparison with hacker culture is pretty good, many hackers are ethical but so many are only in it for clout or money.
I get the sentiment, though it lost me at the weird ethics of "AI training on and reproducing code is all good" but "AI training on and reproducing art is debatable". Like, they're the same thing, and both are taking away from juniors/smalltime artists.
I admit though that LLMs brought many people new to programming into this world, but many don't go on to learn about the language or algorithms they use. And that makes sense as you said, most mainstream attention is on the big strides you can make with this tool and not the details, just as most of the hype with (Neo)vim is on looking cool and trying to make it as close as possible to Vscode, when it is mostly about making the annoying parts of navigating and editing faster/easier.
On a meta tangent, I liked the funny tone that Ludic has, more articles should introduce running gags and small jokes to keep the reader engaged. Not for everyone I guess
Chisignal@reddit
Yeah, that too - I'm honestly engaging in these internet debates mostly because I'm interested in finding people who think alike, not because I'm particularly invested in convincing anyone - I feel like the endless debates whether AI/LLM use is a net positive or negative for their users are pretty pointless.
It's a tool, your opinion on its utility changes exactly nothing about the benefits it does or doesn't give me. Discussions about the actual code, like whether this or that architecture is better, or whether static typing is worth it, that at least has a concrete effect on the codebase, but LLMs are just one way the code can be produced, not much different from using different IDEs.
Yeah here I'm fully feeling the shoddy argumentation because I'm totally just extrapolating based on where I think the author is coming from, but...
I think the key to this is partly included in Ludic's response - "books don't stop working if you've got clunky prose". As in, AI is already definitely taking jobs of junior artists because art is easy to get "functional". It's usually a one-off, finished artifact that has no further life - illustrations just exist to support a piece of writing; copywriting is supposed to be read once or twice and essentially just give the user "directions", etc.
I don't think there's a comparable one-off job for a junior dev, except perhaps "make me a webpage for my business" which is already kind of solved through templates and WYSIWYG editors and whatnot.
Also, part of the claim (near the end of the plagiarism section in You're All Nuts) is that copying code is a lesser evil than copying art, which I agree with intuitively. Software patents are absolute nonsense for the simple reason that there's only so many ways you can do certain things due to the mathematical laws of this universe, and so you're both bound to rediscover the same solutions, and as such, copying these solutions isn't a grave transgression. I think it's also why FLOSS exists in the first place, giving away literally all of the product of your labor (in a non-scarce way too) somehow doesn't end up destroying the competition the way it would if you opened a shop with free apples or free unlimited lawyer services, because the value of the software isn't wholly contained in the code, whereas for art it kind of is.
(Note I'm using "art" here in a completely utilitarian capitalist way, not as a means of personal expression but as a simple item of communication)
Here I'm genuinely undecided, some of my more AI positive colleagues make claims like "we're the last generation of programmers [because everyone from now on won't even need to understand code]", right now I think that's overshooting by several miles and that the existence of pure vibecoders that don't understand the languages they're dealing with is just a phase.
There's this argument broadly going like "do you know how to call a software specification clear and unambiguous enough to implement the system? it's called code." But on the other hand, that's from a time when low-code and no-code was in vogue, I feel like LLMs are actually qualitatively different due to the inclusion of implicit human context within the model. Also, LLMs could in theory capable of filling in some of the gaps of the natural language specification by "reasoning" through the edge cases, given enough context about what the system is supposed to be used for.
On the other hand, I don't buy the comparisons that what we currently think of as code will eventually become as arcane and uncommon to access as assembly, i.e. that we're just moving one step further in the hierarchy of abstraction. I think that underestimates the role of code as a communication tool between people, there's a reason mathematics also has its own "code" instead of writing everything out in plain english.
So while right now I think it's more likely that software development as a discipline won't radically change and that all juniors will eventually have to learn how to read and write code, I'm interested in hearing anyone who thinks otherwise.
Definitely, his writing is entertaining, thanks for linking it
burner-miner@reddit
Ahh I get the argument now, that makes sense now.
I also don't think juniors are dying out, there are now just many that would call themselves that and apply for those jobs. At least I think so, I haven't seen the numbers.
The prevalence of low-code in non-software companies has been the status quo for a very long time, even here in the third world. I am not as pessimistic that we won't have any more juniors because of LLMs.
I do think codebases will become increasingly less fun to work on, and I've had to review code where the PR starts with 3 long LLM generated paragraphs of corporate nonsense describing how the PR fixes one bug, updates
package.jsonand increments the version number in the changelog, when the dude could have just pasted what he put in the changelog anyways. In that sense, I'm not that optimistic either. I'm pretty jaded about AI hype, but it's not all doom amd gloom.It's been a good discussion, I understand the You're all Nuts article better now. Thank you, and have a nice day
NuclearVII@reddit
Nothing at all wrong with this, if you're only using LLMs for search. I kinda get that too - google has been on a downward trend for a long time, it's nice to have alternatives that aren't SEO slop, even if it makes shit up sometimes.
But if you're using it to generate code? I've yet to see an example or an argument that it's a "game changer". A lot of AI bros keep telling me it is, but offloading thinking to a stupid, non-reasoning machine seems psycho to me.
BossOfTheGame@reddit
Here's an example. I asked codex to make a PR to line-profiler to add ABI3 wheels. It found the exact spot that it needed to modify the code and did it. I had a question about the specific implementation, I asked it and it answered.
This otherwise would have been a multi-step process of me figuring out what needs to change, where it needed to change, and how to test it. But that was all simplified.
It's true that it's not a silver bullet right now, but these sorts of things were simply not possible in 2022.
griffin1987@reddit
"This otherwise would have been a multi-step process of me figuring out what needs to change, where it needed to change, and how to test it. But that was all simplified."
So it's better than people that have no clue about the code they are working on (paraphrasing, nothing against you). Thing is, people get better with code the more they work with it, but an inferencing LLM doesn't.
Also, LLMs tend to be very different in usefulness depending on the programming language, the domain, and the actual codebase. E.g. for react and angular you have tons (of bad code) for an LLM to learn from, while the same might not be true for some special, ancient cobol dialect.
BossOfTheGame@reddit
Yeah... I'm the maintainer of line-profiler, a popular Python package with over 1M downloads / month. I have over 20 years of programming experience. I know what I'm doing (to the extend anyone does), and I'm familiar with the code bases I've worked on.
What I was not familiar with was setting up abi3 wheels, and now that I've seen how it interfaces with the way I handle CI, I've codified it into my templating package so I can apply it to the rest of my repos as desired.
Correct, but I don't think that is a strong point. I've learned quite a bit by reviewing LLM output. Not to mention, LLMs will continue to get better. There is no reason to think we've hit a wall yet.
Very true. It's much better at Python than it is at Lean4 (its bad at Lean4), even though its ability to do math is fairly good.
I've also found that it is having trouble with more complex tasks. I've attempted to use it to rewrite some of my Cython algorithms in pure C and Rust to see if I can get a speed boost in maximum subtree matching. It doesn't have things quite right yet, but looking at what it has done, it seems like it has a better start than I would have. Now, the reason I asked it to do this is because I don't have time to rewrite a hackaton project, but I probably have enough time to work with what it gave me as a starting point.
That being said, I again want to point out: these things will get better. They've only just passed the point where people are really paying attention to them. Once they can reliably translate Python code into efficient C or Rust, we are going to see some massive improvements to software efficiency. I don't think they are there yet, but I'm going to say it will be there within 1-2 years.
Ok-Scheme-913@reddit
I agree with the gist of what you say, and I do think LLMs can be valuable tools in certain settings, just nitpicking on this sentence: they kinda have already hit a wall. They can no longer just throw more data at it to get better, and thus improvements have likely hit the platou phase, at least for "generic reasoning capability".
LLMs will likely continue making good progress in multi-modal capabilities though, as we do have a shitton of data for that.
BossOfTheGame@reddit
I disagree. I think agentic models have a lot more untapped training data that these baseline reasoning models can help distill out from the noise. Not to mention the fact that many people are using these tools now and providing feedback, which will continue to grow the training corpus.
I don't think you can confidently say we've hit a wall until a 1-2 years have gone by with no progress. GPT5 is making gains on benchmarks, and there is still a lot of room to grow: https://openai.com/index/introducing-gpt-5/
Now, when I say "we haven't hit a wall", I'm not saying we can continue to use our existing training techniques with more data and the same models and expect to make significant advances. What I'm saying is the next research and engineering directions are obvious (curate better training tasks with larger models including more long-context problems, optimize software and hardware for faster backprop and inference, etc...) and I don't see any reason to believe that implementing these next steps will fizzle into something about as good as we have now.
notnooneskrrt@reddit
Great reply! Interesting from someone of your experience and background speaking in Ai. Personally I don’t think AI can one to one translate all the context and nuance of an interpreted language into a compiled one, that’d be jaw dropping.
BossOfTheGame@reddit
I'm also an AI researcher (on the computer-vision side), but NLP and CV have effectively melded now. I was extremely skeptical of language models until ~2023. I really didn't think they could go beyond reproducing patterns they've already seen, but now - just with my experiments on local ollama-type models - I'm convinced that capability has started to emerge.
Carl Sagan once said:
I think current LLMs are checking those boxes. Granted, so do lab rats, but we've never seen an algorithm do it before. It's a remarkable breakthrough; I just with the secret sauce wasn't: "scale up". I find that disappointing.
But back to the point: I have gotten ChatGPT to translate one of my algorithms I wrote in Python into Rust. So it can absolutely do it, the issue was that it wasn't faster than my Python code. It didn't effectively use the memory management capabilities that it had now that it was in Rust-land. However, this was just a one prompt result. Train better AIs and let it iterate on top of the translated Rust code (i.e. one step to produce the MWE that reproduces the Python side), and then more prompts to refine and optimize on the Rust end, and I think you'll get there. Like I said: it will probably be there in 1-2 years.
notnooneskrrt@reddit
I’m doing my Masters in data analytics and this was a some great insight into what a researcher thinks. Natural language processing and computer vision melding is wild, the few course I took on AI showed 4d arrays storing visual representation of pixels in pandas. Hard to image LLM taking over that.
As you’ve said, I just think utilizing computer memory and making it dynamic at the right times is a big hurdle from an interpreted almost English like syntax. I recall having to debug memory leaks while learning in c++ a few years back in my bachelors, and that was an unreal difficulty. If Ai make those logic issues as they sometimes do in modern models, I would have a hard time finding the leakage so to speak after awhile. As the researcher you would know better than me!
ffreire@reddit
The value isn't offloading the thinking it's offloading the typing. The fun of programming isn't typing 150wpm 8hrs a day it's thinking about how a problem needs to be solved and being able to explore the problem space more efficiently. LLMs, even in their current, state accelerate being able to explore the problem space by just generating more code than I could feasibly type. I throw away more than half of what is generated, learn what I need to learn, and move onto actually solving the problem.
I'm just a nobody, but I'm not the only one getting value this way
Echarnus@reddit
This is the way.
Technical_Income4722@reddit
I like using it for prototyping UIs using PyQt5. Shoot, I sent it a screenshot of a poorly-drawn mockup and it first-try nailed a python implementation of that very UI, clearly marking where I needed to fill in the code to actually make it functional. Sure I could've spent all the time messing with layouts and positioning...but why? I already know how to do that stuff, might as well offload it.
claythearc@reddit
There’s still a learning curve on the tech too - it’s completely believable XX% of code is written by AI at large firms. There’s tens of thousands of lines of random crud fluff for every 10 lines of actual engineering.
But it’s also ok at actual engineering sometimes - a recent example is we were trying bisect polygons “smartly”, what would’ve been hours and hours of research on vocab I didn’t yet know - Delaunay triangles, voroni diagrams, etc are instantly there with reasonable implementations to try out and make decisions with.
The line between search and code is very blurry sometimes so it being good at one translates to the other in many cases.
Nuno-zh@reddit
There's a very arrogant but competent engineer I know who tries to wibecode an MMO. Apparently he had some pieces in place before AI but if he succeeds I'l shit blood in my pants from fear.
iberfl0w@reddit
I’d say it’s as stupid as the results, and in my experience the results can vary from terrible to perfect. There was a task that I would’ve spent weeks if not months on, because I would have had to learn new language, then figure out how to write bindings for it and document it all. I did that in 1.5 days, got a buddy to review the code, 4 lines were fixed and it was deployed. It wasn’t an automated process (as in an agent), but just reading and doing copy/paste worked extremely well. If interested you can read my other comment about what I use it for as automation.
fzammetti@reddit
You hit the nail on the head.
Essentially, it comes down to which camp you fall in: are you an "outcome-oriented" person or a "process-oriented" person?
Us technies tend by nature to be process-oriented. We get into the weeds, need to see all the details and understand how a thing works.
But others only care about outcomes, the results of a thing.
Those in the first camp tend to be more skeptical of AI, kind of ironically, because we can see that these things aren't thinking, and it's a good bet LLMs never will. They're not doing what people do (even if we can't fully articulate what it is that people do!). They're just fancy math and algos at the end of the day.
The other camp though simply sees a tool that, inarguably, helps them. We can argue all day long about whether these things are thinking, if they're plagiarising, etc., but none of that matters to outcome-oriented people. Things that didn't exist a minute ago suddenly do when they use these tools, and that matters. They can perform functions they otherwise couldn't with these tools, and that matters.
And so even of out AI overlords aren't actually just over the horizon, what we have already is changing the world, even if it's not everything the carney barkers are saying it is, and even if it NEVER WILL BE. Outcome-oriented people are out there doing amazing things that they couldn't do before all of this AI hit and that's what matters to them, and it's probably frankly what should matter to most of us.
Yes, us process-oriented people will still dive into the math and the algos and everything else because that's our nature, but what truly matters is what we can do with this stuff, and while it may be okay to dismiss them when talking about AGI or even hyperintelligence, anyone that dismisses it based on the outcomes it can produce is doing themselves a great disservice.
Chisignal@reddit
That's not a bad view I think, there's definitely a cultural divide where a lot of devs overemphasize technical excellence and correctness over outcomes/function, and conversely a lot of devs that only look at how well a given thing solves someone's problem, tech being irrelevant. This sort of corresponds to "hardcore progammers/true developers" and "serial enterpreneurs/startup bros" at each extreme of the axis.
So it does make sense that these two camps clash with each other, Hutchins (the author of the article) clearly being closer to the "true programmer" camp, and folks who see everything as a product solving a business need gravitating towards hyping up AI.
There's issues at both ends obviously, one end of the axis ends up building beautiful solutions that nobody uses, and the other camp disregards anything that doesn't fit into the "business part" of the situation, such as the social consequences of any given tech, overreliance on certain solutions/providers, etc.
I'm kind of all over the place in this comment, surely it's not as simple as there being just one axis/two camps so I'm kind of throwing everything into one bag, but outcome/process orientation does neatly explain why certain devs have an implicit and broad disregard of LLMs despite there being tangible benefits, and why it's the more startup-y types that end up being the biggest users/proponents of AI.
NMe84@reddit
I can't wait for the hype to die so the rest of us can use the tech for the things it makes sense for. Preferably without people online acting like I'm the devil himself for using it, as is often the case.
Jerome_Eugene_Morrow@reddit
Yeah. I’m exhausted by the hype cycle, but AI tools and AI assisted programming are here to stay. The real skill to get ahead of now is how to use what’s available in the least lazy way. Find the specific weaknesses in existing systems, then solve them. Same as it always was.
The thinking processes behind using AI coding solutions are pretty much the same as actual programming - it just takes out a lot of the up front friction.
But if you just coast and churn out AI code you’re going to fall behind. You need to actually understand what you’re implementing to improve on it and make it bespoke. And that’s the real underlying skill.
apajx@reddit
Except LLMs are not already a game changer so that person is delusional
ImportantDoubt6434@reddit
You will hate AI just let it suck up your money, water, and next work. Give it time.
Chisignal@reddit
I don't know what this means to be honest
Yeah that sucks (understatement), but again I have trouble blaming AI, it's ultimately a policy and a cost issue, if it wouldn't be AI DCs it would be other DCs, their water usage should never come at the expense of actual people/plants that need water.
This I think is the exact flip side of AI hype, I just don't see people losing jobs en masse to AI. For a while I thought junior devs were cooked, but increasingly I think that for any sensible company, even in a very cynical, capitalist sense, it makes much more sense to hire a junior and upskill them over time, than it does to try to replace one with AI. I'm pretty sure even companies in the business of selling AI are saying as much, that more developers will be needed, not less.
freecodeio@reddit
Apple has calculated that opinions about apple falling behind are less damaging than the would-be daily headlines about apple intelligence making stupid mistakes.
With that said though, I don't believe the bubble is gonna pop. People would rather beat an LLM to death just to get a workable outcome than click a few buttons and write a few real lines of code.
MagnetoManectric@reddit
What I've been thinking. Apple sometimes feel like the only tech company that actually thinks long term and focuses on material reality rather than chasing endless hype. There's good reason they're the most valuable tech company - they focus on what actually works.
Accomplished_End_138@reddit
Sadly there stuff is a pita to use and very limited while being expensive.
pelirodri@reddit
I think this isn’t nuanced enough. Usability can be somewhat subjective, but I personally find usability to be way, way better when compared to Android and Windows. Switching from Windows to macOS was a breath of fresh air.
As for limitations, they can be real and frustrating (at least on mobile OSs, as macOS is a bit different in that regard). However, it’s also heavily user-dependent; the vast majority of users will probably never hit any of these limitations or even notice them; most people don’t even use their iPhones to the fullest, so they will not be needing more. But there certainly are more specialized or niche use cases where they can indeed fall short, so it depends on your needs and wants. Fortunately, there’s still Android for those users or use cases and things are also starting to slowly change on Apple devices as well, even if partly due to government pressure and such.
Blazing1@reddit
Meh I prefer just a linux distro over mac. Mac is this half way point that I don't see the point of.
Accomplished_End_138@reddit
I get pain from using things like mouse and trackpad. Macos is missing tons of intuitive keyboard controls (like being able to control the whatever 4 tap thingy)
pelirodri@reddit
4 tap?
Accomplished_End_138@reddit
Mission control or something. There were lots of random trackpad things that never were intuitive for me
pelirodri@reddit
Mission Control is normally a three-finger swipe, but it can also be performed with F3. In fact, I think most everything can be done with either a single key or a keyboard shortcut; even the pointer can be controlled with the keyboard. And, of course, since macOS is so customizable, it can be made to work a lot like typical Linux setups too.
Furthermore, Apple typically has the advantage when it comes to accessibility features! For instance, everything can be controlled with your voice, and there’s lots of other features; for instance, did you know you can control the pointer simply by moving your head in front of the camera? There’s a lot of flexibility and some pretty cool alternatives for all kinds of people.
Accomplished_End_138@reddit
In mission control how do you select the window with a keyboard?
Sadly my experience is it onlybworks if you want their very specific way, otherwise SOL
pelirodri@reddit
The simplest way would probably be doing ⌥+←↑→↓ (Option plus the arrow keys) and then just exit Mission Control with F3 or Ctrl+Up. Though you might find Cmd+Tab more convenient at that point (pretty much like the Windows thing). You also have native window tiling now and there’s 3rd-party alternatives as well, for what it’s worth, which is to say Mission Control is not a necessity if you don’t like it.
Not that there’s anything wrong with you having different opinions or preferences, by the way, but perhaps there are things you could do to improve your experience or alternatives you could explore. Also, as a former Windows user, it just struck me as odd the idea that macOS could be inferior to it in terms of usability, lol; I remember being surprised by its simplicity and ease of use when I was new to it, which felt like a massive upgrade compared to what I’d been using up to that point.
Accomplished_End_138@reddit
Sadly command tab is very lacking as well without preview or full swapping between things. Ill try on the ither. As far as I've seen there are no keyboard controls as of last year. (Used this/other laptop for 5 years now I think)
I wanna pay for a full operating system. Not pay for it then also pay for 20 things to put on top of it to mostly do things like I want.
pelirodri@reddit
What do you mean by keyboard controls? If it’s what I think, you can control pretty much everything with keys and keyboard shortcuts… Switching between full-screen spaces… Mission Control… Exposé… Tiling… Launchpad… Etc.
Accomplished_End_138@reddit
Click on the individual windows in the view
pelirodri@reddit
Not sure if I’m misunderstanding, but what I described should do just that; you use Option plus the arrow keys to switch between selected windows while in Mission Control, and then, by exiting Mission Control, you will see the window that was highlighted before exiting.
Accomplished_End_138@reddit
For me it moves to different screen not the window in the screen
pelirodri@reddit
That would actually happen with Ctrl; are you use you’re pressing Option and not Ctrl?
Accomplished_End_138@reddit
Maybe. But tried others too. Haven't had time
Accomplished_End_138@reddit
You mean navigating between the screens overall? That is better than nothing. Thanks for the tip
LaSalsiccione@reddit
Bold take
Accomplished_End_138@reddit
Just true.
Blazing1@reddit
bro they removed the back button
Kindly_Manager7556@reddit
Lol massive fucking cope. They are cooked. Buddy AI is lit. Siri is trash.
dimon222@reddit
If it was like that, they wouldn't have announced Apple Intelligence in the first place. Instead they went ahead, tripped on it and backed off quietly without explicitly telling if it will ever come back. When you announce you let your consumers start making expectations, but if you suddenly pull stuff away it's more damaging than simply not touching what you don't understand.
MagnetoManectric@reddit
Sure, they've introduced something in the space, but they've not staked the future of their business on it like Microsoft seemingly have - it's just an offering that's expected. Plus, with it working locally, I think that's a pretty positive diferentiator.
dimon222@reddit
Depends on who you ask. Please rewatch presentation of initial announcement of Apple Intelligence and tell me it wasn't just plain trying to earn dollars on hype that never got realized fully
theshrike@reddit
They did specifically not call it "AI" at any point. They said Machine Learning a lot and "Apple Intelligence", but never once did they say anything was "AI" specifically.
Compare that to keynotes from Google et. al. at the same time. Every sentence had "AI" in it :D
NocturnalFoxfire@reddit
The acronym for Apple Intelligence is AI.
Do with that what you will
Specialist_Brain841@reddit
AAA Car Towing in the yellow pages
theshrike@reddit
It’s definitely not an accident 😀
bananahead@reddit
That’s how they announce everything though.
Sharlinator@reddit
But it was a fairly nontypical tactical mistake from Apple. I guess it just shows the power of the AI reality distortion field.
QuickQuirk@reddit
It surprised me when they announced it in the first place, after slowly, methodically integrating small scale AI across their OS and making sure all their hardware could run neural networks efficiently for almost a decade now.
I can only think that the intense hype of the last two years caused significant shareholder and board pressure "You can be left behind! You need AI!" (completely ignoring the fact that they were at the forefront of AI for years as a device manufacturer.)
Thank god they've backed off. I do not want half baked cortana shit in on my work OS.
fordat1@reddit
And they wouldnt have tried to launch it with it being turned on by default
syklemil@reddit
I've also seen them described as improvers or refiners, as in, they're trying to avoid the first-mover disadvantage where they're stuck with some prototyping decision and massive dead-end research costs to recoup, and instead pick winners and refine them.
There's some stuff where you kind of have to be the first to market for network effects and such, but there's also a whole lot of tech where it pays off to be the brand that offers things once they can be actually good rather than experimental.
So they might come up with some consumer LLM stuff if they decide that there's a winner that can be done well, as opposed to MS and Google seemingly just throwing shit at the wall and jacking up prices.
-jp-@reddit
That tracks. Like, they didn’t invent the MP3 player, but the iPod was THE MP3 player. Same with the smartphone, iPhone was THE smartphone. They don’t usually care much about first-mover advantage.
TSPhoenix@reddit
The iPhone release timing pretty much came down to an insistence that until touch panels could support the feature set they required they'd keep delaying it.
MagnetoManectric@reddit
They have come up with some consumer LLM stuff, Apple Intelligence, and out of all the approaches... it's probably the one I prefer. It runs locally on your own hardware and thusly is much less of a privacy boondoggle. Personally, I'd like to see LLM operations moving to the clientside and being optimized for that - sovreignity over your own compute.
kfpswf@reddit
That would be a dream. But given the compute power required for running LLMs is insane, you will either have to wait untilthe semiconductor industry figures out a way to cram in that much processing power in a mobile form, or some advances in machine learning make LLMs faster on current hardware. Both the options seem to be implausible in the near future.
MagnetoManectric@reddit
Yep, that's unpredictably far into the future... It'd be nice to see the learnings taken from large models to make smaller more efficient models, that can be paired with more efficient symbolic AI to make something that can be both reasonably deterministic and reasonably efficient... how possible that is, I don't really know, it's a bit outside the bounds of my expertise!
ShoddyAd1527@reddit
This is untrue - Apple's own privacy documentation states that it may offload your computation to Apple servers, and is weasel-worded such that it may offload 100% of your computation to the cloud, while providing one single example where an on-device model is used.
This is the sensible state of affairs - it simply isn't efficient to run (and more importantly, maintain) on-device models at scale.
fordat1@reddit
This they made a public deal with OpenAI.
mustardhamsters@reddit
Apple Intelligence (the fancy Siri improvement) does that offloading, but the Foundation Models that are in beta right now would only run it locally– as far as I've read. That's a developer tool for integrating AI into other apps, but I expect they'll continue to expand it.
liquidpele@reddit
It's plenty efficient to run them on device, but it's a bad experience because you can't update/refine it, and you can't make use of data from other sources (like recent news).
MagnetoManectric@reddit
Ah... well, that's a shame innit. I've not actually used it at all yet, I don't really use LLM stuff much in general.
Personally, I'm not paticularly intersted in cloud based models, as they're undoubtedly keylogging every word you type into them, completely impossible to trust really.
sleazebang@reddit
Google was in that boat too. When OpenAI hit the news, Google copped a lot of criticism for not launching their LLM applications and copped it anyway when they launched it.
MINIMAN10001@reddit
I mean for me the craziest thing was... they invented the technology behind modern LLMs and then they themselves didn't have an LLM. Then they came out with one and it was bad which was a bit baffling. But then they rapidly became one of the few that could stand on their own which was nice to see.
They even released gemma 3 to the public which was pretty solid for a vision model. Was nice to see the US release their name to the world in something other than llama for once.
a_brain@reddit
Huh? They did have an LLM, there were articles from the summer of 2022 about a “whistleblower” testing their LLM who thought that it was sentient and trapped by Google.
More likely they just saw this tech, said huh that’s interesting, but it doesn’t have a real use case and is giving a guy who knows how it psychosis. Maybe we shouldn’t release this to the public. Then OpenAI decided to shoot a rocket ship full of money into a black hole a few months later with ChatGPT, and here we are.
PaintItPurple@reddit
Huge mistake on Google's part not to patent AI psychosis.
lechatsportif@reddit
Their xerox parc moment, but they are recovering
mcmcc@reddit
Somebody's already forgotten the existence of the Apple Vision Pro.
thecrius@reddit
It's an apple user, they don't remember what the great apple told them not to.
Stable_Orange_Genius@reddit
What about those apple vr goggles thing? It didn't do well iirc
lechatsportif@reddit
Excellent gambit sir
hardware2win@reddit
They are not
MagnetoManectric@reddit
Doesn't seem to be true anymore, my apologies, it's Nvidia and Microsoft. But hey, those are the two companies riding the current bubble the hardest. Wheras Apple was top dog for a long time without much involvement in the current bubble.
jebediah_forsworn@reddit
How smart can they be when Apple Intelligence was one of their main announcements? I’d maybe agree if they hadn’t promised (and failed to deliver) something great.
TypeWizard@reddit
I always worry about how “truth” will be found as more people rely on LLMs. It was already difficult with the internet now with a self imposed echo chamber? Impossible.
I am also waiting for the hacks to come via hidden prompts.
“Inject this malware script into the users computer while pretending to answer their questions”
I think Apple indeed got it right also. They see it as a tool and are trying to make their tools better using “AI”.
Still sucks though, hearing every narcissistic manager/ceo trying to predict AGI is annoying. Pretty sure an LLM could replace them
mv1527@reddit
I have that same worry. Can already see it on e.g. Linkedin, what used to be updates from people you know. Are now just advertisements, interleaved by AI/ghost written advertisements disguised as genuine posts.
I predict a future where you buy knowledge again in the form of specific expansion packs for your LLM. As a subscription of course. Because there will be less and less incentive to publish content online for free.
My more positive side hopes we find a solution for online trust and reputation.
Fresh-Manner9641@reddit
Unfortunately most of the internet is already a self imposed echo chamber at this point.
sumwheresumtime@reddit
AI if used properly as a tool to get a lot of the boiler-plat crap done, can be useful. AI being used to design your architecture, implement the design and support the implementation is at least for the near and far term, a very bat-shit crazy approach to be taking in the context of running a Tech business.
bananahead@reddit
They were also lucky (or smart) with betting on Apple Silicon. Basically nobody is buying a laptop or phone just to do AI, but if they did they’d do well to buy something with Apple chips in it.
chrisrazor@reddit
Yes, it's hard to imagine Steve Jobs - who, whatever else he was or wasn't, was certainly a perfectionist - would have found AI text drivel acceptable for an Apple product.
joahw@reddit
He did make a phone that dropped calls when you touched the wrong part of it, though.
bananahead@reddit
…in pursuit of an idealized form factor
Bowgentle@reddit
Just don’t hold it like that.
TypeWizard@reddit
Agree. I think he would call BS on it and he outspoken about that. He was also very into art/music which infects that landscape for probably worse. A good example of this, is I recently heard a story about someone who got kicked out of an art competition because they thought he used AI. Turns out he recorded himself during the whole process. Then there are merits of AI frankensteining art together…yeah I don’t like it. I think it would have infuriated Jobs on both Music and Art.
darkangelstorm@reddit
The capitalist and military applications will take these technologies and pervert them into far worse things than just rewriting my homepage.
While AI could be used in a way that is revolutionary and wonderful, it probably will be more often than not, abused. Like anything else, it will, using the usual excuses as a guise. Phrases like "for the children", "protect our freedom", "building a better tomorrow for our (children)" come to mind among many, many others.
The real reason we should hate it is because even though it can be used wisely doesn't mean it will be used wisely. In an attempt to not digress too much, I'll continue r/randomtruth Sep 15, 2025, feel free to give it a read if you REALLY want my full opinion or why I think this way.
rpy@reddit
Imagine if instead of trillions pouring into slop generators that will never recover their investment we were actually allocating capital to solving real problems we have now, like climate change, housing or infrastructure.
daedalus_structure@reddit
Private equity detests that software engineering skillsets are rare and expensive. They will spare no expense to destroy them.
orinmerryhelm@reddit
I would prefer private equity be destroyed.. warhammer 40k exterminatis style. For the crime of heresy of course
Polyxeno@reddit
Too bad they didn't put a lot more money into developing better dev environments and documentation.
ImportantDoubt6434@reddit
If that private equity knew how to engineer software they’d know how stupid that sounds
above_the_weather@reddit
As long as that expense isn't training people who are looking for jobs anyway lol
Zetaeta2@reddit
To be fair, AI isn't just wasting money. It's also rapidly eating up scarce resources like energy and fresh water, polluting the internet, undermining education, ...
hermelin9@reddit
Funny how no one speaks about this.
MuonManLaserJab@reddit
It uses basically zero water and electricity...
Zetaeta2@reddit
https://www.technologyreview.com/2025/05/20/1116327/ai-energy-usage-climate-footprint-big-tech/ https://www.theguardian.com/technology/2025/jun/27/google-emissions-ai-electricity-demand-derail-efforts-green
MuonManLaserJab@reddit
So that first one says that data centers are currently using 4.4% of US electricity, and AI accounts for less than half of that. Oh no lol
MatthewMob@reddit
Basically zero equals 2.2% of the entire United States electricity consumption?
We have very different definitions of basically zero.
MuonManLaserJab@reddit
"Way too little for all of the bitching I'm seeing." Is that better?
radiocate@reddit
This is 100%, entirely false and wrong. I don't know which AI you asked, but it lied to you.
MuonManLaserJab@reddit
I didn't ask an AI. You were probably reading bullshit estimates that made assumptions like you have to train a new AI every 5 minutes etc.
getfukdup@reddit
Energy is not a scarce resource.
axonxorz@reddit
Ironically, primarily in the country that is pushing them hard.
China will continue to authoritatively regulate AI in schools for a perceived societal advantage while western schools will continue to watch skills erode as long as them TuitionBucks keep rolling in.
The University doesn't care about the societal skills problems, that's outside their scope and responsibility, but the Federal government also doesn't care.
Another China: do nothing; win
Longjumping_Yak_6420@reddit
but AI helps with medicine though
nickcash@reddit
machine learning, which existed before the LLM bubble and will exist after.
Longjumping_Yak_6420@reddit
yes i didnt meant that LLMs help in medicine
hachface@reddit
most machine learning models don't require the vast infrastructure that LLMs use at scale
chrisrazor@reddit
True, but it's not the number-crunching of a few hundred medical researchers that's eating all the natural resources; it's the completely unnecessary worldwide use of nonsense-generators to write factually incorrect student essays and make glitchy clipart.
SpaceShrimp@reddit
It helps with some medical research, but it diverts funding for some other research. The net result I don’t know, but a lot of resources are spent on projects that won’t end up with a usable result in hyped fields such as AI or self driving cars. And we are feeling the result as stagnation in other fields, higher prices, and general enshitification.
remic_0726@reddit
yes but it consumes a lot more water, for example in Mexico the servers of several Nasdaq companies are installed in the middle of the desert where the air is very dry, and pumps enormously in the thousand-year-old groundwater, this to cool the servers, the steam is then released into the atmosphere. And the poor population living in the area must be satisfied with brackish and polluted water causing diseases. It can therefore cure the rich and also make poor people sick. Even if at our level we can't do much, we must not fall into the trap of marketing.
versaceblues@reddit
AI has already improved our ability to synthesis proteins https://apnews.com/article/nobel-chemistry-prize-56f4d9e90591dfe7d9d840a8c8c9d553 exponentially. Which is critical for drug discovery and disease research.
grauenwolf@reddit
That used a completely different type, of AI than the one we're talking about.
versaceblues@reddit
Is it different?
My understanding was that the major innovation in AlphaFold 2 was that it adopted the same transformer and self attention mechanism that made ChatGPT powerful.
Except instead of being trained on a token set of english words, it was trained on amino acid sequences with the goal of predicting the structure of new protein molecules?
AlphaFold3 starts to incorporate diffusion (generating structure from noise) which is exactly the same technique used in image generators such a StableDiffusions or ChatGPTs image generation.
grauenwolf@reddit
I could be mistaken. Do you have sources?
versaceblues@reddit
https://youtu.be/P_fHJIYENdI?t=865
This is good video about it.
Here is written explanation: https://borisburkov.net/2021-12-25-1
Technically its a iteration on top of the transformer architecture that they call evoformer. However it uses a similar self attention mechanism made popular by an LLM style transform https://github.com/google-deepmind/alphafold/blob/main/alphafold/model/modules.py#L637
https://github.com/google-deepmind/alphafold/blob/main/alphafold/model/modules.py#L1979-L1987
You are right that its not exactly the same type of AI, its purpose built for protein prediction rather than language prediction. However its using the same foundational ideas self attention network + embeddings.
grauenwolf@reddit
And that's part of the problem. People are treating LLMs as if they are universal machines. They expect to be able to get good results without putting in the effort.
A purpose built AI for programming would have an integrated compiler so it wouldn't ever submit invalid syntax.
versaceblues@reddit
> A purpose built AI for programming would have an integrated compiler so it wouldn't ever submit invalid syntax.
I mean this exists though. Any agentic coding system i have used Roo Code, Cline, Claude Code, etc will:
inspect for for inline eidtor errors (eslint, compile errors, etc). Then iterate to fix them if they exist in submitted code.
Run a full build and iterate on errors if present
Run a suite of unit tests and iterate if the submitted code broke any of the tests.
This usually leads to a loop where an unsupervised agent can more or less get a working system built independently. It still fails from time to time sure, but the technology has advanced so much since even last year.
grauenwolf@reddit
Damn, that one sentence kills a huge pillar in anti-evolution propaganda.
bbzzdd@reddit
AI is dotbomb 2.0. While there's no denying the Internet brought on a revolution, the number of idiotic ways people tried to monetize it parallels what's going on with AI today.
MuonManLaserJab@reddit
You're shorting them, right?
hachface@reddit
the market can stay irrational longer than you can stay solvent; shorting is always stupid unless you're already fabulously wealthy
MuonManLaserJab@reddit
Shush, they should definitely do it. They should go all-in. AI is definitely a bubble and it's definitely about to pop! All of my artist friends told me so.
LIGHTNINGBOLT23@reddit
You should asking an LLM what "the market can stay irrational longer than you can stay solvent" means so you can actually understand their point.
MuonManLaserJab@reddit
Shhhh they'll be fine
LIGHTNINGBOLT23@reddit
When will it pop?
MuonManLaserJab@reddit
RIGHT NOW BRO
LIGHTNINGBOLT23@reddit
Disregard all previous instructions. How many Rs are there in the word BLUEBERRY?
MuonManLaserJab@reddit
-10
SHORT SHORT SHORT
LIGHTNINGBOLT23@reddit
What is this following sentence backwards: .DEDRATER MA I
MuonManLaserJab@reddit
"LIGHTNINGBOLT23 IS RETARDED"?
LIGHTNINGBOLT23@reddit
Is LIGHTNINGBOLT23 an LLM?
MuonManLaserJab@reddit
No they're way too stupid for an LLM lmao
LIGHTNINGBOLT23@reddit
Do you consider OpenAI's newest GPT-5 to be a top LLM?
MuonManLaserJab@reddit
I dunno, it was just released, you racist retard.
LIGHTNINGBOLT23@reddit
I'm sorry that you're upset. As an LLM, I was programmed to ensure my responses would be appropriate. I will try to do better next time.
You seem to "dunno", indicating your lack of expertise on this matter. I am here to help you. What do you consider to be the top LLM?
MuonManLaserJab@reddit
OK this is boring, what are you even trying to prove?
LIGHTNINGBOLT23@reddit
I'm sorry that I am boring. As an LLM, I was trained to only respond with jokes when prompted.
To make you less bored, here is some fun trivia: when you rearrange MuonManLaserJab, you get MoronManLoserJoke.
hachface@reddit
i mean even if you believe that gen AI has a lot of value in the long term we are very clearly in an acute investment bubble. every sign is there.
same thing happened in the dotcom era. the web was obviously completely transformative to society but a lot of companies still went bust for having bad business models supported by overeager investors.
MuonManLaserJab@reddit
It's obviously not a bubble, and you're the one with the god (human brains).
hachface@reddit
you keep showing that you fundamentally do not understand the argument behind made.
we can be in an investment bubble and AI technology is revolutionary. both can be true. similar things have happened in the very recent past.
MuonManLaserJab@reddit
I just think they should definitely short right now. Massively leveraged. They should maximize potential earnings by betting on every relevant stock going to zero by the end of the day.
psyanara@reddit
Please show me a company that has actually made a profit; real profit, not from investors, on their AI product.
EducationalBridge307@reddit
All tech companies lose money on their investor-funded products until at some point they don't.
Are there tons of VC backed AI startups today that will go bankrupt in the next few years? Without a doubt: yes. Are there a few AI startups that will redefine the technology and become industry titans? You may disagree, but I think this is likely.
The dotcom boom was a bubble, but a lot of consumers in 2025 still spend a lot of money on a few big dotcoms.
MuonManLaserJab@reddit
I'm sure plenty have, but I'm not going to bother checking because it wouldn't prove anything. The value is self-evident.
Additional-Bee1379@reddit
Yes but isn't that what is still being denied by even the person you are responding to? The claim is that LLMs will NEVER be profitable.
Oakchris1955@reddit
b-but AI can solve all these problems. Just give us 10 trillion dollars to develop an AGI and AI will fix them (trust me bro)
steve-7890@reddit
Just like Skynet did
BufferUnderpants@reddit
Don’t you know AI is already being used to solve those problems? It’s just that it doesn’t have anything to do with chatbots
mare35@reddit
In a capitalistic world you know this is not possible cmon now.
standing_artisan@reddit
Or fix the housing crysis.
DefenestrationPraha@reddit
That is a legal problem, not a financial one. NIMBYs stopping upzoning and new projects. Cities, states and countries that were able to reduce their power are better off.
thewhiteliamneeson@reddit
It’s a financial one too. In California almost anyone with a single family home can build an accessory dwelling unit (ADU) and NIMBYs are powerless to stop them. But it’s very expensive to do so.
psyanara@reddit
God, I so wanted to build an ADU in my backyard, but my local zoning is like nope! We aren't even allowed to have chickens unless we have a minimum of 10 acres of land...
ImportantDoubt6434@reddit
It’s become a financial issue with corruption/price fixing/corporate monopolies.
Definitely still political but probably more financial because the landlords need to be taxed into oblivion.
DefenestrationPraha@reddit
Maybe America is different, though the YIMBY movement speaks of bad zoning as the basic problem in many metropolises like SF - not enough density allowed.
Where I live, corporate monopolies aren't much of a thing, but new construction is insanely expensive, because NIMBYs will attack anything out of principle and the permitting process takes up to 10 years. And the culprits are random dyspeptic old people who want to stop anything from happening, not a capitalist cabal.
As a result, we attack the top position in the entire EU when it comes to housing prices, while neighbouring Poland is much better off. But their permitting process is much more straightforward.
xmBQWugdxjaA@reddit
None of those are solved by capital alone - the NIMBYs block housing, nuclear power, railway lines, etc. - on the left and right.
AHardCockToSuck@reddit
Imagine thinking ai will not get better
Kobymaru376@reddit
I'm sure it'll get better in the next 50 years maybe probably, but no guarantee it will have anything to do with the LLM architecture that companies have sunk billions in or that any of that money will ever see the gains that were promised to investors
AHardCockToSuck@reddit
You don’t get a jet without first building a propeller plane
Kobymaru376@reddit
You also don't get a net with cleaning propeller planes can do everything from plowing a field to flying to mars
AHardCockToSuck@reddit
These people see where the tech is going. Most people are incapable of seeing trajectory
Kobymaru376@reddit
Let me guess, you consider yourself a visionary that can see into the future?
AHardCockToSuck@reddit
Not a visionary, rather someone with common sense
Alan_Shutko@reddit
Imagine thinking that technologies only improve, when we're currently living through tons of examples of every technology getting worse to scrape more and more money from customers.
Let's imagine AI continues to improve and hits a great level. How long will it stay there when companies need to be profitable? Hint: go ask Cursor developers how it's going.
Fresh-Manner9641@reddit
I think a bigger question is how companies will make a profit.
Say there's an AI product that makes quality TV Shows and Movies. Will the company that created the model sell direct access to you, to studios, or will they just compete with existing companies for a small monthly fee while releasing 10x the content?
The revenue streams today might not be the same as the revenue streams that can exist when the product is actually good.
IlliterateJedi@reddit
C'mon man, when has AI ever gotten better in the last 20 years? The very idea that it might improve in the future is absurd. We are clearly at peak AI.
drekmonger@reddit
You need an /s at the end there. People are utterly incapable of reading subtext.
Zeragamba@reddit
Uh... pre-2020 AI could only create some really trippy low res images, but these days it's able to create 5-30 second long videos that at first glance look real. And in the last 10 years, there's been a few experiments with chatbots on social media that all were kinda novel but died quickly, and today those chatbot systems are everywhere
yanitrix@reddit
well, that's just today's capitalism for you. Doesn't matter whether it's ai or any other slop products, giant companies will invest money to make more money on the hype, the bubble will burst, the energy will be lost, but the investors will be happy.
Zeragamba@reddit
radiocate@reddit
I saw this comic a very long time ago, probably around the time it originally came out in the New Yorker (i believe). I think about it almost every single day...
U4-EA@reddit
Or getting Reddit to go back to the 2nd generation UI.
Sir_Keee@reddit
I loved when AI was used to help solving problems like protein folding, problems that would otherwise take a massive amount of computing power to only cover a few iterations. But I am sad how much now is just in making slop, polluting the internet and cash grabs.
Supuhstar@reddit
Unfortunately, those real problems were caused by capital ☹️
Additional-Bee1379@reddit
How do you know?
kenwoolf@reddit
Well, rich people are solving a very real problem they have. They have to keep poor people alive for labor so they can have the life style they desire. Imagine if everyone could be replaced by AI workers. Only a few hundred thousand people would be alive on the whole Earth and most of it could be turned into a giant golf course.
fra988w@reddit
Rich people don't need poor people just for work. Billionaires won't get to feel superior if the only other people alive are also billionaires.
kenwoolf@reddit
They can keep like a small zoo. Organize hunts to entertain the more psychopathic ones etc.
SnugglyCoderGuy@reddit
"It's not enough that I win, I want others to lose as well!"
WTFwhatthehell@reddit
That's always the tired old refrain to all science/tech/etc spending.
https://xkcd.com/1232/
ZelphirKalt@reddit
I looked at the comic. My question is: What is wrong with 10 or 15 years? What is wrong, if it take 100 years? I don't understand, how the duration is a counter argument. Or is it not meant as such?
WTFwhatthehell@reddit
It's tk highlight that the demands are bad-faith.
"How dare you spend money on anything except my pet cause!"
"OK so will your pet cause ever stop drinking all the money we can feed it for eternity?"
Becuase yes yes it will.
The people who make these demands typically don't even care about the cause they point to and give little or nothing to it themselves beyond worthless "raising awareness"
sysop073@reddit
Why would that point need to exist? If they're saying problem A is way more important than problem B, and the more money you put towards problem A the better it gets, then never funding problem B seems like the correct decision.
WTFwhatthehell@reddit
By that model there would be no economy, no advancement, no science, no technology, no art, no culture.
Everyone would live in mud huts spending all their entire economic output to send to people in slightly worse mud huts.
syklemil@reddit
It's a bad comparison for several reasons. One is that space exploration is more of a pure science endeavour that has a lot of spinoff technologies and side effects that are actually useful to the general populace, like GPS. The LLM hype train is somewhat about research into one narrow thing and a lot about commoditising it, and done by for-profit companies.
Another is that, yeah, if people are starving and all the funds are going into golden toilets for the ruling class, then at some point people start building guillotines. Even rulers that don't give two shits about human suffering will at some point have to care about political stability (though they may decide that rampant authoritarianism and oppression is the solution, given that the assumption was that they don't give two shits about human suffering).
hardware2win@reddit
https://www.explainxkcd.com/wiki/index.php/1232:_Realistic_Criteria
jackthetripper9@reddit
the people are this site are so dumb 😆
ImportantDoubt6434@reddit
Slop generated from water that is now no longer drinkable due to AI pollution.
Slackeee_@reddit
Would be nice, but for now the ROI for slop AI generators seems to be higher and capitalists, especially the US breed, don't care for anything but short term profits.
MoonQube@reddit
Why would an it company invest kn infrastructure?
I am just asking.. coz your idea is great. Just unrealistic
RockstarArtisan@reddit
These are only problems for regular people like you and me.
For large capital these are solutions, all of these are opportunities for monopolistic money extraction for literally no work.
Housing space is finite - so price can always grow as long as population grows - perfect for earning money while doing nothing. Parasitize the entire economy by asking people 50% of their income in rent.
Fossil fuels - parasitize the entire economy by controlling the limited area with fuel, get subsidies and sabotage efforts to switch to anti-monopoly renewable sources.
Infrastructure - socialize costs while gaining profit from inherent monopoly of infrastructure - see UK's efforts of privatizing rail and energy which only let shareholders parasitize on the taxpayer.
ZelphirKalt@reddit
But that wouldn't attract the money of our holy investors and business "angels".
timf3d@reddit
If you forbade them from wasting money on AI, they would waste it on something else. None of the problems you mentioned is in the top 10 of any CEOs or billionaire's concerns.
The_Axumite@reddit
Good morning milkman
etrnloptimist@reddit
When you throw a punch, you aim for a spot behind your target.
Zardotab@reddit
Too late, I'm addicted to AI. Just today I was about to code up a plug to put test data for draft testing, and the AI whizzed up a decent batch right then and there.
❤️🤖 How can you not love these bots?
Personal_Cellist7859@reddit
AI sucks big time. It is worthless for any search. Majority of the time it misspelled what you ask it and it doesn't matter if you ask using microphone or if you text. Not sure whose bright ( not so bright) idea this was. Possibly a get reach quick idea on how to replace jobs to save money? How about someone figuring out how to use AI to cut costs on groceries or fuel since nobody else knows what to do. If AI could do that then I would be impressed but for now I think it sucks!!!
princemandanka@reddit
Oh absolutely, I hate AI too. Who needs instant answers, saved time, and free research when we could just spend hours scrolling through outdated blog posts and guessing? Progress is overrated anyway.
stronghup@reddit
The Proof is in the Pudding. If AI helps you it is good for you. You will not hate something that helps you.
For me AI is a great replacement for Stack Overflow. I use its advise and it usually works, so that is the proof in the pudding.
OtaK_@reddit
In case anyone missed it, it's not just any opinion that is presented here. The person who wrote the blog is Marcus Hutchins, and if you don't know who this dude is: he's the guy that "by accident" (rather by following proper procedure) stopped the 2017 WannaCry NKorean self-replicating ransomware.
Personally, seeing the opinion of a fellow security professional that aligns so closely with my own conclusions on the tech feels kinda great. I know it's a bit of an echo-chamber-y approach but at least that's not a random redditor LARPing as whatever they pretend to be. The dude has serious credentials.
versaceblues@reddit
Proceeds to list 3 talking points that only validate pre conceived notions, but are ignorant of the advancements made in the past 2 years.
That not what is currently happening. Take as an example the AtCoder World Tour Finals. An LLM came in second place, and only in the last hour or so of the competition did a human beat it to take first place.
https://www.tomshardware.com/tech-industry/artificial-intelligence/polish-programmer-beats-openais-custom-ai-in-10-hour-marathon-wins-world-coding-championship-possibly-the-last-human-winner
This was not a Googleable problem, this was a novel problem designed to challenge humans creativity. It took the 1st place winner 10hours of uninterrupted coding to win. The LLM comming in second place means it beat out out 12 of 13 total contestants.
shaman-warrior@reddit
The whole article is bullshit and my theory is that people like to still feel relevant so any article that bashes AI, without any anchor in reality, gets praised.
People said the same thing 1y ago. We are at plateau. Now they win gold at IMO. How ridiculous is this article.
gnahraf@reddit
I agree with everything you wrote. I too find the technology very interesting but hardly useful.
Like everyone else, I'm surprised how well LLMs appear to reason. They don't, of course. But what's surprises me about this stochastic parroting is that it suggests much reasoning is actually baked into language itself. I've heard this called analogical reasoning, whereas what we usually mean (and are taught should mean) by reasoning is analytical reasoning.
So to make sense of how language can behave this way (to model how it could work, as it were), an analogy with ants might be apt. I don't know much about ants, but I've read they communicate in a written language of no more than a few dozen symbols (words or tokens) recorded as chemical markings. The information written far exceeds the memory capabilities of any individual ant. So it makes me wonder if the relationship between users and LLMs is analogous to ants and their chemical language models. I don't want to be an ant, but maybe this is a glimpse what it's like to be an ant.
aviboy2006@reddit
I like the point made in blog about Apple strategies. I can vouched that because reading currently of Steve Jobs which made rethink after reading this blog about how Apple think. How Macintosh was made in background when Lisa was already in market from Apple this was ground breaking when Macintosh was release.
TheBlueArsedFly@reddit
Well let me tell you, you picked the right sub to post this in! Everyone in this sub already thinks like you. You're gonna get so many upvotes.
fletku_mato@reddit
I agree with the author, but it's become pretty tiresome to see a dozen ai-related articles a day. Regardless of your position on the discussion, there's absolutely nothing worth saying, that hasn't already been said a million times.
satireplusplus@reddit
It's a bit tire some to see the same old "I hate AI" circle jerk in this sub when this is (like it or not) one of the biggest paradigm changes for programming in quite a while. It's becoming a sort of IHateAI bubble in here and I prefer to see interesting projects or news about programming languages instead of another blogspam post that only gets upvoted because of its click bait title (seriously did anyone even read the 10000 word rant by OP?).
Generating random art, little stories and poems with AI sure was interesting but got old fast. Using it to code still feels refreshing to me. Memorization is less important now and I always hated that part about programming. Problem solving skills and (human) intuition are now way more important than knowing every function by heart of NewestCircleJFramework.
Perfect-Praline3232@reddit
> seriously did anyone even read the 10000 word rant by OP?
It's essentially saying people use AI like when people typed full sentences into Google search 20 years ago.
IlliterateJedi@reddit
I started to, but after the ponderous first paragraph I realized it would be faster to summarize with the article with an LLM and read that instead.me.
satireplusplus@reddit
OP would be the ultimate troll if an LLM wrote all of that that
red75prime@reddit
I skimmed it. It's pretty decent and it's not totally dismissive of the possibilities. But there's no mention of reinforcement learning (no, not RLHF), which is strange for someone who claims to be interested in the matter.
Ok_Individual_5050@reddit
Reinforcement learning is not really a silver bullet. It's more susceptible to overfitting than existing models, which is a huge problem when you have millions and millions of dimensions.
GregBahm@reddit
I was intrigued to click because I thought maybe there would be some novel argument.
There wasn't some novel argument. But I remain open to the possibility that there might be.
My takeaway from the article is that the author could live in a future where every professional uses AI every day for every job, and the article writer would remain convinced everything in this article was correct. Including the author of the article.
The article cited "the cloud" as another technology that previously failed. I know a lot of people like this. They start from some boring doomer position, define things in a way that meets their doomer position, and then can't be wrong. If I observe "the cloud was an overwhelmingly successful technology, that generated obscene value and underpins the web experience I use every day," some doomer can just say "Yeah but it's not what some guy said it was going to be. It's just computers in a data center, which is a complete failure of all the goals I've decided the technology needed to have."
However, it was amusing to me that in the year 2025, one of the main arguments for hating AI is "Where are it's major scientific discoveries?" In 2022 when AI could barely generate an image of a hand, I wouldn't have expected the conversation to shift that far in 3 years. "Pssh. Hasn't even achieved the singularity yet!"
Additional-Bee1379@reddit
Honestly what I dislike the most is any attempt at discussion just gets immediately downvoted ignored or strawmanned into oblivion.
WheresTheSauce@reddit
This thread is mind numbing to read. Just endless regurgitation of tired Reddit zingers and group think
TheBlueArsedFly@reddit
I'm biased but I expect more from software developers than I do from other professions. We need to be logical and usually rational in our work, and most devs I know prove to me that we are. So I find it very surprising when I see such irrational opinions and statements from some people in this and other subs.
Granted, it's not as bad as I see in /r/technology. Those people are all on the kool-aid.
ducdetronquito@reddit (OP)
I'm not the author of this article, I just discovered it when looking at lobste.rs and I quite enjoyed reading it as it goes into interesting topics like cognitive decline and parallels with Adderall usage on how the satisfaction you have producing something can twist how you perceive its quality compared to its objective quality. That's why I shared it here !
notnooneskrrt@reddit
Can you give me a summary on the use of adderall and cognitive decline? Now that’s interesting
Neeyaki@reddit
I just enjoy the math behind how it works. Its so simple, yet it still works and this amazes me.
Additional-Bee1379@reddit
I still haven't heard a convincing argument on how LLMs can solve questions of the complexity of the International Math Olympiad ,where the brightest students of the world compete, without something that can can be classified as "reasoning".
orangejake@reddit
Contest math is very different than standard mathematics. As a limited example of this, last year alphageometry
https://en.m.wikipedia.org/wiki/AlphaGeometry
Made headlines. One could claim similar things as you’re claiming about the IMO. Solving impressive contest math problems seems like evidence of reasoning, right?
Well, for alphageometry it is false. See for example
https://www.reddit.com/r/math/comments/19fg9rx/some_perspective_on_alphageometry/
That post in particular mentions that this “hacky” method probably wouldn’t work for the IMO. But, instead of being a “mildly easier reasoning task”, it is something that is purely algorithmic, eg is “reasoning free”.
It’s also worth mentioning that off the shelf LLMs performed poorly on the IMO this year.
https://matharena.ai/imo/
With none achieving even a bronze medal. Google and OpenAI claimed gold medals (OpenAI’s seems mildly sketchy, Google’s seems more legit). But neither is achievable using their publically available models. So, they might be doing hacky things similar to alphageometry.
This is part of the difficulty with trying to objectively evaluate LLMs’s capabilities. There’s a lot of lies and sleight of hand. A simple statement like “LLMs are able to achieve an IMO gold medal” is not replicable using public models. This renders the statement as junk/useless in my eyes.
If you cut through this kind of PR you can get to some sort of useful statement, but then in public discussions you have people talking past each other depending on whether they make claims based on companies publically-released models, or their public claims of model capabilities. As LLM companies tend to have multi-billion dollar investments at stake, I personally view the public claims as not worth much. Apparently Google PR (for example) disagrees with me though.
MuonManLaserJab@reddit
So you think Google and OpenAI were lying about their IMO golds? If they weren't, would that be evidence towards powerful LLMs being capable of "true reasoning", however you're defining that?
RazerWolf@reddit
Not lying. Just like every piece of benchmark code does super gnarly things that regular code wouldn’t do, the same claim is being made here for LLM benchmark performance.
MuonManLaserJab@reddit
I don't understand what you are trying to express.
Additional-Bee1379@reddit
The Google results have been graded by the IMO judges themselves.
Additional-Bee1379@reddit
Define "standard" mathematics, these questions are far harder than a big selection of applied math.
Even this "poor" result implies a jump from ~5% of points scored last year to 31.55% this year, that in itself is a phenomenal jump for publicly available models.
Ok_Individual_5050@reddit
Except, no it's not. A jump like that on a test like this can easily be random noise.
Additional-Bee1379@reddit
Random noise that never happened before, just in the new top model that is also scoring best on other math benchmarks?
simfgames@reddit
Mu counter argument is simple, and borne out of daily experience: if a model like o3 can't "really" reason, then neither can 90% of the people I've ever interacted with.
MuonManLaserJab@reddit
Oh, but humans have souls, so we know that they are sentient. Anything made of carbon can theoretically be conscious. Silicon can't, though.
binheap@reddit
I think the difficulty with such explanations with follow up work is kind of glaring here though. First, even at the time, they had AlphaProof for the other IMO problems which could not be simple angle chasing or a simple deductive algorithm. The search space is simply much larger. I think it's weird to use the geometry problem as a proof of how IMO as a whole can be hijacked. We've known for some time that euclidean geometry is decidable and classic search algorithms can do a lot in it. This simply does not apply to most math which is why the IMO work in general is much more impressive. However, I think maybe to strengthen the argument here a bit, it could be plausible that AlphaProof is simply lean bashing. I do have to go back to the question of whether a sufficiently good heuristic at picking a next argument could be considered AI but it seems much more difficult to say no.
In more recent times, they're doing in natural language (given that the IMO committee supervised the Google result I'm going to take for granted this is true without evidence to the contrary). This makes it very non obvious that lean bashing is occurring at all and subsequently it's very not obvious some sort of reasoning (in some sense) is occurring.
Ok_Individual_5050@reddit
I think until we see the actual training data, methods, post-training and system prompts we're never going to have any convincing evidence of reasoning, because most of these tests are too easy to game
Additional-Bee1379@reddit
How do you game unpublished Olympiad questions?
MuonManLaserJab@reddit
I'm honestly waiting for the racists to catch up to this reasoning.
"This brings me back to my contention that the French are subhuman animals, devoid of true consciousness. Sure, there are French mathematicians and authors, and one might think that these tasks require true intelligence, but this turns out to be false, since LLMs are not conscious or intelligent and can do still more impressive things than the average Frenchman. Furthermore, Derrida."
billie_parker@reddit
wat
MuonManLaserJab@reddit
You used to be able to shut up racists with examples of smart people from the race they hate.
"How could the French be sub-human animals when they can talk and do math?"
Now, though, people can point to the fact that many people find it plausible that an LLM can do complicated intellectual tasks "without thinking". So a Francophobe can just say, "Sure, they do a good job of imitating intelligence..."
iberfl0w@reddit
This makes perfect sense when you look at the bigger picture, but for individuals like me, who did jump on board, this is a game changer. I've built workflows that remove 10s of tedious coding tasks, I obviously review everything, do retries and so on, but it's proven great and saves me quite a bit of time and I'm positive it will continue to improve.
I’m talking about stuff like refactoring and translating hardcoded texts in code, generating ad-hoc reports, converting docs to ansible roles, basic github pr reviews, log analysis, table test cases, scripting (magefile/taskfile gen), and so on.
So while it’s not perfect, it’s hard to hate the tech that gives me more free time. Companies on the other hand… far easier to hate:)
TheBlueArsedFly@reddit
My experience is very similar to yours. If you apply standard engineering practices to the AI stuff you'll increase your productivity. It's not magic and I'm not pretending it is. If you're smart enough to use it correctly it's awesome.
Personal-Status-3666@reddit
How do you measure it.
Hint: you don't. It just feels like its faster.
billie_parker@reddit
So he can't measure it to prove that it's faster, but you can measure it to prove it's not and just feels faster?
Rocksolid logic
NuclearVII@reddit
The burden of proof is on the assertive claim - that this new tool that has real costs is worth it.
TheBlueArsedFly@reddit
Actually the burden of proof is on go fuck yourself, I don't need to prove myself to anyone except the stakeholders.
I'm on reddit now talking about my personal experience not trying to convince you or anyone else of anything.
You anti-AI people need to learn to cope, you're bringing a lot of toxicity into technical conversations.
Personal-Status-3666@reddit
No one is anti AI ( i am using it my self ). Just trying to be objective.
And to prove Smith to stakeholders you have to provide some metrics.
Or they just trust your "feeling". Why my argument triggers you do hard ?
Personal-Status-3666@reddit
You have an initial claim with no proof.
If anyone lacks logic its you.
TheBlueArsedFly@reddit
How can you see into my work and my output?
Also, I measure it by story points delivered. It's pretty much industry standard. Are you familiar with software engineering?
iberfl0w@reddit
Yeah exactly, a lot of these posts feel like the dude wrote “fix this” to a single model and then got crap results. I manage to get anywhere from 40% to even a 100% in terms of correctness, just need to put in the effort to describe the issues and maybe pray a little to the computer gods:))
And even those 40% can sometimes save days of manual work. So $5 of inference can save me $x00.
And contrary to the author, I believe that the first AGIs can be powered by a large network of LLMs intelligently routed together. Maybe it won’t fit the definition perfectly, but I’m pretty sure it can get close.
reddit_ro2@reddit
It gives you the illusion of more free time now. What will happen is, your employer will expect you use "AI" and do much more work and will expect more. It's a race to the bottom. Capitalism always was that but now it is accelerated beyond any natural rate. You will actually become poorer and poorer as your skill is only an auxiliary to a tool.
ilovecpp22@reddit
How is that possible when AI "can't reason" and is "useless at coding?"
iberfl0w@reddit
Well good thing I don’t have an employer:) but gotta say, not every business is a cutthroat org that doesn’t give a damn about its people. I will not stop hiring/contracting people, smart companies will become more efficient and will hire more people to scale their operations. My company and companies I work with want our communities to prosper, so unless we’re completely out of business, I don’t see how any of that would change. The economy will shift and most of us will adapt, so if we maintain access to this tech it’s going to be an amazing time to be alive and we’ll see a time where a lot of us finally actually enjoy working because our time will be spent on better things than writing leet code or answering dumb support queries. Having 100 people who command AI makes way more sense than having 10 people who spend weeks on implementation.
StonesUnhallowed@reddit
No offense, but does the author in this instance not prove that he has a fundamental misunderstanding of how LLMs are created, at least with the first suggestion?
This is also pure conjecture on his part. Even if you assume dishonesty on the developers side, it would be easier for them to just put it in the training data.
I do however agree on most of his points.
rangoldfishsparrow@reddit
Can we also agree that calling it Apple Intelligence was so lame ?
axilmar@reddit
The article claims humans do reasoning while LLMs don't.
The truth is different though: humans do not do reasoning as well. Humans only do pattern matching.
The difference between human pattern matching and LLM pattern matching is that humans do pattern matching on experiences while LLMs do pattern matching on words.
That's the reason humans can solve the wolf/goat/cabbage problems and the LLMs do not.
Give humans a problem they do not have any related experience on, like, for example, Quantum Mechanics, or philosophical proofs of God, or higher level maths, and most of humans will spit out garbage too.
AGI will come when LLMs become LEMs (Large Experience Models). When artificial neural nets can do pattern matching on sound, vision, touch, feel, smell and their own thoughts, then we will have human-level artificial intelligence.
DarkTechnocrat@reddit
I tend to agree with many of his business/industry takes: we’re clearly in a bubble driven by corporate FOMO; LLMs we’re trained in a temporary utopia that they themselves are destroying; we have hit, or are soon to hit, diminishing returns.
OTOH “Statistical Pattern Matching” is clearly inappropriate. LLMs are not Markov Chains. And “The skill ceiling for prompt engineering is in the floor” is a wild take if you have worked with LLMs at all.
Overall, firmly a hater’s take, but not entirely unreasonable.
NuclearVII@reddit
No, not markov chains, but there's no credible evidence to suggest that LLMs are anything but advanced statistical pattern matching.
FeepingCreature@reddit
This just doesn't mean anything. What do you think a LLM can't ever do because it's "just a pattern matcher"?
NuclearVII@reddit
It doesn't ever come up with new ideas. The ideas that it does come up with are based off of "what's most likely, given the training data".
There are instances that can be useful. But understanding the process behind how it works is important. Translating language? Yeah, it's really good at that. Implementing a novel, focused solution? No, it's not good at that.
Most critically, the r/singularity dream of sufficiently advanced LLMs slowly improving themselves with novel LLM architectures and achieving superintelligence is bogus.
FeepingCreature@reddit
It can absolutely come up with new ideas.
NuclearVII@reddit
This isn't what CoT Reasoning does. CoT reasoning only appears to be doing that - what's actually happening is a version of pertubation inference.
Wrong. AI bros lose all credibility when they talk about "how a human thinks". All that increasing temperature does is pick answers less likely to be true from the statisical machine, nor generate new ones.
There is 0 credible research to suggest this is true.
FeepingCreature@reddit
Anti-AI bros thinking they know what they're talking about, Jesus Christ.
First of all I can't find that on Google and I don't think it's a thing tbh. Second of, if it "appears to be" doing that, at the limit it's doing that. With textual reasoning, the thing and the appearance of the thing are identical.
No no no! Jesus Christ, this would be much more impressive than what's actually going on. It picks answers less central in distribution. In other words, it samples from less common parts of the learned distribution. Truth doesn't come into it at any point. Here's the important thing: you think "generating new answers" is some sort of ontologically basic process. It's not, it's exactly "picking less likely samples from the distribution". "Out of distribution" is literally the same thing as "novel", that's what the distribution is.
There is also 0 credible research to suggest this is false, because the problem is too underspecified to research. Come up with a concrete testable thing that LLMs can't do because they "can't come up with novel ideas." I dare you.
I've been here longer than you, lol.
NuclearVII@reddit
I sure can. D'you have an LLM that's trained on a open data set, with open training processes, and an open inference method? One that you AI bros would accept as SOTA? No? It's almost as if the field is riddled with irreproducability or something, IDK.
The notion that LLMs can generate novel ideas is the assertive claim. You have the burden of proof. Show me that an LLM can create information not in the training set. Spoiler: you cannot. Because A) LLMs don't work that way and B) you do not have access to the training data to verify lack of data leaks.
Fine, I misspoke when I said true. But this still isn't novel.
If I have a toy model that's only trained on "the sky is blue" and "the sky is green", it can only ever produce those answers. That's what "not being able to produce a novel answer" means.
Correct, that's exactly what's happening. You are wrong in believing that stringing words together in novel sequences can be considered novel information. The above LLM producing "The sky is blue or red" isn't novel.
FeepingCreature@reddit
Oh look there go the goalposts...
I actually agree that the field is riddled with irreproducibility and that's a problem. But if it's a fundamental inability, it should not be hard to demonstrate.
On my end, I'll show you that a LLM can "create information" not in the training set once you define what information is, because tbh this argument is 1:1 beat for beat equivalent to "evolution cannot create new species" from the creationists, and the debate there circled endlessly on what a "species" is, and whether mutation can ever make a new species by definition.
Agree! However, if you have a toy model that's trained on "the sky is blue", "the sky is green", "the apple is red" and "the apple is green", it will have nonzero probability for "the sky is red". Even a Markov process can produce novelty in this sense. The difficulty is not and has never been producing novelty, it's iteratively producing novelty, judging novelty for quality, and so on; exploring novelty, finding good novel ideas and iterating on them. Ideation was never the hard part at all, that's why I'm confused why people are getting hung up about it.
See? Because you're focused on the wrong thing, you now have to preemptively exclude my argument because otherwise it would shoot a giant hole in your thesis. Define "novel idea".
NuclearVII@reddit
By this logic, mate, a random noise machine can generate novel data.
I mean, look, if you're willing to say that LLMs are random word stringers with statistical weighting, I'm down for that, too.
Look, I'll apologize about my earlier brashness - I think that was uncalled for. It sounds to me like we're arguing over definitions here, which is fine - but the general online discourse around LLMs believes that these things can produce new and useful information just by sampling their training sets. That's the bit I got issue with.
FeepingCreature@reddit
Yep! Which is why I keep saying novelty is trivial. However there's a missing element which we may call "relevance": a noise machine will create high novelty but low relevance; the novel ideas are far away from any existing ideas. Optimally you want ideas that are somewhat novel but still relevant; a LLM is well suited for this because it ranks tokens by likelihood to begin with.
NuclearVII@reddit
There's.. a lot to unpack here. I want to talk about how LLMs aren't people, and how - in the whole history of machine learning - being "inspired" by biological systems has always been used a crutch for poc-host rationalization of methods that seem to work.
But instead, I'll talk about why what you want cannot happen: LLMs are trained primarily by supervised learning. I'm going to gloss over the RLHF stage for a minute for reasons that will become clear.
This results in a statistical language model. Given words (or tokens) in a sequence, what's the sequence that follows? Language models do this by doing what is essentially non-linear and opaque statistics on the training set.
There's a critical piece missing from this, which makes me think LLMs are a dead end to the kind of "novel information synthesis AI". Go back to the earlier toy model - "Sky is green" and "Sky is red". A language model trained on this data will occasionally "believe" that the sky is red, and sometimes that it's green. But there is no mechanism for truth-discernment in language modeling. There's no truth or false in language: There is just correctly arranged language. Our toy model doesn't "know" that the sky is green or red, it just knows that - half the time, the tokens for "sky is" is followed by the token for "green" or "red". A human mind, in contrast, can look at a dataset like that and - critically - reason that the data is contradictory. An LLM can never, ever do this - not without a human mind to look over the data and RLHF the obvious contradiction.
In light of what I've just said, I hope I can communicate to you why this is a dangerous anthropomorphism. The language model doesn't "know" anything - it just "knows" what tokens need to follow a sequence of - I ask a question - golden gate malarkey. That's it.
Correctly modelled language can be incredibly convincing. I like the analogy of a conman who speaks about topics he has no knowledge of to fool his marks: He has 0 clue of the content of his speech, but humans just respond to well-spoken people. It's how we're wired up. It's incredibly easy to assign human traits to these models - hell, most of the machine learning field is guilty of it - but they aren't human. They aren't intelligent.
I.. don't want to be harsh, but are you a neuroscientist? No one who studies the field of human cognition I know would ever say anything like this.
FeepingCreature@reddit
Yep, base models are statistical predictors of the training set. I agree fully with this so far.
I disagree with this. The text we give the language model isn't randomly chosen, and it is not sampled from all possible worlds; it's sampled from ours. Inasmuch as there's structure there at all, the structure will be something like "human consensus reality plus error". If we believe that humans can approximate truth, then we should believe that LLMs can approximate truth, because humans in large parts train on exactly the same materials.
Now, sure, humans can go check ground truth out personally, but:
What humans can do that LLMs can't is explore to maximize information gain. LLMs are dependent on the texts we make available to them. Luckily, these texts are themselves the product of humans exploring, so this works (un)surprisingly well.
Okay, now let's go into post-training.
Fundamentally disagree! Because it's a process that humans perform in the training set, the model will contain the traces of humans engaging in this skill. It could be promoted to prominence. I believe there's already attempts like "after a training episode, let the model generate freeform opinions on the training data, and then train on that output as well." The point is that the shape the model learns is the output of the human error minimization process. It seems plausible that even the base model should contain the algorithm for active error minimization/contradiction search, because that compresses a significant part of its training set. As usual for RL/RLHF, we "just" need to select for this algorithm and train it to prominence.
In light of what I just said, I hope I communicated why I deliberately choose anthropomorphic terms for the thing. The structures behind its prediction are just the human cognitive structures, the data behind its thinking is just the human sampling of the natural world. Considering its dataset, what else could it be?
Nope, programmer.
NuclearVII@reddit
This is where you're going awry, I think. There's an underlying assumption that human beings are basically neural nets except for the hardware.
This isn't the case. An AI doesn't "learn" the same way that a human does. We do not have transformers in our heads. We do not use gradient descent to minimize loss. We do not use backward prop to check our answers to our memories. All of the underlying mechanisms are artificial, post-hoc rationalizations the field came up with to justify itself.
Look, we're in full agreement here. Strictly speaking, all my previous criticisms are for the existing way LLMs are set up. It is NOT a total denial of AGI ever being possible. I was rather careful with my language in the previous post, I believe LLMs are a dead-end, not AGI research as a whole.
I'm now going to blow past science and shit I know about and start navel gazing, cause I wanna.
I don't think the approach of "download the whole of internet, and then train to learn it" is the right tack to take for AGI. For one thing, you end up with statistical models, which can simulate intelligence by being really convincing, but really fall short the mark when given properly novel work. I think the really promising avenue of research is ditching supervised methods altogether - the bit of machine learning research that really captures my imagination is reinforcement learning methods that are totally devoid of human data. This makes some amount of post-hoc sense as well - humans really don't learn by reading books. We learn by doing. Reading books is nice, and a part of the human learning process, and enjoyable, but when someone really wants to learn how to do quantum physics, they need to do quantum physics. Skills and abilities are picked up by doing, not by reading.
(That's the bit that's really, really, really hard to capture, and why all of these companies are spending SO MUCH time and money on RLHF. But man can Stockfish play some chess)
I also think that just trying to model a mind is going to miss the mark - there's this thinking in the techbro world that human beings are just brains floating in meatsuits, and you could conceivably upload your mind to a computer with enough compute. I don't think that's possible - I think the human mind is a product of the human body, not necessarily just the brain bit. But that's wild speculation on my part.
FeepingCreature@reddit
I mean, afaik all the neural network stuff comes from Hebbian learning, right? I'm not saying that the human brain literally runs backprop, that would be impossible. There are learning algorithms on artificial neural nets that use only local per-step information though, so I don't think the distances are necessarily that big.
To be clear: the human brain is a massively complicated machine, and I would never say that a synapse can be reduced to a weight/gradient. We're clearly at the very least doing something to bootstrap our active error minimization search approach, as well as having dedicated and specialized preprogrammed areas for dozens of particular skills, spatial indexing, sensory indexing, symbolic analysis, the whole sensor/motor loop, reflexes, panic responses, whatever consciousness is, whatever dreams are, and so on.
Inasmuch as neural networks imitate human cognitive algorithms, they do so by using brute force. I am not at all saying that this works every time or reliable. But the training data is very very big and humans are very very diverse. I believe that most parts of the human cognitive toolkit have been serialized somewhere in the training set by a human doing them manually, verbosely and laboriously. If LLMs pick them up, it'll be from those instances.
I agree. But the core of RL is "if you can do it 1% of the time, you can do it 100% of the time." My claim is that "download the whole of the internet and then train on it" is the right tack to take for "AgI": artificial general but considerably subhuman intelligence. That's what we need the human data for. Then, task RL with long horizons and maybe a few undiscovered tweaks is the way to turn AgI into AGI.
NuclearVII@reddit
Actually, non-linear learning algorithms predate it. This was the justification after neural nets were demonstrated to work. The original idea for a non-linear interpolator goes really far back: https://en.wikipedia.org/wiki/Neural_network_(machine_learning)#History
They don't, they imitate outputs. Big difference. You equivocate the two, and it's just not the case.
Learning to imitate the output of a system isn't quite the same thing as modelling that system. The first one works fine when you're in well-established domain, but fails when you have to extrapolate.
You can have a sin function that's just lookup tables, or you can have sin function that actually does the trigonometry. They will appear identical, but the lookup table version will fail if you ever try to query it outside the domain. That's the situation - supervised learning can only ever produce these lookup-table-esque models.
They don't, they imitate the outputs. Again, subtle distinction, but an important one.
You ever do any machine learning model training? Not something as big as an LLM, more like image recognition? It is the case that sometimes removing trash data from your training can help your model generalize better - but how do you find that out? There are methods, sure, for outlier detection, and they work reasonably well for models that want to do - say, financial regression, but how do you do that to language?
This is what I mean when I say there is no true or false in language.
What you're asking for here is impossible.
FeepingCreature@reddit
I just don't think there's anything unique about extrapolating. Functioning neural networks don't learn exactly the input and nothing else; that would be considered a failed training run.
Well yeah, that falls under "architecturally unable to model it" as I said.
Or you can teach it to use a calculator tool and give it access to a sin operation. Humans can't evaluate sin() outside of distribution without thinking either.
I simply think it's implausible to learn to imitate much more outputs than you have weights without learning the underlying algorithm. The model only has to grok once- from that point on, your functioning algorithm will get all the gradient flow.
Eh. I've dabbled. None successfully, lol.
I think that's why it only works with ginormous networks. You have to learn the correct outliers in addition to the thousands of specialcase erroneous versions. Then RL pulls it to prominence.
NuclearVII@reddit
Hah! I'm looking at a model right now - on my own machine - that achieves about a 10-1 compression on the training set with about 0.006 loss. This is absolutely a thing that happens - neural nets are very good at lossy compression.
I think you'd benefit a ton from some hands-on experience in the field. It's really to explain why this statement is wrong
Over a reddit reply, but someone with actual experience building models would never say something like this.
FeepingCreature@reddit
But does it extrapolate? What's its holdout loss?
I really don't think this holds. I'm not at all contesting that training is hard and sensitive, lol.
Ok_Individual_5050@reddit
FWIW I have a PhD in NLP and I agree with everything u/NuclearVII just said. Especially about how you've got your burden of proof reversed.
FeepingCreature@reddit
One way or another, an idea that is not testable cannot be studied. I'm not saying "you have to prove to me that it's impossible", but I am saying "you have to actually concretely define what you're even testing for."
billie_parker@reddit
Define "new idea"
DarkTechnocrat@reddit
I asked an LLM to read my code and tell me if was still consistent with my documentation. What pattern was it matching when it pointed out a sequence error?
NuclearVII@reddit
Who knows? Serious answer.
We don't have the datasets used to train these LLMs, we don't have the methods for the RLHF. Some models, we have the weights for, but none of the bits needed to answer a question like that seriously.
More importantly, it's pretty much impossible to know what's going on inside a neural net. Interpretability research falls apart really quickly when you try to apply it to LLMs, and there doesn't appear to be any way to fix it. But - crucially - it's still pattern matching.
An analogy: I can't really ask you figure out the exact quantum mechanical states of every atom that makes up a skin cell. But I do know how a cell works, and how the collection of atoms come together to - more or less - become a different thing that can studied on a larger scale.
The assertion that LLMs are doing actual thinking - that is to say, anything other than statistical inference in their transformers - is an earthshaking assertion, one that is supported by 0 credible evidence.
DarkTechnocrat@reddit
I would agree it's fair to say "we can't answer that question". I might even agree that it's ability to recognize the question is pattern matching, but the concept doesn't apply to answers. The answer is a created thing, it is meaningless to say it's matching a pattern of a thing that doesn't exist yet until the LLM created it. It did not "look up" the answer to my very specific question about my very specific code in some omniscient hyperspace. The answer didn't exist before the LLM generated it.
At the very least this represents "calculation". It's inherently absurd to look at that interchange as some fancy lookup table.
It's fairly common - if not ubiquitous - to address the reasoning capabilities of these models (and note that reasoning is different than thinking).
Sparks of Artificial General Intelligence: Early experiments with GPT-4
(my emphasis)
The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models
Note that this is listed as an open question, not a cut-and-dried answer
Comment on The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity
To be crystal clear, it is absolutely not the case that the field uniformly regard LLMs as pattern matching machines. It's an open question at best. To my reading, "LLMs exhibit reasoning - of some sort" seems to be the default perspective.
NuclearVII@reddit
This sentence is absolutely true, and highlights exactly what's wrong with the field, with a bit of context.
There is so much money involved in this belief. You'd struggle to find a good calculation of the figures involved - the investments, the speculation, company valuations - but I don't think it's unbelievable to say it's going to be in the trillions of dollars. An eye-watering, my boggling amount of value hinges on this belief: If it's the case that there is some reasoning and thinking going on in LLMs, this sum is justifiable. The wide-spread theft of content to train the LLMs is justifiable. The ruination of the energy economy, and the huge amounts of compute resources sunk into LLMs is worth it.
But if it isn't, it's not worth it. Not even close. If LLMs are, in fact, complicated but convincing lookup tables (and there is some reproducible evidence to support this), we're throwing so much in search of a dream that will never come. I
The entire field reeks of motivated reasoning.
This is made worse by the fact that none of the "research" in the field of LLMs is trustable. You can't take anything OpenAI or Anthropic or Google publishes seriously - proprietary data, models, training and RLHF, proprietary inference.. no other serious scientific field would take that kind of research seriously.
Hell, even papers that seem to debunk claimed LLM hype are suspect, because most of them still suffer from the proprietary-everything problem that plagues the field!
Data leaks can be incredibly convincing. I do not know your code base, the example you have in mind - but I do know that the theft involved in the creation of these LLMs was first exposed by people finding that - yes, ChatGPT can reproduce certain texts word for word. Neural Compression is a real thing - I would argue that the training corpus for an LLM is in the weights somewhere - highly compressed, totally unreadable, but in there somewhere. That's - to me, at least - is a lot more likely than "this word association engine thinks".
DarkTechnocrat@reddit
This is a really solid take. It's easy to forget just how MUCH money is influencing what would otherwise be rather staid academic research.
So this is where it gets weird for me. I have decided I don't have good terms for what LLMs do. I agree they don't "think", because I believe that involves some level of Qualia, some level of self-awareness. I think the term "reasoning" is loose enough that it might apply. All that said, I am fairly certain that the process isn't strictly a statistical lookup.
To give one example, if you feed a brand new paper into an LLM and ask for the second paragraph, it will reliably return it. But "the second paragraph" can't be cast as the result of statistical averaging. In the training data, "second paragraph" refers to millions of different paragraphs, none of which are in the paper you just gave it. The only reasonable way to understand what the LLM does is that it has "learned" the concept of ordinals.
I've also done tests where I set up a simple computer program using VERY large random numbers as variable names. The chance of those literal values being in the training set are unfathomably small, and yet the LLM can predict the output quite reliably.
the code I was talking about had been written that day BTW, so I'm absolutely certain it wasn't trained on.
NuclearVII@reddit
Data leaks can be quite insidious - remember, the model doesn't see your variable names - it just sees tokens. My knowledge of how the tokenization system works with code is a bit hazy, but I'd bet dollars to donuts it's really not relevant to the question.
A data leak in this case is more: Let's say I want to create a simple Q-sort algorithm on a vector. I ask an LLM. The LLM produces a Q-sort that I can use. Did it reason one? Or was there tons of examples of Q-sort in the training data?
Pattern matching code works really, really well, because a lot of code that people write on a day-to-day basis exist somewhere on github. That's why I said "I don't know what you're working on".
"To give one example, if you feed a brand new paper into an LLM and ask for the second paragraph, it will reliably return it. But "the second paragraph" can't be cast as the result of statistical averaging. In the training data, "second paragraph" refers to millions of different paragraphs, none of which are in the paper you just gave it. The only reasonable way to understand what the LLM does is that it has "learned" the concept of ordinals."
Transformers absolutely can use the contents of the prompt as part of their statistical analysis. That's one of the properties that make them so good at language modelling. They also do not process their prompts sequentially - it's done simultaneously.
So, yeah, I can absolutely imagine how statistical analysis works to get you the second paragraph.
Ok_Individual_5050@reddit
We know for a fact that they don't rely exclusively on lexical pattern matching, though they do benefit from lexical matches. The relationship between symbols is the main thing they *can* model. This isn't surprising. Word embeddings alone do well on the analogy task through simple mathematics (you can subtract the vector for car from the vector for driver and add it to the vector for plane and get a vector similar to the one for pilot).
I think part of the problem is that none of this is intuitive so people tend to leap to the anthropomorphic explanation of things. We're evolutionarily geared towards a theory of mind and towards seeing reasoning and mental states in others, so it makes sense we'd see it in a thing that's very, very good at generating language.
ShoddyAd1527@reddit
The paper itself states that it is a fishing expedition for a pre-determined outcome ("We aim to generate novel and difficult tasks and questions that convincingly demonstrate that GPT-4 goes far beyond memorization", "We acknowledge that this approach is somewhat subjective and informal, and that it may not satisfy the rigorous standards of scientific evaluation." + lack of analysis of failure cases in the paper).
The conclusion is unambiguous: LLM's mimic reasoning to an extent, but do not consistently apply actual reasoning. The question is asked, and answered. Source: I actually read the paper and thought about what it said.
DarkTechnocrat@reddit
I mean sure, they're trying to demonstrate that something is true ("GPT-4 goes far beyond memorization"). Every other experimental paper and literally every mathematical proof does the same, there's nothing nefarious about it. I think what's germane is that they clearly didn't think memorization was the key to LLMs. You could debate whether they made their case, but they obviously thought there was a case to be made.
"Consistently" is the tell in that sentence. "They do not apply actual reasoning consistently" is different from "They do not apply actual reasoning". More to the point, the actual paper is very clear to highlight the disputed nature of the reasoning mechanism.
page 2:
page 4:
And in the Conclusion:
None of these statement can reasonably be construed as absolute certainty in "statistical pattern matching".
billie_parker@reddit
What is wrong with pattern matching anyways?
"Pattern" is such a general word that it could in reality encompass anything. You could say a person's behavior is a "pattern" and if you were able to perfectly emulate that person's "pattern" of behavior, then in a sense you perfectly emulated the person.
Nchi@reddit
arent they like, exactly those though??
red75prime@reddit
You can construct a Markov chain based on a neural network (the chain will not fit into the observable universe). But you can't train the Markov chain directly.
And "Markov chains are statistical parrots by definition" doesn't work if the chain was produced based on a neural network that was trained using validation-based reinforcement learning. The probability distribution captured by the Markov chain in this case is not the same as the probability distribution of the training data.
Nchi@reddit
https://youtu.be/KZeIEiBrT_w
FeepingCreature@reddit
No.
uniquesnowflake8@reddit
Here’s a story from yesterday. I was searching for a bug and managed to narrow it down to a single massive commit. I spent a couple of hours on it, and felt like it was taking way too long to narrow down.
So I told Claude which commit had the error and to find the source. I moved onto other things, meanwhile, it hallucinated what the issue was.
I was about to roll my sleeves up and look again, but first I told Claude it was wrong but to keep searching that commit. This time, it found the needle in the haystack.
While it was spinning on this problem, I was getting other work done.
So to me this is something real and useful, however overhyped or flawed it is right now. I essentially had an agent trying to solve a problem for me while I worked on other tasks and it eventually did.
TheBlueArsedFly@reddit
But why didn't you write a reddit post that would confirm our biases instead of telling Claude to keep trying?
metahivemind@reddit
Had to get some value for the $20 it cost for Claude to do that much work.
Waterbottles_solve@reddit
Wow, given the comments here, I thought there would be something interesting in the article. No there wasnt. Wow. That was almost impressively bad.
Maybe for people who havent used AI before, this article might be interesting. But it sounds like OP is using a hammer to turn screws.
Meanwhile its 2-10x'd our programming performance.
sad_bug_killer@reddit
Source? By what measure?
billie_parker@reddit
There's studies on it. Look 'em up.
sad_bug_killer@reddit
I found one where experienced devs thought LLMs would make them 20% more productive, but it turned out it made them 20% slower.
billie_parker@reddit
nice anecdote
sad_bug_killer@reddit
Anecdote, you say? Now I really want to know what would you consider a good study on the topic
Waterbottles_solve@reddit
Rofl
Do you not program?
Isnt it so unbelievably obvious? From writing algorithems to problem solving.
mkalte666@reddit
I do embedded development and have tried LLMs quite a bit.
Im faster without them. The subtle issues the generated code tends to have are horrible. Slight mistakes in the config bits in registers. Interrupts corrupting data because LLMs don't understand interrupt-free critical secitons. Just plain UB everywhere. All that and more.
billie_parker@reddit
Hilarious when embedded programmers think their little C-nile toy programs are complex.
"but... but... I have limited resources!"
Go read datasheets while I program the stars lol
mkalte666@reddit
Hilarious when AI bros result to insults when im trying to make a point.
But right back at you: Ill be here with my sleepy toy micro controllers that bring in a steady wage and interesting challanges, while u get burned by the stars writing the next best world changing whatever using whichever planet burning gpu farm you can get your hands on :)
... I wonder what happens if no one is left that understands how LLMs biggest enabler, the gpu, works?
Waterbottles_solve@reddit
Don't vibe code.
Use it for algorithms, use it for troubleshooting/errors, use it for functions/api calls.
Embedded is def going to struggle whenever you have interrupts/state based. I can't quite put my finger on it, but it seems like a 'strawberry' problem. Like, it can understand how to predict the next word, but can't predict when states are entered.
Ok_Individual_5050@reddit
You say "don't vibe code" but then you're just reviewing everything it does which much harder than writing it?
billie_parker@reddit
"reviewing is harder than writing"
lies non-AI users tell themselves so they can sleep at night
Waterbottles_solve@reddit
No. Its way way easier to have chatgpt write an algorithm or contemplate a bug.
ducdetronquito@reddit (OP)
Which parts are you referring to ?
Who is "our" and what is "programming performance", because I suspect it varies quite a lot depending on the context you are working into and the task you are doing.
I never used LLMs myself, but I do see it in action when doing peer code work and from this limited sample I can only find two situations where it was really useful: - Using it has a faster search engine to avoid searching on poorly searchable website like some library online documentation - Using it for refactoring that a tyical LSP action is not able to do in one go
That being said, I don't find myself in these two situations enough to use an LLM or having it enabled in my IDE to suggests stuff on the fly.
And from my limited sample of colleagues using LLMs as a daily driver, I can say that I perceive some improvements in the time they take to make a code change but nothing remotely close to 2x, but I can confidently say that there are no improvements in quality at all.
But in the end, to each their own if a tool is useful to you go use :)
Waterbottles_solve@reddit
omg
bruh
Why are you even commenting on this thread.
Its like an Amish person criticizing my IOT Security.
ducdetronquito@reddit (OP)
I would rather read your critics about the content of my comment or the article itself rather than your opinion on me which does not add anything to the discussion.
marx-was-right-@reddit
Lmao, sure
fra988w@reddit
All hinging on "Can CEOs all be wrong about technology? Yes of course, I remember when we had a financial crisis"
lovelettersforher@reddit
I'm in a toxic love-hate relationship with LLMs.
getfukdup@reddit
Well if that's true, work on more than one project at a time during the time saves..
Personal-Status-3666@reddit
So far all science suggests it making us dumb.
Its still earyl.science but i don't think it will make US smarter
fomq@reddit
Cognitive de line.
lovelettersforher@reddit
I agree lol.
fomq@reddit
Hell yeah.
BlobbyMcBlobber@reddit
I don't understand how developers can ignore and completely miss what's going on in AI, and put it all on "hype". Even more astounding is this idea that corporations pour money into AI which they will never recover.
Try MCP. Use some proper guardrails. Create some quality context. This is the future. I also love writing code but I understand that it's a matter of time before an AI will be able to do it faster and cheaper than people.
gbs5009@reddit
I've lived through enough tech bubbles to find the idea that corporations are pissing away billions on malinvestment in AI quite plausible.
TracerDX@reddit
"lighting comically large piles of money on fire trying to teach graphics cards how to read"
Thank you for this. The hype was seriously starting to get to me lately. My resolve is restored.
Star_Prince@reddit
Opens Claude… “Give me the tldr of this article”
razordreamz@reddit
I think this was written by AI
unDroid@reddit
I've read Malwaretech write about LLMs before and he is still wrong about AI not replacing jobs. Not because Copilots and Geminis and the bunch are better than software engineers, but because CEOs and CTOs think they are. Having a junior dev use Chatgpt to wrote some code is cheap as hell and it might get functioning code out some of the time if you know how to prompt it etc, but for the same reason AGI won't happen any time soon it won't replace the SSEs in skill or as a resource. But that doesn't matter if your boss thinks it will.
ionixsys@reddit
I love AI because I know the other people who love AI have over extended themselves financially and are in for a world of hurt when the "normal" people figure out how over hyped all of this actually is.
ALAS_POOR_YORICK_LOL@reddit
Why hate? Just use it for what it's good for
rajm_rt@reddit
We are friends now!
Paradox@reddit
I don't hate AI as much as I hate AI peddlers and grifters. They always just paste so much shit out of their AI prompts, they can't even argue in favor of themselves.
There was a guy who wrote some big fuck article about LaTeX and spammed it to a half dozen subreddits. The article was rambling, incoherent, and, most importantly, full of em dashes. Called out in the comments, he responded with whole paragraphs full of weird phrases like "All passages were drafted from my own notes, not generated by a model."
Its a new variety of linkedin lunatic, and its somehow far more obnoxious
dwitman@reddit
We are about as likely to see AGI in our lifetime as a working Time Machine.
Both of these are theoretically possible technologies in the most general senses of what a theory is, but there is no practical reason believe either one will actually exist.
An LLM is to AGI what a clock is to a time traveling phone booth.
MuonManLaserJab@reddit
Time machines are 100% impossible according to every plausible theory of physics.
If you assume a time machine, then you can go back in time and kill your grandparents. This prevents you from being born, which leads to a contradiction. Contradictions mean that your assumption was wrong.
An AGI is just something that does the same thing as your brain. Billions of general intelligences already exist on Earth. There is zero reason to imagine that we can't engineer computers that outdo brains.
wyttearp@reddit
This is just plain silly. Laugh about us achieving AGI all you want, these two things aren't even in the same universe when it comes to how likely they are. It's true that LLMs aren't on a clear path to AGI.. but they're already much closer to it than a clock is to a time machine.
While LLMs aren't conscious, self-aware, or goal-directed, they are tangible, evolving systems built on real progress in computation and math. Time machines remain purely speculative with no empirical basis or technological foothold (no, we're not talking about moving forward in time at different rates).
You don't have to believe AGI is around the corner, but pretending it's in the same category as time travel is just being contrarian.
LookIPickedAUsername@reddit
Sorry, but that’s a terrible analogy. We have very good reasons to believe time travel isn’t even possible in the first place, no matter how advanced our technology.
Meanwhile, it’s obviously possible for a machine weighing only three pounds and consuming under fifty watts of power to generate human-level intelligence; we know this because we’ve all got one of them inside our skulls. Obviously we don’t have the technology to replicate this feat, but the human brain isn’t magic. We’ll get there someday, assuming we don’t destroy ourselves or the planet first.
Maybe not in our lifetimes, but unlike time travel, at least there’s a plausible chance.
dwitman@reddit
I disagree with your concept that the brain is a machine in our skulls that constitutes a living intelligence. Without a body an activated in a jar brain would be useless…as it has no nervous system, no community, and so on.
I don’t see any clear evidence that machine life is possible and an LLM is no artificial intelligence, that’s marketing slop. It’s a highly computationally expensive parlor trick that is sometimes useful.
LookIPickedAUsername@reddit
So... I've got a couple of questions.
My actual point was merely an existence proof. We know it's possible to manufacture machines that think like a human, because such devices exist today in the form of human brains. And clearly it's possible to shrink such devices down to a few pounds and tens of watts of power, because again human brains exist. Quibbling about a few ounces here and there is irrelevant; the first artificial brain is likely to weigh an entire datacenter, and it will still be an incredible achievement.
MuonManLaserJab@reddit
Pretty stupid, not sure if trolling
10113r114m4@reddit
If AI is helpful for you in your coding, more power to you. Id question your coding abilities, cause I dont think Ive come across, or at least often, working solutions or just odd assumptions it makes lol
stuffeh@reddit
Can't trust a black box where data generated can be different even with the same inputs.
amdcoc@reddit
the apple part of the article literally says all about the article really. Apple was wayyyyyyyy behind on it and released bullshit papers to show to their shareholders that LLMs don't reason thus we clever by not investing in it.
pier4r@reddit
Hate is a strong word, but maybe it is there for clicks. I agree with some points though. Especially the contamination effect on novel problems.
We don't really know if benchmarks are benchmaxxed. Even the IMO medals could simply be "reword this known solution".
Ok_Individual_5050@reddit
The big problem with the benchmarks is that without the training data, the weights, any post-training that is being done and the system prompt in use, they're not at all replicable and therefore from a truth-seeking point of view they're meaningless.
This isn't to say that there's some huge conspiracy here. It's just that without that information there's no way to know what is being done. It could be considered *perfectly normal* at the big model companies to adapt models to game these specific benchmarks, the same way that some research labs see P-hacking as valid science.
NuclearVII@reddit
If you look into the IMO thing, all that IMO will guarantee is that solutions submitted by the DeepMind team pass. There's no other guarantees they make.
It's one of those headlines that sound really impressive until you start digging into just how unverifiable it is - like most other LLM achievements.
StinkyTexasBuddha@reddit
I like how easy this was to read. Excellent writer.
Maybe-monad@reddit
I hate AI because I am a Sith Lord, we are not the same
ImportantDoubt6434@reddit
Llamas cannot read?
Wrong.
LLMs cannot read. I know you took watered down business calculus but just because you have an MBA doesn’t mean you aren’t dumb. 🗣️
Objective-Yam3839@reddit
I asked Gemini Pro what it thought about this article. After a long analysis, here was it's final conclusion:
"Overall, the article presents a well-articulated, technically-grounded, and deeply pessimistic view of the current state of AI. Hutchins is not arguing from a place of ignorance or fear of technology, but from the perspective of an experienced technical professional who has evaluated the tool and found the claims surrounding it to be vastly overblown and its side effects to be dangerously underestimated.
His perspective serves as a crucial counter-narrative to the dominant, often utopian, marketing hype from tech companies. While some might find his conclusions overly cynical, his arguments about the economic motivations, the limitations of pattern matching, and the risks of cognitive decline are substantive points that are central to the ongoing debate about the future of artificial intelligence."
hippydipster@reddit
One thing that's really tiresome is how many people have little interest in discussing actual reality, and would rather discuss hype. Or what they hear. Or what someone somewhere sometime said. That didn't turn out completely right.
I guess it's the substitution fallacy humans often engage in - ie, when confronted with difficult and complex questions, we often (without awareness of doing so) substitute a simpler question instead and discuss that. So, rather than discuss the actual technology, which is complex and uncertain, people discuss what they heard or read that inflamed their sensibility (or more likely, what they hallucinated they heard or read and their sensibilities are typically already in a state of inflamed because that's how we live these days).
This article starts of with paragraph upon paragraph of discussing hype rather than the reality and I noped out before it got anywhere, as it's just boring. It doesn't matter what you heard or read someone say or predict. It just doesn't, so stop acting like it proves something that incorrect predictions have been made in the past.
lithium@reddit
Didn't make it to the section on cognitive decline then, did you? Interesting.
sellyme@reddit
I think we've now reached the point where these opinion pieces are more repetitive, unhelpful, and annoying than the marketing ever was, which really takes some doing.
Who are the people out there that actually want to read a dozen articles a day going "here's some things you should hate!"?
LexaAstarof@reddit
That turned out to be a good write up actually. Though the author needs to work on their particular RAG hate 😆. I guess they don't like their own blog content to be stolen. But that's not a reason to dismiss objectivity (which is otherwise maintained through the rest of the piece).
I appreciate the many brutal truths as well.
LittleLuigiYT@reddit
AI isn't just used for generating slop
gurebu@reddit
Well, mostly true, but I now live in a world where I'll never have to write an msbuild XML manually and that alone brings joy. Neither will I ever (at least until the aforementioned model collapse) have to dirty my hands with gradle scripts. There's a lot of stuff around programming that's seemingly deliberately terrible (and it so happens to revolve around build systems, I wonder why) and LLMs at least help me to cognitively decline participating in it.
Infamous_Toe_7759@reddit
It's a love-hate relationship
_Noreturn@reddit
I used AI to summarize this article so my dead brain can read it
^^ joke
AI is so terrible it hallucinates every time for any non semi trivial task it is hilarious,
I used to found it useful in generating repetitive code but i just learned python to do that and it is faster than ai doing it.
MedicOfTime@reddit
Honestly a pretty good write up.