Guys that are putting billions of dollars into their AI companies making grand claims about AI replacing everyone in two years. Whoda thunk it
Cloud tech seller sells cloud
The only people who would say this are people that don’t know programming.
LLMs are not going to replace software devs.
I don’t know if you noticed but most of the people making decisions in the industry aren’t programmers, they’re MBAs.
Irrelevant, anyone who tries to replace their devs with LLMs will crash and burn. The lessons will be learned. But yes, many executives will make stupid ass decisions around this tech.
It’s really sad how even techheads ignore how rapidly LLM coding has come in the last 3 years and what that means in the long run.
Just look how rapidly voice recognition developed once Google started exploiting all of its users’ voice to text data. There was a point that industry experts stated ‘There will never be a general voice recognition system that is 90%+ across all languages and dialects.’ And google made one within 4 years.
The natural bounty of a no-salary programmer in a box is too great for this to ever stop being developed, and the people with the money only want more money, and not paying devs is something they’ve wanted since the coding industry literally started.
Yes its terrible now, but it is also in its infancy, like voice recognition in the late 90s it is a novelty with many hiccoughs. That won’t be the case for long and anyone who confidently thinks it can’t ever happen will be left without recourse when it does.
But that’s not even the worst part about all of this but I’m not going into black box code because all of you just argue stupid points when I do but just so you know, human programming will be a thing of the past outside of hobbyists and ultra secure systems within 20 years.
Maybe sooner
Maybe in 20 years. Maybe. But this article is quoting CEOs saying 2 years, which is bullshit.
I think it’s just as likely that in 20 years they’ll be crying because they scared enough people away from the career that there aren’t enough developers, when the magic GenAI that can write all code still doesn’t exist.
yeah 2 years is bullshit but with innovation, 10 years is still reasonable and fucking terrifying.
That’s not what was said. He specifically said coding.
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.
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. 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.
The “Attention Is All You Need” paper that birthed modern AI came out in 2017. Before Transformers, “LLMs” were pretty much just Markov chains and statistical language models.
You’re right, I thought that paper came out in 2016.
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
Transformers, the foundation of modern “AI”, was proposed in 2017. Whatever we called “AI” and “Machine Learning” before that was mostly convolutional networks inspired by the 80’s “Neocognitron”, which is nowhere near as impressive.
The most advanced thing a Convolutional network ever accomplished was DeepDream, and visual Generative AI has skyrocketed in the 10 years since then. Anyone looking at this situation who believes that we have hit bedrock is delusional.
From DeepDream to Midjourney in 10 years is incredible. The next 10 years are going to be very weird.
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.
“at some point” being like 400 years in the future? Sure.
Ok that’s probably a little bit of an exaggeration. 250 years.
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).
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.
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.
I’m saying that devs will use LLM’s in the same way they currently use word processing to send emails instead of handing hand written notes to a secretary to format, grammar/spell check, and type.
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
Iwere 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/12147I 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.”
Will there even be a path for junior level developers?
The same one they have now, perhaps with a steeper learning curve. The market for software developers is already saturated with disillusioned junior devs who attended a boot camp with promises of 6 figure salaries. Some of them did really well, but many others ran headlong into the fact that it takes a lot more passion than a boot camp to stand out as a junior dev.
From what I understand, it’s rough out there for junior devs in certain sectors.
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.
I was trying to use it to write a program in python for this macropad I bought and I have yet to get anything usable out of it. It got me closer than I would have been by myself and I don’t have a ton of coding experience so it’s problems are probably partially on me but everything it’s given me has required me to correct it to work.
I thought by this point everyone would know how computers work.
That, uh, did not happen.
Good take
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.
Nah, it’s built into the editors and repos these days.
?
If no one uses Stack Overflow anymore, then no one posts new answers. So AI has no new info to mine.
They are mining the IDE and GitHub.
Can I join anyone’s band of AI server farm raiders 24 months from now? Anyone forming a group? I will bring my meat bicycle.
It will be interesting to find out if these words will come back and haunt them.
- “I think there is a world market for maybe five computers”.
- “640K ought to be enough for anybody.”
I guess the programmers should start learning how to mine coal…
We will all be given old school Casio calculators a d sent to crunch numbers in the bitcoin mines.
I’m curious about what the “upskilling” is supposed to look like, and what’s meant by the statement that most execs won’t hire a developer without AI skills. Is the idea that everyone needs to know how to put ML models together and train them? Or is it just that everyone employable will need to be able to work with them? There’s a big difference.
I know how to purge one off of a system, does that count?
I’m going with the latter. Even my old college which is heavily focused on development is incorporating AI into the curriculum. Mainly because they’re all using it to solve their assignments anyway. Since it isn’t likely to go away and it’s a ‘tool’ they’ll have available when they hit the workforce they are allowing its use.
I’m not looking forward to seeing code written by some of these people in the wild. Most of the AI code I’ve seen is truly horrendous. I can’t imagine an entire business application of just strung together AI code being maintainable at all.
I’ll just leave this here cause this future reality is even worse since they likely don’t understand the code to begin with.
I’m going to call BS on that unless they are hiding some new models with huge context windows…
For anything that’s not boilerplate, you have to type more as a prompt to the AI than just writing it yourself.
Also, if you have a behaviour/variable that is common to something common, it will stubbornly refuse to do what you want.
Have you ever attempted to fill up one of those monster context windows up with useful context and then let the model try to do some useful task with all the information in it?
I have. Sometimes it works, but often it’s not pretty. Context window size is the new MHz, in terms of misleading performance measurements.
I find there comes a point where, even with a lot of context, the AI just hasn’t been trained to solve the problem. At that point it will cycle you round and round the same few wrong answers until you give up and work it out yourself.
Guys selling something claim it will make you taller and thinner, your dick bigger, your mother in law stop calling, and work as advertised.
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 fromseen somewhere elseIt can also throw things against the wall with no concern for fitness-to=purpose. See “None pizza, left beef”.
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.
That’d be an exciting world, since it’d massively increase access to software.
I am also very dubious about that claim.
In the long run, I do think that AI can legitimately handle a great deal of what humans do today. It’s something to think about, plan for, sure.
I do not think that anything we have today is remotely near being on the brink of the kind of technical threshold required to do that, and I think that even in a world where that was true, that it’d probably take more than 2 years to transition most of the industry.
I am enthusiastic about AI’s potential. I think that there is also – partly because we have a fair number of unknowns unknowns, and partly because people have a strong incentive to oversell the particular AI thing that they personally are involved with to investors and the like – a tendency to be overly-optimistic about the near-term potential.
I have another comment a while back talking about why I’m skeptical that the process of translating human-language requirements to machine-language instructions is going to be as amenable as translating human-language to human-consumable output. The gist, though, is that:
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Humans rely on stuff that “looks to us like” what’s going on in the real world to cue our brain to construct something. That’s something where the kind of synthesis that people are doing with latent diffusion software works well. An image that’s about 80% “accurate” works well enough for us; the lighting being a little odd or maybe an extra toe or something is something that we can miss. Ditto for natural-language stuff. But machine language doesn’t work like that. A CPU requires a very specific set of instructions. If 1% is “off”, a software package isn’t going to work at all.
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The process of programming involves incorporating knowledge about the real world with a set of requirements, because those requirements are in-and-of-themselves usually incomplete. I don’t think that there’s a great way to fill in those holes without having that deep knowledge of the world. This “deep knowledge and understanding of the world” is the hard stuff to do for AI. If we could do that, that’s the kind of stuff that would let us create a general artificial intelligence that could do what a human does in general. Stable Diffusion’s “understanding” of the world is limited to statistical properties of a set of 2D images; for that application, I think that we can create a very limited AI that can still produce useful output in a number of areas, which is why, in 2024, without producing an AI capable of performing generalized human tasks, we can still get some useful output from the thing. I don’t think that there’s likely a similar shortcut for much by way of programming. And hell, even for graphic arts, there’s a lot of things that this approach just doesn’t work for. I gave an example earlier in a discussion where I said “try and produce a page out of a comic book using stuff like Stable Diffusion”. It’s not really practical today; Stable Diffusion isn’t building up a 3D mental model of the world, designing an entity that stably persists from image to image, and then rendering that. It doesn’t know how it’s reasonable for objects and the like to interact. I think that to reach that point, you’re going to have to have a much-more-sophisticated understanding of the world, something that looks a lot more like what a human’s looks like.
The kind of stuff that we have today may be a component of such an AI system. But I don’t think that the answer here is going to be “take existing latent diffusion software and throw a lot of hardware at it”. I think that there’s going to have to be some significant technical breakthroughs that have not happened yet, and that we’re probably going to spend some time heading down dead-end approaches before we get to that. There’s probably going to be a lot of hard R&D before we get there, and that’s going to take time.
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I wonder how they think that’s possible, the attempts I’ve made at having an “AI” produce working code have failed spectacularly.
All the manufacturers of mechanical keyboards just cried 🥺
I feel sorry for all those people in AWS that now have him as a leader…
A company I used to work for outsourced most of their coding to a company in India. I say most because when the code came back the internal teams anways had to put a bunch of work in to fix it and integrate it with existing systems. I imagine that, if anything, LLMs will just take the place of that overseas coding farm. The code they spit out will still need to be fixed and modified so it works with your existing systems and that work is going to require programmers.
So instead of spending 1 day writing good code, we’ll be spending a week debugging shitty code. Great.
