I don't care if LLMs are good at coding or bad at it (in my experience the answer is "it depends"). I don't care how good are they at anything else. What matters in the end is that this tech is not to empower a common person (although it could). It is not here to make our lives better, more worthwhile, more satisfying (it could do these as well). It is there to reduce our agency, to make it easier to fire us, to put us in even more precarious position, to suck even more wealth from those that have little to those that have a lot.
Yet what I see are pigs discussing the usefulness of bacon-making machine just because it also happens to be able to produce tasty soybean feed. They forget that it is not soybean feed that their owner bought this machine for, and that their owner expects a return from such investment.
How do you figure? 20 dollars/month is insanely cheap for what OpenAI/Anthropic/Google offer. That absolutely qualifies as "empowering a common person". It lowers barriers!
A lot of the anti-AI sentiment on HN concerns people losing their jobs. I don't think this will happen: programmers who know what they're doing are going to be way, way more effective at using AIs to generate code than others.
But even if it is true and we do see job losses in tech: are software devs really "in a precarious position"? Do they really qualify as "those that have little"? Seems like a fantasy to me. Computer programmers have done great over the past 30 years.
More broadly, anti-AI sentiment comes from people who dislike change. It's hard to argue someone out of that position. You're allowed to prefer stasis. But the world moves on and I think it's best to remain optimistic, keep an open mind, and make the most of it.
$20/month in return for measurable reductions in quality of life is not an amazing deal. It's "Heads I win, tails you lose."
Or maybe, if you're thinking of it as an enabler for a side hustle or some other project with a low probability of a high payoff, it can slightly more optimistically be regarded as a moderately expensive lottery ticket.
That's not pessimism; it's just a realistic understanding of how the tech industry actually works, informed by decades' worth of experience.
Can you share those studies? I'm pretty skeptical of this effect. I find that AI has made my job easier and less stressful.
In general, I think your atittude is not realistic, it's just general pessimism about the world ("everything new is bad") that is basically unfounded.
Block just laid off 40% of their company citing AI.
It's not because of pandemic overhiring, and if that were true, the layoffs in 2021-2022 would have handled it. It's 2026. The people getting laid off (on average) haven't worked at these companies since before the pandemic, they got hired in ~2023 (average tenure at a tech company is ~3 years).
It's not because of AI either. Nobody is replacing jobs with AI, AI can't do anyone's job.
It's not because of interest rates. People hired like crazy when interest rates were this high in the oughts.
It's because Elon Musk's Twitter purchase and subsequent management convinced every executive in tech that you can cut to the bone, fuck your product's quality completely, and be totally fine. It's not true, but the downsides come later and the cash influx comes now, so they're doing it anyway.
I agreed with you up to this point. Twitter largely operated in the red for its entire existence prior to his "restructuring" to make it leaner and profitable. In my opinion, twitter went to shit when the incentive for creating engagement switched from gaining social capital to gaining... erm... actual capital. The laissez-faire attitude about allowing fairly terrible behavior on there gave it a PR black eye that probably didn't help either in the eyes of advertisers.
If I had to guess what happened with Block (and that's what we're all doing, guessing): a CEO's job is to make the line go up, and saying you introduced tools to increase productivity with half the staff (especially if you're overstaffed) seems to me a pretty easy way to do that. I saw someone on here refer to it as "Vibe CEOing", which I think is pretty on point. Again, just my opinion/guess.
Because the company was being horribly run and over hired and "pivoted to blockchain" for no fucking reason.
> citing AI.
Because it's 2026 and they thought that would work to bullshit a few people about point one, which apparently it did.
Could be. It could also end up freeing us from every commercial dependency we have. Write your own OS, your own mail app, design your own machinery to farm with.
It’s here, so I don’t know where you’re going with “I’m unhappy this is happening and someone should do something”
I'm a small business owner, and AI has drastically increased my agency. I can do so much more - I've built so many internal tools and automated so many processes that allow me to spend my time on things I care about (both within the business but also spending time with my kids).
It is, fortunately, and unfortunately, the nature of a lot of technology to disempower some people while making lives better for others. The internet disempowered librarians.
It isn't, it just a matter of seeing ahead of the curve. Delegating stuff to AI and agents by necessity leads to atrophy of skills that are being delegated. Using AI to write code leads to reduced capability to write code (among people). Using AI for decision-making reduces capability for making decisions. Using AI for math reduces capability for doing math. Using AI to formulate opinions reduces capability to formulate opinions. Using AI to write summaries reduces capability to summarize. And so on. And, by nature, less capability means less agency.
Once men turned their thinking over to machines in the hope that this would set them free. But that only permitted other men with machines to enslave them
Not to mention utilizing AI for control, spying, invigilation and coercion. Do I need to explain how control is opposed to agency?
I used to use a bookkeeper, but I got Claude a QuickBooks API key and have had it doing my books since then. I give it the same inputs and it generates all the various journal entries, etc. that I need. The difference between using it and my bookkeeper is I can ask it all kinds of questions about why it's doing things and how bookkeeping conventions work. It's much better at explaining than my bookkeeper and also doesn't charge me by the hour to answer. I've learned more about bookkeeping in the past month than in my entire life prior - very much the opposite of skill atrophy.
Claude does a bunch of low-skill tasks in my business, like copying numbers from reports into different systems into a centralized Google Sheet. My muscle memory at running reports and pulling out the info I want has certainly atrophied, but who cares? It was a skill I used because I needed the outcome, not because the skill was useful.
You say that using AI reduces all these skills as though that's an unavoidable outcome over which people have no control, but it's not. You can mindlessly hand tasks off to AI, or you can engage with it as an expert and learn something. In many cases the former is fine. Before AI ever existed, you saw the same thing as people progressed in their careers. The investment banking analyst gets promoted a few times and suddenly her skill at making slide decks has atrophied, because she's delegating that to analysts. That's a desirable outcome, not a tragedy.
Less capability doesn't necessarily mean less agency. If you choose to delegate a task you don't want to do so you can focus on other things, then you are becoming less capable at that skill precisely because you are exercising agency.
Now in fairness I get that I am very lucky in that I have full control of when and how I use AI, while others are going to be forced to use it in order to keep up with peers. But that's the way technology has always been - people who decided they didn't want to move from a typewriter to a word processor couldn't keep up and got left behind. The world changes, and we're forced to adapt to it. You can't go back, but within the current technological paradigm there remains plenty of agency to be had.
Yeah, companies that develop and push this tech definitely have this in mind.
> I don’t know where you’re going with “I’m unhappy this is happening and someone should do something
I am not surprised because I didn't write anything like it.
> I am not surprised because I didn't write anything like it.
You're right, there was no "someone should do something" call to action in your original comment.
It could also end up freeing us from every commercial dependency we have. Write your own OS, your own mail app, design your own machinery to farm with.
edit: also the solution here isn't that every one writes their own software (based on open source code available on the internet no doubt) we just use that open source software, and people learn to code and improve it themselves instead of off-loading it to a machine
LLMs are bad at counting the number of rows in a spreadsheet. LLMs are great at "write a Python script that counts the number of rows in this spreadsheet".
It'll fail miserably at making it human-friendly though, and attempt to pilfer existing popular designs. If it builds a GUI, it's be a horrible mashup of Windows 7/8/10/11, various versions of OSX / MacOS, iOS, and Android. It won't 'get' the difference between desktop, laptop, mobile, or tablet. It might apply HIG rules, but that would end up with a clone at best.
In short, it would most likely make something technically passable but nightmareish to use.
Given a century the only unreasonable part is oneshotting with no details, context, or follow up questions. If you tell Linus Torvalds "write a python script that generates and OS", his response won't be the script, it'll be "who are you and how did you get into my house".
If you're expecting OSX, AI will certainly be able to make that and better "in the next 100 years". Though perhaps not oneshotting off something as vague as "make an OS" without followup questions about target architecture and desired features.
3 years ago LLMs couldn’t solve 7x8.
Now they’re building complex applications in one shot, solving previously unsolved math and science problems.
Heck, one company built a (prototype but functional) web browser
And you say it’s crazy that in the future it’ll be able to build a mail app or OS?
Another distraction is AGI that which is a danger to humanity- the only danger is people...
Simply put, no it is not.
But on the reverse, the first danger with AI is people.
Over the longer term it will look like this. The rich 'win' the world by using AI to enslave the rest of mankind and claim ownership over everything. This will suck and a lot of us will die.
The problem is this doesn't solve the greed that cause the problem in the first place. The world will still be limited in a resources of something which will end with the rich in a dick measuring contest and to win that contest they will put more and more power in AI and they connive and fight each other. Eventually the AI has enough power that it kills us all, intentionally or not.
We'll achieve nearly unlimited capability long before we solve the problem of unlimited greed and that will spell our end.
Ive worked in "AI" for 20 years, through 2 winters, and run an alignment shop and AIRT... The problem is people. People will use the problem as a scapegoat.
And walls between France and Germany were effective, until they weren't.
Hell, even the 'people' is the problem doesn't work well for things like Moloch problems. Which people? The problem can no longer be pointed at any individual but a super-organizational response. Once you have an issue that is abstracted from it's base components, then any agent capable of parsing the abstraction can be part of influencing it and becoming part of Moloch.
I think new jobs will be created because AI is always limited by hardware and its current capabilities. Businesses, in order to compete, want to do things their competitors aren't currently doing. Those business needs always go beyond the current technological capabilities until the tech catches up and then they lather, rinse, repeat.
With shrinking and aging population?
Complete bullshit.
The individual has never had as much ability to take on large projects as they do now. They’ve never been able to learn as easily as they can now.
>to make it easier to fire us
As of now, the technology increases productivity in the average user. The companies that take advantage of that and expand their offering will outperform the ones that simply replace workers and don’t expand or improve offerings.
More capable employees make companies more money in general. Productivity increases lead to richer societies and yes, even more jobs, just as it always has.
You could say this is the story of society, it makes us dependent on each other, reduces our agency, puts us in precarious positions (like WW2). But nobody would argue against society like that.
What happens here is that we become empowered by AI and gain some advantages which we immediately use and become dependent on, eventually not being able to function without them - like computers and even thermostats.
Does anyone think how would economy operate without thermostats? No fridges, no data centers, no engines... they all need thermostats. We have lost some freedom by depending on them. But also gained.
I was able to feel wool scarves made in europe from the middle ages. (In museum storage, under the guidance of a curator) They are a fundamentally different product than what is produced in woolen mills. A handmade (in the old traditiona) woolen scarf can be pulled through a ring, because it is so thin and fine. Not so for a modern mill-made scarf.
Another interesting thing is that we do not know how they made them so fine. The technique was never recorded or documented in detail, as it was passed down from parent to child. So the knowledge is actually lost forever.
Weavers in Kashmir work a similar level of quality, but their wool is different, their needs and techniques are different, so while we still have craftsman that can produce wool by hand, most of the traditions and techniques are lost.
Is it a tragedy? I go back and forth. Obviously the heritage fabrics are phenomenal and luxurious. Part of me wishes that the tradition could have been maintained through a luxury sector.
Automation is never a 1:1 improvement. It's not just about the speed or process. The process itself changes the product. I don't know where we will net out on software, and I do think the complaints are justified - but the Luddites were also justified. They were *Right*. Their whole argument was that the mills could not product fabric of the same quality. But being right is not enough.
I'm already seeing vibe-coded internal tools at an org I consult at saving employees hundreds of hours a month, because a non-technical person was empowered to build their own solution. It was a mess, and I stepped in to help optimize it, but I only optimized it partially, making it faster. I let it be the spaghetti mess it was for the most part - why? because it was making an impact already. The product was succeeding. And it was a fundamentally different product than what internal tools were 10 years ago.
And they also like to emphasise how long it takes for someone to become a master at a given trade.
Though, given Amodei and Altman’s behavior (along with the rest of the billionaire class) that shouldn’t be a surprise to anyone.
Eating raw chicken is risky even in Japan. There are cultures that eat raw chicken, pork, and other meat products by choice but it’s always a risk. There are outbreaks of serious food borne illness in Japan from raw chicken: https://pubmed.ncbi.nlm.nih.gov/18406474/
Survivor bias plays a role in glorifying the past.
With programming, we documented a lot of it, so it's unlikely to go the way of fine weaving. People will always be able to learn to think and be great programmers.
Maybe if the wool weavers had internet, they could have blogged, made youtube videos, and cataloged their profession so it could last Millenia.
Long term though, I’ve always wondered if the Amish turn out to be the only survivors.
Its entirely possible that old manufacturing methods produced things that are different, but I would be entirely surprised if they are entirely better overall. If the defining metric for scarves is how well they fit through rings, I am sure they would all be made so you could fit 3 through a ring if people were willing to pony up for that. If you look at a lot of old clothes, they are generally a lot heavier, but I am not sure I would really want to wear them, they look quite uncomfortable. I also think its wonderful that today you can get a set of clothes for a few hours of minimum wage work while in the past this was a major investment. You can also choose to pay thousands for a shirt if you wish, but from 10 feet away its going to be hard to tell the difference.
Looked up Pucci - looks like a designer that makes silk scarves. Silk is a totally different material. The Luddites were wool and cotton weavers.
Making wool thin enough for a meter long scarf to go thorough a ring requires the individual strand of wool to be very thin. Both making it thin and weaving that thin strand is the craft that was lost. Go look at wool yarn next time you are at a store and see how thin they can get it.
As for "Are they better?" Yes. Thinner wool is incredible, soft. High quality merino wool is one of the most expensive fabrics. Look up this brand "Made in Rosia Montana" if you are curious. It's not like what the Luddites made, but its as good as it gets in the modern world. Getting stuff from the Kashmir region is difficult - I got mine because I knew someone who ran a school in the area. Most "Cashmere" stuff in department stores is fake/chemically processed for fake softness which makes it nice but it doesn't last. Real quality wool lasts a lifetime. The chemically processed stuff is ok if you want to see how it "feels"
EDIT: also, wool is naturally waterproof! I can walk in the rain with my scarf from kashmir on my head, its pretty thin but absolutely no water goes through even in heavy rain. it has to do with the springiness of the fibers and its natural oils. I will stop nerding out on fibers now!!
This labeling tactic became pretty common and tries to build a narrative that software engineers are going away. Artisan coders, craftsmen,
First and foremost, wool craftsmen are not engineers (which doesn't make their work less valuable).
Second, most software engineers, especially not in FAANG-like companies, don't engineer a shit. My spouse worked at a large telecom company in US and employees with "software engineer" title were doing mechanical tasks following some scripts, like daily system reload - run the script, verify status, open a ticket for a sub-contractor if anything is wrong, support the contractor via the ticket system until it's resolved. To be fair, two of my close family members work in FAANG and say COVID over-hire created a similar landscape there too.
My point is, creating CRUD internal tools was not an engineering to begin with, it was a craft, matching most craftsmen features such as small-scale, bespoke work, hands-on practice, tacit knowledge, apprenticeship-like learning (even if it's SO or tutorial), iterative refinement, tool mastery, adaptation during build.
Now we're way past the point of no return.
At the moment, a single line of production code costs hundreds of dollars. I'm not talking about the bedrock of technology, like compilers, mysql, the Linux kernel, which represent hundreds of billions of value. I'm talking about the shitty code that powers Salesforce and ERP integration, Drupal modules, intranet customisation, insurance company call centre agent policy workflows, the thrice cursed apps that ship with cheap Chinese android phones, the putrid code to analyse our shopping loyalty card purchases and turn it into business insights.
All that code is shit, and it costs a fortune. Meanwhile regular people have no code. Even I run my life on almost no code, I have to use SaaS (like Gmail and Docs). If I want something like a financial analysis to be understood by my family I don't code it in python, I use Excel. I use whatever automation comes in my car. But once simple code becomes a process of thinking about what you want rather than knowing esoterica like calling conventions, allocation lifetimes etc, then we have made custom software accessible to billions of people, people who are clever and industrious.
So stay in your cathedral and illuminate your manuscript if you like, there is a need for excellent code, and tooling like Lean that can define what correct means, but let the people eat.
This is a rather extreme failure on their part. There’s nothing admirable about hoarding knowledge and forcing it to only be passed down in person.
I don’t see this as the Luddites being right at all because they were clearly incredibly wrong about their chosen method of knowledge storage. This was a highly predictable and preventable outcome. If we were talking about a company today that forgot how to manage their servers because they refused to document anything and only passed it down from person to person we wouldn’t be speaking in awe and wonder, we’d be rightly criticizing their terrible decision making.
That aside, every time I hear that knowledge has been lost forever it turns out to be an exaggeration from those trying to amplify the mystique of the past. If we wanted to make ultra-thin scarves we could do it. We could study those ultra thin museum pieces with our endless array of modern tools and then use our vast quantities of modern wool to experiment until we got something similar.
But you missed the reason why we wouldn’t want to: An ultra-thin scarf isn’t going to work as well as a thicker one for keeping someone warm. It will be less durable. It would be a fundamentally inferior product. It’s interesting to see as a museum antique that has to be treated with utmost delicacy, but not so much as a practical garment.
I have no doubt in the future there will be a class of vibe software and it will be known as distinctly lower quality than human understood software. I do think the example you describe is a good use of vibing. I also think tech orgs mandating 100% LLM code generation are short sighted and stupid.
A lot of this push for “slop” is downstream of our K shaped economy. Give the people more money and quality becomes a lot more important. Give them less, and you’re selling to their boss who is often insulated from the effects of low quality.
Mass production makes things accesible, and if the handmade product cannot compete relative to it, then it's clearly not that much better or some people would still pay that premium to keep it around.
With programming that's very much the case, nobody's gonna vibe code a self driving car stack or a production grade DBMS. Even the cheapest scarf still works as a scarf in 99.9% of use cases though, if maybe not for as long.
Before mechanised farming, the men were forced to spend all day in the fields.
Never again.
The software engineer who thinks they’d be happier working in a field is largely just a grass is always greener phenomenon. It turns out that for most people, they don’t like work whatever it is, because work is done not by choice but by necessity.
Nobody's really stopping you from studying agriculture and working in that proverbial field either.
Sounds like a tautology. If you deliberately hoard knowledge of course it’s going to be hard to obtain.
The difference between automating the creation of software and automating the creation of physical products is that software is everywhere. It is relied on for most tools and processes that keep our civilization alive. Cutting corners on that front, and deciding to entrust our collective future to tech bros and VC firms fiending for their next payout, seems like an incredibly dumb and risky proposition.
The Luddites were right in the sense that the social order had changed in a negative way. In a careless way.
In the same way that we look at America now that has effectively put a plutocracy in absolute control of the country, at the same time that there is going to be a massive devaluing of labour. Elon Musk likes to talk about the coming golden age of automation, but I hope Americans realize that unless they happen to be a billionaire, they will enjoy zero fruits of that advance. Quite contrary, plump yourself up to be Soylent Green because it turns out that giving a bunch of psychopaths/sociopaths absolute control of government isn't good for the average person.
>One of the claims of the Luddites is that quality would go down
Then people will choose the better quality items and it will be easy for them to compete? Right?
luddites were right
I get there are many things happening today that are frustrating or moving some element of human life in negative or ambiguous directions, but we really have to keep perspective on these things.
Nearly every problem today is a problem with a solution.
The feelings of panic we have that things are going wrong are useful signals to help guide and motivate us to implement those solutions, but we really must avoid letting the doomerism dominate. Just because we hear constant negative news doesn't mean things are lost. Doesn't even mean things are bad.
It just means we have been hearing a lot of negative news.
This is what it looks like for progress to not be monotonically increasing.
Most of what we do is programming is some small novel idea at high level and repeatable boilerplate at low level. A fair question is: why hasn’t the boilerplate been automated as libraries or other abstractions? LLMs are especially good at fuzzy abstracting repeatable code, and it’s simply not possible to get the same result from other manual methods.
I empathise because it is distressing to realise that most of value we provide is not in those lines of code but in that small innovation at the higher layer. No developer wants to hear that, they would like to think each lexicon is a creation from their soul.
If development velocity was truly an important factor in these businesses, we'd migrated away from that gang of four ass Java 8 codebase, given these poor souls offices, or at least cubicles to reduce the noise, we wouldn't make them spend 3 hours a day in ceremonial meetings.
The reason none of this happens is that even if these developers crank out code 10x faster, by the time it's made it past all the inertia and inefficiencies of the organization, the change is nearly imperceptible. Though the bill for the new office and the 2 year refactoring effort are much more tangible.
What that means is anyone’s guess, but it seems like it should result in a Cambrian explosion of disruptive new companies, limited in scope by the idea space.
The thing about small teams is, with a few exceptions, the biggest challenges are typically funnels for users and product-market fit, overcoming and exploitation of network effects, etc… so even in small orgs, if you make 30 percent of the problem 4x faster/smaller you still have the other 70 percent which is now 92.5% of the problem.
This applies even more acutely in larger organizations… so for them, 99 percent of the problem remains.
Intangibles in an organization like reluctance, education, and organizational inertia fill the gap left by software acceleration, and in the end you only see tiny gains, if any.
What really happened, on an organizational scale, is that software development costs went down. We wouldn’t expect a wage collapse in coding to foment an explosive revolution in company profitability or dynamism. We shouldn’t expect those things of LLM assistance.
We should look at it as a reduction in cost with potentially dangerous side effects if not managed carefully, with an especially big reduction in r&d development costs.
For example, we have to plan 8 to 12 sprints in advance. Full acceptance criteria, story points, and slotted into a sprint with points balanced across the team. Of course this is utterly useless, anything past the second sprint is going to be wrong, but they want it done. LLMs got me through that in a few hours instead of a few days.
Honestly you can probably use this as a means to measure the amount of regulation, graft, and corruption in an economy.
In a wild west free for all code velocity would likely be very fast with software popping up, changing rapidly, and some quickly disappearing.
But in an economy that doesn't care what you make, then who you pay or what laws you buy is far more important.
If the janitors swept the floors 10x faster, we wouldn't see any KPIs to shoot up from that. You still need it to happen regularly and reliably and on demand in case there's a mess, but it doesn't need to be fast.
I mean I'd expect that you'd see a decrease in the number of janitor staff as was convenient per the companies policies. That and janitorial services would turn into contracted services rather than in house positions. Oddly enough both these things have already occurred as tooling to janitors became better and the legal incentives made it cheaper.
Abstractions are the source of bloat. Without abstractions you can always reduce bloat, or you can reduce bloat in your glue, but you can't reduce glue.
It takes discipline to NOT create arbitrary function signatures and short-lived intermediate data structures or type definitions. This is the beginning of boilerplate.
So many advances in removing boilerplate are realizing your 5 function calls and 10 intermediate data structures or type definitions, essentially compute a thing that you can do with 0 function calls and 0 custom datatypes and less lines of code.
The abstraction hides how simple the thing you want is.
Problem is that all open source code looks like the bloat described above, so LLMs have no idea how to actually write code that is without boilerplate. The only place where I've seen it work is in shaders, which are usually written to avoid common pitfalls of abstraction.
LLMs are incapable of writing a big program in 1 function and 1 file, that does what you want. Splitting the program into functions or even multiple files, is a step you do after a lot of time, yet all open source looks nothing like that.
I don’t think I agree. Here is an example.
QTcpSocket socket; socket.connectToHost(QHostAddress::LocalHost, 1234);
Vs:
int clientSocket = socket(AF_INET, SOCK_STREAM, 0);
sockaddr_in serverAddr;
serverAddr.sin_family = AF_INET;
serverAddr.sin_port = htons(1234);
inet_pton(AF_INET, "127.0.0.1", &serverAddr.sin_addr);
connect(clientSocket, (sockaddr*)&serverAddr, sizeof(serverAddr))Of course it is not. It is needed, by definition.
> or doesn’t carry novelty.
Of course it does not. Why would a piece of code that simply fills a large C structure with constants be innovative?
> Every project is different and not everything can be made from a generic out-of-shelf product
Tangential to use of LLMs for boring boilerplate stuff.
from foundations import ConcreteStrip
ConcreteStrip(x,y,z)
Doesn't work for houses
It's weird to look at something that recent and think how dated it reads today. I also wrote about the Turing test as some major milestone of AI development, when in fact the general response to programs passing the Turing test was to shrug and minimize it
They have such obvious patterns and tells that humans have already picked up on them and they can eventually sus out that they're talking to an LLM
For instance I heard recently about someone talking (verbally) with an AI voiced customer support. They were very convinced, so they asked the support agent to calculate the product of two large numbers, and it replied with the result instantly
I would argue that fails the chinese room
To me, a function is a single sentence within a book. It may approach the larger picture, but that sentence can be reviewed, changed, switched around, killed by an editor.
Some programmers believe they're fantastic sentence writers. They brag about how good of a sentence they write, they're entire worldview has been built on being good sentence creators. Especially within enterprises, you may spend your entire life writing sentences without ever really understanding the whole book.
If your worldview has been built on sentence creation, and suddenly there's a sentence creator AI, you're going to be deathly afraid of it replacing you as a sentence writer.
This is not the same when it comes to books and music.
However, LLMs destroy this economic incentive utterly. It now seems most productive to code in fairly low level TypeScript and let the machines spew tons of garbage code for you.
Because our ways of programming computers are still woefully inadequate and rudimentary. This is why we have a tons of technique for code reuse, yet we keep reinventing the wheel because they shatter in contact with reality. OOP was supposed to save us all in the 1990s, we've seen how it went.
In other fields we've had a lot of time to figure out basic patterns and components that can be endlessly reused. Imagine if car manufacturers had to reinvent the screw, the piston, the gear and lubricants for every new car model.
One example that has bugged me for a decade is: we've been in the Internet era for decades at this point, yet we spend a lot of time reinventing communication. An average programmer can't spend two days without having to deal with JSON serialization, or connectivity, or sending notifications about the state of a process. What about adding authentication and authorization? There is a whole cottage industry to simplify something that should be, by now, almost as basic as multiplying two integers. Isn't that utter madness? It is a miracle we can build complex systems at all when we have to focus on this minutiae that pop up in every single application.
Now we have intelligences that can create code, using the same inadequate language of grunts and groans we use ourselves in our day to day.
The question is, at what point of progress will it benefit the software industry.
It feels like the era of standardization already came and went. It seems like product designers are now being deliberately obtuse so that their product quirks create lock-in and therefore more revenue. I expect that genAI will put fuel on this fire.
Probably the original sin here is that we started calling them programming languages instead of just 'computer code'.
Also - most of your work is far more than mere novelty! There are intangibles like your intellectual labor and time.
There is also the cost reason, somebody trying to sell an abstraction will try to monetize it and this means not everyone will want/be able to use it (or it will take forever/be unfinished if it's open/free).
There's also the platform lockin/competition aspect...
Because a lot of programmers don't know how to copy-paste or make packages for themselves? We have boilerplate at my work, which comprises of some ready made packages that we can drop in, and tweak as needed, no LLMs required
I also don't know what work you do, but I would not characterize the codebases I work in as "small bits of novelty" on boilerplate. Software engineering is always a holistic systems undertaking, where every subcomponent and the interactions between them have to be considered.
Sometimes it has. The amount of generated code that selected count(distinct id) from customers would produce is huge.
I still think LLMs as fancy autocomplete is the truth and not even a dig. Autocomplete is great. It works best when there’s one clear output desired (even if you don’t know exactly what it is yet). Nobody is surprised when you type “cal” and California comes up in an address form, why should we be surprised when you describe a program and the code is returned?
Knowledge has the same problem as cosmology, the part we can observe doesn’t seem to account for the vast majority of what we know us out there. Symbolic knowledge encompasses unfathomable multitudes and will eventually be solved by AI but the “dark matter” of knowledge that can’t easily be expressed in language or math is still out in the wild
LLM type systems are the final level of abstraction that lifts it up to literal natural language. Any dev with decent self awareness would admit they were just copying shit from stackoverflow half the time before LLMs anyway, high level languages and libraries just streamline that process with canonical implementations.
The value we provide is turning "person with problem" -> "person with solution to said problem" with as few caveats as possible. A programmer is that arrow, we solve problems. The more code we have to write to solve that problem, the worse we are at our job.
FORTRAN ("formula translator") was one of the first programs ever written and it was supposed to make coding obsolete. Scientists will now be able to just type in formulas and the computer will just calculate the result, imagine that!
Yes, it is. Literally every programming innovation claims to "make coding obsolete". I've seen a half dozen in my own lifetime.
The period is now. Just add "be a great teacher but don't attempt to write code" in the prompt.
(yes, it's a teacher who gets things wrong from time to time. You still need to refer to the source and ground truth just like when you're taught by a human teacher.)
I'm not sure if you ever had a teacher or instructor that you didn't trust, because they were a compulsive liar or addiction or any other issue. I didn't (as least not that I can remember) but I know I would be VERY on guard about it. I imagine I would consequently be quite stressed learning with them, even if they were brilliant, kind, etc.
It would feel a bit like walking on thin ice to get to a beautiful island. Sure, it's not infeasible and if you somehow make it, it might be worth the risk, but honestly wouldn't you prefer a slower boat?
I think that's actually deeply different. If a human keeps on apologizing because they are being caught in a lie, or just a mistake, you distrust them a LOT more. It's not normal to shrug off a problem then REPEAT it.
I imagine the cost of a mistake is exponential, not linear. So when somebody says "oops, you got me there!" I don't mistrust them just marginally more, I distrust them a LOT more and it will take a ton of effort, if even feasible, to get back to the initial level of trust.
I do not think it's at all equivalent to what "Real humans" do. Yes, we do mistake, but the humans you trust and want to partner with are precisely the one who are accountable when they make mistakes.
Unfortunately, individual people are not anywhere as reliable as a compiler for ensuring compliance to reality. We are particularly susceptible to flattery and other emotional manipulation, which LLMs frequently employ. This becomes particularly problematic when you ask for feedback on an idea.
In that case, a useful hack is to frame prompts as if you're an impartial observer and want help evaluating something, not as if the idea under evaluation is your own.
I think you can build a very easy workflow that reinforces rather than replaces learning, I've used a citation flow to link and put into practice a ton of more advanced programming techniques, that I found incredibly difficult to locate and research before AI.
I'd say the comparison is faulty, it's more akin to swimming to an island (no-ai) vs using a boat. You control the speed and direction of the boat, which also means you have the responsbility of directing it to the correct location.
PS: sorry if the analogy is a bit wonky but it's quite dear to me as I do ice skating on frozen lakes and it's basically a life or death information "game" that I can relate to. It might not be a great analogy for others.
I guess in my view - the main alternative you'd have beforehand is just to drown.
For me, AI sits in a space where if you know how to use it, it can tell you all the thin spots of the ice accurately. You can then verify those spots, but there's a level of personal responsibility of verification.
I'd agree there's currently a ton of people that are using these tools to essentially just find the specific route - but i'd argue those people probably shouldn't be skating in the first place, and would've fallen one way or the other.
Right, but AFAICT most people just venture over the ice and don't bother to check. In fact a lot of people venture there, do check once or twice, then check less and less frequently. The fact that you do it is great but others seem a lot less careful, until cracks start to show and then it might be too late.
I'd only argue that people were doing this before AI, slop development was just copy pasting from the first stack overflow issue that matched the question rather than thinking
So i'd argue there's a part of it that is just personal responsibility with how these tools are used
Before most who didn't know the ice didn't went out on it, today a lot of people who shouldn't be there go far out on the ice.
Maybe "Artisanal Coding" will be a thing in the future?
Programming via LLMs is just the logical conclusion to this niche of industrialized software development which favours quantity over quality. It's basically replacing human bots which translate specs written by architecture astronauts into code without having to think on their own.
And good riddance to that type of 'spec-in-code-out' type of programming, it should never have existed in the first place. Let the architecture astronauts go wild with LLMs implementing their ideas without having to bother human programmers who actually value their craft ;)
People still pay for hand-knit fabrics (there's one place in Italy that makes silk by hand and it costs 5 figures per foot), but the vast majority is machine made.
Same thing will happen to code, unless the bubble bursts really badly. Most bulk API Glue CRUD stuff and basic web UI work will be mostly automated and churned off automated agentic production lines.
But there will still be a market for that special human touch in code, most likely when you need safety/security or efficiency/speed.
Steve Gibson was hand-coding assembly (often beautifully and making for very compact binaries) long after almost everyone else had switched to C language or higher in abstraction. This is the closest analogy I can think of it.
It had it's own cult following, but I wouldn't say it was a massive movement.
Like you can still make Karelian pies[0] anywhere, but unless you follow the exact recipe, you can't sell them as "Karelian pies". It's good for the heritage and good for the customers.
You can also make any cheeses and wines and whatever you like, it's just how you name them and market them that's regulated.
But the comment you reply to explicitly points out the process is in fact relevant as it is itself a cultural artifact. You're not replying to their main point.
How are the customers hurt if their pie has not been baked by a babushka in Petrozavodsk using the old original recipe, but by an anonymous migrant worker in a dark kitchen using an optimized recipe if the end result is objectively the same? The packaging doesn't have to say who it was made by.
I also don't see the problem with the heritage. The comment I replied to already said anyone could call their pies Karelian, so there was no restriction that benefitted the residents of a specific region. I can see a PDO-like carveout that goes "we want to preserve the traditional pie-making of Karelia, so we want this activity to remain economically viable. Therefore, only pies baked in Karelia can be sold as Karelian pies." But I don't see how Sysco baking the same pies and distributing them nationwide helps maintain the heritage.
Even if you make something that tastes and looks exactly like the original, you still can't call it Specific Thing because the process wasn't followed as it's an integral part of the product. Think of it like a trademark. You can't create some brown sugary stuff and sell it as Coca-Cola - even if it tastes EXACTLY like it does.
Nothing about this is about profit or economic viability, it's not even a small part of the equation. The purpose is to preserve cultural heritage and not dilute it with shitty imitations calling themselves something they are not.
The issue is how this will be handled in law. Can the law define this in a way that is not overly strict or overly permissive? The current attempts is effectively the law doing this, but with an overly strict approach of what counts as 'objectively the same' by judging the process and not purely the outcome. Would it be possible to make the law's definition of this more permissive, to focus only on the product produced, without accidentally becoming overly permissive?
This is an absolute chef-kiss double-entendre.
[1] https://knowyourmeme.com/sensitive/memes/time-to-penis-ttp
We are only craftsmen to ourselves and each other. To anyone else we are factory workers producing widgets to sell. Once we accept this then there is little surprise that the factory owners want us using a tool that makes production faster, cheaper. I imagine that watchmakers were similarly dismayed when the automatic lathe was invented and they saw their craft being automated into mediocrity. Like watchmakers we can still produce crafted machines of elegance for the customers who want them. But most customers are just going to want a quartz.
I like Simon Willison's take on this: "Your job is to deliver code you have proven to work". If someone is spitting out LLM trash and shipping it, that means the job isn't being done properly. That can be done with and without an LLM.
Sure, no one really cares about the code but the quality of the code matters more for some products (and in different ways) than others.
It's certainly intellectually stimulating to create it, but I've learned to take joy in discarding vast swathes of it when it's no longer required.
I will just copy paste my comment from another thread but still very relevant>
Coding isn’t creative, it isn’t sexy, and almost nobody outside this bubble cares
Most of the world doesn’t care about “good code.” They care about “does it work, is it fast enough, is it cheap enough, and can we ship it before the competitor does?”
Beautiful architecture, perfect tests, elegant abstractions — those things feel deeply rewarding to the person who wrote them, but they’re invisible to users, to executives, and, let’s be honest, to the dating market.
Being able to refactor a monolith into pristine microservices will not make you more attractive on a date. What might is the salary that comes with the title “Senior Engineer at FAANG.” In that sense, many women (not all, but enough) relate to programmers the same way middle managers and VCs do: they’re perfectly happy to extract the economic value you produce while remaining indifferent to the craft itself. The code isn’t the turn-on; the direct deposit is.
That’s brutal to hear if you’ve spent years telling yourself that your intellectual passion is inherently admirable or sexy. It’s not. Outside our tribe it’s just a means to an end — same as accounting, law, or plumbing, just with worse dress code and better catering.
So when AI starts eating the parts of the job we insisted were “creative” and “irreplaceable,” the threat feels existential because the last remaining moat — the romantic story we told ourselves about why this profession is special — collapses. Turns out the scarcity was mostly the paycheck, not the poetry.
I’m not saying the work is meaningless or that system design and taste don’t matter. I’m saying we should stop pretending the act of writing software is inherently sexier or more artistically noble than any other high-paying skilled trade. It never was.
Your perspective is a path with only one logical end. That nothing you do or think or believe matters unless someone you're attracted to finds it attractive.
That is not how I or most others live. We take pride in and derive satisfaction from our accomplishments without the need for external validation.
Yeah, only I care whether the solution I found to a problem today was elegant, or whether my kitchen was pristine and well organized after I prepped for next week's lunches, but so what? I care and it injects more than enough meaning into my life to be worth it.
(Worked in Firefox on macOS, doesn't seem to work in Mobile Safari)
In the meantime the only way to really sort this is to have models that have only been trained on some particular kind of license - there is quite a big corpus of GPL'd code out there so a GPL based model could potentially be one of the first, and of course the output could only be GPL.
With the notable exception of Minecraft terrain generation, which I think most would say was successful in what it set out to achieve.
It's feels like a modern twist on a bygone time of the web.
Love it. Calling it "Copilot" in itself is a lie. Marketing speak to sell you an idea that doesn't exist. The idea is that you are still in control.
Guilty until proven innocent will satisfy the author's LLM-specific point of contention, but it is hardly a good principle.
He is proposing to not make a judgement at all. If the AI company CLAIMS something they have to prove it. Like they do in science or something. Any claim is treated as such, a claim. The trick is to not even claim anything, let the users all on their own come to the conclusion that it's magic. And it's true that LLMs by design cannot cite sources. Thus they cannot by design tell you if they made something up with disregard to it making sense or working, if they just copy and pasted it, something that either works or is crap, or if they somehow created something new that is fantastic.
All we ever see are the success stories. The success after the n-th try and tweaking of the prompt and the process of handling your agents the right way. The hidden cost is out there, barely hidden.
This ambiguity is benefitting the AI companies and they are exploiting it to the maximum. Going even as far as illegally obtaining pirated intellectual property from an entity that is banned in many countries on one end of their utilization pipeline and selling it as the biggest thing ever at the other end. And yes, all the doomsday stories of AI taking over the world are part of the marketing hype.
>AI output should be treated like a forgery
Who's passing this judgement this? Author? Civil society?
On a philosophical level I do not get the discussions about paintings. I love a painting for what it is not for being the first or the only one. An artist that paints something that I can't distinguish from a Van Gogh is a very skillful artist and the painting is very beautiful. Me labeling "authentic" it or not should not affect it's artistic value.
For a piece of code you might care about many things: correctness, maintainability, efficiency, etc. I don't care if someone wrote bad (or good) code by hand or uses LLM, it is still bad (or good code). Someone has to take the decision if the code fits the requirements, LLM, or software developer, and this will not go away.
> but also a specific geographic origin. There's a good reason for this.
Yes, but the "good reason" is more probably the desire of people to have monopolies and not change. Same as with the paintings, if the cheese is 99% the same I don't care if it was made in a region or not. Of course the region is happy because means more revenue for them, but not sure it is good.
> To stop the machines from lying, they have to cite their sources properly.
I would be curious how can this be applied to a human? Should we also cite all the courses, articles that we have read on a topic when we write code?
Even if you aren't in the group, there is clearly a group of people who appreciate seeing the original, the thing that modified our collective artistic trajectory.
Forgeries and master studies have a long history in art. Every classically trained worth their salt has a handful of forgeries under their belt. Remaking work that you enjoy helps you appreciate it further, understand the choices they made and get a better for feel how they wielded the medium. Though these forgeries are for learning and not intended to be pieces in their own right.
Generally you get a much better ‘view’ of the artwork in a museum. It’s higher ‘resolution’ you can view it from multiple angles etc.
There are some exceptions. You’re probably going to get a better look at the Mona Lisa online than if you try and see it at the Louvre.
I go to a museum to see a curated collection with explanations in a place that prevents distractions (I can't open a new tab) and going with people that might be interested to talk about what they see and feel. It's as well a social and personal experience on top information gathering.
> there is clearly a group of people who appreciate seeing the original,
There are many people interested in many things, do you want to say that "because some people think it is important, it must be important"? There were many people with really weird and despicable ideas along history and while I am neutral to this one, they definitely don't convince me just by their numbers.
> simply looking at a jpg.
Technically a jpg would not work because is lossy compression. But a png at the correct resolution might do the trick for some things (paintings that you see from far), but not for others. Museum have multiple objects that would be hard to put in an image (statues, clothes, bones, tables, etc.). You definitely can't put https://en.wikipedia.org/wiki/Comedian_(artwork) in a jpg - but the discussion surrounding it touches topics discussed here.
The problem with automated imitation generators is that they can produce thousands of painting that imitate Van Gogh, but does not have the same soul.
It is the same reason why these things cannot create genuinely funny jokes. They cannot assess the funnyness of the themselves. They cannot feel, and cannot do the filtering based on emotion.
It is easy to recognize the emptiness of a joke, but not so easy for a painting, or some other form of art.
This is why it will never work for art. But the sad thing is that that will not stop them from being used to create art. Because it just needs to sell.
I would say that for art, at least for most of the movies, music etc, this was already the case. So nothing much to lose.
Define soul, how about a legal/scientific description that accurately covers all bases?
The funny jokes thing is funny too, if someone told you a joke and you thought it was funny, then they told you it was from an LLM, would it stop being funny.
>if someone told you a joke and you thought it was funny, then they told you it was from an LLM, would it stop being funny.
No. It wouldn't. But they can't generate funny jokes.
I would be really happy if I am wrong, and it is possible to laugh all day by reading endless jokes from an LLM...
The value of a piece is definitely not completely tied to its physical attributes, but the story around it. The story is what creates its scarcity and generates the value.
It is similar for collectible items. If I had in my possession the original costume that Michael Jackson wore in thriller, I am sure I could sell it for thousands of dollars. I can also buy a copy for less than a hundred.
Same with luxury brands. Their price is not necessarily linked to their quality, but to the status they bring and the story they tell (i.e. wearing this transforms me into somebody important).
It can seem quite silly, but I think we are all doing it to some extent. While you said that a good forgery shouldn't affect one's opinion on the object (and I agree with you), what about AI-generated content? If I made a novel painting in the style of Van Gogh, you might find it beautiful. What if I told you I just prompted it and painted it? What if I just printed it? There are levels of involvement that we are all willing to accept differently.
There are a lot such artists who can do that after having seen Van Gogh's paintings before. Only Van Gogh (as far as we know) did paint those without having seen anything like it before - in other words, he had a new idea.
Should we also say "if you can implement Dijkstra's algorithm" it's irrelevant because "you did not have the idea"?
It's great to credit people that have an idea first. I fail to see how using an idea is that "bad" or "not worthy", ideas should be spread and used, not locked by the first one that had them (except some small time period maybe).
Yea this is the kind of BS and counter-productiveness that irrational radicals try to push the crowd towards.
The idea that one owns your observations of their work and can collect rent on it is absurd.
1. re-establishes mind mapping of the code base 2. separates out noise from signal. 3. makes it a breeze to refactor to reduce code complexity.
Notice anything yet?
If I replace the AI code input with my own curated techniques from known and measured code sources in long-term I save more time than those who rely upon AI vibe coding.
While LLMs are surely used to generate a lot of slop-code and overwhelm (open source) code bases, this surely isn't the only thing they can do. I dislike discussing the potential of a technology exclusively by looking at its negative impact.
LLMs in proper hands don't create code which is "stolen", they also shouldn't create unnecessary code and definitely don't remove any of the ownership of the programmer, at least not any more than using a mighty IDE does.
The problem seems to be in the usage of LLMs. These effects definitely do happen when just releasing an agent on a codebase without any oversight. But they can also largely be mitigated by using frameworks such as Openspec or Spec-Kit, properly designing a spec, plan, granular tasks and manually reviewing all code yourself. The LLM should not be responsible for any creative idea, it should at most verify the practicality against the codebase. When doing that, the entire creative control is in the hands of the programmer and so is the mechanical execution. The LLM is reduced to a very powerful autocomplete with a strict harness around it. Obviously this also doesn't lead to 10x or even 100x improvements in speed like some AI merchants promise, but in my personal experience the speedup is still significant enough to make LLMs a very, very useful technology.
btw you can make git commits with AI as author and you as commiter. Which makes git blame easier
What helped us most was treating outputs like untrusted input: retrieval + citations for factual claims, strict tool permissions, and an explicit “I don’t know” path that’s rewarded instead of punished. That doesn’t make LLMs truthful, but it does make them a lot less brittle.
So to me the key question isn’t “are models liars?” but “what product and engineering constraints make wrong answers cheap and detectable?”
A Private (system) Investigator. :)
In order to lie, one needs to understand what truth and objective reality are.
Even with people, when a flat-earther tells you the earth is flat, they're not lying, they're just wrong.
All LLM output is speculation. All speculation, by definition, has some probability of being incorrect.
---
We can go even deeper in a philosophical sense. If I made the audacious claim that 2 +2 = 4, I may think it's true, but I'm still speculating that the objective reality I experience is the same one others also experience, and that my senses and mental faculties, and therefore the qualia making up my reality, are indeed intact, correct, and functional. So is there a degree of speculation when I made that claim?
Regardless, I am able to agree upon a shared reality with the rest of the world, and I also share a common understanding of truth and untruth. If I lied, it can only be caused by an intention to mislead others. For example, if I claimed to be the president of the united states, of course that would be incorrect (thankfully!), but since we all agree that no one reading this post would actually be mislead into thinking I am the POTUS, then it isn't a lie. Perhaps sarcasm, a failed attempt at humor, or just trolling. it is untruth, but it isn't a lie, no one was mislead. You need intent (LLM isn't capable of one), and that intent needs to be at least in part, an intent to mislead.
I’ve seen reluctance to refactor even 10+-year-old garbage long before LLMs were first made available to the broader public.
I've seen a lot of 'fixes' for 10 year old 'garbage' that turned out to be regressions for important use cases that the author of the 'fix' wasn't aware of.
> If someone produces a painting in the style of Van Gogh, and passes it off as being made by Van Gogh, by putting his signature on it, that painting is a forgery.
Which is true. But the implication that follows is false.
Van Gogh's artwork is valuable specifically because of his identity. I find much of his artwork particularly hideous. That's fine! Someone else finds value in it specifically because of who wrote it.
This metaphor doesn't appear to apply to code at all. The entire value of code is what it does not who wrote it.
Honestly, I stopped reading after the first bullet point because these types of arguments feel lazy and the attitude of the people writing these articles frequently comes across as holier than thou.
You don't like LLMs? Great, don't use them. Using Van Gogh's paintbrush doesn't mean I'm making a forgery. I'm just painting, my friend.
I have accepted that reading 100% of the generated code is not possible.
I am attempting to find methods to allow for clean code to be generated none the less.
I am using extremely strict DDD architecture. Yes it is totally overkill for a one man project.
Now i only have to be intimate with 2 parts of the code:
* the public facade of the modules, which also happens to be the place where authorization is checked.
* the orchestrators, where multiple modules are tied together.
If the inners of the module are a little sloppy (code duplication and al), it is not really an issue, as these do not have an effect at a distance with the rest of the code.
I have to be on the lookout though. It happens that the agent tries to break the boundaries between the modules, cheating its way with stuff like direct SQL queries.
>What's the Excel of JSON
Ever heard of CUE that's compatible with JSON and YAML introduced by ex-Googlers? It seamlessly support both types and values, whereas Excel supports ephemeral values [1].
Both CUE and original Excel are non-Turing complete so they don't have the notorious and tricky halting problem.
Someone need to seamlessly integrate LLM with CUE, its NLP deterministic distant cousin based on lattice-valued logic [2],[3].
Truth be told LLM are like the automated loom machine during 19th CE Britain that kick started the industrial revolution. Heck the Toyota conglomerate was once the pioneer of the modern automated loom manufacturer, and looks where they are now after embracing change and pivoted to vehicle manufacturing.
The automated loom machine commoditize the manual looming industry (not unlike modern software engineering) to its oblivion in India, that turned the rich Moghul India with the highest GDP in the whole wide world into the lowest GDP for India during colonial time (include Indian sub-continent namely Afghanistan, Pakistan and Bangladesh here if you want apple to apple comparison) [4].
Ignore LLM at your peril in the name of so-called moral authenticity/forgery/lie/etc, and you can go the way of 20th CE India and its sub-continent, settling only at a fraction of its Moghul empire in term of GDP at its very peak.
> Is there a standard CRDT-like protocol for syncing editable graphs yet?
It's for other HN comments but spoiler alert it's called D4M by the nice folks from MIT [5]. We probably don't need full CRDT, local-first capability with eventual consistency will be more than suffice for most things that are of importance.
[1] CUE lang:
[2] The Logic of CUE:
https://cuelang.org/docs/concept/the-logic-of-cue/
[3] Guardrailing Intuition: Towards Reliable AI:
https://cue.dev/blog/guardrailing-intuition-towards-reliable...
[4] Economy of the Mughal Empire:
https://en.wikipedia.org/wiki/Economy_of_the_Mughal_Empire
[5] D4M: Dynamic Distributed Dimensional Data Model:
If a woman gets married at 25 and her kid is 25, how old is she?
This is what LLMs are dealing with. You dont tell them everything they need to know and they are left to fill in the gaps. Which may, and sometimes often means they lie.
That's what Agentic does differently, it'll go find the gaps before answering.
Agentic is AGI. You can hire many minimum wage workers who are generally intelligent who dont even go to that level.
That's pretty rude to say, at minimum.
Claude makes me mad: even when I ask for small code snippets to be improved, it increasingly starts to comment "what I could improve" in the code I stead of generating the embarrassingly easy code with the improvement itself.
If I point it to that by something like "include that yourself", it does a decent job.
That's so _L_azy.
If you don't have the copyright, then you can't license or litigate it under the common rules of software. If someone 'steals' it you can at best go after them with some trade secret case, and I suspect this would be limited if you had already shared the code with them, e.g. because they helped you synthesise it.
“Look at me and the code that took me eons to perfect. It’s handcrafted and genuine.”
Newsflash: nobody cares, especially if it’s expensive, time consuming, or doesn’t work. They also don’t care about “artisanal” PDO cheese with a 30% tariff that still tastes like shit.
“The posers are stealing our thunder. Forgery!! They terk r jerbs!”
Sink or swim. You decide.
A short design note and tribute to Richard Stallman (RMS) and St. IGNUcius for the term Pretend Intelligence (PI) and the ethic behind it: don’t overclaim, don’t over-trust, and don’t let marketing launder accountability.
https://github.com/SimHacker/moollm/blob/main/designs/PRETEN...
1. What PI Is
Richard Stallman proposes the term Pretend Intelligence (PI) for what the industry calls “AI”: systems that pretend to be intelligent and are marketed as worthy of trust. He uses it to push back on hype that asks people to trust these systems with their lives and control.
From his January 2026 talk at Georgia Tech (YouTube, event, LibreTech Collective):
https://www.youtube.com/watch?v=YDxPJs1EPS4
> "So I've come up with the term Pretend Intelligence. We could call it PI. And if we start saying this more often, we might help overcome this marketing hype campaign that wants people to trust those systems, and trust their lives and all their activities to the control of those systems and the big companies that develop and control them." — Richard Stallman, Georgia Tech, 2026-01-23. Source: YouTube (full talk) — "Dr. Richard Stallman @ Georgia Tech - 01-23-2026," Alex Jenkins, CC BY-ND 4.0; transcript in video description.
So PI is both a label (call it PI, not AI) and a stance: resist the campaign to make people trust and hand over control to systems and vendors that don’t deserve that trust. In MOOLLM we use the same framing: we find models useful when we don’t overclaim — advisory guidance, not a guarantee (see MOOAM.md §5.3).
[...]
Richard Stallman critiques AI, connected cars, smartphones, and DRM (slashdot.org) 42 points by MilnerRoute 38 days ago | hide | past | favorite | 10 comments
https://news.ycombinator.com/item?id=46757411
https://news.slashdot.org/story/26/01/25/1930244/richard-sta...
Gnu: Words to Avoid: Artificial Intelligence:
https://www.gnu.org/philosophy/words-to-avoid.html#Artificia...
...currently not responding... archive.org link:
https://web.archive.org/web/20260303004610/https://www.gnu.o...
The use of loaded and pejorative language like "forgery" emphasizes that this is not a logical argument, but a moral one. The repeated comparisons to "true craft" reveals the author would prefer that code be regarded like artisanal cheese.
Beyond the pretension, it's head in the sand to imply that the technology hasn't progressed. It's just very clearly not true to anyone who is paying attention - longer tasks, better code, less errors. I'm somebody who actively despises the hype bullshit-machine that SV has turned into, but technology is an industry for pragmatists that can leverage what works. And LLMs do.
If you don't like the technology, you have every right to scream that from the mountaintops. As it stands, this just serves as no more than a rallying cry to the ignorant.
No, you won't be rewarded magic beans for churning out crappy dashboards any more. But if you're serious about shipping quality, nothing is stopping you here.
Has this really been people's experience?
I develop and maintain several small FOSS projects, some of which are moderately popular (e.g. 90,000-user Thunderbird extension; a library with 850 stars on GitHub). So, I'm no superstar or in the center of attention but also not a tumbleweed. I've not received a single AI-slop pull request, so far.
Am I an exception to the rule? Or is this something that only happens for very "fashionable" projects?
Is it so hard to accept that humans are more capable of doing intricate and impressive work than any LLM will ever be?
This has upvotes?
Anyway, as I train people in LLMs/AI. I unapologetically will say "DONT LISTEN TO IT, IT LIES!" and send commands like "Try again, try harder"
No, it's simply untrue. Players only object against AI art assets. And only when they're painfully obvious. No one cares about how the code is written.
If you actually read the words used in Steam AI survey you'll know Steam has completely caved in for AI-gen code as well. It's specifically worded like this:
> content such as artwork, sound, narrative, localization, etc.
No 'code' or 'programming.'
If game players are the most anti-AI group then it's crystal clear that LLM coding is inevitable.
> This stands in stark contrast to code, which generally doesn't suffer from re-use at all, or may even benefit from it, if it's infrastructure.
Yeah, exactly. And LLM help developers save time from writing the same thing that has be done by other developers for a thousand times. I don't know how one can spins this as a bad thing.
> Classic procedural generation is noteworthy here as a precedent, which gamers were already familiar with, because by and large it has failed to deliver.
Spore is well acclaimed. Minecraft is literally the most sold game ever. The fact one developer fumbled it doesn't make the idea of procedural generation bad. This is a perfect example of that a tool isn't inherently good or bad. It's up to the tool's wielder.
Yes, this is a wildly uneducated perspective.
Procedural generation has often been a key component of some incredibly successful, and even iconic games going back decades. Elite is a canonical example here, with its galaxies being procedurally generated. Powermonger, from Bulldog, likewise used fractal generation for its maps.
More recently, the prevalence of procedurally generated rogue-likes and Metroidvanias is another point against. Granted, people have got a bit bored of these now, but that's because there were so many of them, not because they were unsuccessful or "failed to deliver".
And it's used to power effect where you might not expect it (Stardew Valley mines).
What procedural generation does NOT work at is generating "story elements" though perhaps even that can fall, Dwarf Fortress already does decently enough given that the player will fill in the blanks.
Apparently Stardew Valley's mines are not procedurally generated, but rather hand-crafted. Per their recent 10 year anniversary video, the developer did try to implement procedural generation for the mines, but ended up scrapping it:
https://www.stardewvalley.net/stardew-valley-10-year-anniver...
It’s not even in the same ballpark as Elite, NMS, terraria, or Minecraft.
The levels are all hand drawn, not generated by an algorithm, even if they’re shuffled. Eric Barone, the developer, has publicly said as much. Are you calling him a liar?
It’s like the difference between sudoku/crossword and conways game of life
It looks like the Super Mario Bros series has a good showing, but it is the first one. I bet 3 falls into an unlucky valley where the game-playing population was not quite as large as it is now, but it isn’t early enough to get the extreme nostalgia of the first one.
https://en.wikipedia.org/wiki/List_of_best-selling_video_gam...
Of course this assumes sales=popularity, but the latter is too hard to measure.
So procedural generation is extremely prevalent in most AAA games and has been for a long time.
Procedural generation is good when variety matters more than quality, which is a relatively rare occurrence.
Now with an LLM I could have AD&D-like campaigns, photorealistic renders of my character and the NPCs. I could give it the text of an AD&D campaign as a DM and have it generate walking and talking NOCs.
The art of those great fantasy artists is definitely being stolen in generated images, and application of VLMs should require payment into some sort of art funding pool. But modern artists could well profit by being the intermediary between user and VLM, crafting prompts, both visual and textual, to give a consistent look and feel to a game.
The essay author is smoking crack.
But this wouldn't make sense anyway. Game companies won't foot the bill for real-time renders of your character, let alone a world of generated NPCs. If/when costs are low enough, and players accept a recurring subscription to play games, then this could happen, sure. No way in hell will artists be available in real-time to keep the generated imagery consistent.
Even when I play a game like Expedition 33 or Elden Ring, my brain (for whatever reason) makes a solid split between the cutscene versions of the characters and the gameplay version. I mean, in some games the gameplay characters is a wandering murderer, while the cutscene characters have all sorts of moral compunctions about killing the big-bad. They are clearly different dudes.
In any case, I agree that gamers by and large don’t care to what extent the game creation was automated. They are happy to use automated enemies, automated allies, automated armies and pre-made cut scenes. Why would they stop short at automated code gen? I genuinely think 90% wouldn’t mind if humans are still in the loop but the product overall is better.
Yes. It is "wildly uneducated" to have, and express, strong opinions about ANY field of endeavour where you are unfamiliar with large parts of that field.
Elite is a bit more obscure, but really anybody who aims to be familiar with the history of games should recognize the name at least. Metroidvania isn't a game, but is a combination of the names of Metroid and Castlevania and you absolutely should know about both of those.
Powermonger is new to me.
And while the comment in question didn't mention it, others have: Minecraft. If you're not familiar with Minecraft you must be Rip Van Winkle. This should be the foremost game that comes to mind when anybody talks about procedural generation.
Before LLMs we did already have a way to "save developers time from writing the same thing that has been done by other developers for a thousand times", you know? A LLM doing the same thing the 1001st time is not code reuse. Code reuse is code reuse.
The whole history of programming tool is exploring how to properly reuse code: are functions or objects the fundamental unit of reuse? is diamond inheritance okay? should a language have an official package management? build system? should C++ std have network support? how about gui support? should editors implement their own parsers or rely on language server? And none of these questions has a clear answer after thousands if not millions of smart people attempted. (well perhaps except the function vs object one)
Electron is the ultimate effort of code reuse: we reuse the tens of thousands of human-years invested to make a markup-based render engine that covers 99% of use case. And everyone complains about it, the author of OP article included.
LLM-coding is not code reuse. It's more like throwing hands up and admitting humans are yet not smart enough to properly reuse code except for some well-defined low level cases like compiling C into different ISA. And I'm all for that.
(Many small dependencies can be avoided by letting the LLM just re-implememt the desired behavior; ~ with tradeoffs, of course)
The issue is orchestrating this local reuse into a coherent global codebase.
You didn't park your car in your driveway so anyone could take it to get groceries
If this is what I think it is, I consider it very lopsided view, failure to recognize what model fits for what case and looking at everything from a hammer point of view
Of course it's just my opinion.
See how it's a matter of what you're looking at?
https://news.ycombinator.com/item?id=47260385
You might have a more compelling argument if instead of syntax and semantics you contrasted semantics and pragmatics.
> Syntactic reuse would be macros
Well sure. My point is that what can be reused is decided ahead of time and encoded in the syntax. Whereas with LLMs it is not, and is encoded in the semantics.
> Pragmatics
Didn't know what that is. Consider my post updated with the better terms.
The industry has known how to reuse codes for two decades now (npm was released 16 years ago; pip 18 years ago). Using LLMs for code reuse is a step in the wrong direction, at least if you care about maintaining your code.
LLMs generate what you tell them to, which means it will be slop if you're careless and good if you're careful, just like programming in general.
This reminded me of a conversation about AI I had with an artist last year. She was furious and cursing and saying how awful it is for stealing from artists, but then admitted she uses it for writing descriptions and marketing posts to sell her art.
The world makes waay more sense when you really internalize that. It doesn't necessarily mean people are selfish, large groups often have aligned interests, but when an individuals interest alignment changes, then their group membership almost always changes too.
I'd bet she has a bunch of pirated content and anti-copyright remarks from the golden age of piracy as well.
There _have_ however been studies that show that this attitude is prevalent in (neoclassical) economics students and others who are exposed to (neoclassical) economic thinking: https://www.sciencedirect.com/science/article/abs/pii/S22148...
It's very effective propaganda. And we have a good example of it here. (Mot saying you're spreading it maliciously, but you are spreading it).
The big eye-opener for me in college was taking a class that put me up-close with artists and learning that there were, in the whole class, a grand total of two students who hadn't started doing 3D modeling on a cracked copy of Maya (and the two, if memory serves, learned on Blender).
I can rage against guns and gun manufacturers for their negative effects on our nation and hate when they are used for monstrous evil, but also believe that police should have firearms and that the second amendment is important. It’s a tool. You can hate the way it’s made and marketed, and hate many of its popular use cases, and still think there are acceptable ways to use and market it without requiring a total abolition.
To summarize this era we live in: my AI usage is justified but all the other people are generating slop.
[0]: https://www.youtube.com/watch?v=z8fFM6kjZUk
[1]: Disclaimer: I deeply respect Sinix as an art educator. If it weren't him I wouldn't have learnt digital painting. But it's still quite a weird take of him.
And the users may not care about code directly, but they definitely do indirectly. The less optimized and more off-the-shelf solutions have seen a stark decrease in performance but allowing game development to be more approachable.
LLMs saving engineers and developers time is an unfounded claim because immediate results does not mean net positive. Actually, I'd argue that any software engineer worth their salt knows intimately that more immediate results is usually at the expense of long term sustainability.
They fundamentally misunderstood what they were promising, it’s the same as making a pirate game where you never steer the ship or drop anchor.
You can prove people are not bored with the concept as new gamers still start playing fallout new Vegas or skyrim today despite them being old and janky.
It was really a combination of mini-games:
- you got steer a ship (or fleet of ships) around the Caribbean
- ship to ship combat
- fencing
- dancing (with the Governors' daughters)
- trading (from port to port or with captured goods0
- side quests
Each time I played it with my oldest, it felt like a brand new game.
https://en.wikipedia.org/wiki/Sid_Meier%27s_Pirates!
The same could be said about Hollywood movies and series.
When agenda is more important than fun, books, movies, games are not labour of love but neglet.
And yet "No Mans Sky" is massively popular.
> ny software engineer worth their salt knows intimately that more immediate results is usually at the expense of long term sustainability.
And any software engineer worth their salt realizes there are 100s if not 1000s of problems to be solved and trying to paint a broad picture of development is naive. You have only seen 1% (at best) of the current software development field and yet you're confidently saying that a tool that is being used by a large part of it isn't actually useful. You'd have to have a massive ego to be able to categorically tell thousands of other people that what they're doing is both wrong and not useful and that they things they are seeing aren't actually true.
I don't think it has anything to do with ego. There are studies on the topic of AI and productivity and I assume we have a way to go before we can say anything concretely. Software workflows permeate the industry you're in. You're putting words in my mouth, I said nothing about what people are doing is wrong or not useful. I said the claim that generative AI is making engineers more productive is an unfounded one. What code you shit out isn't where the work starts or ends. Using expedient solutions and having to face potentially more work in the future isn't even something that is a claim about software, I can make that claim about life.
You need to evaluate what you read rather than putting your own twist on what I've said.
> LLMs saving engineers and developers time is an unfounded claim
By whom exactly? If I say it saves me time, and another developer says the same, and so on, than it is categorically not unfounded. In fact, it's the opposite.
You've completely missed the point if you don't understand how telling other people that their own experience in such a large field is "unfounded" simply because it doesn't line up with your experience.
> we have a way to go before we can say anything concretely
No YOU do. It's quite apparent to me how it can save time in the myriad of things I need to perform as a software developer (and have been doing).
Household name game studios have had custom AI art asset tooling for a long time that can create art quickly, using their specific style.
AI is a tool and as Steve Jobs said, you can hold it wrong. It's like plastic surgery, you only notice the bad ones and object to them. An expert might detect the better jobs, but the regular folk don't know and for the most part don't care unless someone else tells them to care.
And then they go around blaming EVERYTHING as AI.
_Everyone_ (and their grandmother) can instantly tell a ChatGPT generated image, it has a very distinct style - and in my experience no amount of prompting will make it go away. Same for Grok and to a smaller degree Google's stuff.
What the industry needs (and uses) is something they can feed a, say, wall texture into and the AI workflow will produce a summer, winter and fall variant of that - in the exact style the specific game is using.
"So you hated the TV Series Ugly Betty then?"
"What? that's not CGI!"
This video is 15 years old
https://www.youtube.com/watch?v=rDjorAhcnbY
In that specific 15 year old example they're mostly composited, you're right about that.
https://www.youtube.com/watch?v=RxD6H3ri8RI
His Blender Conference talk about photogrammetry / camera projection / projection mapping was fantastic:
World Building in Blender - Ian Hubert
https://www.youtube.com/watch?v=whPWKecazgM
Actually if you look at the scene from Greys Anatomy [0:54] you can see where CGI is used to improve the scene (rather then cut costs), and you get this amazing scene of the Washington State Ferry crash.
I think you can see the parallels here. When people say they hate AI they are generally referring to the sloppy stuff it generates. It has enabled a proliferation of cheap slop. And with few exception it seems like generating cheap slop is all it does (these exception being specialized tools e.g. in image processing software).
Won 3 Primetime Emmys
52 wins & 124 nominations total
https://www.imdb.com/title/tt0805669/awards/
I guess it's just too lowbrow for you.
However, my counter examples included Grey’s Anatomy, Mad Max, and Titanic. None of these are considered high literature exactly (and all of them are award winning as well).
> That said, Steam's policy has been recently updated to exclude dev tools used for "efficiency gains", but which are not used to generate content presented to players.
I only quoted the first paragraph, but there is more.
I love procedural generation, and there is definitely a craft to it. Creating a process that generates a playable level or world is just very interesting to explore as an emergent system. I don't think LLMs will make these system more interesting by default. Of course there are still things to explore in this new space.
It's similar to generative/plotter art compared to a midjourney piece of slop. The craft that goes into creating the code for the plotter is what makes it interesting.
Its creature creator was, but as a game it was always mediocre to bad. They had to drop something like 90% of the features and extremely dumb down the stages to get it released.
It was also what introduced a lot of us to SecuROM DRM - it bricked my laptop in the middle of a semester.
I would overstate:
No one even cares how architecture is done. Unless you are the one fixing it or maintaining it.
Sorry, no one. We all know Apple did some great stuff with their code, but we care more about the awful work done on the UI, right? I mean - the UI seems to not be breaking in these new OSs which is amazing feature... for a game perhaps, and most likely the code is top notch. But we care about other things.
This is the reality, and the blind notion that so-many people care about code is super untrue. Perhaps someone putting money on developers care, but we have so many examples already of money put on implementation no matter what the code is. We can see everywhere funds thrown at obnoxious implementations, and particularly in large enterprises, that are only sustained by the weird ecosystem of white-collar jobs that sustains this impression.
Very few people care about the code in total, and this can be observed very easy, perhaps it can be proved no other way around is possible.
You say you don't care, but I bet you do when you're dealing with a problem caused by poor code quality or bad choices made by the developer.
Just like how most people don't care how well a bridge is designed... until it collapses.
and for what is worth - the reason of failure may not be because of particular nut, but the combination of them all.
the whole idea that most software is done in good faith is just plainly wrong, and most of the software we rely on a daily basis - all the enterprise bullshit - is very very very often not done in good faith, but rather just made, seamed, helped into some weird equilibrium of temporary performance.
perhaps hardware is done right more than software ever is.
Be careful of reading any viewpoint on the internet. Apparently no one used facebook or instagram and everyone boycotts anything with ai in it.
In reality i think you’d be foolish not to make use of the tools available. Arc Raiders did the right thing by completely ignoring those sorts of comments. There may be a market for 100% organic video games but there’s also a market for mainstream ‘uses every ai tool available’ type of games.
Spore was fun (IMHO) but at the time of release was considered a disappointment compared to its hype.
I can type up what I want much faster and be sure it's at least solving the right problem, even if it may have bugs.
There are also tools to generate boilerplate that work much much better than LLMs. And they're deterministic.
LLMs are physically incapable of generating something “well thought out”, because they are physically incapable of thinking.
You are just repeating that because you read that before somewhere else. Like a stochastic parrot. Quite ironic. ;)
This process cannot produce reasoning.
1) an LLM cannot represent the truth value of statements, only their likelihood of being found in its training data.
2) because it uses lexical data, an LLM will answer differently based on the names / terms used in a prompt.
Both of these facts contradict the idea that the LLM is reasoning, or "thinking".
This isn't really a very hit take either, I don't think I've talked to a single researcher who thinks that LLMs are thinking.
In fact, the more details I give it about a specific problem, the more it seems to hallucinate. Presumably because it is more outside the training set.
This reads like a skill issue on your end, in part at least in the prompting side.
It does take time to reach a point where you can prompt an LLM sufficiently well to get a correct answer in one shot, developing an intuitive understanding of what absolutely needs to be written out and what can be inferred by the model.
In the past 2 months I've been using all the SOTA models to help me design a new DSL for narrative scripting (such as game story telling) and a c# runtime implementation o the script player engine.
The language spec and design is about 95% authored by me up to this point; I have the LLMs work on the 2nd layer: the implementation specs/guidelines and the 3rd layer: concrete c# implementation.
Since it's a new language, I consider it's somewhat new/novel tasks for LLMs (at least, not like boilerplate stuff like HTTP API or CRUD service). I'd say, these LLMs have been very helpful - you can tell they sometimes get confused and have trouble to comply to the foreign language spec and design - but they are mostly smart enough to carry out the objectives, and they get better and better after the project got on track and has plenty of files/resources to read and reference.
And I'd also say "prompt better" is a important factor, just much more nuanced/complicated. I started with 0 experience with LLM agents and have learned a lot about how to tame them, and developed a protocol to collaborate with agents, these all comes from countless trial and errors, but in the end get boiled down to "prompt better".
I’ve never personally caught the language implementation bug. I appreciate your perspective here.
But I feel like the rationale would still stands: Considering LLMs' natures, common boilerplate tasks are easy because they can kind of just "decompress" from training data. But for a new language design, unless the language is almost identical to some other captured by the model, "decompression" would just fail.
And even semantic analysis is at least very similar in most PLs. Even DSLs. Assuming you're using concepts like variables and functions.
When it comes to codegen / interpreter runtimes, things start to diverge. But this also depends on the use case. More often than not a DSL is a one-to-one map to an existing language, with syntactic sugar on top.
I'm curious, what's the DSL you're working on?
The points you brought up all are valid. Lexer, parser and general concepts are not language-specific, yes, and I wasn't talking about how the implementation is different.
When I said "you can tell they sometimes get confused and have trouble to comply to the foreign language spec and design", I was thinking about the many times they just fail to write in my language even when provided will full language specs. LLMs don't "think" and boilerplate is easy for LLMs because highly similar syntax structure even identical code exist in their training data, they are kind of just copying stuff. But that doesn't work that well when they are tasked to write in a original language that is... too creative.
Sure, I can get the task done by delegating everything to an agentic workflow, but it just adds a bunch of useless overhead to my work.
I still need to know what the code does at the end of the day, so I can document it and reason about it. If I write the code myself, it's easy. If an LLM does it, it's a chore.
And even without those concerns, the LLM is still slower than me. Unless it's trivial boilerplate, in which case other tools serve me better and cheaper.
I'll note that a compiler is one of the most well understood and implemented software projects, much of it open source, which means the LLM has a lot of prior art that it can copy.
It's less "git gud; prompt better", and more, "be able to explain (well) what you want as the output". If someone messages the IT guy and says "hey my computer is broken" - what sort of helpful information can the IT guy offer beyond "turn it on and off again"?
Here are some well known names who are now saying they regularly use LLM's for development. For many of these folks, that wasn't true 1-2 years ago:
- Donald Knuth: https://www-cs-faculty.stanford.edu/%7Eknuth/papers/claude-c...
- Linus Torvalds: https://arstechnica.com/ai/2026/01/hobby-github-repo-shows-l...
- John Carmack: https://x.com/ID_AA_Carmack/status/1909311174845329874
My point being - some random guy on the internet says LLM's have never been useful for them and they only output garbage vs. some of the best engineers in the field using the same tools, and saying the exact opposite of what you are.
This is a huge overstatement that isn't supported by your own links.
- Donald Knuth: the link is him acknowledging someone else solved one of his open problems with Claude. Quote: "It seems that I’ll have to revise my opinions about “generative AI” one of these days."
- Linus Torvalds: used it to write a tool in Python because "I know more about analog filters—and that’s not saying much—than I do about python" and he doesn't care to learn. He's using it as a copy-paste replacement, not to write the kernel.
- John Carmack: he's literally just opining on what he thinks will happen in the future.
I read them all, and in none of them do any of the three say that they "regularly use LLMs for development".
Carmack is speculating about how the technology will develop. And Carmack has a vested interest in AI, so I would not put any value on this as an "engineers opinion".
Torvalds has vibe coded one visualizer for a hobby project. That's within what I might use to test out LLM output: simple, inconsequential, contained. There's no indication in that article that Linus is using LLMs for any serious development work.
Knuth is reporting about somebody else using LLMs for mathematical proofs. The domain of mathematical proofs is much more suitable for LLM work, because the LLM can be guided by checking the correctness of proofs.
And Knuth himself only used the partial proof sent in by someone else as inspiration for a handcrafted proof.
I don't mind arguing this case with you, but please don't fabricate facts. That's dishonest
1. Personal experience. Lazy prompting vs careful prompting.
2. They're coincidentally good at things I'm good at, and shit at things I don't understand.
3. Following from 2, when used by somebody who does understand a problem space which I do not, they easily succeed. That dog vibe coding games succeeded in getting claude to write games because his master knew a thing or two about it. I on the other hand have no game Dev experience, even almost no hobby experience with games specifically, so I struggle to get any game code that even remotely works.
> 2. They're coincidentally good at things I'm good at, and shit at things I don't understand.
This may well be! In the perfect world this would be balanced with the knowledge that maybe “the things you’re good at” are objectively* easier than “things you don’t understand”. Speaking for myself, I’m proficient in many more easy things than hard things.
*inasmuch as anything can be “objectively” easier
I think you can observe this in action by making vague requests, seeing how it does, then roll back that work and make a more precise request using relevant jargon and compare the results. For example, I asked claude to make a system that recommends files with similar tags. It gave me a recommender that just orders files by how many tags they had in common with the query file. This is the kind of solution that somebody may think up quick but it doesn't actually work great in practice. Then I reverted all of that and instead specified that it should use a vector space model with cosine similarity. It did pretty good but there was something subtly off. That is however about the limit of my expertise in this direction, so I tabbed over to a session with ChatGPT and discussed the problem on a high level for about 20 minutes, then asked ChatGPT to write up a single terse technically precise paragraph describing the problem. I told ChatGPT to use no bullet points and write no psuedocode, telling it the coding agent was already an expert in the codebase so let it worry about the coding. I give that paragraph to claude and suddenly it clicks, it bangs out a working solution without any drama. So I conclude the quality of the prompting determined the quality of the results.
The uncomfortable fact remains that one cannot really expect to get much better results from an LLM without putting some work themselves. They aren't magical oracles.
I am talking about using LLMs in general, not for boiler plate specifically.
My point about boilerplate is that I have tools that solve this for me already, and do it in a more predictable way.
Restaurant-goers only object against you spitting in their food if it's painfully obvious (i.e. they see you do it, or they taste it)
Players are buying your art. They are valuing it based on how you say you made it. They came down hard on asset-flipping shovelware before the rise of AI (where someone else made the art and you just shoved it together... and the combination didn't add up to much) and they come down hard on AI slop today, especially if you don't disclose it and you get caught.
That’s not what I remember, I remember PUBG being a viral hit that extensively used asset flipping.
Another example comes from Getting Over It with Bennett Foddy, which despite the fact it uses a lot of pre-bought art assets, the entire game has the indisputable hallmark of Bennett Foddy -- it has a ridiculously tricky control mechanism, and the whole game world you play in, should you make any mistakes, has a strong likelyhood of dropping you right back at the start, and it's all your own fault for not being able to recover from your mistakes under pressure. You can see this theme in his other games like QWOP and Baby Steps
And yet it also effectively ended Will Wright's career. Rave press reviews are not a good indicator of anything, really.
And you wouldn't really have any idea this was the case if you weren't there when it happened.
Personally, I do appreciate good localisation, Nintendo usually does a pretty impressive job there. I play games in their original language as long as I actually speak that language, so I don't have too many touch points with translations though.
My favourite example I saw was where Google translated an information page of the Italian branch of a large multinational as “this is the UK branch of [multinational]”, presumably because the LLM thought that was more contextually appropriate in English.
Do you ever ask why you're writing the same thing over and over again? That's literally the foundational piece of being an engineer; understanding when you're reinventing the wheel when there's a perfectly good wheel nearby.
With LLMs, the parameterisation goes into semantic space. This makes code more reusable.
A model trained on all of GitHub can reuse all that code regardless of whether they are syntactically reusable or not. This is semantic reuse, which is naturally much broader.
First, I am not arguing for reusability. Reusability is one of the most common mistakes you can make as a software engineer because you are over-generalizing what you need before you need it. Code should be written for your specific use case, and only generalized as problems appear. But if you can recognize that your specific use case fits a known problem, then you can find the best way to solve that problem, faster.
Second, when you're using an LLM to make your code more 'reusable' you are taking full responsibility for everything that LLM vomits out. You're no longer assembling a car from well known parts, taking care to tailor it to your use case as needed. You're now building everything in said car, from the tires to the engine and the rearview mirror.
Coding is a constant balance between understanding what you're solving for and what can solve it. Using LLMs takes the worst of both worlds, by offloading both your understanding of the problem and your understanding of the solution.
If you are anything above a mid level ticket taker, your responsibility exceeds what you personally write. When I was an “architect” responsible for the implementation and integration work of multiple teams at product companies - mostly startups - and now a tech lead in consulting, I’m responsible for knowing how a lot of code works that I further write and I’m the person called to carpet by the director/CTO then and the customer now.
I was responsible for what the more junior developers “vomit out”, the outside consulting company doing the Salesforce integration or god forbid for a little while the contractors in India. I no more cars about whether the LLM decided to use a for loop or while loop than I cared about the OSQL (not a typo) that the Salesforce consultants used. I care about does the resulting implementation meet the functional and non functional requirements.
On my latest two projects, I understand the customer from talking to sales before I started, I understand the business requirements from multiple calls with the customer, I understand the architecture because I designed it myself from the diagrams and 8 years of working with and (in a former life at AWS) and reviewing it with the customer.
As far as reusability? I’ve used the same base internal management web app across multiple clients.
I built it (with AI) for one client. Extracted the reusable parts and removed the client specific parts and deployed a demo internally (with AI) and modified it and added features (with AI) for another client. I haven’t done web development since 2002 seriously except a little copy paste work. I didn’t look at a line of code. I used AWS Cognito for authentication. I verified the database user permissions.
Absolutely no one in the value chain cares if the project was handcrafted or written by AI - as long as it was done on time, on budget and meets requirements.
Before the gatekeeping starts, I’ve been working for 30 years across 10 jobs and before that I was a hobbyist for a decade who started programming in 65C02 assembly in 1986.
My point is that the very act of training an LLM on any corpus of code, automatically makes all of that code reusable, in a much broader semantic way rather than through syntax. Because the LLM uses a compressed representation of all that code to generate the function you ask it to. It is like having an npm where it already has compressed the code specific to your situation (like you were saying) that you want to write.
People definitely do care. Nobody wants vibe-coded buggy slop code for their game.
They want well designed and optimized code that runs the game smoothly on reasonable hardware and without a bunch of bugs.
As proof, ask yourself which of the following two options you would prefer:
1. buggy code that was hand-written 2. optimized code that was vibe-coded
I'll bet most people will choose 2.
So I personally do care and I am someone, so the answer is not no one.
The gaming industry is absolutely overwhelmed with outrageously inefficient, garbage, crash-prone code. It has become the norm, and it has absolutely nothing to do with AI.
Like https://nee.lv/2021/02/28/How-I-cut-GTA-Online-loading-times.... That something so outrageously trash made it to a hundreds-of-million dollar game, cursing millions to 10+ minute waits, should shame everyone involved. It's actually completely normal in that industry. Trash code, thoughtless and lazily implemented, is the norm.
Most game studios would likely hugely improve their game, har har, if they leveraged AI a lot more.
People spin all kinds of things if they believe (accurately or not) that their livelihood is on the line. The knee-jerk "AI universally bad" movement seems just as absurd to me as the "AGI is already here" one.
> Spore is well acclaimed. Minecraft is literally the most sold game ever.
Counterpoint: Oblivion, one of the first high-profile games to use procedural terrain/landscape generation, seemed very soulless to me at the time.
As I see it, it's all a matter of how well it's executed. In the best case, a skilled artist uses automation to fill in mechanical rote work (in the same way that e.g. renaissance artists didn't make every single brushstroke of their masterpieces themselves).
In the worst (or maybe even average? time will tell) case, there are only minimal human-made artistic decisions flowing into a work and the output is a mediocre average of everything that's already been done before, which is then rightfully perceived as slop.
Is that even a counter point? Nobody in their right mind would ever claim that procedural generation is impossible to fuck up. The reason Minecraft/etc are good examples is because they prove procedural generation can work, not that it always works.
I might be misremembering but wasn't the Oblivion proc-gen entirely in the development process, not "live" in the game, which means...
> "In the best case, a skilled artist uses automation to fill in mechanical rote work"
...is what Bethesda did, no?
The problem with procedural generation is it's hard to make it as action-packed and desirable as WoW zones, and even those quickly become fly-over territory.