> LLM’s amplify what you already have: opinions, structure, frameworks.
So far, so agreeable, but…
> If you have thoughts, they come out sharper and faster.
I can’t help but wonder whether constant use of “agent” harnesses will lead to an atrophy of the software engineering (or really any field) muscles.
Actual muscles need exercise to stay in shape (let alone grow), so does the brain. Can we really be sure that thoughts, opinions, taste will still come out sharper and faster after five, ten, 20 years of using these tools almost every day?
Conversely, I also am a user of LLMs (true shocker these days, I know), and am noticing a speedup in areas I was already familiar with, and a quicker introduction to new ones. The obvious benefit cannot be denied, and doing so regardless makes you look uninformed. [0]
So what’s the ideal “middle ground” in this situation? Stoically continuing to sharpen your skills on your own, but risking being left in the dust productivity-wise? Or taking an “agent first” approach and trying to learn and improve more only on the side, as more of an afterthought?
[0] Excluding people who don’t want anything to do with LLMs out of moral principle, which curiously just like the overarching topic I also both respect and understand, but on the other hand don’t do myself.
I guess I'm lucky to have taken the career path I have, but I write very little code these days being in pre-sales now. "Productivity" is pretty stark and obvious in sales. You can output all the text you want, maybe it even compiles and passes test suites and looks pretty in a demo. But does it sell? It's nice to be able to have the only true measure that matters actually be measurable. I have yet to use an LLM for anything at all, haven't touched an agent, not because I'm ideologically opposed, but I just haven't seen the need. Part of it is the largely advisory role. I'm largely expected on the customer side to field and answer questions and render technical assistance. I'm not much good at that if I'm just deferring to Claude. The customer can do that themselves. I need to know more than Claude. The other part is just I'm not being paid to generate text. I'm being paid to generate revenue. If there's a convincing argument that LLMs can help me, well, it's an argument, but truth on the ground is I'm performing every bit as good if not better than any of my peers that are blowing a grand a month on tokens. They're definitely having a lot of fun and flooding internal Github orgs with new repos and tooling, but it demonstrably isn't generating new revenue.
I'm frankly baffled by the number of commenters saying they've seen little productivity gains using LLMs/agents. My team got cut in half at the end of last year and we've probably increased our output by 25% primarily due to LLMs. Personally it's probably doubled my productivity. And we generally use a pretty structured spec based approach with consistent human code review, and six months into adoption we've had virtually no bugs attributable to an AI mistake (in fact production issues have gone down, knock on wood). My point being we aren't vibe coding.
I speculate the gains must be heavily context dependent (no pun intended). Different teams, tech stacks, product types, and company policies perhaps have an outsized effect on what gains LLMs give. We are allowed to be pretty independent and don't have much of a restriction on token usage.
What is your metric ? How do you calculate ROI ? Strange that you didn't specify that. Also, are you sure your "25% increase" is due to LLMs ? Every time I see this claim, it is always for a short time period or the increase was not due to LLMs at all
For what it is worth, my colleagues who use these tools more liberally see a much higher regression rate and are left refactoring more as well. While they definitely put out more PRs, I can confidently say that mine are more valuable and better stand the test of time. Whether that matters or not is up to debate as I guess you could argue there may be some point where the productivity overcomes their regression rate.
My position is, if the technology will be so good and progressing so fast to the point that I don't have to write code anymore and I'm just dictating our needs and what I want architecturally, then I don't really feel the need to put in the time to learn the tools as they are now. That seems way easier to learn than anything computer science related that I had to learn to build robust systems.
The part of brain which cares about efficiency and waste have "atrophied" decades ago in most devs. More likely they just never cared...
E.g.
> A 1.6GB Docker image for a Node app. Every code change rebuilds npm install from scratch. CI takes 8 minutes for a one-line fix.
> The team's proposal: add more CI runners.
Would it be surprising if that team makes a React app which does 10x more requests than necessary, has 10x more JS code than necessary, flickers, etc. People just don't care, and it has nothing to do with AI. "Engineering" is too big of a word for this kind of activity.
On the other hand, I think AI can amplify capabilities of those people who still care, and it's a good thing.
> Actual muscles need exercise to stay in shape (let alone grow), so does the brain. Can we really be sure that thoughts, opinions, taste will still come out sharper and faster after five, ten, 20 years of using these tools almost every day?
It occurred to me recently how because the LLMs have to potential to lead to intellectual/skill atrophy, they are a factor which could lead to an Idiocracy-esque [0] situation.
And while it's a well-established Fermi Paradox [1] hypothesis that AI could lead to the violent destruction of a civilization, I think it's also worth considering that they could non-violently lead to their decline due to intellectual atrophy.
I think you can use them critically. Probe the code, see how it behaves when tweaked, can you break it etc.? without slowing you down too much. And in doing so you probably learn 80% of what you would have if you'd written it yourself by hand.
But then there are definitely cases where LLMs are slower and less reliable than someone using tooling by hand, especially like deterministic refactoring operations can be done close to instantly if you already know what needs to be done and how to do it.
The amplification is what is causing the skills atrophy. When everyone is chasing max lines of code, we generate more code than what we can realistically write.
And it's not like we can not use LLM because others are using it in a craze to get the next promotion so you have to use it and maximize your output even if your skills are atrophing
Use it or lose it, but the problem is very old. The introduction of the alphabet/literacy almost certainly made people's memories worse -- up to that point they had to hold a lot more in their heads. At the same time it enabled a different better way to share information and learn. The question is thus, will it make the world better, is it a book or a TV?
The trust problem feels more important than whether the code was AI-assisted. A small, reproducible change with clear tests is reviewable; a huge opaque diff is not, regardless of who typed it.
The implication is that LLM critics don't use LLMs at all, or that the author is not an LLM critic, but both of those things are incorrect. We are very good at inventing entire people out of single opinions we read, and the AI arguments are maybe the best example of that I've ever seen in my many years watching internet arguments (not least due to the expansiveness of AI, and the sheer breadth of pros and cons it holds within).
>as a senior, you don’t need juniors anymore. The mundane tasks, at least I find that a lot of people agree with that one, can be fully outsourced to an LLM
Master craftsmen didn't take on apprentices to give them chores.
> And this is where the value is for me: I can simply make things higher quality than I could do them alone.
Yeah, that's the thing for me. LLMs have made my work easier and faster, and they've made my side projects easier and faster. I think there are very sensible and valid critiques but so far the tool works for me.
Criticizing use of agents for skill atrophy is valid, it definitely atrophies blank slate coding ability, though I don't think it atrophies engineering abilities unless you just YOLO all decisions to the agent. The data center/oligarchy complaints are also valid.
Saying agents produce shitty code is a bad argument though. They produce shitty codebase organization, but at a micro level their code is solid if not elegant. If you let them turn your codebase into a spaghetti mess, that's on you.
One thing about open source is a lot of people are throwing low quality PRs that should have been an issue or even a discussion, so you can understand what was the problem the person encountered that motivated such PR. This is hard to get because usually people use LLMs to also answer questions you make about the PR. I am tending towards blocking PRs from people outside the main developers in most of my open source projects. If the person CAN discuss the problem, authoring the PR is easier if I do it myself, even if using an LLM, because reviewing a PR authored by random internet people/bots is hard because of how much the entire code tends to change after minimal questions are asked. What I am sad of this approach is that I did met a few interesting people through receiving PRs and establishing trust and some relationships in the past (eight years ago and before)
The critics are absolutely right, LLMs have a lot of faults. But they also have a few great benefits. I use them every day for the benefits, fully aware of the faults, and I watch those faults like a hawk.
> I think the core issue here is trust. You should never trust random people on the internet anyway. But before LLMs, there was this base thing: creating a proper PR with proper descriptions would require at least some human time, so it would keep trolls and low quality submissions out. Or at least you could easily filter them out within a couple of seconds. So even if a new person came in, you could trust that this person would have at least spent a couple of hours on that. And then it was probably worth taking a closer look at it.
Ding ding ding. This is my biggest gripe with AI. Even the SEO blogspam, the fluff in front of every recipe, yarnwork or DIY instruction, it all was clearly written by a human. Someone had invested time (and money) in getting something in front of my eyes.
But now, it's all just slop. Everywhere. And hell I'm tired because the onslaught breaks my trust filters.
Maybe I think this is an age thing. Boomers? They trust everything written down somewhere. No matter what, and no matter if they didn't spend half my childhood to "never trust what people write on the Internet", and now they fall for scams left and right. My generation as said grew up with this "never trust, always verify" thing. And the younger generation? They DGAF about anything any more, all they care about is trying to survive.
> And b), the teaching, aka “How do we teach new people?”: previously, there was this balance aka “the junior does some pretty mundane tasks, but for this the senior reviews it together with him and helps him to grow”.
GOD YES YES YES THIS x1000.
There is barely anything more rewarding than teaching someone something, to watch the other person grow - and eventually surpassing your own abilities. That is when you know you did right and well. My wife is the best example, she started out at "can you help me with Excel", and these days, she pulls off stuff that would make more than a few finance people blush.
Good article! This matches how I feel about the situation.
It's really not incongruent to use LLMs and be in awe of their frankly incredible capabilities while at the same time recognize the risks and frankly real damage we are already seeing to junior training and hiring, open source communities and (in my opinion) very soon the entire fabric of our society.
I respect that people don't want to use agents themselves for whatever personal reason.
I respect maintainers not accepting AI-authored contributions. It's a tradeoff between progress, growing new contributors and maintainer sanity. Though I do feel that categoric opposition to anything AI will likely be futile in the mid-term.
I respect people pushing for regulation of AI or a global pause or whatever.
I don't particularly respect people dismissing everything AI authored as slop. Categorically refusing to read an article because it contains em-dashes or the term "load-bearing" is silly. While this is slowly changing now, many people are still in complete denial as to what the frontier AI is capable of.
Love it, hate it - I don't care, but at least respect it, goddamit.
I think both the LLM critics and the LLM advocates are right.
Even this article has some cognitive dissonance in it. What it really comes down to is how much you trust your own verification process. The branches of questions an LLM generates are still trapped within the biases of its training data. Of course, the authority to craft that initial prompt, the very first question, comes from human experience and learning.
But I think thought itself is the easiest resource to outsource. People say the human did the thinking and the LLM just amplified it, but the truth is, the LLM outsources the thinking. Otherwise, when the result is good, people say "human thought was present," and when it's bad, they say "human thought was absent." But a part of the actual thinking really is outsourced. The alternatives, the counterexamples, the sentence structure. In programming terms, the reader's experience gets outsourced. When you write a blog post, you find yourself thinking about how to make something you understand easy for someone else to understand. With an LLM, that part gets outsourced.
But at the same time, I don't get the argument that you shouldn't use it at all. We don't "think" about everything. We have limited cognitive resources. So we study deeply the things we care about, but for the things we don't need, we mostly leave them to "common sense" or prejudice. We just skim the surface.
I think of "common sense" as "the largest collection of prejudice." Because what we call common sense usually just amounts to surface level knowledge, the kind of thing we know just enough about to get by.
That's why I think LLMs are good. The reason is simple. I don't think deeply about everything in the world anyway. For everything else, I'm buried in some kind of bias. You see it on HN all the time, right? People fight over some technology, but they often don't think about its internal structure or why it works the way it does. They just treat it as an identity. They fight over a particular language, a framework, an operating system, but they rarely check how that technology actually works internally or why it was designed that way. Why use MVC, why a different architecture might be better for my case, it's easier to just go with what's popular. Put more elegantly, "job mobility" gets bundled in there too. I use Windows. In my country, if it's not Windows, you literally can't do anything. You can't even do basic online banking. From regional context like that all the way down to personal interests, people are bound to be different. So I'm just going to use LLMs. The most common excuse you hear around this is the whole "reinventing the wheel" thing.
So yeah, I'm going to use LLMs. Because I recognize that I bias myself toward only thinking about what I want to think about. And I know that bias isn't cognitively healthy. But on the flip side, I think what the world values, whether it's knowing a lot or knowing one thing deeply, is going to change.
Honestly, I don't know what's right. I think both the advocates and the critics are making valid points. I respect the people who don't use it, and the people who do just have their own workflow. There's really no reason to fight over whose workflow is superior.
It depends upon why you have issues with LLMs. If you’re just concerned about quality then sure the dissonance isn’t intolerable. If you’re concerned about their ethics then this becomes a much more challenging position to have.
But that's the thing - people can live with the contradictions. You can find people at a gas station who will totally agree that air pollution and CO2 emissions are killing people, but still fill up the tank.
Because it's a machine, not an oracle. If you "hold it right" you are more productive. When you catch them being "dumb" you're reinforcing your own deep knowledge on the topic. When it is correct, you're either learning something new or your task is complete. You are still at risk when you know little and trust it to work alone.
I'm saying it in jest, but it's also a bit true. Not necessarily because we use it any differently. But because my use of AI saves me time. But their use of AI adds more to my plate, no matter if it's slop or not.
So much this. When coding I use my LLM to hone the code to perfection before sending it to review, including running AI reviews on it multiple times and fixing obvious bugs so that reviewers have less work to do. I also test all my code manually (LLM can be helpful by giving me a list of test scenarios to consider). When reviewing PRs I carefully read the AI reviewer output, consider which points are relevant, which are irrelevant and only post the relevant issues when requesting changes.
I have coworkers who routinely post PRs with comments stating "all tests green, ready to review" and then I actually start the application, test it manually and it doesn't work. And don't get me started on reviewers who post the raw AI output of their "reviewing skill" and then I have to read through 30 paragraphs of "findings" most of which are irrelevant (just today I got the "valuable" feedback of "View is @api_view(["POST"]); no test asserts GET/PUT/PATCH/DELETE → 405")...
You're all in denial when you criticize LLMs. It's not necessarily that the criticism isn't true. It's more how self assured the criticism is. That's the biggest problem because AI is a moving target. It is getting better, and it is getting better fast. A lot of the criticism can become outdated in a year or six months. The change is happening in front of your very eyes and yet you can always reliably come on HN and find some sort of self assured criticism to say AI can't design, AI code must always be reviewed. Blah blah blah.
The big thing people used to call AI was that it was a stochastic parrot and all it did was summarize things. Clearly. None of this is/was true anymore. And very likely all the current criticism will be eliminated soon and we have to find new excuses about AI that makes us feel we are superior.
The status quo is about to change. Every 6 months. And you will always think of yourself as superior to LLMs. Your current criticisms will evolve as most of them will be rendered not true pretty soon.
> "Last but not least, even when just researching with LLMs, they have the natural tendency to silently sneak in the thoughts of the majority of the training materials, or sometimes even the political convictions of the ones who created the model."
> "Yet I still write all of my texts with LLMs"
So I'm guessing the author is actually ok with the point they put in the "LLMs are bad" part of the article?
> a genuinely good tool that enriches your thinking
A smartphone is also a genuinely good all-around tool. Even social media is a genuinely good tool for connecting people.
Yet, I feel like we've been overly optimistic about the impact of said tools on us and our societies in the past two decades.
Smartphones are so good, in fact, in some societies, half of us are addicted to them. Billions of people world-wide.
I ask myself: Will LLMs enrich my thinking in the long run, or will they ruin it?
And what about most people? Will half of us outsource most of our thinking in a decade from now?
Given the speed and global scale that we're running these experiments with, it's fair, I think, to be a bit sceptical of the conclusion that, in the long run, LLMs will enrich our thinking.
The LLM critics are right, and that's why I try not to use AI in the ways critics point out are bad.
I use AI to code tools for myself, but I don't pretend anything I make is production quality. Duct tape engineering has always been a bit sloppy, and AI just made it faster.
I use AI to troubleshoot issues and plan out strategies, but I basically consider the AI draft of anything to be "draft 0", and use it as a framework for writing my own works for a real first draft of anything I write that will be read by other people. Sometimes the AI spits out a perfect paragraph that I might copy, but I don't ever blindly trust it or let it speak for me. I also double-check everything it says that I don't have existing knowledge of, rather than trust it to be right.
AI images, video, and music are all entertaining, but I only generate these things as a form of self-entertainment and maybe online meming. I could never in good conscience pass these creations off as my own, or publish them online on a personal or business website when something non-synthetic would suffice.
And I am never personally confiding in an LLM like it were a person. I have had it help me brainstorm options for office politics stuff, but I'm not about to ask it for relationship advice or to be my friend.
I do love that it accelerates the tedious stuff, and helps me learn new things pretty quickly if used right. It has definite utility. But I am always really distrustful of it. Sometimes at work we are asked to share how we use AI, and I have actually refused before, on the grounds that I may have found a useful way to use the AI, but I am worried that others will use my same method badly (e.g., not verifying eveything the AI says first), and I would rather not share.
It's like I have a finicky gun. I might be comfortable shooting it since I know its quirks and how to keep it from accidentally discharging, but I'm not loaning it out to anyone I wouldn't want to accidentally shoot themselves with it.
That seems unlikely given the diverse nature of mutually exclusive opinions that exist out there.
Critics seem to run the gamut from LLMs being incapable of even the most basic of functions to already sentient creatures secretly plotting our destruction with steganographic messages to each other.
It's maybe a bell curve with some wacky at those tails, but there's some fairly significant differences of opinion amongst the positions that are more mainstream.
Just the difference between critics of all LLMs and crutics of all closed weights models are a pretty big gap.
Similarly for those who criticise them for over censorship vs those who criticise them for unrestricted generation.
69 comments
[ 3.5 ms ] story [ 82.1 ms ] threadSo far, so agreeable, but…
> If you have thoughts, they come out sharper and faster.
I can’t help but wonder whether constant use of “agent” harnesses will lead to an atrophy of the software engineering (or really any field) muscles.
Actual muscles need exercise to stay in shape (let alone grow), so does the brain. Can we really be sure that thoughts, opinions, taste will still come out sharper and faster after five, ten, 20 years of using these tools almost every day?
Conversely, I also am a user of LLMs (true shocker these days, I know), and am noticing a speedup in areas I was already familiar with, and a quicker introduction to new ones. The obvious benefit cannot be denied, and doing so regardless makes you look uninformed. [0]
So what’s the ideal “middle ground” in this situation? Stoically continuing to sharpen your skills on your own, but risking being left in the dust productivity-wise? Or taking an “agent first” approach and trying to learn and improve more only on the side, as more of an afterthought?
[0] Excluding people who don’t want anything to do with LLMs out of moral principle, which curiously just like the overarching topic I also both respect and understand, but on the other hand don’t do myself.
>> You would probably be surprised how few actually have the agency to do even this small step.
I speculate the gains must be heavily context dependent (no pun intended). Different teams, tech stacks, product types, and company policies perhaps have an outsized effect on what gains LLMs give. We are allowed to be pretty independent and don't have much of a restriction on token usage.
A spec is NOT CODE.
Someone still needs to understand what the code means.
C optimizers are not the same as the radical departure of an LLM.
https://arxiv.org/pdf/2604.04721
For what it is worth, my colleagues who use these tools more liberally see a much higher regression rate and are left refactoring more as well. While they definitely put out more PRs, I can confidently say that mine are more valuable and better stand the test of time. Whether that matters or not is up to debate as I guess you could argue there may be some point where the productivity overcomes their regression rate.
My position is, if the technology will be so good and progressing so fast to the point that I don't have to write code anymore and I'm just dictating our needs and what I want architecturally, then I don't really feel the need to put in the time to learn the tools as they are now. That seems way easier to learn than anything computer science related that I had to learn to build robust systems.
E.g.
> A 1.6GB Docker image for a Node app. Every code change rebuilds npm install from scratch. CI takes 8 minutes for a one-line fix.
> The team's proposal: add more CI runners.
Would it be surprising if that team makes a React app which does 10x more requests than necessary, has 10x more JS code than necessary, flickers, etc. People just don't care, and it has nothing to do with AI. "Engineering" is too big of a word for this kind of activity.
On the other hand, I think AI can amplify capabilities of those people who still care, and it's a good thing.
It occurred to me recently how because the LLMs have to potential to lead to intellectual/skill atrophy, they are a factor which could lead to an Idiocracy-esque [0] situation.
And while it's a well-established Fermi Paradox [1] hypothesis that AI could lead to the violent destruction of a civilization, I think it's also worth considering that they could non-violently lead to their decline due to intellectual atrophy.
[0]: https://www.imdb.com/title/tt0387808/
[1]: https://en.wikipedia.org/wiki/Fermi_paradox
But then there are definitely cases where LLMs are slower and less reliable than someone using tooling by hand, especially like deterministic refactoring operations can be done close to instantly if you already know what needs to be done and how to do it.
And it's not like we can not use LLM because others are using it in a craze to get the next promotion so you have to use it and maximize your output even if your skills are atrophing
Master craftsmen didn't take on apprentices to give them chores.
Yeah, that's the thing for me. LLMs have made my work easier and faster, and they've made my side projects easier and faster. I think there are very sensible and valid critiques but so far the tool works for me.
Saying agents produce shitty code is a bad argument though. They produce shitty codebase organization, but at a micro level their code is solid if not elegant. If you let them turn your codebase into a spaghetti mess, that's on you.
Ding ding ding. This is my biggest gripe with AI. Even the SEO blogspam, the fluff in front of every recipe, yarnwork or DIY instruction, it all was clearly written by a human. Someone had invested time (and money) in getting something in front of my eyes.
But now, it's all just slop. Everywhere. And hell I'm tired because the onslaught breaks my trust filters.
Maybe I think this is an age thing. Boomers? They trust everything written down somewhere. No matter what, and no matter if they didn't spend half my childhood to "never trust what people write on the Internet", and now they fall for scams left and right. My generation as said grew up with this "never trust, always verify" thing. And the younger generation? They DGAF about anything any more, all they care about is trying to survive.
> And b), the teaching, aka “How do we teach new people?”: previously, there was this balance aka “the junior does some pretty mundane tasks, but for this the senior reviews it together with him and helps him to grow”.
GOD YES YES YES THIS x1000.
There is barely anything more rewarding than teaching someone something, to watch the other person grow - and eventually surpassing your own abilities. That is when you know you did right and well. My wife is the best example, she started out at "can you help me with Excel", and these days, she pulls off stuff that would make more than a few finance people blush.
It's really not incongruent to use LLMs and be in awe of their frankly incredible capabilities while at the same time recognize the risks and frankly real damage we are already seeing to junior training and hiring, open source communities and (in my opinion) very soon the entire fabric of our society.
I respect that people don't want to use agents themselves for whatever personal reason.
I respect maintainers not accepting AI-authored contributions. It's a tradeoff between progress, growing new contributors and maintainer sanity. Though I do feel that categoric opposition to anything AI will likely be futile in the mid-term.
I respect people pushing for regulation of AI or a global pause or whatever.
I don't particularly respect people dismissing everything AI authored as slop. Categorically refusing to read an article because it contains em-dashes or the term "load-bearing" is silly. While this is slowly changing now, many people are still in complete denial as to what the frontier AI is capable of.
Love it, hate it - I don't care, but at least respect it, goddamit.
Even this article has some cognitive dissonance in it. What it really comes down to is how much you trust your own verification process. The branches of questions an LLM generates are still trapped within the biases of its training data. Of course, the authority to craft that initial prompt, the very first question, comes from human experience and learning.
But I think thought itself is the easiest resource to outsource. People say the human did the thinking and the LLM just amplified it, but the truth is, the LLM outsources the thinking. Otherwise, when the result is good, people say "human thought was present," and when it's bad, they say "human thought was absent." But a part of the actual thinking really is outsourced. The alternatives, the counterexamples, the sentence structure. In programming terms, the reader's experience gets outsourced. When you write a blog post, you find yourself thinking about how to make something you understand easy for someone else to understand. With an LLM, that part gets outsourced.
But at the same time, I don't get the argument that you shouldn't use it at all. We don't "think" about everything. We have limited cognitive resources. So we study deeply the things we care about, but for the things we don't need, we mostly leave them to "common sense" or prejudice. We just skim the surface.
I think of "common sense" as "the largest collection of prejudice." Because what we call common sense usually just amounts to surface level knowledge, the kind of thing we know just enough about to get by.
That's why I think LLMs are good. The reason is simple. I don't think deeply about everything in the world anyway. For everything else, I'm buried in some kind of bias. You see it on HN all the time, right? People fight over some technology, but they often don't think about its internal structure or why it works the way it does. They just treat it as an identity. They fight over a particular language, a framework, an operating system, but they rarely check how that technology actually works internally or why it was designed that way. Why use MVC, why a different architecture might be better for my case, it's easier to just go with what's popular. Put more elegantly, "job mobility" gets bundled in there too. I use Windows. In my country, if it's not Windows, you literally can't do anything. You can't even do basic online banking. From regional context like that all the way down to personal interests, people are bound to be different. So I'm just going to use LLMs. The most common excuse you hear around this is the whole "reinventing the wheel" thing.
So yeah, I'm going to use LLMs. Because I recognize that I bias myself toward only thinking about what I want to think about. And I know that bias isn't cognitively healthy. But on the flip side, I think what the world values, whether it's knowing a lot or knowing one thing deeply, is going to change.
Honestly, I don't know what's right. I think both the advocates and the critics are making valid points. I respect the people who don't use it, and the people who do just have their own workflow. There's really no reason to fight over whose workflow is superior.
The tech world does not care about woke ideology, german technical illiteracy and self importance.
LLMs are useful and here to stay.
I'm saying it in jest, but it's also a bit true. Not necessarily because we use it any differently. But because my use of AI saves me time. But their use of AI adds more to my plate, no matter if it's slop or not.
I have coworkers who routinely post PRs with comments stating "all tests green, ready to review" and then I actually start the application, test it manually and it doesn't work. And don't get me started on reviewers who post the raw AI output of their "reviewing skill" and then I have to read through 30 paragraphs of "findings" most of which are irrelevant (just today I got the "valuable" feedback of "View is @api_view(["POST"]); no test asserts GET/PUT/PATCH/DELETE → 405")...
The big thing people used to call AI was that it was a stochastic parrot and all it did was summarize things. Clearly. None of this is/was true anymore. And very likely all the current criticism will be eliminated soon and we have to find new excuses about AI that makes us feel we are superior.
The status quo is about to change. Every 6 months. And you will always think of yourself as superior to LLMs. Your current criticisms will evolve as most of them will be rendered not true pretty soon.
> "Yet I still write all of my texts with LLMs"
So I'm guessing the author is actually ok with the point they put in the "LLMs are bad" part of the article?
A smartphone is also a genuinely good all-around tool. Even social media is a genuinely good tool for connecting people.
Yet, I feel like we've been overly optimistic about the impact of said tools on us and our societies in the past two decades.
Smartphones are so good, in fact, in some societies, half of us are addicted to them. Billions of people world-wide.
I ask myself: Will LLMs enrich my thinking in the long run, or will they ruin it?
And what about most people? Will half of us outsource most of our thinking in a decade from now?
Given the speed and global scale that we're running these experiments with, it's fair, I think, to be a bit sceptical of the conclusion that, in the long run, LLMs will enrich our thinking.
I use AI to code tools for myself, but I don't pretend anything I make is production quality. Duct tape engineering has always been a bit sloppy, and AI just made it faster.
I use AI to troubleshoot issues and plan out strategies, but I basically consider the AI draft of anything to be "draft 0", and use it as a framework for writing my own works for a real first draft of anything I write that will be read by other people. Sometimes the AI spits out a perfect paragraph that I might copy, but I don't ever blindly trust it or let it speak for me. I also double-check everything it says that I don't have existing knowledge of, rather than trust it to be right.
AI images, video, and music are all entertaining, but I only generate these things as a form of self-entertainment and maybe online meming. I could never in good conscience pass these creations off as my own, or publish them online on a personal or business website when something non-synthetic would suffice.
And I am never personally confiding in an LLM like it were a person. I have had it help me brainstorm options for office politics stuff, but I'm not about to ask it for relationship advice or to be my friend.
I do love that it accelerates the tedious stuff, and helps me learn new things pretty quickly if used right. It has definite utility. But I am always really distrustful of it. Sometimes at work we are asked to share how we use AI, and I have actually refused before, on the grounds that I may have found a useful way to use the AI, but I am worried that others will use my same method badly (e.g., not verifying eveything the AI says first), and I would rather not share.
It's like I have a finicky gun. I might be comfortable shooting it since I know its quirks and how to keep it from accidentally discharging, but I'm not loaning it out to anyone I wouldn't want to accidentally shoot themselves with it.
That seems unlikely given the diverse nature of mutually exclusive opinions that exist out there.
Critics seem to run the gamut from LLMs being incapable of even the most basic of functions to already sentient creatures secretly plotting our destruction with steganographic messages to each other.
It's maybe a bell curve with some wacky at those tails, but there's some fairly significant differences of opinion amongst the positions that are more mainstream.
Just the difference between critics of all LLMs and crutics of all closed weights models are a pretty big gap.
Similarly for those who criticise them for over censorship vs those who criticise them for unrestricted generation.
It's sad that this can be true because if you did them alone the quality would be non-existent.