The only people who would say this are people that don’t know programming.
LLMs are not going to replace software devs.
Wrong, this is also exactly what people selling LLMs to people who can’t code would say.
It’s this. When boards and non-tech savvy managers start making decisions based on a slick slide deck and a few visuals, enough will bite that people will be laid off. It’s already happening.
There may be a reckoning after, but wall street likes it when you cut too deep and then bounce back to the “right” (lower) headcount. Even if you’ve broken the company and they just don’t see the glide path.
It’s gonna happen. I hope it’s rare. I’d argue it’s already happening, but I doubt enough people see it underpinning recent lay offs (yet).
AI as a general concept probably will at some point. But LLMs have all but reached the end of the line and they’re not nearly smart enough.
LLMs have already reached the end of the line 🤔
I don’t believe that. At least from an implementation perspective we’re extremely early on, and I don’t see why the tech itself can’t be improved either.
Maybe it’s current iteration has hit a wall, but I don’t think anyone can really say what the future holds for it.
LLMs have been around since roughly 2016 2017 (comment below corrected me that Attention paper was 2017). While scaling the up has improved their performance/capabilities, there are fundamental limitations on the actual approach. Behind the scenes, LLMs (even multimodal ones like gpt4) are trying to predict what is most expected, while that can be powerful it means they can never innovate or be truth systems.
For years we used things like tf-idf to vectorize words, then embeddings, now transformers (supped up embeddings). Each approach has it limits, LLMs are no different. The results we see now are surprisingly good, but don’t overcome the baseline limitations in the underlying model.
I’m not trained in formal computer science, so I’m unable to evaluate the quality of this paper’s argument, but there’s a preprint out that claims to prove that current computing architectures will never be able to advance to AGI, and that rather than accelerating, improvements are only going to slow down due to the exponential increase in resources necessary for any incremental advancements (because it’s an NP-hard problem). That doesn’t prove LLMs are end of the line, but it does suggest that additional improvements are likely to be marginal.
we’re extremely early on
Oh really! The analysis has been established since the 80’s. Its so far from early on that statement is comical
I can see the statement in the same way word processing displaced secretaries.
There used to be two tiers in business. Those who wrote ideas/solutions and those who typed out those ideas into documents to be photocopied and faxed. Now the people who work on problems type their own words and email/slack/teams the information.
In the same way there are programmers who design and solve the problems, and then the coders who take those outlines and make it actually compile.
LLM will disrupt the programmers leaving the problem solvers.
There are still secretaries today. But there aren’t vast secretary pools in every business like 50 years ago.
It’ll have to improve a magnitude for that effect. Right now it’s basically an improved stack overflow.
…and only sometimes improved. And it’ll stop improving if people stop using Stack Overflow, since that’s one of the main places it’s mined for data.
There is no reason to believe that LLM will disrupt anyone any time soon. As it stands now the level of workmanship is absolutely terrible and there are more things to be done than anyone has enough labor to do. Making it so skilled professionals can do more literally just makes it so more companies can produce quality of work that is not complete garbage.
Juniors produce progressively more directly usable work with reason and autonomy and are the only way you develop seniors. As it stands LLM do nothing with autonomy and do much of the work they do wrong. Even with improvements they will in near term actually be a coworker. They remain something you a skilled person actually use like a wrench. In the hands of someone who knows nothing they are worth nothing. Thinking this will replace a segment of workers of any stripe is just wrong.
I wrote a comment about this several months ago on my old kbin.social account. That site is gone and I can’t seem to get a link to it, so I’m just going to repost it here since I feel it’s relevant. My kbin client doesn’t let me copy text posts directly, so I’ve had to use the Select feature of the android app switcher. Unfortunately, the comment didn’t emerge unscathed, and I lack the mental energy to fix it due to covid brain fog (EDIT: it appears that many uses of I were not preserved). The context of the old post was about layoffs, and it can be found here: https://kbin.earth/m/asklemmy@lemmy.ml/t/12147
I want to offer my perspective on the Al thing from the point of view of a senior individual contributor at a larger company. Management loves the idea, but there will be a lot of developers fixing auto-generated code full of bad practices and mysterious bugs at any company that tries to lean on it instead of good devs. A large language model has no concept of good or bad, and it has no logic. happily generate string- templated SQL queries that are ripe for SQL injection. I’ve had to fix this myself. Things get even worse when you have to deal with a shit language like Bash that is absolutely full of God awful footguns. Sometimes you have to use that wretched piece of trash language, and the scripts generated are horrific. Remember that time when Steam on Linux was effectively running rm -rf /* on people’s systems? I’ve had to fix that same type of issue multiple times at my workplace.
I think LLMs will genuinely transform parts of the software industry, but I absolutely do not think they’re going to stand in for competent developers in the near future. Maybe they can help junior developers who don’t have a good grasp on syntax and patterns and such. I’ve personally felt no need to use them, since spend about 95% of my time on architecture, testing, and documentation.
Now, do the higher-ups think the way that do? Absolutely not. I’ve had senior management ask me about how I’m using Al tooling, and they always seem so disappointed when I explain why I personally don’t feel the need for it and what feel its weaknesses are. Bossman sees it as a way to magically multiply IC efficiency for nothing, so absolutely agree that it’s likely playing a part in at least some of these layoffs.
Basically, I think LLMs can be helpful for some folks, but my experience is that the use of LLMs by junior developers absolutely increases the workload of senior developers. Senior developers using LLMs can experience a productivity bump, but only if they’re very critical of the output generated by the model. I am personally much faster just relying on traditional IDE auto complete, since I don’t have to change from “I’m writing code” mode to “I’m reviewing code mode.”
The one colleague using AI at my company produced (CUDA) code with lots of memory leaks that required two expert developers to fix. LLMs produce code based on vibes instead of following language syntax and proper coding practices. Maybe that would be ok in a more forgiving high level language, but I don’t trust them at all for low level languages.
The problem with this take is the assertion that LLMs are going to take the place of secretaries in your analogy. The reality is that replacing junior devs with LLMs is like replacing secretaries with a network of typewriter monkeys who throw sheets of paper at a drunk MBA who decides what gets faxed.
The one thing that LLMs have done for me is to make summarizing and correlating data in documents really easy. Take 20 docs of notes about a project and have it summarize where they are at so I can get up to speed quickly. Works surprisingly well. I haven’t had luck with code requests.
It’ll replace brain dead CEOs before it replaces programmers.
I’m pretty sure I could write a bot right now that just regurgitates pop science bullshit and how it relates to Line Go Up business philosophy.
Edit: did it, thanks ChatJippity
def main():
# Check if the correct number of arguments are provided
if len(sys.argv) != 2:
print("Usage: python script.py <PopScienceBS>")
sys.exit(1)
# Get the input from the command line
PopScienceBS = sys.argv[1]
# Assign the input variable to the output variable
LineGoUp = PopScienceBS
# Print the output
print(f"Line Go Up if we do: {LineGoUp}")
if __name__ == "__main__":
main()
if lineGoUp {
CollectUnearnedBonus()
} else {
FireSomePeople()
CollectUnearnedBonus()
}
I think we need to start a company and commence enshittification, pronto.
I know just enough about this to confirm that this statement is absolute horseshit
Sounds like the no-ops of a decade ago and cloud will remove the need for infrastructure engineers. 😂🤣😂🤣😂🤣😂😂😂🤣
It isn’t that AI will have replaced us in 24 months, it’s that we will be enslaved in 24 months. Or in the matrix. Etc.
I’ll take “things business people dont understand” for 100$.
No one hires software engineers to code. You’re hired to solve problems. All of this AI bullshit has 0 capability to solve your problems, because it can only spit out what it’s already stolen from seen somewhere else
I’ve worked with a few PMs over my 12 year career that think devs are really only there to code like trained monkeys.
I’m at the point where what I work on requires such a depth of knowledge that I just manage my own projects. Doesn’t help that my work’s PM team consistently brings in new hires only to toss them on the difficult projects no one else is willing to take. They see a project is doomed to fail so they put their least skilled and newest person on it so the seniors don’t suffer any failures.
Simplifying things to a level that is understandable for the PMs just leads to overlooked footguns. Trying to explain a small subset of the footguns just leads to them wildly misinterpreting what is going on, causing more work for me to sort out what terrible misconceptions they’ve blasted out to everyone else.
If you can’t actually be a reliable force multiplier, or even someone I can rely on to get accurate information from other teams, just get out of my way please.
Guys that are putting billions of dollars into their AI companies making grand claims about AI replacing everyone in two years. Whoda thunk it
