Firing programmers for AI will destroy everything

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.

> 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.

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.

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.

> 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.

I agree, and I also share your experience (guess I was a bit earlier with PHP).

I think what's left out though is that this is the experience of those who are really interested and for whom "it's not satisfying" to stay there.

As tech has turned into a money-maker, people aren't doing it for the satisfaction, they are doing it for the money. That appears to cause more corner cutting and less learning what's underneath instead of just doing the quickest fix that SO/LLM/whatever gives you.

> And then they backfill their knowledge over time.

If only. There are too many devs who've learnt to write JS or Python, and simply won't change. I've seen one case where someone ported an existing 20k C++ app to a browser app in the most unsuitable way with emscripten, where a 1100 lines of typescript do a much better job.

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.

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.

> 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.

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.

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.

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.

I wonder about this too - and also wonder what the difference of order is between the historical shifts you mention and the one we're seeing now (or will see soon).

Is it 10 times the "abstracting away complexity and understanding"? 100, 1000, [...]?

This seems important.

There must be some threshold beyond which (assuming most new developers are learning using these tools) fundamental ability to understand how the machine works and thus ability to "dive in and figure things out" when something goes wrong is pretty much completely lost.

> 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.

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.

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

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.

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.

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!

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

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.

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.

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.

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.

Have they bled users?

We gotta put AI Crypto in the Blockchain Metaverse!

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.

Amazingly, there is industry outside tech that uses software. We are an old school tangible goods manufacturing company. We use stacks of old grumbling equipment to do product verification tests, and LLMs to write the software that synchronizes them and interprets what they spit back out.

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.