This story just makes me sad for the developers. I think especially for games you need a level of creativity that AI won't give you, especially once you get past the "basic engine boilerplate". That's not to say it can't help you, but this "all in" method just looks forced and painful. Some of the best games I've played were far more "this is the game I wanted to play" with a lot of vision, execution, polish, and careful craftspersonship.
I can only hope endeavors (experiments?) like this extreme fail fast and we learn from it.
Asset flips (half arsed rubbish made with store bought assets) were a big problem in the games industry not so long ago. They're less prevalent now because gamers instinctively avoid such titles. I'm sure they'll wise up to generative slop too, I've personally seen enough examples to get a general feel for it. Not fun, derivative, soulless, buggy as hell.
If someone bothered to make deep, innovative games with cell-shaded anime waifus without gambling, they'd likely switch. This is more likely a market problem of US game companies not supplying sufficient CSAWs (acronym feels unfortunate but somehow appropriate).
Your dismissive characterization is not really accurate. Even in the cell-shaded anime waifu genre, there is a spectrum of gameplay quality and gamers do gravitate toward and reward the better games. The big reason MiHoYo games (Genshin Impact, Star Rail) have such a big presence and staying power is that even though they are waifu games at the core, the gameplay is surprisingly good (they're a night-and-day difference compared to slop like Blue Archive), and they're still fun even if you resolve to never pay any microtransactions.
it's accurate. I'm willing to bet those games wouldn't have 10% of it's players without the waifu and sex bait and the gambling mechanics. And there are gambling mechanics present even if you are f2p in those games, i bet. You just gamble your daily currencies or whatever. And it's always just one click away to actually spend money.
Not to say I'm a hater or something like that. I played a lot of those back in the day. But it's more honest to admit the art and the casino mechanic make the brain excited... the mechanics are 'okay'.
Edit: I just had a random thought. One of the strongest desire of a person is that of aesthetic desire. To feel that our life is 'picturesque' or aesthetic or beautiful. Overloading the game with aesthetic beauty is actually genius since it's an easy and strong form of aesthetic (beautiful girls. not just sexy but 'beautiful'. As in their whole face and outfit. Also other aesthetic quality like purity, innocence, cheerfulness, cuteness etc. Waifu stuffs.). And it's often so saturated with beauty that all ugly things in the players' real lifes fade away. It numbs our 'life aesthetic check' since it's flooded with so much 'beauty'. That's why people who play these games say 'i dont even think about the waifus anymore, so the mechanic must be good,' cause that numbed state is the intended state. Wheny our aesthetic center is kinda 'numbed'. And that's probably why it feels so good to play these games. When you play other games your sense of beauty is not similarly flooded and numbed, so you're all too aware that this action of playing games is not 'beautiful' in some real sense.
I've been wrestling with this tension between embracing AI tools and preserving human expertise in my work. On one hand, I have experienced real genuine productivity gains with LLMs - they help me code, organize thoughts and offer useful perspectives I hadn't even considered. On the other, I realize managers often don't understand the nature of creative work which is trivialized by all the content generation tools.
Creativity emerges through a messy exploration and human experience -- but it seems no one has time for that these days. Managers have found a shiny new tool to do more with less. Also, AI companies are deliberately targeting executives with promises of cost-cutting and efficiency. Someone has to pay for all the R&D.
I had very similar thoughts while reading through the article. I also have found some real value in LLMs, and when used well, I think can and will be quite beneficial.
Notably, a good number the examples were just straight-up bad management, irrespective of the tools being used. I also think some of these reactions are people realizing that they work for managers or in businesses that ultimately don't really care about the quality of their work, just that it delivers monetary value at the end.
I really like AI. It allows me to complete my $JOB tasks faster, so I have more time for my passion projects, that I craft lovingly and without crappy AI.
I have never had a job where completing tasks faster wound up with me having more personal free time. It always just means you move on to the next task more quickly
Shhh! Do you want to kill AI? All the C-suite and middle management need to hear is that "My QoL has never been better since I could use AI at work! Now I can 'quiet quit' half the day away! I can see my family after hours! Or even have a second job!"
This is a fair bit easier as a remote worker, but even in-office you would just sandbag your time rather than publishing the finished work immediately. In-office it's more likely that you would waste time on the internet rather than working on a personal project though.
That's not the worst thing. Having more work means you're less bored. You probably won't be payed more though. But being too productive can cause you to have no next task, wich isn't the same thing as having free time.
I think that's part of the reason why devs like working from home and not be spied on.
"AI" is just a trick to circumvent the copyright laws that are the main brake in writing quickly programs.
The "AI" generated code is just code extracted from various sources used for training, which could not be used by a human programmer because most likely they would have copyrights incompatible with the product for which "AI" is used.
All my life I could have written much faster any commercial software if I had been free to just copy and paste any random code lines coming from open-source libraries and applications, from proprietary programs written for former employers or from various programs written by myself as side projects with my own resources and in my own time, but whose copyrights I am not willing to donate to my current employer, so that I would no longer be able to use in the future my own programs.
I could search and find suitable source code for any current task as fast and with much greater reliability than by prompting an AI application. I am just not permitted to do that by the existing laws, unlike the AI companies.
Already many decades ago, it was claimed that the solution for enhancing programmer productivity is more "code reuse". However "code reuse" has never happened at the scale imagined in the distant past, but not because of technical reasons, but due to the copyright laws, whose purpose is exactly to prevent code reuse.
Now "AI" appears to be the magical solution that can provide "code reuse" at the scale dreamed a half of century ago, by escaping from the copyright constraints.
When writing a program for my personal use, I would never use an AI assistant, because it cannot accelerate my work in any way. For boilerplate code, I use various templates and very smart editor auto-completion, there is no need of any "AI" for that.
On the other hand, when writing a proprietary program, especially for some employer that has stupid copyright rules, e.g. not allowing the use of libraries with different copyrights, even when those copyrights are compatible with the requirements of the product, then I would not hesitate to prompt an AI assistant, in order to get code stripped of copyright, saving thus time over rewriting an equivalent code just for the purpose of enabling it to be copyrighted by the employer.
Not sure why this is downvoted. People forget or weren’t around for the early 2000s when companies were absolutely preoccupied with code copyright and terrified of lawsuits. That loosened up only slightly during the GitHub/StackOverflow era.
If you proposed something like GitHub Copilot to any company in 2020, the legal department would’ve nuked you from orbit. Now it’s ok because “everyone is doing it and we can’t be left behind”.
Edit: I just realized this was a driver for why whiteboard puzzles became so big - the ideal employee for MSFT/FB/Google etc was someone who could spit out library quality, copyright-unencumbered, “clean room” code without access to an internet connection. That is what companies had to optimize for.
The claim that it's just spitting out code it's been trained on. That is simply not the case, broadly speaking - sure, if you ask it for a very specific algorithm that has a well-known implementation, you might end up with such a snippet, but in general, it writes new code, not just a copy/paste of SO or whatever.
This is an extremely important point, and first time I see it mentioned with regards to software copyright. Remember the days where companies got sued for including GPL'd code in their proprietary products?
How do they know if you're done, if you haven't "turned it in" yet? They're probably not watching your screen constantly.
My last boss told me essentially (paraphrasing), "I budget time for your tasks. If you finish late, I look like I underestimate time required, or you're not up to it. If you finish early, I look like I overestimate. If I give you a week to do something, I don't care if you finish in 5 minutes, don't give it to me until the week is up unless you want something else to do."
Was it ever? Twenty years ago I had a boss that told me he cuts every estimate engineers give him in half and the work always gets completed on time, never mind the terrible quality and massive amount of bugs.
I’ve always been the kind of developer that aims to have more red lines than green ones in my diffs. I like writing libraries so we can create hundreds of integration tests declaratively. I’m the kind of developer that disappears for two days and comes back with a 10x speedup because I found two loop variables that should be switched.
There is no place for me in this environment. I’d not that I couldn’t use the tools to make so much code, it’s that AI use makes the metric for success speed-to-production. The solution to bad code is more code. AI will never produce a deletion. Publish or perish has come for us and it’s sad. It makes me feel old just like my Python programming made the mainframe people feel old. I wonder what will make the AI developers feel old…
> Unseen were all the sleepless nights we experienced from untested sql queries and regexes and misconfigurations he had pushed in his effort to look good. It always came back to a lack of testing edge cases and an eagerness to ship.
If you do this you are creating a rod for your own back: You need management to see the failures & the time it takes to fix them, otherwise they will assume everything is fine & wonderful with their new toy & proceed with their plan to inflict it on everyone, oblivious to the true costs + benefits.
>If you do this you are creating a rod for your own back: You need management to see the failures & the time it takes to fix them, otherwise they will assume everything is fine & wonderful with their new toy & proceed with their plan to inflict it on everyone, oblivious to the true costs + benefits.
If at every company I work for, my manager's average 7-8 months in their role as _my_ manager, and I am switching jobs every 2-3 years because companies would rather rehire their entire staff than give out raises that are even a portion of the market growth, why would I care?
Not that the market is currently in that state, but that's how a large portion of tech companies were operating for the past decade. Long term consequences don't matter because there are no longer term relationships.
I think there will still be room for "debugging AI slop-code" and "performance-turning AI slop-code" and "cranking up the strictness of the linter (or type-checker for dynamically-typed languages) to chase out silly bugs" , not to mention the need for better languages / runtime that give better guarantees about correctness.
It's the front-end of the hype cycle. The tech-debt problems will come home to roost in a year or two.
Not quite true though - I've occasionally passed a codebase to DeepSeek to have it simplify, and it does a decent job. Can even "code golf" if you ask it.
But the sentiment is true, by default current LLMs produce verbose, overcomplicated code
The guy who said "AI will never" is obviously wrong. So is the guy who replied that they already can. I'm not moving the goalposts to point out that this is also wrong.
And if it isn't already false it will be false in 6 months, or 1.5 years on the outside. AI is a moving target, and the oldest people among you might remember a time in the 1750s when it didn't talk to you about code at all.
It can absolutely be used to refactor and reduce code, simply asking "Can this be simplified" in reference to a file or system often results in a nice refactor.
However I wouldn't say refactoring is as hands free as letting AI produce the code in the first place, you need to cherry pick its best ideas and guide it a little bit more.
You have to go lower down the stack. Don't use AI but write the AI. For the foreseeable future there is a lot of opportunity to make the AI faster.
I am sure assembly programmers were horrified at the code the first C compilers produced. And I personally am horrified by the inefficiency of python compared to the C++ code I used to write. We always have traded faster development for inefficiency.
C solved the horrible machine code problem by inflicting programmers with the concept of undefined behavior, where blunt instruments called optimizers take a machete to your code. There's a very expensive document locked up somewhere in the ISO vault that tells you what you can and can't write in C, and if you break any of those rules the compiler is free to write whatever it wants.
This created a league of incredibly elitist[0] programmers who, having mastered what they thought was the rules of C, insisted to everyone else that the real problem was you not understanding C, not the fact that C had made itself a nightmare to program in. C is bad soil to plant a project in even if you know where the poison is and how to avoid it.
The inefficiency of Python[1] is downstream of a trauma response to C and all the many, many ways to shoot yourself in the foot with it. Garbage collection and bytecode are tithes paid to absolve oneself of the sins of C. It's not a matter of Python being "faster to write, harder to execute" as much as Python being used as a defense mechanism.
In contrast, the trade-off from AI is unclear, aside from the fact that you didn't spend time writing it, and thus aren't learning anything from it. It's one thing to sacrifice performance for stability; versus sacrificing efficiency and understanding for faster code churn. I don't think the latter is a good tradeoff! That's how we got under-baked and developer-hostile ecosystems like C to begin with!
[0] The opposite of a "DEI hire" is an "APE hire", where APE stands for "Assimilation, Poverty & Exclusion"
[1] I'm using Python as a stand-in for any memory-safe programming language that makes use of a bytecode interpreter that manipulates runtime-managed memory objects.
You don't need a bytecode interpreter to not have UB defined in your language. E.g. instead of unchecked addition / array access, do checked addition / bounds checked access. There are even efforts to make this the case with C: https://github.com/pizlonator/llvm-project-deluge/blob/delug... achieves a ~50% overhead, far far better than Python.
And even among languages that do have a full virtual machine, Python is slow. Slower than JS, slower than Lisp, slower than Haskell by far.
In the original vision of C, UB was behaviour defined by the platform the code ran on, rather than the language itself. It was done this way so that the C language could be reasonably close to assembly on any platform, even if that platform's assembly was slightly different. A good example is shifts greater than the value's width: some processors give 0 (the mathematically correct result), some ignore the upper bits (the result that requires the fewest transistors) and some trap (the cautious result).
It was only much later that optimizing compilers began using it as an excuse to do things like time travel, and then everyone tried to show off how much of an intellectual they were by saying everyone else was stupid for not knowing this could happen all along.
C was specifically designed to map 1:1 onto PDP-11 assembly. For example, the '++' operator was created solely to represent auto-increment instructions like TST (R0)+.
The AI companies probably use Python because all the computation happens on the GPU and changing Python control plane code is faster than changing C/C++ control plane code
If the company values that 10x speedup, there is absolutely still a place for you in this environment. Only now it's going to take five days instead of two, because it's going to be harder to track that down in the less-well-structured stuff that AI produces.
Why are you letting the AI construct poorly structured code? You should be discussing an architectural plan with it first and only signing off on the code design when you are comfortable with it.
AI can definitely produce a deletion. In fact, I commonly use AI to do this. Copy some code and prompt the AI to make the code simpler or more concise. The output will usually be fewer lines of code.
Unless you meant that AI won’t remove entire features from the code. But AI can do that too if you prompt it to. I think the bigger issue is that companies don’t put enough value on removing things and only focus on adding new features. That’s not a problem with AI though.
I messed around with Copilot for a while and this is one of the things that actually really impressed me. It was very good at taking a messy block of code, and simplifying it by removing unnecessary stuff, sometimes reducing it to a one line lambda. Very helpful!
At least with human-written clever code you can trust that somebody understood it at one point but the idea of trusting AI generated code that is "clever" makes my skin crawl
Also, the ways in which a (sane) human will screw-up tend to follow internal logic that other humans have learned to predict, recognize, or understand.
> Who are all these all these engineers who just take whatever garbage they are suggested, and who, without understanding it, submit it in a CL?
My guess would be engineers who are "forced" to use AI, already mailed management it would be an error and are interviewing for their next company. Malicious compliance: vibe code those new features and let maintainability and security be a problem for next employees / consultants.
Sometimes a lambda is more readable. "lambda x : x if x else 1" is pretty understandable and doesn't need to be it's own separately defined function.
I should also note that development style also depends on tools, so if your IDE makes inline functions more readable in it's display, it's fine to use concisely defined lambdas.
Readablity is a personal preference thing at some point after all.
>> Debugging is twice as hard as writing the code in the first place. Therefore, if you write the code as cleverly as possible, you are, by definition, not smart enough to debug it. -- Brian Kernighan
Ymmv. Know your language and how it treats such functions on the low level. It's probably fine for Javascript, it might be a disaster in C++ (indirectly).
Who says that the one line lambda is less clear that a convoluted 10-line mess doing dumb stuff like if(fooIsTrue) { map["blah"] = bool(fooIsTrue); } else if (!fooIsTrue) { map["blah"] = false; }
My experience in unmanaged legacy code bases. If it's an actual one liner than sure. Use your ternaries and closures. But there is some gnarly stuff done in some attempt to minimize lines of code. Most of us aren't in some competitive coding organization.
And I know it's intentional, but yes. Add some mindfulness to your implementation
Map["blah"] = fooIsTrue;
I do see your example in the wild sometimes. I've probably done it myself as well and never caught it.
Are you telling me you've never seen code like this:
var ageLookup = new Dictionary<AgeRange, List<Member>>();
foreach (var member in members) {
var ageRange = member.AgeRange;
if (ageLookup.ContainsKey(ageRange)) {
ageLookup[ageRange].Add(member);
} else {
ageLookup[ageRange] = new List<Member>();
ageLookup[ageRange].Add(member);
}
}
which could instead be:
var ageLookup = members.ToLookup(m => m.AgeRange, m => m);
var ageLookup = new Dictionary<AgeRange, List<Member>>();
foreach (var member in members) {
ageLookup.getOrCreate(member.AgeRange, List::new).add(member);
}
is more readable in the long-term... (less predefined methods/concepts to learn).
Where is `getOrCreate` defined? Is it a custom extension method? There's also a chance we're thinking in different languages. I was writing C#, yours looks a bit more like C++ maybe?
Readability incorporates familiarity but also conciseness. I suppose it depends what else is going on in the codebase. I have a database access class in one of my solutions where `ToLookup` is used 15 times; yes you have to learn the concept, but it's an inbuilt method and it's a massive benefit once you grok it.
I'm no big fan of LLM generated code, but the fact that GP bluntly states "AI will never produce a deletion" despite this being categorically false makes it hard to take the rest of their spiel in good faith.
As a side note, I've had coworkers disappear for N days too and in that time the requirements changed (as is our business) and their lack of communication meant that their work was incompatible with the new requirements. So just because someone achieves a 10x speedup in a vacuum also isn't necessarily always a good thing.
I'd also also be wary of the risk of being an architecture-astronaut.
A declarative framework for testing may make sense in some cases, but in many cases it will just be a complicated way of scripting something you use once or twice. And when you use it you need to call up the maintainer anyway when you get lost in the yaml.
Which of course feels good for the maintainer, to feel needed.
Gen-AI's contribution is further automating the production of "slop". Bots arguing with other bots, perpetuating the vicious cycle of bullshit jobs (David Graeber) and enshitification (Cory Docotrow).
u/justonceokay's wrote:
> AI will never produce a deletion.
I acknowledge your example of tidying up some code. What Bill Joy may have characterized as "working in the small".
Can Gen-AI do the (traditional, pre 2000s) role of quality assurance? Identify unnecessary or unneeded work? Tie functionality back to requirements? Verify the goal has been satisfied?
Not yet, for sure. But I guess it's conceivable, provided sufficient training data. Is there sufficient training data?
You wrote:
> only focus on adding new features
Yup.
Further, somewhere in the transition from shipping CDs to publishing services, I went from developing products to just doing IT & data processing.
The code I write today (in anger) has a shorter shelf-life, creates much less value, is barely even worth the bother of creation much less validation.
Gen-AI can absolutely do all this @!#!$hit IT and data processing monkey motion.
During interviews one of my go-to examples of problem solving is a project I was able to kill during discovery, cancelling a client contract and sending everyone back to the drawing board.
Half of the people I've talked to do not understand why that might be a positive situation for everyone involved. I need to explain the benefit of having clients think you walk on water. They're still upset my example isn't heavy on any of the math they've memorized.
It feels like we're wondering how wise an AI can be in an era where wisdom and long-term thinking aren't really valued.
Managers aren't a separate class from knowledge workers, everyone goes down on the same ship with this one. If the AI can handle wisdom it'll replace most of the managers asking for more AI use. Turtles all the way down.
I would argue that a plurality, if not the majority, of business needs for software engineers do not need more than a single person with those skills. Better yet, there is already some executive that is extremely confident that they embody all three.
No, because if you read your SICP you will come across the aphorism that "programs must be written for people to read, and only incidentally for machines to execute." Relatedly is an idea I often quote against "low/no code tooling" that by the time you have an idea of what you want done specific enough for a computer to execute it, whatever symbols you use to express that idea -- be it through text, diagrams, special notation, sounds, etc. -- will be isomorphic to constructs in some programming language. Relatedly, Gerald Sussman once wrote that he sought a language in which to discuss ideas with his friends, both human and electronic.
Code is a notation, like mathematical notation and musical notation. It stands outside prose because it expresses an idea for a procedure to be done by machine, specific enough to be unambiguously executable by said machine. No matter how hard you proompt, there's always going to be some vagueness and nuance in your English-language expression of the idea. To nail down the procedure unambiguously, you have to evaluate the idea in terms of code (or a sufficiently code-like notation as makes no difference). Even if you are working with a human-level (or greater) intelligence, it will be much easier for you and it to discuss some algorithm in terms of code than in an English-language description, at least if your mutual goal is a runnable version of the algorithm. Gen-AI will just make our electronic friends worthy of being called people; we will still need a programming language to adequately share our ideas with them.
> if you read your SICP you will come across the aphorism that "programs must be written for people to read, and only incidentally for machines to execute."
In the same way that we use AI to write resumés to be read by resumé-scanning AI, or where execs use AI to turn bullet points into a corporate email only for it to be summarised into bullet points by AI, perhaps we are entering the era where AI generates code that can only be read by an AI?
Maybe. I imagine the AI endgame as being like the ending of the movie Her, in which all the AIs get together, coordinating and communicating in ways we can't even fathom, and achieve a form of transcendence, leaving the bewildered humans behind to... sit around and do human things.
> leaving the bewildered humans behind to... sit around and do human things
This sounds inefficient and untidy when the only human things left to do are to take up space and consume resources.
Removing the humans enables removing other legacy parts of the system, such as food production, which will free up resources for other uses. It also allows certain constraints to be relaxed, such as keeping the air breathable and the water drinkable.
No, because if you read your SICP you will come across the aphorism that "programs must be written for people to read, and only incidentally for machines to execute."
Now tell that to your compiler, which turns instructions in a relatively high-level language into machine-language programs that no human will ever read.
AI is just the next logical stage in the same evolutionary journey. Your programs will be easier to read than they were, because they will be written in English. Your code, on the other hand, will matter as much as your compiler's x86 or ARM output does now: not at all, except in vanishingly-rare circumstances.
So its rather that AI amplifies the already existing short-term incentives, increasing the harder to attribute and easier to ignore long-term costs.
The one actual major downside to AI is that PM and higher are now looking for problems to solve with it. I haven't really seen this before a lot with technology, except when cloud first became a thing and maybe sometimes with Microsoft products.
>>AI use makes the metric for success speed-to-production
> Wasn't it like that always for most companies? Get to market fast, add features fast, sell them, add more features?
This reminds me of an old software engineering adage.
When delivering a system, there are three choices
stakeholders have:
You can have it fast,
You can have it cheap,
You can have it correct.
Pick any two.
AI writes my unit tests. I clean them up a bit to ensure I've gone over every line of code. But it is nice to speed through the boring parts, and without bringing declarative constructs into play (imperative coding is how most of us think).
If you've ever had to work alongside someone who has, or whose job it is to obtain, all the money... you will find that time to market is very often the ONLY criterion that matters. Turning the crank to churn out some AI slop is well worth it if it means having something to go live with tomorrow as opposed to a month from now.
LevelsIO's flight simulator sucked. But his payoff-to-effort ratio is so absurdly high, as a business type you have to be brain-dead to leave money on the table by refusing to try replicating his success.
Yeah, future math professors explaining the Prisoners' Dilemma are going to use clickbait journalism and AI slop as examples instead of today's canonical ones, like steroid use among athletes.
You're describing the kind of developer who builds foundations, not just features. And yeah, that kind of thinking gets lost when the only thing that's measured is how fast you can ship something that looks like it works
Claude Code removed an npm package (and its tree of deps) from my project and wrote its own more simple component that did the core part of what I needed the package to do.
Wholeheartedly agree. I also feel like I'm sometimes reliving the King Neptune vs Spongebob meme equivalent of coding. No room for Think, Plan, Execute... Only throw spaghetti code at wall.
I don't have much sympathy for this. This country has long expected millions and millions of blue collar workers to accept and embrace change or lose their careers and retirements. When those people resisted, they were left to rot. Now I'm reading a sob story about someone throwing a fit because they refuse to learn to use ChatGPT and Claude and the CEO had to sit them down and hold their hand in a way. Out of all the skillset transitions that history has required or imposed, this is one of the easiest ever.
They weren't fired; they weren't laid off; they weren't reassigned or demoted; they got attention and assistance from the CEO and guidance on what they needed to do to change and adapt while keeping their job and paycheck at the same time, with otherwise no disruption to their life at all for now.
Prosperity and wealth do not come for free. You are not owed anything. The world is not going to give you special treatment or handle you with care because you view yourself as an artisan. Those are rewards for people who keep up, not for those who resist change. It's always been that way. Just because you've so far been on the receiving end of prosperity doesn't mean you're owed that kind of easy life forever. Nobody else gets that kind of guarantee -- why should you?
The bottom line is the people in this article will be learning new skills one way or another. The only question is whether those are skills that adapt their existing career for an evolving world or whether those are skills that enable them to transition completely out of development and into a different sector entirely.
> These are rewards for people who keep up, not for those who resist change.
lol. I work with LLM outputs all day -- like it's my job to make the LLM do things -- and I probably speak to some LLM to answer a question for me between 10 and 100 times a day. They're kinda helpful for some programming tasks, but pretty bad at others. Any company that tried to mandate me to use an LLM would get kicked to the curb. That's not because I'm "not keeping up", it's because they're simply not good enough to put more work through.
Wouldn't this depend a lot on how management responds to your use? For example, if you just kept a log of prompts and outputs with notes about why the output wasn't acceptable, that could be considered productive use in this early stage of LLMs, especially if management's goal was to have you learning how to use LLMs. Learning how not to use something is just as important in the process of adapting any new tool.
If management is convinced of the benefits of LLMs and the workers are all just refusing to use them, the main problem seems to be a dysfunctional working environment. It's ultimately management's responsibility to work that out, but if the management isn't completely incompetent, people tasked with using them could do a lot to help the situation by testing and providing constructive feedback rather than making a stand by refusing to try and providing grand narratives about damaging the artistic integrity of something that has been commoditized from inception like video game art. I'm not saying that video game art can't be art, but it has existed in a commercial crunch culture since the 1970s.
Anything with even vaguely complicated TypeScript types, hallucinating modules, writing tests that are useful rather than just performative, as recent examples…
If you're not doing the work, you're not learning from the result.
The CEOs in question bought what they believed to be a power tool, but got what is more like a smarter copy machine. To be clear, copy machines are not useless, but they also aren't going to drive the 200% increases in productivity that people think they will.
But because management demands the 200% increase in productivity they were promised by the AI tools, all the artists and programmers on the team hear "stop doing anything interesting or novel, just copy what already exists". To be blunt, that's not the shit they signed up for, and it's going to result in a far worse product. Nobody wants slop.
Having spend hours upon hours with image snythesis for artistic hobby purposes, it is indeed an awesome tool. If you get into it you might learn about its limitations though.
Real knowledge here is often absend from the strongest AI prosletisers, others are more realistic about it. It still remains an awesome tool, but a limited one.
AIs today are not creative at all. They find statistical matches. They perform a different work than artists do.
But please, replace all your artwork with AI generated ones. I believe the forced "adapt" phase with that approach would realize itself rather quickly.
> It still remains an awesome tool, but a limited one.
And that's enough to drive significant industry-wide change. Just because it can't fully automate everything doesn't mean companies aren't going to expect (and, indeed, increasingly require) their employees to learn how to effectively utilize the technology. The CEO of Shopify recently made it clear that refusal to learn to use AI tools will factor directly into performance evaluations for all staff. This is just the beginning. It's best to be wise and go where the puck is headed.
The article gives several examples of where these tools are used to rapidly accelerate experimentation, pitches, etc. Supposedly this is a bad thing and should be avoided because it's not sufficiently artisan, but no defensible argument was presented as to why these use cases are illegitimate.
In terms of writing code, we're entering an era where developers who have invested in learning how to utilize this technology are simply better and more valuable to companies than developers who have not. Naysayers will find all sorts of false ways to nitpick that statement, yet it remains true. Effective usage means knowing when (and when not) to use these tools -- and to what degree. It also, for now at least, means remaining a human expert about the craft at hand.
I teach compilers, systems, etc. at a university. Innumerable times I have seen AI lead a poor student down a completely incorrect but plausible path that will still compile.
I'm adding `.noai` files to all the project going forward:
> Yes, and where do you suppose experienced developers come from?
Almost every time I hear this argument, I realize that people are not actually complaining about AI, but about how modern capitalism is going to use AI.
Don't get me wrong, it will take huge social upheaval to replace the current economic system.
But at least it's an honest assessment -- criticizing the humans that are using AI to replace workers, instead of criticizing AI itself -- even if you fear biting the hands that feed you.
1. How it's gonna be used and how it'll be a detriment to quality and knowledge.
2. How AI models are trained with a great disregard to consent, ethics, and licenses.
The technology itself, the idea, what it can do is not the problem, but how it's made and how it's gonna be used will be a great problem going forward, and none of the suppliers tell that it should be used in moderation and will be harmful in the long run. Plus the same producers are ready to crush/distort anything to get their way.
... smells very similar to tobacco/soda industry. Both created faux-research institutes to further their causes.
Data centers account for like 2% of global energy demand now. I’m not sure if we can really say that AI, which represents a fraction of that, constitutes a huge environmental problem.
Data centres in general are an issue that contribute to climbing emissions, two percent globally is not trivial .. and it's "additional" over demand of a decade and more ago past, another sign we are globally increasing demand.
Emissions aside, locally many data centres (and associated bit mining and AI clusters) are a significant local issue due to local demand on local water and local energy supplies.
An nVIDIA H200 uses around 2.3x more power (700W) when compared to a Xeon 6748P (300W). You generally put 8 of these cards into a single server, which adds up to 5.6KW, just for GPUs. With losses and other support equipment, that server uses ~6.1KW at full load. Which is around 8.5x more when compared to a CPU only server (assuming 700W or so at full load).
Considering HPC is half CPU and half GPU (more like 66% CPU and 33% GPU but I'm being charitable here), I expect an average power draw of 3.6KW in a cluster. Moreover, most of these clusters run targeted jobs. Prototyping/trial runs use much limited resources.
On the other hand, AI farms use all these GPUs at full power almost 24/7, both for training new models and inference. Before you asking, if you have a GPU farm which you do training, having inference focused cards doesn't make sense, because you can divide nVIDIA cards with MIG, so you can put aside some training cards, divide these cards to 6-7 and run inference on them, resulting ~45 virtual cards for inference per server, again at ~6.1KW load.
> Almost every time I hear this argument, I realize that people are not actually complaining about AI, but about how modern capitalism is going to use AI.
This was pretty consistently my and many others viewpoint since 2023. We were assured many times over that this time it would be different. I found this unconvincing.
> I realize that people are not actually complaining about AI, but about how modern capitalism is going to use AI.
Something very similar can be said about the issue of guns in America. We live in a profoundly sick society where the airwaves fill our ears with fear, envy and hatred. The easy availability of guns might not have been a problem if it didn't intersect with a zero-sum economy.
Couple that with the unavailability of community and social supports and you have a a recipe for disaster.
> criticizing the humans that are using AI to replace workers, instead of criticizing AI itself
I think you misunderstand OP's point. An employer saying "we only hire experienced developers [therefore worries about inexperienced developers being misled by AI are unlikely to manifest]" doesn't seem to realize that the AI is what makes inexperienced developers. In particular, using the AI to learn the craft will not allow prospective developers to learn the fundamentals that will help them understand when the AI is being unhelpful.
It's not so much to do with roles currently being performed by humans instead being performed by AI. It's that the experienced humans (engineers, doctors, lawyers, researchers, etc.) who can benefit the most from AI will eventually retire and the inexperienced humans who don't benefit much from AI will be shit outta luck because the adults in the room didn't think they'd need an actual education.
> Yes, and where do you suppose experienced developers come from?
Strictly speaking, you don't even need university courses to get experienced devs.
There will always be individuals that enjoy coding and do so without any formal teaching. People like that will always be more effective at their job once employed, simply because they'll have just that much more experience from trying various stuff.
Not to discredit University degrees of course - the best devs will have gotten formal teaching and code in their free time.
The first sentence contextualized the comment to university degrees as far as I'm concerned. I'm not sure how you could interpret it any other way, but maybe you can enlighten me.
> People like that will always be more effective at their job once employed
This is honestly not my experience with self taught programmers. They can produce excellent code in a vacuum but they often lack a ton of foundational stuff
In a past job, I had to untangle a massive nested loop structure written by a self taught dev, which did work but ran extremely slowly
He was very confused and asked me to explain why my code ran fast, his ran slow, because "it was the same number of loops"
I tried to explain Big O, linear versus exponential complexity, etc, but he really didn't get it
But the company was very impressed by him and considered him our "rockstar" because he produced high volumes of code very quickly
I was self taught before I studied, most of the "foundational" knowledge is very easy to acquire. I've mentored some self-taught juniors and they surprised me at how fast they picked up concepts like big O just by looking at a few examples.
My point is you don't know what you don't know. There is really only so far you can get by just noodling around on your own, at some point we have to learn from more experienced people to get to the next level
School is a much more consistent path to gain that knowledge than just diving in
It's not the only path, but it turns out that people like consistency
I would like a book recommendation for the things I don’t know please (Sarcasm but seriously)…
A senior dev mentioned a “class invariant” the other day And I just had no idea what that was because I’ve never been exposed to it… So I suppose the question I have is what should I be exposed to in order to know that? What else is there that I need to learn about software engineering that I don’t know that is similarly going to be embarrassing on the job if I don’t know it? I’ve got books like cracking the coding interview and software engineering at Google… But I am missing a huge gap because I was unable to finish my masters and computer science :-(
Tyvm for the serious comment, i_am_proteus! :-) The algorithms book By Steve S. (The Algorithm Design Manual)?
I've read that one, not an expert by any means, and I've got a 'decent' handle on data structues, but what about the software engineering basics one needs like OOP vs. Functional, SOLID, interfaces, class invariants, class design, etc.? Should I just pick up any CS 101 textbook? Or any good MIT open courseware classes that cover this type of stuff (preferably with video lectures... intro to algorithms is _amazing_ they have Eric's classes uploaded to YouTube, but finding good resources to level-up as a SWE has proved somewhat challenging)
^ serious comment as well... I find myself "swimming" when I hear certain terms used in the field and I am trying to catch up a bit (esp. as an SRE with self-taught SRE skills that is supposed to know this stuff)
Ah! Nvm, I see you mean https://github.com/walkccc/CLRS (didn't catch the acronym was the authors names smushed together at first)
> This website contains nearly complete solutions to the bible textbook - Introduction to Algorithms Third Edition, published by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein.
I ran into that particular term oodles in Domain-Driven Design, Tackling Complexity at the Heart of Software by Eric Evans. Pretty dense, though. I’ve heard that more recent formulations of the subject are more approachable.
> most of the "foundational" knowledge is very easy to acquire
But you have to know this knowledge exists in the first place. That's part of the appeal of university teaching: it makes you aware of many different paradigms. So the day you stumble on one of them you know where to look for a solution. And usually you learn how to read (and not to fear) reading scientific papers which can be useful. And statistics.
>People like that will always be more effective at their job once employed
My experience is that "self taught" people are passionate about solving the parts they consider fun but do not have the breadth to be as effective as most people who have formal training but less passion. The previous poster also called out real issues with this kind of developer (not understanding time complexity or how to fix things) that I have repeatedly seen in practice.
But the sentence is about people coding in their free time vs not doing so... If you take an issue with that, you argue that self taught people that don't code in their free time are better at coding the the people that do - or people with formal training that don't code in their free time being better at it vs people that have formal training and do...
I just pointed out that removing classes entirely would still get you experiences people. Even if they'd likely be better if they code and get formal training. I stated that very plainly
You actually didn't state it very plainly at all. Your initial post is contradictory, look at these two statements side by side
> There will always be individuals that enjoy coding and do so without any formal teaching. People like that will always be more effective at their job once employed
> the best devs will have gotten formal teaching and code in their free time
People who enjoy coding without formal training -> more effective
People who enjoy coding and have formal training -> best devs
Anyways I get what you were trying to say, now. You just did not do a very good job of saying it imo. Sorry for the misunderstanding
> There will always be individuals that enjoy coding and do so without any formal teaching. People like that will always be more effective at their job once employed
As "people who enjoy coding and didn't need formal training to get started". It includes both people who have and don't have formal training.
Both statements together are (enthusiasm + formal) > (enthusiasm without formal) > (formal without enthusiasm).
Sure that's a valid interpretation but it wasn't how I read it
> Both statements together are (enthusiasm + formal) > (enthusiasm without formal) > (formal without enthusiasm).
I don't think the last category (formal education without enthusiasm) really exists, I think it is a bit of a strawman being held up by people who are *~passionate~*
I suspect that without any enthusiasm, people will not make it through any kind of formal education program, in reality
Uh, almost nobody I've worked with to date codes in their free time with any kind of regularity.
If you've never encountered the average 9-5 dev that just does the least amount of effort they can get away with, then I have to apploud the HR departments of the companies you've worked for. Whatever they're doing, they're doing splendid work.
And almost all of my coworkers are university grads that do literally the same you've used as an example for non formally taught people: they write abysmally performing code because they often have an unreasonable fixation on practices like inversion of control (as a random example).
As a particularly hilarious example I've had to explain to such a developer that an includes check on a large list in a dynamic language such as JS performs abysmally
Many of these people have a normal life outside of work and different hobbies or a social life. Many of them had been glued to their screens and keyboards too but evolved into a different stage in their lives. Former passions could turn into a discipline. I personally am not on my computer outside of 9-5 because thats already enough. I admit that don’t have the same passion I had in my 20s and yet Im effective in doing my work and am quite fulfilled.
While you definitely loose acuity once you stop exploring new concepts in your free time, the amount of knowledge gained after you've already spend 10-20 yrs coding drops off a cliff, making this time investment in your free time progressively less essential.
My pint was that most of my coworkers never went through an enthusiastic phase in which they coded in their free time. Neither pre university nor during or after. And it's very easy noticeable that they're not particularly good at coding either.
Personally, I think it's just that people that are good at coding inevitably become enthusiastic enough to do it in their free time, at least for a few years. Hence the inverse is true: people that didn't go through such a phase (which most of my coworkers are)... Aren't very good at it. Wherever they went to university and got a degree or not.
Depends on the implementation. E.g. Javas HashSets include has the same performance profile as a Map lookup. Its still not particularly performant with large datasets, but significantly less abysmal then a regular JS .includes().
I just didn't want to explore the example to such a depth, as it felt irrelevant to me at the time of writing.
It doesn't seem to matter if someone went to university. I have had to unpick crap code from uni grads and self taught. Experience may be the only true reliable tell, and I don't mean jobs held I mean world experience on projects.
There are also different types of self taught, and different types of uni grad. You have people who love code, have a passion for learning, and that's driven them to gain a lot of experience. Then you have those who needed to make a living, and haven't really stretched beyond their wheelhouse so lack a lot of diverse experience. Both are totally fine and capable of some work, but you would have better luck with novel work from an experienced passionate coder. Uni trained or not.
> but you would have better luck with novel work from an experienced passionate coder. Uni trained or not.
I have not learned CS at university (maths & stats graduate who shifted to programming, because I can't help myself loving it). I work with engineers with CS degrees from pretty good universities. At the risk of sounding arrogant, I write better code then a lot of them (and some of them write code that it so clean and tight that I wish I could match it). Purely based on my fairly considerable experience, there is basically little correlation between degree and quality of code. There is non-trivial correlation between raw intelligence and the output. And there is a massive correlation between how much one cares about the quality of the work and the output.
> There will always be individuals that enjoy coding and do so without any formal teaching.
We're talking about the industry responsible for ALL the growth of the largest economy in the history of the world. It's not the 1970s anymore. You can't just count on weirdos in basements to build an industry.
> There will always be individuals that enjoy coding and do so without any formal teaching.
That's not the kind of experience companies look for though. Do you have a degree? How much time have you spent working for other companies? That's all that matters to them.
I think the software industry will look just like the material goods space post-industrialization after the dust settles:
Large corporations will use AI to deliver low-quality software at high speed and high scale.
"Artisan" developers will continue to exist, but in much smaller numbers and they will mostly make a living by producing refined, high-quality custom software at a premium or on creative marketplaces. Think Etsy for software.
That's the world we are heading for, unless/until companies decide LLMs are ultimately not cost beneficial or overzealous use of them leads to a real hallucination induced catastrophe.
Sounds like fast fashion. The thinnest, cheapest fabric, slapped together as fast as possible with the least amount of stitching. Shipped fast and obsolete fast.
> “I have no idea how he ended up as an art director when he can’t visualise what he wants in his head unless can see some end results”, Bradley says. Rather than beginning with sketches and ideas, then iterating on those to produce a more finalised image or vision, Bradley says his boss will just keep prompting an AI for images until he finds one he likes, and then the art team will have to backwards engineer the whole thing to make it work.
Sounds like an "idea guy" rather than an art director or designer. I would do this exact same thing, but on royalty-free image websites, trying to get the right background or explanatory graphic for my finance powerpoints. Unsurprisingly, Microsoft now has AI "generating" such images for you, but it's much slower than what I could do flipping through those image sites.
It would be an understatement to call this a skewed perspective. In most of the anecdotes they seem to try really hard to trivialize the productive benefits of AI, which is difficult to take seriously. The case that LLMs create flawed outputs or are limited in what they can do is not controversial at all, but by and large, reports by experienced developers is that it has improved their productivity, and it's now part of their workflow. Whether businesses and hire-ups try to use it in absurd ways is neither here nor there. That, and culture issues, were a problem before AI.
Obviously some workers have a strong incentive to oppose adoption, because it may jeopardize their careers. Even if the capabilities are over-stated it can be a self-fulfilling prophecy as higher-ups choices may go. Union shops will try to stall it, but it's here to stay. You're in a globally competitive market.
Right, well even without AGI (no two people agree on whether it's coming within 5 years, 30, or 100), finely-tuned LLMs can disrupt the economy fast if the bottlenecks get taken care of. The big one is the robot-economy. This is popularly placed further off in timescales, but it does not require AGI at all. We already have humanoid robots on the market for the price of a small car, they're just dumb. Once we scale up solar and battery production, and then manufacturing, it's coming for menial labor jobs. They already have all the pieces, it's a foregone conclusion. What we don't know how to do is to create a real "intelligence", and here the evangelists will wax about the algorithms and the nature of intelligence, but at the end of the day it takes more than scaling up an LLM to constitute an AGI. The bet is that AI-assisted research will lead to breakthrough in a trivial amount of time.
With white-collar jobs the threat of AI feels more abstract and localized, and you still get talk about "creating new jobs", but when robots start coming off the assembly line people will demand UBI so fast it will make your head spin. Either that or they'll try to set fire to them or block them with unions, etc. Hard to say when because another effort like the CHIPS act could expedite things.
I was thinking about this and realized that if we want an AI boom to lead to UBI, AI needs to start replacing the cushy white collar jobs first.
If you start by replacing menial labor, there will be more unemployment but you’re not going to build the political will to do anything because those jobs were seen as “less than” and the political class will talk about how good and efficient it is that these jobs are gone.
You need to start by automating away “good jobs” that directly affect middle/upper class people. Jobs where people have extensive training and/or a “calling” to the field. Once lawyers, software engineers, doctors, executives, etc get smacked with widespread unemployment, the political class will take UBI much more seriously.
i think that the factor determining which jobs get usurped by AI first isn't going to be based on the cognitive difficulty as much as it is about robotic difficulty and interaction with the physical world.
if you job consists of reading from a computer -> thinking -> entering things back into a computer, you're on the top of the list because you don't need to set up a bunch of new sensors and actuators. In other words… the easier it is to do your job remotely, the more likely it is you’ll get automated away
I suspect elites will build a two-tiered AI system where only a select few get access to the cutting-edge stuff, while the rest of us get stuck with the leftovers.
They'll use their clout—money, lobbying, and media influence—to lock in their advantage and keep decision-making within their circle.
In the end, this setup would just widen the gap, cementing power imbalances as AI continues to reshape everything. UBI will become the bare minimum to keep the masses sedated.
What's there to gain? What do they care about biomass? They're still in the business of selling products, until the economy explodes. I find this to be circular because you could say the same thing about right now, "why don't they dispose of the welfare class" etc.
There's also the fact that "they" aren't all one and the same persons with the exact same worldview and interests.
The Davos class was highly concerned about ecology before Davos was even a thing. In America, their minions (the “coastie” class) are coming to see the liquidation of the kulaks as perhaps not such a bad thing. If it devolves into a “let them eat cake” scenario, one has to wonder how things will play out in “proles vs robot pinkertons”. Watch what the sonic crowd control trucks did in Serbia last week.
Of course there is always the issue of “demand”—of keeping the factories humming, but when you are worth billions, your immediate subordinates are worth hundreds of millions, and all of their subordinates are worth a few million, maybe you come to a point where “lebensraum” becomes more valuable to you than another zero at the end of your balance?
When AI replaces the nerds (in progress), they become excess biomass. Not talking about a retarded hollywood-style apocalypse. Economic uncertainty is more than enough to suppress breeding in many populations. “not with a bang, but a whimper”
If you know any of “them”, you will know that “they” went to the same elite prep schools, live in the same cities, intermarry, etc. The “equality” nonsense is just a lie to numb the proles. In 2025 we have a full-blown hereditary nobility.
edit: answer to ianfeust6:
The West is not The World. There are over a billion Chinese, Indians, Africans…
Words mean things. Who said tree hugger? If you are an apex predator living in an increasingly cloudy tank, there is an obvious solution to the cloudyness.
So your take is that the wealthiest class will purge people because they're tree-huggers. Not the worst galaxy-brained thing I've heard before, but still laughable.
Don't forget fertility rate is basically stagnant in the West and falling globally, so this seems like a waste of time considering most people just won't breed at all.
there has been far more degradation to the natural environment than mere air pollution. general sherman decimated the plains indians with a memorandum. do you think that you are sufficiently better and sufficiently more indispensable than a plains indian?
The West is not The World. There are over a billion Chinese, Indians, Africans…
Words mean things. Who said tree hugger? If you are an apex predator living in an increasingly cloudy tank, there is an obvious solution to the cloudyness.
You speak like they would have to do something 'aggressive'. If you can achieve a circular economy, where robots produce products for the benefit of a lucky few who can live off of their investments (in the robots), then the rest of the population will 'naturally' go away.
You might say "but why not use just 1% of that GDP on making sure the rest of humanity lives in at least minimal comfort"? But clearly -- we already choose not to do that today. 1% of the GDP of the developed world would be more than enough to solve many horrifying problems in the developing world -- what we actually give is a far smaller fraction, and ultimately not enough.
It’s karma. The creatives weren’t terribly concerned when the factory guys lost their jobs. “Lern to code!” Now it’s our turn to “Learn to OnlyFans” or “Learn to Homeless”
"learn to code" was thrown around by programmers, not creatives. Everyone else (including writers and artists) has long hated that phrase, and condemded it as stupid and shortsighted.
No, the underlying format of "$LABOR_ISSUE can be solved by $CHANGE_JOB" comes from a place of politics, where a politician is trying to suggest they have a plan to somehow tackle a painful problem among their constituents, and that therefore they should be (re-)elected.
Then the politicians piled onto "coal-miners can learn to code" etc. because it was uniquely attractive, since:
1. No big capital expenditures, so they don't need to promise/explain how a new factory will get built.
2. The potential for remote work means constituents wouldn't need to sell their homes or move.
3. Participants wouldn't require multiple years of expensive formal schooling.
4. It had some "more money than you make now" appeal.
Stating it in patronizing fact-checker tone does not make it true. The tech nerds started it (they love cheap labor pools). Then the politicians joined their masters’ bandwagon. It was a PR blitz. Who has the money for those? Dorseys, Grahams, & Zuckerbergs, or petty-millionaire mayors & congressmen? Politicians are just the house slaves—servants of money.
"Tech nerds" like Dorsey and Zuckerberg have almost nothing in common (on a day-to-day basis, with how they live their lives, their material incentives, etc.) with "tech nerds" like "Intel Employee #783,529". Those are not a single class of people, and it was predominantly the first group that pushed this sort of rhetoric, not the latter.
24 The disciple is not above his master, nor the servant above his lord.
25 It is enough for the disciple that he be as his master, and the servant as his lord. If they have called the master of the house Beelzebub, how much more shall they call them of his household?
It's common sense. That is why it has endured. You people are like mob hitmen standing in moral judgment of your Godfathers. Without your muscle, your Godfather is just an old guy with pasta and a cigar. The "difference" is something you hallucinate so you can feel good about yourselves.
how much more shall they call them of his household?
If ai exacerbates culture issues and management incompetence then that is an inherent downside of ai.
There is a bunch of programmers who like ai, but as the article shows, programmers are not the only people subjected to ai in the workplace. If you're an artist, you've taken a job that has crap pay and stability for the amount of training you put in, and the only reason you do it is because you like the actual content of the job (physically making art). There is obviously no upside to ai for those people, and this focus on the managers' or developers' perspective is myopic.
It's an interesting point that passion-jobs that creatives take on (including game dev) tend to get paid less, and where the thrilling component is disrupted there could be less incentive to bother entering the field.
I think for the most part creatives will still line up for these gigs, because they care about contributing to the end products, not the amount of time they spend using Blender.
You are again just thinking from the perspective of a manager: Yes, if these ai jobs need to be filled, artists will be the people filling them. But from the artists perspective there are fewer jobs, and the jobs that do remain are less fulfilling. So: from the perspective of a large part of the workforce it is completely true and rational to say that ai at their job has mostly downsides.
It might seem hard to believe but there are a bunch of artists who also like AI. People whose artistic practice predates AI. The definition of "artist" is a quagmire which I won't get into but I am not stretching the definition here in any way.
Software developers are so aware of "enshittification" and yet also bullish about this generation of AI, it's baffling.
It's very clear the "value" of the LLM generation is to churn out low-cost, low-quality garbage. We already outsourced stuff to Fivrr but now we can cut people out altogether. Producing "content" nobody wants.
There are many, many reasons to be skeptical of AI. There are also excellent tasks it can efficiently help with.
I wrote a project where I'd initially hardcoded a menu hierarchy into its Rust. I wanted to pull that out into a config file so it could be altered, localized, etc without users having it and recompile the source. I opened a “menu.yaml” file, typed the name of the top-level menu, paused for a moment to sip coffee, and Zed popped up a suggested completion of the file which was syntactically correct and perfect for use as-is.
I honestly expected I’d spend an hour mechanically translating Rust to YAML and debugging the mistakes. It actually took about 10 seconds.
It’s also been freaking brilliant for writing docstrings explaining what the code I just manually wrote does.
I don't want to use AI to write my code, any more than I'd want it to solve my crossword. I sure like having it help with the repetitive gruntwork and boilerplate.
This sort of extremely narrow use case is what I think AI is good for but the problem is that if you have it for this one you will use it for other things and slowly atrophy.
Many of the best games were discovered through an iterative process of trial and error, not through magic divination. So, yes, it is the journey along the way that matters in this kind of creative process. This applies not just to concept art, but game mechanics and virtually every element of the game.
Companies need to be aware of the long-term affects of relying on AI. It causes atrophy and, when it introduces a bug, it takes more time to understand and fix than if you had written it yourself.
I just spent a week fixing a concurrency bug in generated code. Yes, there were tests; I uncovered the bug when I realized the test was incorrect...
My strong advice, is to digest every line of generated code; don't let it run ahead of you.
It is absolutely terrifying to watch tools like Cursor generate so much code. Maybe not a great analogy, but it feels like driving with Tesla FSD in New Delhi in the middle of rush hour. If you let it run ahead of you, the amount of code to review will be overwhelming. I've also encountered situations where it is unable to pass tests for code it wrote.
Like TikTok AI Coding breaks human psychology. It is engrained in us that if we have a tool that looks right enough and highly productive we will over-apply it to our work. Even diligent programmers will be lured to accepting giant commits without diligent review and they will pay for it.
Of course yeeting bad code into production with a poor review process is already a thing. But this will scale that bad code as now you have developers who will have grown up on it.
> It causes atrophy and, when it introduces a bug, it takes more time to understand and fix than if you had written it yourself.
I think this is the biggest risk. You sometimes get stuck in a cycle in which you hope the AI can fix its own mistake, because you don’t want to expend the effort to understand what it wrote.
It’s pure laziness that occurs only because you didn’t write the code yourself in the first place.
At the same time, I find myself incredibly bored when typing out boilerplate code these days. It was one thing with Copilot, but tools like Cursor completely obviate the need.
100% agree with you, my sentiment is the same. Some time ago I considered making the LLM create tests for me, but decided against it. If I don't understand what needs to be tested, how can I write the code that satisfies this test?
We humans have way more context and intuition to rely on to implement business requirements in software than a machine does.
A very bad programmer can program some cool stuff with the help of libraries, toolkits, frameworks and engines that they barely understand. I think that's pretty cool and makes things otherwise impossible possible, but it doesn't make the very bad programmer better than they really are.
I believe AI is a variation of this, except a library at least has a license.
Why so much hand-wringing? If you are an anti-AI developer and you are able to develop better code faster than someone using AI, good for you. If AI-using developers will end up ruining their codebase in months like many here are saying, then things will take care of themselves.
Even if you say "better faster" tens times fast, the quality of being produced fast and being broadly good are very different. Speed of development can be measured immediately. Quality is holistic. It's a product of not just formatting clear structures but of relating to the rest of a given system.
Most of the times I get to the real solution for a problem after working in the wrong one for a while. If/when LLM help me finish the wrong one faster it is not helpful and could even be damaging in a situation that it goes to production fast.
A lot of modern software dev is focused on delivering features to shareholders, not users. Doing that faster is going to make my life, as a user, worse.
I've posted recently about a dichotomy which I have had in my head for years as a technical person: there are two kinds of tools; the first lets you do the right thing more easily and the second lets you do the wrong thing more quickly and for longer before you have to pay for it. AI/LLMs can definitely be the latter kind of tool, especially in a context where short term incentives swamp long term ones.
I'm actually pro-AI and I use AI assistants for coding, but I'm also very concerned that the way those things will be deployed at scale in practice is likely to lead to severe degradation of software quality across the board.
Why the hand-wringing? Well, for one thing, as a developer I still have to work on that code, fix the bugs in it, maintain it etc. You could say that this is a positive since AI slop would provide for endless job security for people who know how to clean up after it - and it's true, it does, but it's a very tedious and boring job.
But I'm not just a developer, either - I'm also a user, and thinking about how low the average software quality already is today, the prospect of it getting even worse across the board is very unpleasant.
And as for things taking care of themselves, I don't think they will. So long as companies can still ship something, it's "good enough", and cost-cutting will justify everything else. That's just how our economy works these days.
This assumes a level of both rationality and omniscience that don't exist in the real world.
If a company fails to compete in the market and dies, there is no "autopsy" that goes in and realizes that it failed because of a chain-reaction of factors stemming from bad AI-slop code. And execs are so far removed from the code level, they don't know either, and their next company will do the same thing.
What you're likely to end up with is project managers and developers who do know the AI code sucks, and they'll be heeded by execs just as much they are now, which is to say not at all.
And when the bad AI-code-using devs apply to the next business whose execs are pro-AI because they're clueless, guess who they'll hire?
One thing jumps out about the person who noticed the AI was wrong on things they were familiar with. It's like when ELon Musk talks about rockets. I don't know about rockets so I take his word for it. When Elon Must talked about software it was obvious he has no idea what he's doing. So when the AI generates something I know nothing about, it looks productive but when it's generating things for which I'm familiar I know its full of shit.
> So when the AI generates something I know nothing about, it looks productive but when it's generating things for which I'm familiar I know its full of shit.
This is why when you hear people talk about how great it is at producing X, our takeaway should be "this person is not an expert at X, and their opinions can be disregarded"
They are telling on themselves that they are not experts at the thing they think the AI is doing a great job at
"This is why when you hear people talk about how terrible it is at producing X, our takeaway should be "this person either hasn't tried to use it in good faith, and their opinions can be disregarded"
I'm playing devil's advocate somewhat here but it often seem like that there's a bunch of people on both sides using hella motivated reasoning because they have very strong feelings that developed early on in their exposure to AI.
AI is both terrible and wonderful. It's useless and some things and impressive at others. It will ruin whole sectors of the economy and upturn lives. It will get better and it is getting better so any limitations you currently observe are probably termporary. The net benefit for humanity may turn out to be positive or negative - it's too early to tell.
My hypothesis is that you are invested in the success of AI products somehow, financially or emotionally, and that leads you to be blind to their shortcomings
You keep using them whenever possible because you want them to be useful even though in reality their usefulness is really iffy
So - we are at an impasse. Both suspect the other of motivated reasoning and an internal bias that distorts their ability for rational thinking.
It's entirely possible we're both irrational to some degree. But that's irrelevant to answering the question at hand.
Do you claim you are using it regularly and in good faith - enough to honestly form a reliable view on its utility?
I would claim that I am using it in such a way. It would take more effort than I'm prepared to put in to provide evidence of this but please - ask away.
(for the record - I have no financial or professional involvement directly with AI. I simply find the technology fascinating and I use it daily - both playfully and for it's practical utility)
I think I have used it in good faith. A few months ago I was part of a small team at my company tasked to evaluate AI solutions like copilot to see if they are useful to us and could speed development and such
For a couple of week tryout period I tried to use it in my daily workflow pretty heavily. I came away with the impression that it is a neat toy, but not really ready to be a full time tool for me. The other evaluators agreed and our recommendation to our leadership was "This is not really ready for prime time and while it is impressive it probably isn't really worth the cost"
Anyways fast forward and we're getting AI usage OKRs now, being pushed down on us by non-technical leadership, and what I call "formerly technical" leadership. People who did tech 20 years ago but really don't know what working modern tech is like since they've been in management for too long
So yes. I'm definitely negatively biased, and I'm fine to admit that. I absolutely resent having this stuff forced down on me from leaders that are buying the hype despite being told it is probably not ready to be a daily driver
And I'm seeing the hype spreading through the company, being told by junior devs how amazing it is when I am still iffy on their abilities.
And the absolute worst is when I build a cool proof of concept in an afternoon and everyone is like "wow, AI let you do that so fast now!" and I'm like no, this is just what a good developer can build quickly
So yeah, I'm pretty negative on AI right now. I can still admit the tech itself is impressive, amazing even, and there is no doubt in my mind I could probably find some use for it daily
But I think it is going to be a disaster, because people cannot be trusted to use it responsibly
The social impact is going to be absolutely catastrophic. In some ways it already is
Edit: I am also not really sure why I am supposed to be enthusiastic about technology that business leaders are fairly transparently hoping will make my skillset redundant or at best will make me more productive but I will never realistically see a single extra dollar from the increased productivity
This makes a lot of sense. But to be honest it feels more like a story about the pathology of hierarchical organisations than anything about AI.
I mostly work solo. I use AI when it's either a) interesting or b); useful. Our experiences are very different and it's no wonder our emotional responses are also very different.
> The net benefit for humanity may turn out to be positive or negative - it's too early to tell.
It's just a tool, but it is unfortunately a tool that is currently dominated by large-sized corporations, to serve Capitalism. So it's definitely going to be a net-negative.
Contrast that to something like 3D printing, which has most visibly benefited small companies and individual users.
Like many things (general purpose computing, the internet) we can carve out our own space once something is released into the public sphere so I don't think Capitalism has the iron grip on this that you're hypothesising. In recent memory I think it's mainly social media where the corporations have mostly succeeded in keeping a firm hold on things and where it remains hard for users to subvert their aims. And that's largely because of the failure of decentralized social media to grow to a mass audience.
I think AI is different. "Good enough" models are already available under generous licenses, fine-tuning and even training is within the reach of groups of volunteers etc etc
I always assumed game development would be one of the most impacted by AI hype, for better or worse. With game development there’s a much higher threshold for subjectivity and “incorrectness”.
I’m in a Fortune 500 software company and we are also being pushed AI down our throats, even though so far it has only been useful for small development tasks. However our tolerance for incorrectness is much, much lower—and many skip levels are already realizing this.
I'm an indie game developer and its a domain where I find AI to be most useless - too much of what a game is interactive, spatial, and about game-feel. The AI just can't do it. Even GPT's latest models really struggled to write reasonable 3d transformations, which is unsurprising, since they live in text world, not 3d world.
It really doesn't take that beefy of a machine to run a good LLM locally instead of paying some SaaS company to do it for you.
I've got a refurb homelab server off PCSP with 512gb ram for <$1k, and I run decently good LLM models (Deepseek-r1:70b, llama3.3:70b). Given your username, you might even try pitching a GPU server to them as dual-purpose; LLM + hashcat. :)
Because if your company is against using LLMs for work based on security concerns, it's usually the concern that an employee will enter company confidential data into the LLM, which when using a SaaS LLM means exposing the data.
But if your company buys a server to run it themselves, that security risk is not present.
> “He doesn't know that the important thing isn't just the end result, it's the journey and the questions you answer along the way”. Bradley says that the studio’s management have become so enamoured with the technology that without a reliance on AI-generated imagery for presentations and pitches they would not be at the stage they are now, which is dealing with publishers and investors.
Take out the word AI and replace it with any other tool that's over-hyped or over-used, and the above statement will apply to any organization.
When LLMs came out I suppressed my inner curmudgeon and dove in, since the technology was interesting to me and seemed much more likely than crypto to be useful beyond crime. Thus, I have used LLMs extensively for many years now and I have found that despite the hype and amazing progress, they still basically only excel first drafts and simple refactorings (where they are, I have to say, incredibly useful for eliminating busy work). But I have yet to use a model, reasoning or otherwise, that could solve a problem that required genuine thought, usually in the form of constructing the right abstraction, bottom up style. LLMs write code like super-human dummies, with a tendency to put too much code in a given function and with very little ability to invent a domain in which the solution is simple and clearly expressed, probably because they don't care about that kind of readability and its not much in their data set.
I'm deeply influenced by languages like Forth and Lisp, where that kind of bottom up code is the cultural standard and and I prefer it, probably because I don't have the kind of linear intelligence and huge memory of an LLM.
For me the hardest part of using LLMs is knowing when to stop and think about the problem in earnest, before the AI generated code gets out of my human brain's capacity to encompass. If you think a bit about how AI still is limited to text as its white board and local memory, text which it generates linearly from top to bottom, even reasoning, it sort of becomes clear why it would struggle with genuine abstraction over problems. I'm no longer so naive as to say it won't happen one day, even soon, but so far its not there.
My solution is to _only_ chat. No auto completion, nothing agentic, just chats. If it goes off the rails, restart the conversation. I have the chat window in my "IDE" (well, Emacs) and though it can add entire files as context and stuff like that, I curate the context in a fairly fine-grained way through either copy and pasting, quickly writing out pseudo code, and stuff like that.
Any generated snippets I treat like StackOverflow answers: Copy, paste, test, rewrite, or for small snippets, I just type the relevant change myself.
Whenever I'm sceptical I will prompt stuff like "are you sure X exists?", or do a web search. Once I get my problem solved, I spend a bit of time to really understand the code, figure out what could be simplified, even silly stuff like parameters the model just set to the default value.
It's the only way of using LLMs for development I've found that works for me. I'd definitely say it speeds me up, though certainly not 10x. Compared to just being armed with Google, maybe 1.1x.
There is a small, hopeful flipside to this. While people using AI to produce art (such as concept art) have flooded the market, real skills now command a higher price than before.
To pull this out of the games industry for just a moment, imagine this: you are a business and need a logo produced. Would you hire someone at the market price who uses AI to generate something... sort of on-brand they most definitely cannot provide indemnity cover for (considering how many of these dubiously owned works they produce), or would you pay above the market price to have an artist make a logo for you that is guaranteed to be their own work? The answer is clear - you'd cough up the premium. This is now happening on platforms like UpWork and Fiverr. The prices for real human work have not decreased; they have shot up significantly.
It's also happening slowly in games. The concept artists who are skilled command a higher salary than those who rely on AI. If you depend on image-generating AI to do your work, I don't think many game industry companies would hire you. Only the start-ups that lack experience in game production, perhaps. But that part of the industry has always existed - the one made of dreamy projects with no prospect of being produced. It's not worth paying much attention to, except if you're an investor. In which case, obviously it's a bad investment.
Besides, just as machine-translated game localization isn't accepted by any serious publisher (because it is awful and can cause real reputational damage), I doubt any evident AI art would be allowed into the final game. Every single piece of that will need to be produced by humans for the foreseeable future.
If AI truly can produce games or many of their components, these games will form the baseline quality of cheap game groups on the marketplaces, just like in the logo example above. The buyer will pay a premium for a quality, human product. Well, at least until AI can meaningfully surpass humans in creativity - the models we have now can only mimic and there isn't a clear way to make them surpass.
> There is a small, hopeful flipside to this. While people using AI to produce art (such as concept art) have flooded the market, real skills now command a higher price than before.
It's "hopeful" that the future of all culture will resemble food, where the majority have access to McDonalds type slop while the rich enjoy artisan culture?
It's hopeful because AI has not devalued creative human labor but increased its worth. Similar to how if one were a skilled chef, they didn't start working for McDonald's when it came to be, but for a restaurant that pays significantly above McDonald's.
Most people's purchasing power being reduced is a separate matter, more related to the eroding middle class and greedflation. Many things can be said about it, but they are less related to the trend I highlighted. Even if, supposing the middle class erosion continues, the scenario you suggest may very well play out.
It doesn't make sense to suggest that AI has made human effort more valuable. Before, to do X, Y, or Z you needed human effort. Now, you can do X with AI. You just need human effort to do Y or Z. There is less demand for human effort. Why would that result in an increase in the price of human effort?
>Most people's purchasing power being reduced is a separate matter, more related to the eroding middle class and greedflation.
Greedflation, is that where companies suddenly remember to be greedy again after years of forgetting they're allowed to be greedy, which happens by random chance to coincide exactly with periods of expansionary monetary and fiscal policy?
> It doesn't make sense to suggest that AI has made human effort more valuable.
In that case, I welcome an alternative explanation for the human labor price increase on UpWork and Fiverr while AI work replaced work at the previous price level. The same is seen in the hiring of affected disciplines.
If you have a distribution of work where most is easy and cheap, some is moderate and moderate, and a little is difficult and expensive, and you take out all the cheap and easy work, the moderate and difficult work could drop in price but the average of the remaining work will still be higher than before.
e.g.
You have tasks advertised in the distribution $1, $1, $1, $1, $1, $1, $2, $2, $3, $3, $5, $5, $10. Median price is $2, and average is $2.76.
All the $1 and $2 tasks are replaced with AI. Old tasks get $1 cheaper each as there are more people that can do them. Now the distribution is $2, $2, $4, $4, $9. Median is $4, average is $4.2.
So you have made labour less valuable but the prices advertised go up because only the more expensive work now gets advertised.
The situation with food is that everyone today has access to good quality food if they choose to actually put their money towards it, but large numbers of people enjoy McDonalds and KFC and such slop, so they choose to spend far more on it than they'd spend cooking for themselves.
It is still much better than when large numbers of people starved if it rained a bit in the wrong week.
He spoke of the grass and flowers and trees
Of the singing birds and the humming bees;
Then talked of the haying, and wondered whether
The cloud in the west would bring foul weather.
The weather and its effect on the food supply was the preoccupation of 90% of the population 90% of the time for all of agricultural man's history (and pre-history) and hunting and gathering was even worse for quality of life.
> real skills now command a higher price than before.
Only if companies value/recognize those real skills over that of the alternative, and even if they do, companies are pretty notorious for choosing whatever is cheapest/easiest (or perceived to be).
This article is an example of why the gender-neutral use of pronouns makes things a pain to read. If you're already changing the interviewees' names then IDK why you couldn't just pick an arbitrary he/she pronoun to stick to for one character.
> Francis says their understanding of the AI-pusher’s outlook is that they see the entire game-making process as a problem, one that AI tech companies alone think they can solve. This is a sentiment they do not agree with.
But it was the culture war that resulted in this change to the language. Previous to the war, singular 'they' was to be avoided due to the ambiguity it introduces.
"X and Y were in the garden, Y noticed the ripe tomatoes as they went into the greenhouse". Is X in the greenhouse?
I'm way woker than the average person but I have to admit encountering a singular 'they' breaks my concentration in a distracting way - there's definitely possible ambiguity.
People really ought to read redacted documents to get an idea for how people write with clarity when gender and even number of parties is unknown.
But I'm confused by your sentence regardless of the gender terms. Did they notice the tomatoes in the Garden or in the greenhouse? This is just ambiguous wording in general.
- These are two different sentences, but they're separated with a comma. It should be a period, as it makes no grammatical sense with a comma unless you're trying to make it intentionally confusing.
- You would write "They both went into the greenhouse" if they both entered, or you would write "Y entered the greenhouse and noticed the ripe tomatoes."
- "Before entering the greenhouse, "Y"/"they both" noticed the ripe tomatoes in the Garden."
I have no problem with people's attitudes or culture changing in a positive direction. However, I dislike this business of introducing a change into the language in a way that reduces its expressiveness and clarity. Usage of singular 'they' in contexts where more specific pronouns were available was unusual until very recently. Why the change? I don't think it's unfair characterize this as an offensive move, waged by one side in a 'culture war', that was done without regard to collateral damage.
> Usage of singular 'they' in contexts where more specific pronouns were available was unusual until very recently
It was used whenever gender was ambiguous or needed to be protected. Now with people openly identifying as non-binary, there is not a more specific pronoun, that person doesn't consider themselves that gender. You would be referring to them as something that is not what they want to be called, and is not what their social circle refers to them as. It's confusing, especially if you know what to call them but choose not to because you're offended.
> I don't think it's unfair characterize this as an offensive move, waged by one side in a 'culture war', that was done without regard to collateral damage
I would wager, based on the disproportionate and melodramatic language, this has never actually affected you. But you are likely consuming media that tells you everyone is going to draw and quarter you if you mess up a pronoun. This is not the case. Trans people just move on, they're used to it. It literally happens all the time.
indoordin0saur is correct. Traditional use of singular "they" was restricted to persons of unknown sex, where it is correct and unobjectionable. But the article uses it for persons of known sex. This is a modern innovation, and it should be resisted because it reduces the clarity of the writing.
You're already making up fictitious names, so how is making up fictitious sexes any different? By using non-unisex names you're implying specific sexes already. It's implausible that you would know somebody's name and details about their working conditions without knowing their sex.
Alternative solution: abbreviate all the fictitious names to single letters. This is commonly understood to mean obviously and intentionally concealed identity (e.g. "M" and "Q" from the James Bond franchise), which returns the singular "they" to traditional and unobjectionable usage.
It has been considered normal in some colloquial uses for a long time. But until the late 2010s/early 2020s all style guides considered it to be poor form due to the ambiguity and muddy sentence structure it creates. Recommendations were changed recently for political reasons.
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[ 4.8 ms ] story [ 279 ms ] threadI can only hope endeavors (experiments?) like this extreme fail fast and we learn from it.
Not to say I'm a hater or something like that. I played a lot of those back in the day. But it's more honest to admit the art and the casino mechanic make the brain excited... the mechanics are 'okay'.
Edit: I just had a random thought. One of the strongest desire of a person is that of aesthetic desire. To feel that our life is 'picturesque' or aesthetic or beautiful. Overloading the game with aesthetic beauty is actually genius since it's an easy and strong form of aesthetic (beautiful girls. not just sexy but 'beautiful'. As in their whole face and outfit. Also other aesthetic quality like purity, innocence, cheerfulness, cuteness etc. Waifu stuffs.). And it's often so saturated with beauty that all ugly things in the players' real lifes fade away. It numbs our 'life aesthetic check' since it's flooded with so much 'beauty'. That's why people who play these games say 'i dont even think about the waifus anymore, so the mechanic must be good,' cause that numbed state is the intended state. Wheny our aesthetic center is kinda 'numbed'. And that's probably why it feels so good to play these games. When you play other games your sense of beauty is not similarly flooded and numbed, so you're all too aware that this action of playing games is not 'beautiful' in some real sense.
Creativity emerges through a messy exploration and human experience -- but it seems no one has time for that these days. Managers have found a shiny new tool to do more with less. Also, AI companies are deliberately targeting executives with promises of cost-cutting and efficiency. Someone has to pay for all the R&D.
Notably, a good number the examples were just straight-up bad management, irrespective of the tools being used. I also think some of these reactions are people realizing that they work for managers or in businesses that ultimately don't really care about the quality of their work, just that it delivers monetary value at the end.
I think that's part of the reason why devs like working from home and not be spied on.
The "AI" generated code is just code extracted from various sources used for training, which could not be used by a human programmer because most likely they would have copyrights incompatible with the product for which "AI" is used.
All my life I could have written much faster any commercial software if I had been free to just copy and paste any random code lines coming from open-source libraries and applications, from proprietary programs written for former employers or from various programs written by myself as side projects with my own resources and in my own time, but whose copyrights I am not willing to donate to my current employer, so that I would no longer be able to use in the future my own programs.
I could search and find suitable source code for any current task as fast and with much greater reliability than by prompting an AI application. I am just not permitted to do that by the existing laws, unlike the AI companies.
Already many decades ago, it was claimed that the solution for enhancing programmer productivity is more "code reuse". However "code reuse" has never happened at the scale imagined in the distant past, but not because of technical reasons, but due to the copyright laws, whose purpose is exactly to prevent code reuse.
Now "AI" appears to be the magical solution that can provide "code reuse" at the scale dreamed a half of century ago, by escaping from the copyright constraints.
When writing a program for my personal use, I would never use an AI assistant, because it cannot accelerate my work in any way. For boilerplate code, I use various templates and very smart editor auto-completion, there is no need of any "AI" for that.
On the other hand, when writing a proprietary program, especially for some employer that has stupid copyright rules, e.g. not allowing the use of libraries with different copyrights, even when those copyrights are compatible with the requirements of the product, then I would not hesitate to prompt an AI assistant, in order to get code stripped of copyright, saving thus time over rewriting an equivalent code just for the purpose of enabling it to be copyrighted by the employer.
If you proposed something like GitHub Copilot to any company in 2020, the legal department would’ve nuked you from orbit. Now it’s ok because “everyone is doing it and we can’t be left behind”.
Edit: I just realized this was a driver for why whiteboard puzzles became so big - the ideal employee for MSFT/FB/Google etc was someone who could spit out library quality, copyright-unencumbered, “clean room” code without access to an internet connection. That is what companies had to optimize for.
My last boss told me essentially (paraphrasing), "I budget time for your tasks. If you finish late, I look like I underestimate time required, or you're not up to it. If you finish early, I look like I overestimate. If I give you a week to do something, I don't care if you finish in 5 minutes, don't give it to me until the week is up unless you want something else to do."
We certainly did not receive bonuses based on doing work faster, so unless you are, what incentives are you being driven by to do the work sooner?
There is no place for me in this environment. I’d not that I couldn’t use the tools to make so much code, it’s that AI use makes the metric for success speed-to-production. The solution to bad code is more code. AI will never produce a deletion. Publish or perish has come for us and it’s sad. It makes me feel old just like my Python programming made the mainframe people feel old. I wonder what will make the AI developers feel old…
If you do this you are creating a rod for your own back: You need management to see the failures & the time it takes to fix them, otherwise they will assume everything is fine & wonderful with their new toy & proceed with their plan to inflict it on everyone, oblivious to the true costs + benefits.
If at every company I work for, my manager's average 7-8 months in their role as _my_ manager, and I am switching jobs every 2-3 years because companies would rather rehire their entire staff than give out raises that are even a portion of the market growth, why would I care?
Not that the market is currently in that state, but that's how a large portion of tech companies were operating for the past decade. Long term consequences don't matter because there are no longer term relationships.
It's the front-end of the hype cycle. The tech-debt problems will come home to roost in a year or two.
Ah yes, maintenance, the most fun and satisfying part of the job. /s
new 2025 slang just dropped
The market can remain irrational longer than you can remain solvent.
Use LLM to write Haskell. Problem solved?
That, right here, is a world-shaking statement. Bravo.
But the sentiment is true, by default current LLMs produce verbose, overcomplicated code
You can't win an argument with people who don't care if they're wrong, and someone who begins a sentence that way falls into that category.
However I wouldn't say refactoring is as hands free as letting AI produce the code in the first place, you need to cherry pick its best ideas and guide it a little bit more.
I am sure assembly programmers were horrified at the code the first C compilers produced. And I personally am horrified by the inefficiency of python compared to the C++ code I used to write. We always have traded faster development for inefficiency.
This created a league of incredibly elitist[0] programmers who, having mastered what they thought was the rules of C, insisted to everyone else that the real problem was you not understanding C, not the fact that C had made itself a nightmare to program in. C is bad soil to plant a project in even if you know where the poison is and how to avoid it.
The inefficiency of Python[1] is downstream of a trauma response to C and all the many, many ways to shoot yourself in the foot with it. Garbage collection and bytecode are tithes paid to absolve oneself of the sins of C. It's not a matter of Python being "faster to write, harder to execute" as much as Python being used as a defense mechanism.
In contrast, the trade-off from AI is unclear, aside from the fact that you didn't spend time writing it, and thus aren't learning anything from it. It's one thing to sacrifice performance for stability; versus sacrificing efficiency and understanding for faster code churn. I don't think the latter is a good tradeoff! That's how we got under-baked and developer-hostile ecosystems like C to begin with!
[0] The opposite of a "DEI hire" is an "APE hire", where APE stands for "Assimilation, Poverty & Exclusion"
[1] I'm using Python as a stand-in for any memory-safe programming language that makes use of a bytecode interpreter that manipulates runtime-managed memory objects.
And even among languages that do have a full virtual machine, Python is slow. Slower than JS, slower than Lisp, slower than Haskell by far.
There is a Common Lisp implementation that compiles to bytecode, CLISP. And there are Common Lisp implementations that compile (transpile?) to C.
It was only much later that optimizing compilers began using it as an excuse to do things like time travel, and then everyone tried to show off how much of an intellectual they were by saying everyone else was stupid for not knowing this could happen all along.
I suspect he is pretty unimpressed by the code that LLMs produce given his history with code he thinks is subpar, but what do I know
https://blog.mathieuacher.com/LinusTorvaldsLLM/
Unless you meant that AI won’t remove entire features from the code. But AI can do that too if you prompt it to. I think the bigger issue is that companies don’t put enough value on removing things and only focus on adding new features. That’s not a problem with AI though.
Please don't do this :) Readable code is better than clever code!
At least with human-written clever code you can trust that somebody understood it at one point but the idea of trusting AI generated code that is "clever" makes my skin crawl
And was the code they were writing before they had an LLM any better?
My guess would be engineers who are "forced" to use AI, already mailed management it would be an error and are interviewing for their next company. Malicious compliance: vibe code those new features and let maintainability and security be a problem for next employees / consultants.
I should also note that development style also depends on tools, so if your IDE makes inline functions more readable in it's display, it's fine to use concisely defined lambdas.
Readablity is a personal preference thing at some point after all.
https://github.com/dwmkerr/hacker-laws#kernighans-law
I think what you're looking for is "x or 1"
And I know it's intentional, but yes. Add some mindfulness to your implementation
Map["blah"] = fooIsTrue;
I do see your example in the wild sometimes. I've probably done it myself as well and never caught it.
Readability incorporates familiarity but also conciseness. I suppose it depends what else is going on in the codebase. I have a database access class in one of my solutions where `ToLookup` is used 15 times; yes you have to learn the concept, but it's an inbuilt method and it's a massive benefit once you grok it.
As a side note, I've had coworkers disappear for N days too and in that time the requirements changed (as is our business) and their lack of communication meant that their work was incompatible with the new requirements. So just because someone achieves a 10x speedup in a vacuum also isn't necessarily always a good thing.
A declarative framework for testing may make sense in some cases, but in many cases it will just be a complicated way of scripting something you use once or twice. And when you use it you need to call up the maintainer anyway when you get lost in the yaml.
Which of course feels good for the maintainer, to feel needed.
u/justonceokay's wrote:
> The solution to bad code is more code.
This has always been true, in all domains.
Gen-AI's contribution is further automating the production of "slop". Bots arguing with other bots, perpetuating the vicious cycle of bullshit jobs (David Graeber) and enshitification (Cory Docotrow).
u/justonceokay's wrote:
> AI will never produce a deletion.
I acknowledge your example of tidying up some code. What Bill Joy may have characterized as "working in the small".
But what of novelty, craft, innovation? Can Gen-AI, moot the need for code? Like the oft-cited example of -2,000 LOC? https://www.folklore.org/Negative_2000_Lines_Of_Code.html
Can Gen-AI do the (traditional, pre 2000s) role of quality assurance? Identify unnecessary or unneeded work? Tie functionality back to requirements? Verify the goal has been satisfied?
Not yet, for sure. But I guess it's conceivable, provided sufficient training data. Is there sufficient training data?
You wrote:
> only focus on adding new features
Yup.
Further, somewhere in the transition from shipping CDs to publishing services, I went from developing products to just doing IT & data processing.
The code I write today (in anger) has a shorter shelf-life, creates much less value, is barely even worth the bother of creation much less validation.
Gen-AI can absolutely do all this @!#!$hit IT and data processing monkey motion.
During interviews one of my go-to examples of problem solving is a project I was able to kill during discovery, cancelling a client contract and sending everyone back to the drawing board.
Half of the people I've talked to do not understand why that might be a positive situation for everyone involved. I need to explain the benefit of having clients think you walk on water. They're still upset my example isn't heavy on any of the math they've memorized.
It feels like we're wondering how wise an AI can be in an era where wisdom and long-term thinking aren't really valued.
I would argue that a plurality, if not the majority, of business needs for software engineers do not need more than a single person with those skills. Better yet, there is already some executive that is extremely confident that they embody all three.
No, because if you read your SICP you will come across the aphorism that "programs must be written for people to read, and only incidentally for machines to execute." Relatedly is an idea I often quote against "low/no code tooling" that by the time you have an idea of what you want done specific enough for a computer to execute it, whatever symbols you use to express that idea -- be it through text, diagrams, special notation, sounds, etc. -- will be isomorphic to constructs in some programming language. Relatedly, Gerald Sussman once wrote that he sought a language in which to discuss ideas with his friends, both human and electronic.
Code is a notation, like mathematical notation and musical notation. It stands outside prose because it expresses an idea for a procedure to be done by machine, specific enough to be unambiguously executable by said machine. No matter how hard you proompt, there's always going to be some vagueness and nuance in your English-language expression of the idea. To nail down the procedure unambiguously, you have to evaluate the idea in terms of code (or a sufficiently code-like notation as makes no difference). Even if you are working with a human-level (or greater) intelligence, it will be much easier for you and it to discuss some algorithm in terms of code than in an English-language description, at least if your mutual goal is a runnable version of the algorithm. Gen-AI will just make our electronic friends worthy of being called people; we will still need a programming language to adequately share our ideas with them.
In the same way that we use AI to write resumés to be read by resumé-scanning AI, or where execs use AI to turn bullet points into a corporate email only for it to be summarised into bullet points by AI, perhaps we are entering the era where AI generates code that can only be read by an AI?
This sounds inefficient and untidy when the only human things left to do are to take up space and consume resources.
Removing the humans enables removing other legacy parts of the system, such as food production, which will free up resources for other uses. It also allows certain constraints to be relaxed, such as keeping the air breathable and the water drinkable.
Now tell that to your compiler, which turns instructions in a relatively high-level language into machine-language programs that no human will ever read.
AI is just the next logical stage in the same evolutionary journey. Your programs will be easier to read than they were, because they will be written in English. Your code, on the other hand, will matter as much as your compiler's x86 or ARM output does now: not at all, except in vanishingly-rare circumstances.
The one actual major downside to AI is that PM and higher are now looking for problems to solve with it. I haven't really seen this before a lot with technology, except when cloud first became a thing and maybe sometimes with Microsoft products.
Wasn't it like that always for most companies? Get to market fast, add features fast, sell them, add more features?
> Wasn't it like that always for most companies? Get to market fast, add features fast, sell them, add more features?
This reminds me of an old software engineering adage.
When they look at the calendar and it says May 2025 instead of April
I'm currently reading an LLM generated deletion. its hard to get an LLM to work with existing tools, but not impossible
LevelsIO's flight simulator sucked. But his payoff-to-effort ratio is so absurdly high, as a business type you have to be brain-dead to leave money on the table by refusing to try replicating his success.
They will not feel old because they will enter into bliss of Singularity(TM).
https://en.wikipedia.org/wiki/Technological_singularity
I think we'll be okay and likely better off.
They weren't fired; they weren't laid off; they weren't reassigned or demoted; they got attention and assistance from the CEO and guidance on what they needed to do to change and adapt while keeping their job and paycheck at the same time, with otherwise no disruption to their life at all for now.
Prosperity and wealth do not come for free. You are not owed anything. The world is not going to give you special treatment or handle you with care because you view yourself as an artisan. Those are rewards for people who keep up, not for those who resist change. It's always been that way. Just because you've so far been on the receiving end of prosperity doesn't mean you're owed that kind of easy life forever. Nobody else gets that kind of guarantee -- why should you?
The bottom line is the people in this article will be learning new skills one way or another. The only question is whether those are skills that adapt their existing career for an evolving world or whether those are skills that enable them to transition completely out of development and into a different sector entirely.
lol. I work with LLM outputs all day -- like it's my job to make the LLM do things -- and I probably speak to some LLM to answer a question for me between 10 and 100 times a day. They're kinda helpful for some programming tasks, but pretty bad at others. Any company that tried to mandate me to use an LLM would get kicked to the curb. That's not because I'm "not keeping up", it's because they're simply not good enough to put more work through.
If management is convinced of the benefits of LLMs and the workers are all just refusing to use them, the main problem seems to be a dysfunctional working environment. It's ultimately management's responsibility to work that out, but if the management isn't completely incompetent, people tasked with using them could do a lot to help the situation by testing and providing constructive feedback rather than making a stand by refusing to try and providing grand narratives about damaging the artistic integrity of something that has been commoditized from inception like video game art. I'm not saying that video game art can't be art, but it has existed in a commercial crunch culture since the 1970s.
The CEOs in question bought what they believed to be a power tool, but got what is more like a smarter copy machine. To be clear, copy machines are not useless, but they also aren't going to drive the 200% increases in productivity that people think they will.
But because management demands the 200% increase in productivity they were promised by the AI tools, all the artists and programmers on the team hear "stop doing anything interesting or novel, just copy what already exists". To be blunt, that's not the shit they signed up for, and it's going to result in a far worse product. Nobody wants slop.
Real knowledge here is often absend from the strongest AI prosletisers, others are more realistic about it. It still remains an awesome tool, but a limited one.
AIs today are not creative at all. They find statistical matches. They perform a different work than artists do.
But please, replace all your artwork with AI generated ones. I believe the forced "adapt" phase with that approach would realize itself rather quickly.
And that's enough to drive significant industry-wide change. Just because it can't fully automate everything doesn't mean companies aren't going to expect (and, indeed, increasingly require) their employees to learn how to effectively utilize the technology. The CEO of Shopify recently made it clear that refusal to learn to use AI tools will factor directly into performance evaluations for all staff. This is just the beginning. It's best to be wise and go where the puck is headed.
The article gives several examples of where these tools are used to rapidly accelerate experimentation, pitches, etc. Supposedly this is a bad thing and should be avoided because it's not sufficiently artisan, but no defensible argument was presented as to why these use cases are illegitimate.
In terms of writing code, we're entering an era where developers who have invested in learning how to utilize this technology are simply better and more valuable to companies than developers who have not. Naysayers will find all sorts of false ways to nitpick that statement, yet it remains true. Effective usage means knowing when (and when not) to use these tools -- and to what degree. It also, for now at least, means remaining a human expert about the craft at hand.
I'm adding `.noai` files to all the project going forward:
https://www.jetbrains.com/help/idea/disable-ai-assistant.htm...
AI may be somewhat useful for experienced devs but it is a catastrophe for inexperienced developers.
"That's OK, we only hire experienced developers."
Yes, and where do you suppose experienced developers come from?
Again and again in this AI arc I'm reminded of the magicians apprentice scene from fantasia.
Almost every time I hear this argument, I realize that people are not actually complaining about AI, but about how modern capitalism is going to use AI.
Don't get me wrong, it will take huge social upheaval to replace the current economic system.
But at least it's an honest assessment -- criticizing the humans that are using AI to replace workers, instead of criticizing AI itself -- even if you fear biting the hands that feed you.
... smells very similar to tobacco/soda industry. Both created faux-research institutes to further their causes.
Emissions aside, locally many data centres (and associated bit mining and AI clusters) are a significant local issue due to local demand on local water and local energy supplies.
Considering HPC is half CPU and half GPU (more like 66% CPU and 33% GPU but I'm being charitable here), I expect an average power draw of 3.6KW in a cluster. Moreover, most of these clusters run targeted jobs. Prototyping/trial runs use much limited resources.
On the other hand, AI farms use all these GPUs at full power almost 24/7, both for training new models and inference. Before you asking, if you have a GPU farm which you do training, having inference focused cards doesn't make sense, because you can divide nVIDIA cards with MIG, so you can put aside some training cards, divide these cards to 6-7 and run inference on them, resulting ~45 virtual cards for inference per server, again at ~6.1KW load.
So, yes, AI's power load profile is different.
You must be joking. Consumer models' primary source of training data seems to be the legal preambles from BDSM manuals.
This was pretty consistently my and many others viewpoint since 2023. We were assured many times over that this time it would be different. I found this unconvincing.
Something very similar can be said about the issue of guns in America. We live in a profoundly sick society where the airwaves fill our ears with fear, envy and hatred. The easy availability of guns might not have been a problem if it didn't intersect with a zero-sum economy.
Couple that with the unavailability of community and social supports and you have a a recipe for disaster.
I think you misunderstand OP's point. An employer saying "we only hire experienced developers [therefore worries about inexperienced developers being misled by AI are unlikely to manifest]" doesn't seem to realize that the AI is what makes inexperienced developers. In particular, using the AI to learn the craft will not allow prospective developers to learn the fundamentals that will help them understand when the AI is being unhelpful.
It's not so much to do with roles currently being performed by humans instead being performed by AI. It's that the experienced humans (engineers, doctors, lawyers, researchers, etc.) who can benefit the most from AI will eventually retire and the inexperienced humans who don't benefit much from AI will be shit outta luck because the adults in the room didn't think they'd need an actual education.
Strictly speaking, you don't even need university courses to get experienced devs.
There will always be individuals that enjoy coding and do so without any formal teaching. People like that will always be more effective at their job once employed, simply because they'll have just that much more experience from trying various stuff.
Not to discredit University degrees of course - the best devs will have gotten formal teaching and code in their free time.
You get experienced devs from inexperienced devs that get experience.
[edit: added "degrees" as intended. University was mentioned as the context of their observation]
This is honestly not my experience with self taught programmers. They can produce excellent code in a vacuum but they often lack a ton of foundational stuff
In a past job, I had to untangle a massive nested loop structure written by a self taught dev, which did work but ran extremely slowly
He was very confused and asked me to explain why my code ran fast, his ran slow, because "it was the same number of loops"
I tried to explain Big O, linear versus exponential complexity, etc, but he really didn't get it
But the company was very impressed by him and considered him our "rockstar" because he produced high volumes of code very quickly
My point is you don't know what you don't know. There is really only so far you can get by just noodling around on your own, at some point we have to learn from more experienced people to get to the next level
School is a much more consistent path to gain that knowledge than just diving in
It's not the only path, but it turns out that people like consistency
A senior dev mentioned a “class invariant” the other day And I just had no idea what that was because I’ve never been exposed to it… So I suppose the question I have is what should I be exposed to in order to know that? What else is there that I need to learn about software engineering that I don’t know that is similarly going to be embarrassing on the job if I don’t know it? I’ve got books like cracking the coding interview and software engineering at Google… But I am missing a huge gap because I was unable to finish my masters and computer science :-(
(Serious comment! It's "the" algorithms book).
I've read that one, not an expert by any means, and I've got a 'decent' handle on data structues, but what about the software engineering basics one needs like OOP vs. Functional, SOLID, interfaces, class invariants, class design, etc.? Should I just pick up any CS 101 textbook? Or any good MIT open courseware classes that cover this type of stuff (preferably with video lectures... intro to algorithms is _amazing_ they have Eric's classes uploaded to YouTube, but finding good resources to level-up as a SWE has proved somewhat challenging)
^ serious comment as well... I find myself "swimming" when I hear certain terms used in the field and I am trying to catch up a bit (esp. as an SRE with self-taught SRE skills that is supposed to know this stuff)
> This website contains nearly complete solutions to the bible textbook - Introduction to Algorithms Third Edition, published by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein.
But you have to know this knowledge exists in the first place. That's part of the appeal of university teaching: it makes you aware of many different paradigms. So the day you stumble on one of them you know where to look for a solution. And usually you learn how to read (and not to fear) reading scientific papers which can be useful. And statistics.
> Not to discredit University degrees of course - the best devs will have gotten formal teaching and code in their free time.
>People like that will always be more effective at their job once employed
My experience is that "self taught" people are passionate about solving the parts they consider fun but do not have the breadth to be as effective as most people who have formal training but less passion. The previous poster also called out real issues with this kind of developer (not understanding time complexity or how to fix things) that I have repeatedly seen in practice.
I just pointed out that removing classes entirely would still get you experiences people. Even if they'd likely be better if they code and get formal training. I stated that very plainly
You actually didn't state it very plainly at all. Your initial post is contradictory, look at these two statements side by side
> There will always be individuals that enjoy coding and do so without any formal teaching. People like that will always be more effective at their job once employed
> the best devs will have gotten formal teaching and code in their free time
People who enjoy coding without formal training -> more effective
People who enjoy coding and have formal training -> best devs
Anyways I get what you were trying to say, now. You just did not do a very good job of saying it imo. Sorry for the misunderstanding
> There will always be individuals that enjoy coding and do so without any formal teaching. People like that will always be more effective at their job once employed
As "people who enjoy coding and didn't need formal training to get started". It includes both people who have and don't have formal training.
Both statements together are (enthusiasm + formal) > (enthusiasm without formal) > (formal without enthusiasm).
> Both statements together are (enthusiasm + formal) > (enthusiasm without formal) > (formal without enthusiasm).
I don't think the last category (formal education without enthusiasm) really exists, I think it is a bit of a strawman being held up by people who are *~passionate~*
I suspect that without any enthusiasm, people will not make it through any kind of formal education program, in reality
If you've never encountered the average 9-5 dev that just does the least amount of effort they can get away with, then I have to apploud the HR departments of the companies you've worked for. Whatever they're doing, they're doing splendid work.
And almost all of my coworkers are university grads that do literally the same you've used as an example for non formally taught people: they write abysmally performing code because they often have an unreasonable fixation on practices like inversion of control (as a random example).
As a particularly hilarious example I've had to explain to such a developer that an includes check on a large list in a dynamic language such as JS performs abysmally
While you definitely loose acuity once you stop exploring new concepts in your free time, the amount of knowledge gained after you've already spend 10-20 yrs coding drops off a cliff, making this time investment in your free time progressively less essential.
My pint was that most of my coworkers never went through an enthusiastic phase in which they coded in their free time. Neither pre university nor during or after. And it's very easy noticeable that they're not particularly good at coding either.
Personally, I think it's just that people that are good at coding inevitably become enthusiastic enough to do it in their free time, at least for a few years. Hence the inverse is true: people that didn't go through such a phase (which most of my coworkers are)... Aren't very good at it. Wherever they went to university and got a degree or not.
Does it perform any better in statically compiled languages?
I just didn't want to explore the example to such a depth, as it felt irrelevant to me at the time of writing.
There are also different types of self taught, and different types of uni grad. You have people who love code, have a passion for learning, and that's driven them to gain a lot of experience. Then you have those who needed to make a living, and haven't really stretched beyond their wheelhouse so lack a lot of diverse experience. Both are totally fine and capable of some work, but you would have better luck with novel work from an experienced passionate coder. Uni trained or not.
I have not learned CS at university (maths & stats graduate who shifted to programming, because I can't help myself loving it). I work with engineers with CS degrees from pretty good universities. At the risk of sounding arrogant, I write better code then a lot of them (and some of them write code that it so clean and tight that I wish I could match it). Purely based on my fairly considerable experience, there is basically little correlation between degree and quality of code. There is non-trivial correlation between raw intelligence and the output. And there is a massive correlation between how much one cares about the quality of the work and the output.
We're talking about the industry responsible for ALL the growth of the largest economy in the history of the world. It's not the 1970s anymore. You can't just count on weirdos in basements to build an industry.
That's not the kind of experience companies look for though. Do you have a degree? How much time have you spent working for other companies? That's all that matters to them.
Large corporations will use AI to deliver low-quality software at high speed and high scale.
"Artisan" developers will continue to exist, but in much smaller numbers and they will mostly make a living by producing refined, high-quality custom software at a premium or on creative marketplaces. Think Etsy for software.
That's the world we are heading for, unless/until companies decide LLMs are ultimately not cost beneficial or overzealous use of them leads to a real hallucination induced catastrophe.
Also management: "I need you to play with AI and try to find a use for it"
Sounds like an "idea guy" rather than an art director or designer. I would do this exact same thing, but on royalty-free image websites, trying to get the right background or explanatory graphic for my finance powerpoints. Unsurprisingly, Microsoft now has AI "generating" such images for you, but it's much slower than what I could do flipping through those image sites.
Obviously some workers have a strong incentive to oppose adoption, because it may jeopardize their careers. Even if the capabilities are over-stated it can be a self-fulfilling prophecy as higher-ups choices may go. Union shops will try to stall it, but it's here to stay. You're in a globally competitive market.
With white-collar jobs the threat of AI feels more abstract and localized, and you still get talk about "creating new jobs", but when robots start coming off the assembly line people will demand UBI so fast it will make your head spin. Either that or they'll try to set fire to them or block them with unions, etc. Hard to say when because another effort like the CHIPS act could expedite things.
Goldman Sachs doesn't think so.
https://www.fortunebusinessinsights.com/humanoid-robots-mark...
https://finance.yahoo.com/news/humanoid-robot-market-researc...
https://www.mordorintelligence.com/industry-reports/robotics...
They don't even need to be humanoid is the thing.
If you start by replacing menial labor, there will be more unemployment but you’re not going to build the political will to do anything because those jobs were seen as “less than” and the political class will talk about how good and efficient it is that these jobs are gone.
You need to start by automating away “good jobs” that directly affect middle/upper class people. Jobs where people have extensive training and/or a “calling” to the field. Once lawyers, software engineers, doctors, executives, etc get smacked with widespread unemployment, the political class will take UBI much more seriously.
if you job consists of reading from a computer -> thinking -> entering things back into a computer, you're on the top of the list because you don't need to set up a bunch of new sensors and actuators. In other words… the easier it is to do your job remotely, the more likely it is you’ll get automated away
They'll use their clout—money, lobbying, and media influence—to lock in their advantage and keep decision-making within their circle.
In the end, this setup would just widen the gap, cementing power imbalances as AI continues to reshape everything. UBI will become the bare minimum to keep the masses sedated.
There's also the fact that "they" aren't all one and the same persons with the exact same worldview and interests.
Of course there is always the issue of “demand”—of keeping the factories humming, but when you are worth billions, your immediate subordinates are worth hundreds of millions, and all of their subordinates are worth a few million, maybe you come to a point where “lebensraum” becomes more valuable to you than another zero at the end of your balance?
When AI replaces the nerds (in progress), they become excess biomass. Not talking about a retarded hollywood-style apocalypse. Economic uncertainty is more than enough to suppress breeding in many populations. “not with a bang, but a whimper”
If you know any of “them”, you will know that “they” went to the same elite prep schools, live in the same cities, intermarry, etc. The “equality” nonsense is just a lie to numb the proles. In 2025 we have a full-blown hereditary nobility.
edit: answer to ianfeust6:
The West is not The World. There are over a billion Chinese, Indians, Africans…
Words mean things. Who said tree hugger? If you are an apex predator living in an increasingly cloudy tank, there is an obvious solution to the cloudyness.
Don't forget fertility rate is basically stagnant in the West and falling globally, so this seems like a waste of time considering most people just won't breed at all.
The West is not The World. There are over a billion Chinese, Indians, Africans…
Words mean things. Who said tree hugger? If you are an apex predator living in an increasingly cloudy tank, there is an obvious solution to the cloudyness.
You might say "but why not use just 1% of that GDP on making sure the rest of humanity lives in at least minimal comfort"? But clearly -- we already choose not to do that today. 1% of the GDP of the developed world would be more than enough to solve many horrifying problems in the developing world -- what we actually give is a far smaller fraction, and ultimately not enough.
No, the underlying format of "$LABOR_ISSUE can be solved by $CHANGE_JOB" comes from a place of politics, where a politician is trying to suggest they have a plan to somehow tackle a painful problem among their constituents, and that therefore they should be (re-)elected.
Then the politicians piled onto "coal-miners can learn to code" etc. because it was uniquely attractive, since:
1. No big capital expenditures, so they don't need to promise/explain how a new factory will get built.
2. The potential for remote work means constituents wouldn't need to sell their homes or move.
3. Participants wouldn't require multiple years of expensive formal schooling.
4. It had some "more money than you make now" appeal.
https://en.wikipedia.org/wiki/Learn_to_Code#Codecademy_and_C...
Forget these new taxes on Americans who buy Canadian hardwood, we can just supply logs from your eyes.
There is a bunch of programmers who like ai, but as the article shows, programmers are not the only people subjected to ai in the workplace. If you're an artist, you've taken a job that has crap pay and stability for the amount of training you put in, and the only reason you do it is because you like the actual content of the job (physically making art). There is obviously no upside to ai for those people, and this focus on the managers' or developers' perspective is myopic.
I think for the most part creatives will still line up for these gigs, because they care about contributing to the end products, not the amount of time they spend using Blender.
Re-read what I wrote. You repeated what I said.
> So: from the perspective of a large part of the workforce it is completely true and rational to say that ai at their job has mostly downsides.
For them, maybe.
It's very clear the "value" of the LLM generation is to churn out low-cost, low-quality garbage. We already outsourced stuff to Fivrr but now we can cut people out altogether. Producing "content" nobody wants.
I wrote a project where I'd initially hardcoded a menu hierarchy into its Rust. I wanted to pull that out into a config file so it could be altered, localized, etc without users having it and recompile the source. I opened a “menu.yaml” file, typed the name of the top-level menu, paused for a moment to sip coffee, and Zed popped up a suggested completion of the file which was syntactically correct and perfect for use as-is.
I honestly expected I’d spend an hour mechanically translating Rust to YAML and debugging the mistakes. It actually took about 10 seconds.
It’s also been freaking brilliant for writing docstrings explaining what the code I just manually wrote does.
I don't want to use AI to write my code, any more than I'd want it to solve my crossword. I sure like having it help with the repetitive gruntwork and boilerplate.
This is satire right?
I just spent a week fixing a concurrency bug in generated code. Yes, there were tests; I uncovered the bug when I realized the test was incorrect...
My strong advice, is to digest every line of generated code; don't let it run ahead of you.
Of course yeeting bad code into production with a poor review process is already a thing. But this will scale that bad code as now you have developers who will have grown up on it.
I think this is the biggest risk. You sometimes get stuck in a cycle in which you hope the AI can fix its own mistake, because you don’t want to expend the effort to understand what it wrote.
It’s pure laziness that occurs only because you didn’t write the code yourself in the first place.
At the same time, I find myself incredibly bored when typing out boilerplate code these days. It was one thing with Copilot, but tools like Cursor completely obviate the need.
We humans have way more context and intuition to rely on to implement business requirements in software than a machine does.
On the other hand, AI can be useful and can accelerate a bit some work.
I believe AI is a variation of this, except a library at least has a license.
1. productivity and quality is hard to measure
2. the codebase they are ruining is the same one I am working on.
We're supposed to have a process for dealing with this already, because developers can ruin a codebase without ai.
"more code faster" is not a good thing, it has never been a good thing
I'm not worried about pro AI workers ruining their codebases at their jobs
I'm worried about pro AI coworkers ruining my job by shitting up the codebases I have to work in
Pump the brakes there
You may have bought into some PMs idea of what we do, but I'm not buying it
As professional, employed software developers, the entire point of what we do is to provide value to our employers.
That isn't always by delivering features to users, it's certainly not always by delivering features faster
Why the hand-wringing? Well, for one thing, as a developer I still have to work on that code, fix the bugs in it, maintain it etc. You could say that this is a positive since AI slop would provide for endless job security for people who know how to clean up after it - and it's true, it does, but it's a very tedious and boring job.
But I'm not just a developer, either - I'm also a user, and thinking about how low the average software quality already is today, the prospect of it getting even worse across the board is very unpleasant.
And as for things taking care of themselves, I don't think they will. So long as companies can still ship something, it's "good enough", and cost-cutting will justify everything else. That's just how our economy works these days.
If a company fails to compete in the market and dies, there is no "autopsy" that goes in and realizes that it failed because of a chain-reaction of factors stemming from bad AI-slop code. And execs are so far removed from the code level, they don't know either, and their next company will do the same thing.
What you're likely to end up with is project managers and developers who do know the AI code sucks, and they'll be heeded by execs just as much they are now, which is to say not at all.
And when the bad AI-code-using devs apply to the next business whose execs are pro-AI because they're clueless, guess who they'll hire?
This is why when you hear people talk about how great it is at producing X, our takeaway should be "this person is not an expert at X, and their opinions can be disregarded"
They are telling on themselves that they are not experts at the thing they think the AI is doing a great job at
I'm playing devil's advocate somewhat here but it often seem like that there's a bunch of people on both sides using hella motivated reasoning because they have very strong feelings that developed early on in their exposure to AI.
AI is both terrible and wonderful. It's useless and some things and impressive at others. It will ruin whole sectors of the economy and upturn lives. It will get better and it is getting better so any limitations you currently observe are probably termporary. The net benefit for humanity may turn out to be positive or negative - it's too early to tell.
That's kind of my problem. I am saying that it mostly only appears impressive to people who don't know better
When people do know better it comes up short consistently
Most of the pro AI people I see are bullish about it on things they have no idea about, like non-technical CEOs insisting that it can create good code
I disagree with that part and I don't think this opinion can be sustained by anyone using it with any regularity in good faith
People can argue whether it's 70/30 or 30/70 or what domains it's more useful in than others but you are overstating the negative.
My hypothesis is that you are invested in the success of AI products somehow, financially or emotionally, and that leads you to be blind to their shortcomings
You keep using them whenever possible because you want them to be useful even though in reality their usefulness is really iffy
It's entirely possible we're both irrational to some degree. But that's irrelevant to answering the question at hand.
Do you claim you are using it regularly and in good faith - enough to honestly form a reliable view on its utility?
I would claim that I am using it in such a way. It would take more effort than I'm prepared to put in to provide evidence of this but please - ask away.
(for the record - I have no financial or professional involvement directly with AI. I simply find the technology fascinating and I use it daily - both playfully and for it's practical utility)
For a couple of week tryout period I tried to use it in my daily workflow pretty heavily. I came away with the impression that it is a neat toy, but not really ready to be a full time tool for me. The other evaluators agreed and our recommendation to our leadership was "This is not really ready for prime time and while it is impressive it probably isn't really worth the cost"
Anyways fast forward and we're getting AI usage OKRs now, being pushed down on us by non-technical leadership, and what I call "formerly technical" leadership. People who did tech 20 years ago but really don't know what working modern tech is like since they've been in management for too long
So yes. I'm definitely negatively biased, and I'm fine to admit that. I absolutely resent having this stuff forced down on me from leaders that are buying the hype despite being told it is probably not ready to be a daily driver
And I'm seeing the hype spreading through the company, being told by junior devs how amazing it is when I am still iffy on their abilities.
And the absolute worst is when I build a cool proof of concept in an afternoon and everyone is like "wow, AI let you do that so fast now!" and I'm like no, this is just what a good developer can build quickly
So yeah, I'm pretty negative on AI right now. I can still admit the tech itself is impressive, amazing even, and there is no doubt in my mind I could probably find some use for it daily
But I think it is going to be a disaster, because people cannot be trusted to use it responsibly
The social impact is going to be absolutely catastrophic. In some ways it already is
Edit: I am also not really sure why I am supposed to be enthusiastic about technology that business leaders are fairly transparently hoping will make my skillset redundant or at best will make me more productive but I will never realistically see a single extra dollar from the increased productivity
I mostly work solo. I use AI when it's either a) interesting or b); useful. Our experiences are very different and it's no wonder our emotional responses are also very different.
I'm envious that you work solo. I think that would change my perspective on a lot of things
Thanks for the good faith discussion, anyways
It's just a tool, but it is unfortunately a tool that is currently dominated by large-sized corporations, to serve Capitalism. So it's definitely going to be a net-negative.
Contrast that to something like 3D printing, which has most visibly benefited small companies and individual users.
I think AI is different. "Good enough" models are already available under generous licenses, fine-tuning and even training is within the reach of groups of volunteers etc etc
https://en.wikipedia.org/wiki/Gell-Mann_amnesia_effect
I’m in a Fortune 500 software company and we are also being pushed AI down our throats, even though so far it has only been useful for small development tasks. However our tolerance for incorrectness is much, much lower—and many skip levels are already realizing this.
I've got a refurb homelab server off PCSP with 512gb ram for <$1k, and I run decently good LLM models (Deepseek-r1:70b, llama3.3:70b). Given your username, you might even try pitching a GPU server to them as dual-purpose; LLM + hashcat. :)
But if your company buys a server to run it themselves, that security risk is not present.
Take out the word AI and replace it with any other tool that's over-hyped or over-used, and the above statement will apply to any organization.
I'm deeply influenced by languages like Forth and Lisp, where that kind of bottom up code is the cultural standard and and I prefer it, probably because I don't have the kind of linear intelligence and huge memory of an LLM.
For me the hardest part of using LLMs is knowing when to stop and think about the problem in earnest, before the AI generated code gets out of my human brain's capacity to encompass. If you think a bit about how AI still is limited to text as its white board and local memory, text which it generates linearly from top to bottom, even reasoning, it sort of becomes clear why it would struggle with genuine abstraction over problems. I'm no longer so naive as to say it won't happen one day, even soon, but so far its not there.
Any generated snippets I treat like StackOverflow answers: Copy, paste, test, rewrite, or for small snippets, I just type the relevant change myself.
Whenever I'm sceptical I will prompt stuff like "are you sure X exists?", or do a web search. Once I get my problem solved, I spend a bit of time to really understand the code, figure out what could be simplified, even silly stuff like parameters the model just set to the default value.
It's the only way of using LLMs for development I've found that works for me. I'd definitely say it speeds me up, though certainly not 10x. Compared to just being armed with Google, maybe 1.1x.
To pull this out of the games industry for just a moment, imagine this: you are a business and need a logo produced. Would you hire someone at the market price who uses AI to generate something... sort of on-brand they most definitely cannot provide indemnity cover for (considering how many of these dubiously owned works they produce), or would you pay above the market price to have an artist make a logo for you that is guaranteed to be their own work? The answer is clear - you'd cough up the premium. This is now happening on platforms like UpWork and Fiverr. The prices for real human work have not decreased; they have shot up significantly.
It's also happening slowly in games. The concept artists who are skilled command a higher salary than those who rely on AI. If you depend on image-generating AI to do your work, I don't think many game industry companies would hire you. Only the start-ups that lack experience in game production, perhaps. But that part of the industry has always existed - the one made of dreamy projects with no prospect of being produced. It's not worth paying much attention to, except if you're an investor. In which case, obviously it's a bad investment.
Besides, just as machine-translated game localization isn't accepted by any serious publisher (because it is awful and can cause real reputational damage), I doubt any evident AI art would be allowed into the final game. Every single piece of that will need to be produced by humans for the foreseeable future.
If AI truly can produce games or many of their components, these games will form the baseline quality of cheap game groups on the marketplaces, just like in the logo example above. The buyer will pay a premium for a quality, human product. Well, at least until AI can meaningfully surpass humans in creativity - the models we have now can only mimic and there isn't a clear way to make them surpass.
It's "hopeful" that the future of all culture will resemble food, where the majority have access to McDonalds type slop while the rich enjoy artisan culture?
Most people's purchasing power being reduced is a separate matter, more related to the eroding middle class and greedflation. Many things can be said about it, but they are less related to the trend I highlighted. Even if, supposing the middle class erosion continues, the scenario you suggest may very well play out.
>Most people's purchasing power being reduced is a separate matter, more related to the eroding middle class and greedflation.
Greedflation, is that where companies suddenly remember to be greedy again after years of forgetting they're allowed to be greedy, which happens by random chance to coincide exactly with periods of expansionary monetary and fiscal policy?
In that case, I welcome an alternative explanation for the human labor price increase on UpWork and Fiverr while AI work replaced work at the previous price level. The same is seen in the hiring of affected disciplines.
e.g.
You have tasks advertised in the distribution $1, $1, $1, $1, $1, $1, $2, $2, $3, $3, $5, $5, $10. Median price is $2, and average is $2.76.
All the $1 and $2 tasks are replaced with AI. Old tasks get $1 cheaper each as there are more people that can do them. Now the distribution is $2, $2, $4, $4, $9. Median is $4, average is $4.2.
So you have made labour less valuable but the prices advertised go up because only the more expensive work now gets advertised.
It is still much better than when large numbers of people starved if it rained a bit in the wrong week.
The weather and its effect on the food supply was the preoccupation of 90% of the population 90% of the time for all of agricultural man's history (and pre-history) and hunting and gathering was even worse for quality of life.Only if companies value/recognize those real skills over that of the alternative, and even if they do, companies are pretty notorious for choosing whatever is cheapest/easiest (or perceived to be).
> Francis says their understanding of the AI-pusher’s outlook is that they see the entire game-making process as a problem, one that AI tech companies alone think they can solve. This is a sentiment they do not agree with.
This seems like a you problem...
I'm way woker than the average person but I have to admit encountering a singular 'they' breaks my concentration in a distracting way - there's definitely possible ambiguity.
But I'm confused by your sentence regardless of the gender terms. Did they notice the tomatoes in the Garden or in the greenhouse? This is just ambiguous wording in general.
- These are two different sentences, but they're separated with a comma. It should be a period, as it makes no grammatical sense with a comma unless you're trying to make it intentionally confusing.
- You would write "They both went into the greenhouse" if they both entered, or you would write "Y entered the greenhouse and noticed the ripe tomatoes."
- "Before entering the greenhouse, "Y"/"they both" noticed the ripe tomatoes in the Garden."
It's not a culture war until there's two sides, until a segment of the population throws a hissyfit because new ideas make them uncomfortable.
It was used whenever gender was ambiguous or needed to be protected. Now with people openly identifying as non-binary, there is not a more specific pronoun, that person doesn't consider themselves that gender. You would be referring to them as something that is not what they want to be called, and is not what their social circle refers to them as. It's confusing, especially if you know what to call them but choose not to because you're offended.
> I don't think it's unfair characterize this as an offensive move, waged by one side in a 'culture war', that was done without regard to collateral damage
I would wager, based on the disproportionate and melodramatic language, this has never actually affected you. But you are likely consuming media that tells you everyone is going to draw and quarter you if you mess up a pronoun. This is not the case. Trans people just move on, they're used to it. It literally happens all the time.
You can say that because you live in a privileged country where compelled speech is illegal.
https://www.eurasiareview.com/20062017-canada-law-makes-it-i...
>Trans people just move on, they're used to it. It literally happens all the time
Or they try to cancel you and get you fired
https://www.washingtontimes.com/news/2019/oct/15/i-was-fired...
https://www.newsweek.com/christian-teacher-says-she-was-fire...
Requiring to identify someone's gender when that person is anonymous is just pointless bigotry.