Firing programmers for AI is a mistake

> Why wouldn't this be the case for people using LLM like it was for everyone else?

I feel like it's a bit different this time because LLMs aren't just an abstraction.

To make an analogy: Ruby on Rails serves a similar role as highways—it's a quick path to get where you're going, but once you learn the major highways in a metro area you can very easily break out and explore and learn the surface streets.

LLMs are a GPS, not a highway. They tell you what to do and where to go, and if you follow them blindly you will not learn the layout of the city, you'll just learn how to use the GPS. I find myself unable to navigate a city by myself until I consciously force myself off of Google Maps, and I don't find that having used GPS directions gives me a leg up in understanding the city—I'm starting from scratch no matter how many GPS-assisted trips I've taken.

I think the analogy helps both in that the weaknesses in LLM coding are similar and also that it's not the end of the world. I don't need to know how to navigate most cities by memory, so most of the time Google Maps is exactly what I need. But I need to recognize that leaning on it too much for cities that I really do benefit from knowing by heart is a problem, and intentionally force myself to do it the old-fashioned way in those cases.

Difference here being that you actually learned the information about Ruby on Rails, whereas the modern programmer doesn't learn anything. They are but a clipboard-like vessel that passes information from an LLM onto a text editor, rarely ever actually reading and understanding the code. And if something doesn't work, they don't debug the code, they debug the LLM for not getting it right. The actual knowledge here never gets stored in the brain, making any future learning or evolving impossible.

I've had to work with developers that are over dependent on LLM's, one didn't even know how to undo code, they had to ask an LLM to undo. Almost as if the person is a zombie or something. It's scary to witness. And as soon as you ask them to explain their rationale for the solution they came up with - dead silence. They can't, because they never actually _thought_.

I think you're right; I can see it in the accelerating growth curve of my good Junior devs; I see grandOP's vision in my bad Junior devs. Optimistically, I think this gives more jr devs more runway to advance deeper into more sophisticated tech stacks. I think we're gonna need more SW devs, not fewer, as these tools get better: things that were previously impossible will be possible.

> "But it's not satisfying to stay at that level of ignorance very long"

It's not about satisfaction: it's literally dangerous and can bankrupt your employer, cause immense harm to your customers and people at home, and make you unhirable as an engineer.

Let's take your example of "an S3 avatar upload system", which you consider finished after writing 2 lines of code and a couple of packages installed. What makes sure this can't be abused by an attacker to DDOS your system, leading to massive bills from AWS? What happens after an attacker abuses this system and takes control of your machines? What makes sure those avatars are "safe-for-work" and legal to host in your S3 bucket?

People using LLMs and feeling all confident about it are the equivalent of hobby carpenters after watching a DIY video on YouTube and building a garden shed over the weekend. You're telling me they're now qualified to go build buildings and bridges?

> "It's like presuming that StackOverflow will keep you as a question-asker your whole life when nobody here would relate to that."

I meet people like this during job interviews all of the time, if I'm hiring for a position. Can't tell you how many people with 10+ years of industry experience I met recently that can't explain how to read data from a local file, from the machine's file system.

At present, LLMs are basically Stack Overflow with infinite answers on demand... of Stack Overflow quality and relevance. Prompting is the new Googling. It's a critical base skill, but it's not sufficient.

The models I've tried aren't that great at algorithm design. They're abysmal at generating highly specific, correct code (e.g. kernel drivers, consensus protocols, locking constructs.) They're good plumbers. A lot of programming is plumbing, so I'm happy to have the help, but they have trouble doing actual computer science.

And most relevantly, they currently don't scale to large codebases. They're not autonomous enough to pull a work item off the queue, make changes across a 100kloc codebase, debug and iterate, and submit a PR. But they can help a lot with each individual part of that workflow when focused, so we end up in the perverse situation where junior devs act as the machine's secretary, while the model does most of the actual programming.

So we end up de-skilling the junior devs, but the models still can't replace the principal devs and researchers, so where are the principal devs going to come from?

>Why wouldn't this be the case for people using LLM like it was for everyone else?

Because of the mode of interaction.

When you dive into a framework that provides a ton of scaffolding, and "backfill your knowledge over time" (guilty! using Nikola as a SSG has been my entry point to relearn modern CSS, for example), you're forced to proceed by creating your own loop of experimentation and research.

When you interact with an LLM, and use forums to figure out problems the LLM didn't successfully explain to you (about its own output), you're in chat mode the whole time. Even if people are willing to teach you to fish, they won't voluntarily start the lesson, because you haven't shown any interest in it. And the fish are all over the place - for now - so why would you want to learn?

>It's like presuming that StackOverflow will keep you as a question-asker your whole life when nobody here would relate to that.

Of course nobody on HN would relate to that first-hand. But as someone with extensive experience curating Stack Overflow, I can assure you I have seen it second-hand many times.

> But in reality pretty much anyone who enters software starts off cutting corners just to build things instead of working their way up from nand gates.

The article is right in a zoomed-in view (fundamental skills will be rare and essential), but in the big picture the critique in the comment is better (folks rarely start on nand gates). Programmers of the future will have less need to know code syntax the same way current programmers don't have to fuss with hardware-specific machine code.

The people who still do hardware-specific code, are they currently in demand? The marketplace is smaller, so results will vary and probably like the article suggests, be less satisfactory for the participant with the time-critical need or demand.

Great points. I see my journey from an offshore application support contractor to full time engineer and learning a lot along the way. Along the journey I've seen folks who held good/senior engineering roles just stagnated or moved to management role.

Industry is now large enough to have all sort of people. Growing, stagnating, moving out, moving in, laid off, retiring early, or just plain retiring etc.

I don't think the value of senior developers is so much in knowing how more things work, but rather that they've learnt (over many projects of increasing complexity) how to design and build larger more complex systems, and this knowledge mostly isn't documented for LLMs to learn from. An LLM can do the LLM thing and copy designs it has seen, but this is cargo-cult behavior - copy the surface form of something without understanding why it was built that way, and when a different design would have been better for a myriad of reasons.

This is really an issue for all jobs, not just software development, where there is a large planning and reasoning component. Most of the artifacts available to train an LLM on are the end result of reasoning, not the reasoning process themselves (the day by day, hour by hour, diary of the thought process of someone exercising their journeyman skills). As far as software is concerned, even the end result of reasoning is going to have very limited availability when it comes to large projects since there are relatively few large projects that are open source (things like Linux, gcc, etc). Most large software projects are commercial and proprietary.

This is really one of the major weaknesses of LLM-as-AGI, or LLM-as-human-worker-replacement - their lack of ability to learn on the job and pick up a skill for themselves as opposed to needing to have been pre-trained on it (with the corresponding need for training data). In-context learning is ephemeral and anyways no substitute for weight updates where new knowledge and capabilities have been integrated with existing knowledge into a consistent whole.

Just because there are these abstractions layers that happened in the past does not mean that it will continue to happen that way. For example, many no-code tools promised just that, but they never caught on.

I believe that there's a "optimal" level of abstraction, which, for the web, seems to be something like the modern web stack of HTML, JavaScript and some server-side language like Python, Ruby, Java, JavaScript.

Now, there might be tools that make a developer's life easier, like a nice IDE, debugging tools, linters, autocomplete and also LLMs to a certain degree (which, for me, still is a fancy autocomplete), but they are not abstraction layers in that sense.

> Modern web-dev comes around and now the old Java hands are annoyed that these new kids are just slamming NPM packages together and polyfills everywhere and no one understands Real Software Design.

The real issue here is that a lot of the modern tech stacks are crap, but won for other reasons, e.g. JavaScript is a terrible language but became popular because it was the only one available in browsers. Then you got a lot of people who knew JavaScript so they started putting it in places outside the browser because they didn't want to learn another language.

You get a similar story with Python. It's essentially a scripting language and poorly suited to large projects, but sometimes large projects start out as small ones, or people (especially e.g. mathematicians in machine learning) choose a language for their initial small projects and then lean on it again because it's what they know even when the project size exceeds what the language is suitable for.

To slay these beasts we need to get languages that are actually good in general but also good at the things that cause languages to become popular, e.g. to get something better than JavaScript to be able to run in browsers, and to make languages with good support for large projects to be easier to use for novices and small ones, so people don't keep starting out in a place they don't want to end up.

My son is a CS major right now, and since I've been programming my whole adult life, I've been keeping an eye on his curriculum. They do still teach CS majors from the "ground up" - he took system architecture, assembly language and operating systems classes. While I kind of get the sense that most of them memorize enough to pass the tests and get their degree, I have to believe that they end up retaining some of it.

And also these old C hands don't seem to get paid (significantly) more than a regular web-dev who doesn't care about hardware, memory, performance etc. Go figure.

The real hardcore experts should be writing libraries anyway, to fully take advantage of their expertise in a tiny niche and to amortize the cost of studying their subproblem across many projects. It has never been easier to get people to call your C library, right? As long as somebody can write the Python interface…

Numpy has delivered so many FLOPs for BLAS libraries to work on.

Does anyone really care if you call their optimized library from C or Python? It seems like a sophomoric concern.

Yea, every progeammer should write at least a cpu emulator in their language of choice, its such a undervalued exercise that will teach you so much about how stuff really works.

Notably, I don't think there was a mass disemployment of "old C hands". They just work on different things.

> as far as I can see it's the amount and quality of training data that counts

Well there's your reason. OS code is not as in demand or prevalent as crud web app code, so there's less relevant data to train your models on.

They'll probably go a step further and use an ORM instead of writing queries.

Since ORMs generally write crap unoptimized sql for all but the simplest of queries, this will lead to performance issues once things scale up.

> There will always be high demand for software engineers who actually know what they're doing

Unfortunately they won’t be found due to horrible tech interviews focused on “culture” (*-isms), leetcode under the gun, or resume thrown in trash at first sight from lack of full degree. AMHIK.

> I wouldn't want to be entering the job market in the next couple of years....

I bet there's a software dev employment boom about 5 years away once it becomes obvious competent people are needed to unwind and rework all the llm generated code.

I've found it very easy to end up "generating" yourself into a corner with a total mess with no clear control flow that ends up more convoluted than need be, by a mile.

If you're in mostly (or totally) unfamiliar territory, you can end up in a mess, fast.

I was playing around with writing a dead-simple websocket server in go the other evening and it generated some monstrosity with multiple channels (some unused?) and a tangle of goroutines etc.

Quite literally copying the example from Gorilla's source tree and making small changes would have gotten me 90% of the way there, instead I ended up with a mostly opaque pile of code that *looks good* from a distance, but is barely functional.

(This wasn't a serious exercise, I just wanted to see how "far" I could get with Copilot and minimal intervention)

In a similar situation at my workplace.

What models are you using that you feel comfortable trusting it to understand and operate on 10-20k LOC?

Using the latest and greatest from OpenAI, I've seen output become unreliable with as little as ~300 LOC on a pretty simple personal project. It will drop features as new ones are added, make obvious mistakes, refuse to follow instructions no matter how many different ways I try to tell it to fix a bug, etc.

Tried taking those 300 LOC (generated by o3-mini-high) to cursor and didn't fare much better with the variety of models it offers.

I haven't tried OpenAI's APIs yet - I think I read that they accommodate quite a bit more context than the web interface.

I do find OpenAI's web-based offerings extremely useful for generating short 50-200 LOC support scripts, generating boilerplate, creating short single-purpose functions, etc.

Anything beyond this just hasn't worked all that well for me. Maybe I just need better or different tools though?

Did you have to do any preparation steps before you asked from a model to do the large scale change or there were no steps involved? For example, did you simply ask for the change or did you give a model a chance to learn about the codebase. I am genuinely asking, I'm curious because I haven't had a chance to use those models at work.

Yeah, that would be very helpful to track. Anecdotally, I have found in my own projects that the larger they get the less I can lean on agent/chat models to generate new code that works (without needing enough tweaks that I may as well have just written it myself). Having been written with models does seem to help, but it doesn't get over the problem that eventually you run out of useful context window.

What I have seen is that autocomplete scales fine (and Cursor's autocomplete is amazing), but autocomplete supplements a software engineer, it doesn't replace them. So right now I can see a world where one engineer can do a lot more than before, but it's not clear that that will actually reduce engineering jobs in the long term as opposed to just creating a teller effect.

The question isn't whether the code is complex algorithmically, the question is whether the code is:

* Too large to fit in the useful context window of the model,

* Filled with bunch of warts and landmines, and

* Connected to external systems that are not self-documenting in the code.

Most stuff that most of us are working on meets all three of these criteria. Even microservices don't help, if anything they make things worse by pulling the necessary context outside of the code altogether.

And note that I'm not saying that the tools aren't useful, I'm saying that they're nowhere near good enough to be threatening to anyone's job.

Compute power increases and algorithmic efficiency improvements have been rapid and regular. I'm not sure why you thought that Back to the Future was a documentary film.

Why do you think this is a fundamental hurdle, rather than just one more problem that can be solved? I dont have strong evidence either way, but I've seen a lot of 'fundamental unsurmountable problems' fall by the wayside over the past few years. So I'm not sure we can be that confident that a problem like this, for which we have very good classic algorithms, is a fundamental issue.

This kind of error doesn't really matter in programming where the output can be verified with a feedback loop.

ChatGPT 4 was released 2 years ago. Personally I don't think things have moved on significantly since then.

We dont know what the human brain does, but we know it can produce symbolic theories or models of abstract worlds (in the case of math) or real worlds (in the case of science). It can also produce the "symbolic" turing machine which serves as an abstraction for all computation we use (cpu/gpu/etc)

Yes, but the LLM driven MVP-s are not only builerplates but actual functioning apps. The "create-" is somewhat good, but it's usually throwaway code and do it properly later. While my LLM made boilerplate is the actual first few steps to get the boring parts done. It also needs refactoring and polishing, but it's an order of magnitude better than the "MVP helper tooling" before.

How old are you? All it takes is one bad experience to show you the emperor has no clothes. Corporations, executives, and middle managers are almost by definition self interested and short sighted.

Just look at the over hiring during covid and the methods used to cull that workforce after they realized their mistake. Back handed and inhumane. Executives are more followers than a junior dev is. They just have a lot more terminology to obscure that fact. But they are basically professional bullshitters, like consultant firms.

This is excluding executives with vision. But the market and corporate structure bias towards eliminating those leaders as they are not consistently profitable over every month.

I'm happy you've only worked for altruistic, not-for-profit minded companies that care about employee growth and takes pride in their tach stack above all else. I have not had as fortunate an experience.

>I expect that once many begin to do this, there will be some who do use it for productivity and they will set the bar.

Yeah, probably. I've had companies so pinpointed on "velocoity" instead of quality. I imagine they will definitely try to expect triple the velocity just because one person "gets so much done". Not realizing how much of that illusion is correcting the submissions.

Theranos made bold claims too according to wikipedia Theranos claimed they devised diagnostic using blood tests that required very small amounts of blood. Then their PR machine made them raise $9 billion.

The thing is: how much

0.2x, 2x, 5x, 50x?

Doesn't sound like improving dramically.

It’s an emergent technology and no one knows for certain how far it can be pushed, not even mathematicians.

> he's not, just another delusional venture capitalist that hasn't bothered to look up the counter arguments to his point of view, done by mathematicians

Don’t hate on it, just spin up some startup with “ai” and LLM hype. Juice that lemon.

That's an assumption. Most/all neural network based tech faces a similar problem of exponentially diminishing returns. You get from 0 to 80 in no time. A bit of effort and you eventually ramp it up to 85, and it really seems the goal is imminent. Yet suddenly each percent, and then each fraction of a percent starts requiring exponentially more work. And then you can even get really fun things like you double your training time and suddenly the resultant software starts scoring worse on your metrics, usually due to overfitting.

And it seems, more or less, clear that the rate of change in the state of the art has already sharply decreased. So it's likely LLMs have already entered into this window.

But some say that Moore Law is dead :)

Anyway, the mumber of tiktok users coorelates with advancements in AI too!

Before tiktok the progress was slower, then when tiktok appeared it progressed as hell!

However, an increase in computing quality doesn't necessarily mean an increase in output quality, as you need compute power + data to train these models.

Just increasing compute power will increase the performance/training speed of these models, but you also need to increase the quality of the data that you are training these models on.

Maybe... the reason why these models show a high school level of understanding is because most of the data on the internet that these models have been trained on is of high school graduate quality.

Agreed, I often see AI discussed as if most companies wouldn't take 10x more developers if they could have them for free

>You can escape that rat race if you want to.

I'm working on it. But it takes money and the overlords definitely are trying to squeeze as of late.

And yes, while I don't think I'm being replaced in months or years, I can a possibility in a decade or two of the ladder being pulled up on most programming jobs. We'll either be treated as well as artists (assuming we still don't unionize) or we'll have to rely on our own abilities to generate value without corporate overlords.

Interesting. So it sounds like I need to get into the market and charge half as much and steal all of his customers.

These things eventually always end up as a Red Queen’s race, where you have to run as fast as you can to stay in the same place.

Groundbreakingness is different from the type of novelty that's relevant to an LLM. The script I was trying to write yesterday wasn't groundbreaking at all: it just needed to pull some code from a remote repository, edit a specific file to add a hash, then run a command. But it had to do that _within our custom build system_, and there's few examples of that, so our coding assistant couldn't figure out how to do it.

> Even new innovative products generally boil down to the same database fetches and CRUD glue and JSON parsing and front end form filling code.

The simplest version of that is some CGI code a PHP script. Which everyone should be writing according to your description. But why so many books have been written to be able to do this seemingly simple task? So many frameworks, so many patterns, so many methodologies....

I don't know man

It can't do anything in these random Phaser games I'm making and even translating my 10,000 line XNA game to Phaser. It is totally hopeless.

Phaser has been out forever now, and XNA used to be too.

Try Devin or OpenHands. OpenHands isn't quite ready for production, but it's informative on where things are going and to watch the LLM go off and "do stuff", kinda on its own, from my prompt (while I drink coffee).

I completely agree with Jevron's paradox being the right way to think about this. Much like ERP and HR software made it so you needed less back of office staff to accomplish the same task, but it allows these huge, multi-national companies to exist. I don't think these tens of thousands or hundreds of thousands employee companies would be possible without ERP and HR software.

I think another way of thinking about this is with low-code/no code tools. Another comment in this post said they never really took off and they didn't in the way some people expected. But a lot of large companies use them quite a bit for automating internal processes such as document/data aggregation and manipulation. JP Morgan has multiple job listings right now for RPA developers. Before this would needed to be done by actual developers.

I suspect (and hope) AI will follow a similar trajectory. I hope the future is exciting and we build new, more complex systems we can build that wasn't possible before due to lack of

People definitely said this about SO!

> It's all fun and games until you realise you can't run a consumer economy without consumers.

If the issue is that the AI can't code, then yes you shouldn't replace the programmers: not because they're good consumers, just because you still need programmers.

But if the AI can replace programmers, then it's strange to argue that programmers should still get employed just so they can get money to consume, even though they're obsolete. You seem to be arguing that jobs should never be eliminated due to technical advances, because that's removing a consumer from the market?

I don't think this is anyone's plan. It's the biggest argument against why it won't be the plan: who'll pay for all of it? Unless we can Factorio the world, it seems more likely we just won't do that.

It seems the consensus is that we will reach level 3 pretty quickly given the pace of development in the past 2 years. Not sure about 4 but I’d say in 10 years we’ll be there.

I’ve done freelance translating (not my day job) for 20 years. What you describe is true for certain types of specialized translations, particularly anything that is literary in nature. But that is a very small segment. The vast majority of translation work is commercial in nature and for that companies don’t care whether a human or machine did it.

How do they know that a human is doing the translation? What's to stop someone from just c&ping the text into an LLM, giving it a quick proofread, then sending it back to the client and saying "I translated this"?

Sounds like easy money, maybe I should get into the translation business.

Right. But it only takes 1 person, or maybe a handful, to formulate an idea that might take 100 people to implement. You will still need that one person but not the 100.

20 years as in real replacement, maybe. But change will be cca gradual if looking at whole market, even if composed of many smaller jumps. Top management of companies are now itching for that promised paradise of minimal IT with just few experts. Then comes inevitable sobering up, but the direction is clear.

I wouldn't be considering programming if choosing university studies now. With that smart, many other fields look more stable, albeit demand curve and how comfy later years of career looks like is very different (maybe lawyers, doctors, for blue collars some trades but look at long term health effects with ie back or knee issues).

We gotta put AI Crypto in the Blockchain Metaverse!

Have they bled users?

On the other hand, Instagram has been called one of the greatest acquisitions of all time only below the Apple/Next acquisition.

Really, that's what you're going with, arguing against the business acumen of the world's second richest person, and the only one at that scale with individual majority control over their company?

As for the Metaverse, it was always intended as a very long-term play which is very early to be judged, but as an owner of a Quest headset, it's already going great for me.

I'd be willing to bet that "planning to" means the plan is being executed.

https://www.msn.com/en-us/money/other/meta-starts-eliminatin...

> AI will also enable your competitors to cut costs.

which is why it puts pressure on your own company to cut costs

it's the same reason why nearly all US companies moved their manufacturing offshore; once some companies did it, everyone had to follow suit or be left behind due to higher costs than their competitors

If this works at all, they'll be telling AIs to start multiple companies and keeping the ones that work best.

But if that works, it won't take long for "starting companies" and "being a CEO" to look like comically dated anachronisms. Instead of visual and content slop we'll have a corporate stonk slop.

If ASI becomes a thing, it will be able to understand and manipulate the entirety of human culture - including economics and business - to create ends we can't imagine.

> Instead, those programmers will be starting their own companies and competing with you

If so, then why am I not seeing a lot of new companies starting while we're in this huge down-turn in the development world?

Or, is everyone like me and trying to start a business with only their savings, so not enough to hire people?

Just a paperclip maximizer, with all humans reduced to shareholders in the paperclip maximizer, and also possibly future paperclips.

Yep. It has the same rhyme of the worst case being 'wishes made by fools' too where they don't realize that they themselves don't truly know what to ask for, so getting exactly what they asked for ruins them.

Technomancer. AI is far more like the undead than like a deity, at least for now.

More important than cultural differences is timezone differences. Communication and collaboration is harder when you only have a couple hours of overlap between your working day and their working day. Much harder if you have no overlap at all. This isn't even a feature of outsourcing - it's a challenge for any globally distributed team.

> Now shipping it to production? That's something AI will struggle at, but humans also struggle at that :(

Why? Just because that's where the rubber hits the road? It's a different skillset but AI can do systems design too and probably direct a knowledgable but unpracticed implementer.

I'm sorry but I have to call BS.

>Salaries of the remaining people tend to go up when that happens.

You're telling me with a straight face that after a company replaces part of its workforce with tech/automation, salaries go up? Really? Please show me some data on that because every single graph I've ever seen of salaries must be wrong then. We've had an enormous amount of innovation and breakthroughs in the last decades, but weirdly enough all salaries remain stagnated. If this is true, they should be going up constantly every time we offshore some work or get more efficient technology.

The company can have 50000% growth and salaries will NOT go up. They basically never go up unless the companies want to retain an employee that's at risk of leaving and the replacement cost is high.

The objective of a company is to give money to its owners, nothing else. Salaries are viewed as a cost, so they will never willingly increase their costs unless it's absolutely necessary.

> And costs tend to go down for the general public.

Assuming there aren't monopolies involved and it's a commodity, yes, that sometimes happens. If there's any monopoly involved, unfortunately companies will simply pocket the difference.

The market, in its majestic equality, allows the rich as well as the poor to buy stocks, trade bitcoin, and to own property.

Do I really own Intel/Tesla/Microsoft by buying their stock? No I don't.

I can't influence anything on any of these companies unless I was already a billionaire with a real seat at the table.

Even on startups where, in theory, employees have some skin in the game, it's not really how it works is it? You still can't influence almost anything and you're susceptible to all the bad decisions the founders will make to appease investors.

Call me crazy but to say I own something, I have to at least be able to control some of it. Otherwise it's wishful thinking.