I started out very sceptical. When Claude Code landed, I got completely seduced — borderline addicted, slot machine-style — by what initially felt like a superpower. Then I actually read the code. It was shockingly bad. I swung back hard to my earlier scepticism, probably even more entrenched than before.
Then something shifted. I started experimenting. I stopped giving it orders and began using it more like a virtual rubber duck. That made a huge difference.
It’s still absolute rubbish if you just let it run wild, which is why I think “vibe coding” is basically just “vibe debt” — because it just doesn’t do what most (possibly uninformed) people think it does.
But if you treat it as a collaborator — more like an idiot savant with a massive brain but no instinct or nous — or better yet, as a mech suit [0] that needs firm control — then something interesting happens.
I’m now at a point where working with Claude Code is not just productive, it actually produces pretty good code, with the right guidance. I’ve got tests, lots of them. I’ve also developed a way of getting Claude to document intent as we go, which helps me, any future human reader, and, crucially, the model itself when revisiting old code.
What fascinates me is how negative these comments are — how many people seem closed off to the possibility that this could be a net positive for software engineers rather than some kind of doomsday.
Did Photoshop kill graphic artists? Did film kill theatre? Not really. Things changed, sure. Was it “better”? There’s no counterfactual, so who knows? But change was inevitable.
What’s clear is this tech is here now, and complaining about it feels a bit like mourning the loss of punch cards when terminals showed up.
[0]: https://matthewsinclair.com/blog/0178-why-llm-powered-progra...
Core to Ptacek's point is that everything has changed in the last 6 months. As you and I presume he agree, the use of off-the-shelf LLMs in code was kinda garbage. And I expect the skepticism he's knocking here ("stochastic parrots") was in fact accurate then.
But it did get a lot of people (and money) to rush in and start trying to make something useful. Like the stone soup story, a lot of other technology has been added to the pot, and now we're moving in the direction of something solid, a proper meal. But given the excitement and investment, it'll be at least a few years before things stabilize. Only at that point can we be sure about how much the stone really added to the soup.
Another counterfactual that we'll never know is what kinds of tooling we would have gotten if people had dumped a few billion dollars into code tool improvement without LLMs, but with, say, a lot of more conventional ML tooling. Would the tools we get be much better? Much worse? About the same but different in strengths and weaknesses? Impossible to say.
So I'm still skeptical of the hype. After all, the hype is basically the same as 6 months ago, even though now the boosters can admit the products of 6 months ago sucked. But I can believe we're in the middle of a revolution of developer tooling. Even so, I'm content to wait. We don't know the long term effects on a code base. We don't know what these tools will look like in 6 months. I'm happy to check in again then, where I fully expect to be again told: "If you were trying and failing to use an LLM for code 6 months ago †, you’re not doing what most serious LLM-assisted coders are doing." At least until then, I'm renewing my membership in the Boring Technology Club: https://boringtechnology.club/
This was actually the only point in the essay with which I disagree, and it weakens the overall argument. Even 2 years ago, before agents or reasoning models, these LLMs were extremely powerful. The catch was, you needed to figure out what worked for you.
I wrote this comment elsewhere: https://news.ycombinator.com/item?id=44164846 -- Upshot: It took me months to figure out what worked for me, but AI enabled me to produce innovative (probably cutting edge) work in domains I had little prior background in. Yes, the hype should trigger your suspicions, but if respectable people with no stake in selling AI like @tptacek or @kentonv in the other AI thread are saying similar things, you should probably take a closer look.
Maybe? Social proof doesn't mean much to me during a hype cycle. You could say the same thing about tulip bulbs or any other famous bubble. Lots of smart people with no stake get sucked in. People are extremely good at fooling themselves. There are a lot of extremely smart people following all of the world's major religions, for example, and they can't all be right. And whatever else is going on here, there are a lot of very talented people whose fortunes and futures depend on convincing everybody that something extraordinary is happening here.
I'm glad you have found something that works for you. But I talk with a lot of people who are totally convinced they've found something that makes a huge difference, from essential oils to functional programming. Maybe it does for them. But personally, what works for me is waiting out the hype cycle until we get to the plateau of productivity. Those months that you spent figuring out what worked are months I'd rather spend on using what I've already found to work.
Sure, but I would argue that the UX is the product, and that has radically improved in the past 6-12 months.
Yes, you could have produced similar results before, manually prompting the model each time, copy and pasting code, re-prompting the model as needed. I would strenuously argue that the structuring and automation of these tasks is what has made these models broadly usable and powerful.
In the same way that Apple didn't event mobile phones nor touchscreens nor OSes, but the specific combination of these things resulted in a product that was different in kind than what came before, and took over the world.
Likewise, the "putting the LLM into a structured box of validation and automated re-prompting" is huge! It changed the product radically, even if its constituent pieces existed already.
[edit] More generally I would argue that 95% of the useful applications of LLMs aren't about advancing the SOTA model capabilities and more about what kind of structured interaction environment we shove them into.
Building a mental model of a new domain by creating a logical model that interfaces with a domain I'm familiar with lets me test my assumptions and understanding in real time. I can apply previous experience by analogy and verify usefulness/accuracy instantly.
> Upshot: It took me months to figure out what worked for me, but AI enabled me to produce innovative (probably cutting edge) work in domains I had little prior background in. Yes, the hype should trigger your suspicions[...]
Part of the hype problem is that describing my experience sounds like bullshit to anyone who hasn't gone through the same process. The rate that I pick up concepts well enough to do verifiable work with them is literally unbelievable.
Different people have different weird tendencies in different directions. Some people irrationally assume that things aren’t going to change much. Others see a trend and irrationally assume that it will continue on a trend line.
Synthesis is hard.
Understanding causality is even harder.
Savvy people know that we’re just operating with a bag of models and trying to choose the right combination for the right situation.
This misunderstanding is one reason why doomers, accelerations, and “normies” talk past each other or (worse) look down on each other. (I’m not trying to claim epistemic equivalence here; some perspectives are based on better information, some are better calibrated than others! I’m just not laying out my personal claims at this point. Instead, I’m focusing on how we talk to each other.)
Another big source of misunderstanding is about differing loci of control. People in positions of influence are naturally inclined to think about what they can do, who they know, and where they want to be. People farther removed feel relatively powerless and tend to hold onto their notions of stability, such as the status quo or their deepest values.
Historically, programmers have been quite willing to learn new technologies, but now we’re seeing widespread examples where people’s plasticity has limits. Many developers cannot (or are unwilling to) wrap their minds around the changing world. So instead of confronting the reality they find ways to deny it, consciously or subconsciously. Our perception itself is shaped by our beliefs, and some people won’t even perceive the threat because it is too strange or disconcerting. Such is human nature: we all do it. Sometimes we’re lucky enough to admit it.
I do of course agree that some people are just refusing to "wrap their minds around the changing world". But anybody with enough experience in tech can count a lot more instances of "the world is about to change" than "the world really changed". The most recent obvious example being cryptocurrencies, but there are plenty of others. [1] So I think there's plenty of room here for legitimate skepticism. And for just waiting until things settle down to see where we ended up.
I picked coding again a couple of days back and I’m blown away by how much things have changed
It was all manual work until a few months back. Suddenly, its all agents
I think it's very useful if one wants to properly weigh the value of LLMs in a way that gets beyond the hype. Which I do.
You'll not only never know this, it's IMHO not very useful to think about at all, except as an intellectual exercise.
I wish i could impress this upon more people.
A friend similarly used to lament/complain that Kotlin sucked in part because we could have probably accomplished it's major features in Java, and maybe without tons of work, or migration cost.
This is maybe even true!
as an intellectual exercise, both are interesting to think about. But outside of that, people get caught up in this as if it matters, but it doesn't.
Basically nothing is driven by pure technical merit alone, not just in CS, but in any field. So my point to him was the lesson to take away from this is not "we could have been more effective or done it cheaper or whatever" but "my definition of effectiveness doesn't match how reality decides effectiveness, so i should adjust my definition".
As much as people want the definition to be a meritocracy, it just isn't and honestly, seems unlikely to ever be.
So while it's 100% true that billions of dollars dumped into other tools or approaches or whatever may have have generated good, better, maybe even amazing results, they weren't, and more importantly, never would have been. Unknown but maybe infinite ROI is often much more likely to see investment than more known but maybe only 2x ROI.
and like i said, this is not just true in CS, but in lots of fields.
That is arguably quite bad, but also seems unlikely to change.
I think it's very useful if one wants to properly weigh the value of LLMs in a way that gets beyond the hype. Which I do.
Busy code I need to generate is difficult to do with AI too. Because then you need to formalize the necessary context for an AI assistant, which is exhausting with an unsure result. So perhaps it is just simpler to write it yourself quickly.
I understand comments being negative, because there is so much AI hype without having to many practical applications yet. Or at least good practical applications. Some of that hype is justified, some of it is not. I enjoyed the image/video/audio synthesis hype more tbh.
Test cases are quite helpful and comments are decent too. But often prompting is more complex than programming something. And you can never be sure if any answer is usable.
I'd challenge this one; is it more complex, or is all the thinking and decision making concentrated into a single sentence or paragraph? For me, programming something is taking a big high over problem and breaking it down into smaller and smaller sections until it's a line of code; the lines of code are relatively low effort / cost little brain power. But in my experience, the problem itself and its nuances are only defined once all code is written. If you have to prompt an AI to write it, you need to define the problem beforehand.
It's more design and more thinking upfront, which is something the development community has moved away from in the past ~20 years with the rise of agile development and open source. Techniques like TDD have shifted more of the problem definition forwards as you have to think about your desired outcomes before writing code, but I'm pretty sure (I have no figures) it's only a minority of developers that have the self-discipline to practice test-driven development consistently.
(disclaimer: I don't use AI much, and my employer isn't yet looking into or paying for agentic coding, so it's chat style or inline code suggestions)
Combine that with when you’re reading the code it’s often much easier to develop a prototype solution as you go and you end up with prompting feeling like using 4 men to carry a wheelbarrow instead of having 1 push it.
I think we'll find that over the next few years the first really big win will be AI tearing down the mountain of tech & documentation debt. Bringing efficiency to corporate knowledge is likely a key element to AI working within them.
The fastest way I can transcribe a design is with code or pseudocode. Converting it into English can be hard.
It reminds me a bit of the discussion of if you have an inner monologue. I don't and turning thoughts into English takes work, especially if you need to be specific with what you want.
I agree, but even smaller than thinking in agile is just a tight iteration loop when i'm exploring a design. My ADHD makes upfront design a challenge for me and I am personally much more effective starting with a sketch of what needs to be done and then iterating on it until I get a good result.
The loop of prompt->study->prompt->study... is disruptive to my inner loop for several reasons, but a big one is that the machine doesn't "think" like i do. So the solutions it scaffolds commonly make me say "huh?" and i have to change my thought process to interpet them and then study them for mistakes. My intution and iteration is, for the time being, more effective than this machine assited loop for the really "interesting" code i have to write.
But i will say that AI has been a big time saver for more mundane tasks, especially when I can say "use this example and apply it to the rest of this code/abstraction".
Sure it was easier to do it myself. But putting in the time to train, give context, develop guardrails, learn how to monitor etc ultimately taught me the skills needed to delegate effectively and multiply the teams output massively as we added people.
It's early days but I'm getting the same feeling with LLMs. It's as exhausting as training an overconfident but talented intern, but if you can work through it and somehow get it to produce something as good as you would do yourself, it's a massive multiplier.
But you're not training LLMs as you use them really - do you mean that it's best to develop your own skill using LLMs in an area you already understand well?
I'm finding it a bit hard to square your comment about it being exhausting to catherd the LLM with it being a force multiplier.
You just explained how your work was affected by a big multiplier. At the end of training an intern you get a trained intern -- potentially a huge multiplier. ChatGPT is like an intern you can never train and will never get much better.
These are the same people who would no longer create or participate deeply in OSS (+100x multipler) bragging about the +2x multiplier they got in exchange.
Yes, but you're expensive.
And these models are getting better at solving a lot of business-relevant problems.
Soon all business-relevant problems will be bent to the shape of the LLM because it's cost-effective.
When it works it’s brilliant.
There is a threshold point as part of the learning curve where you realize you are in a pile of spaghetti code and think it actually saves no time to use LLM assistant.
But then you learn to avoid the bad parts - thus they don’t take your time anymore - and the good parts start paying back in heaps of the time spent learning.
They are not zero effort tools.
There is a non-trivial learning cost involved.
We need to understand what kind of guard rails to put these models on for optimal results.
We don’t even have a solid education program for software engineering - possibly for the same reason.
The industry loves to run on the bleeding edge, rather than just think for a minute :)
I just don't think the interest of the profession control. The travel agents had interests too!
Getting AI to hallucinate its way into secure and better quality code seems like the antithesis of this. Why don't we have AI and robots working for humanity with the boring menial tasks - mowing laws, filing taxes, washing dishes, driving cars - instead of attempting to take on our more critical and creative outputs - image generation, movie generation, book writing and even website building.
But the analysis doesn't stop there, because after the raw quality wash, we have to consider things LLMs can do profoundly better than human coders can. Codebase instrumentation, static analysis, type system tuning, formal analysis: all things humans can do, spottily, on a good day but that empirically across most codebases they do not do. An LLM can just be told to spend an afternoon doing them.
I'm a security professional before I am anything else (vulnerability research, software security consulting) and my take on LLM codegen is that they're likely to be a profound win for security.
I mean, we do have automation for literally all of those things, to varying degrees of effectiveness.
There's an increasing number of little "roomba" style mowers around my neighborhood. I file taxes every year with FreeTaxUSA and while it's still annoying, a lot of menial "form-filling" labor has been taken away from me there. My dishwasher does a better job cleaning my dishes than I would by hand. And though there's been a huge amount of hype-driven BS around 'self-driving', we've undeniably made advances in that direction over the last decade.
Technical? Yes. Hardcore expert premium technical, no. The people who want the service can pay someone with basic to moderate skills a few hundred bucks to spend a day working on it, and that's all good.
Could I get an LLM to do much of the work? Yes, but I could also do much of the work without an LLM. Someone who doesn't understand the first principles of domains, Wordpress, hosting and so on, not so much.
Or maybe shouldn't enthusiastically repeat the destruction of the open web in favor of billionaire-controlled platforms for surveillance and manipulation.
> Employment of travel agents is projected to grow 3 percent from 2023 to 2033, about as fast as the average for all occupations.
The last year there is data for claims 68,800 people employed as travel agents in the US. It's not a boom industry by any means, but it doesn't appear they experienced the apocalypse that Hacker News believes they did, either.
I don't know how to easily find historical data, unfortunately. BLS publishes the excel sheets, but pulling out the specific category would have to be done manually as far as I can tell. There's this, I guess: https://www.travelagewest.com/Industry-Insight/Business-Feat...
It appears at least that what happened is, though it may be easier than ever to plan your own travel, there are so many more people traveling these days than in the past that the demand for travel agents hasn't crashed.
Has some stats. It seems pretty clear the interests of travel agents did not count for much in the face of technological change.
40% of all travel agent jobs lost between 2001 and 2025. Glad I'm not a travel agent.
It was just 2 weeks ago when the utter incompetence of these robots were in full public display [1]. But none of that will matter to greedy corporate executives, who will prioritize short-term cost savings. They will hop from company to company, personally reaping the benefits while undermining essential systems that users and society rely on with robot slop. That's part of the reason why the C-suites are overhyping the technology. After all, no rich executive has faced consequences for behaving this way.
It's not just software engineering jobs that will take a hit. Society as a whole will suffer from the greedy recklessness.
Ultimately the thing that impresses me is that LLMs have replaced google search. The thing that disappoints me is that their code is often convincing but wrong.
Coming from a hard-engineering background, anything that is unreliable is categorized as bad. If you come from the move-fast-break-things world of tech, then your tolerance for mistakes is probably a lot higher.
I tried the latest Claude for a very complex wrapper around the AWS Price APIs who are not easy to work with. Down a 2,000 line of code file, I found Claude faking some API returns by creating hard coded values. A pattern I have seen professional developers being caught on while under pressure to deliver.
This will be a boon to the human skilled developers, that will be hired at $900 dollars an hour to fix bugs of a subtlety never seen before.
"Claude gives up and hardcodes the answer as a solution" - https://www.reddit.com/r/ClaudeAI/comments/1j7tiw1/claude_gi...
Of course, in aggregate AI makes me capable in a far broader set of problem domains. It would be tough to live without it at this stage, but needs to be used for what it is actually good at, not what we hope it will be good at.
I haven't tried Claud code yet however. Maybe that approach is more on point.
To a first approximation, the answer to both of these is "yes".
There is still a lot of graphic design work out there (though generative AI will be sucking the marrow out of it soon), but far less than there used to be before the desktop publishing revolution. And the kind of work changed. If "graphic design" to you meant sitting at a drafting table with pencil and paper, those jobs largely evaporated. If that was a kind of work that was rewarding and meaningful to you, that option was removed for you.
Theatre even more so. Yes, there are still some theatres. But the number of people who get to work in theatrical acting, set design, costuming, etc. is a tiny tiny fraction of what it used to be. And those people are barely scraping together a living, and usually working side jobs just to pay their bills.
> it feels a bit like mourning the loss of punch cards when terminals showed up.
I think people deserve the right to mourn the loss of experiences that are meaningful and enjoyable to them, even if those experiences turn out to no longer be maximally economically efficient according to the Great Capitalistic Moral Code.
Does it mean that we should preserve antiquated jobs and suffer the societal effects of inefficiency without bound? Probably not.
But we should remember that the ultimate goal of the economic system is to enable people to live with meaning and dignity. Efficiency is a means to that end.
I think this ends up being recency bias and terminology hairsplitting, in the end. The number of people working in theatre mask design went to nearly zero quite a while back but we still call the stuff in the centuries after that 'theatre' and 'acting'.
I think "theatre" is a fairly well-defined term to refer to live performances of works that are not strictly musical. Gather up all of the professions necessary to put those productions on together.
The number of opportunities for those professions today is much smaller than it was a hundred years ago before film ate the world.
There are only so many audience members and a night they spend watching a film or watching TV or playing videogames is a night they don't spend going to a play. The result is much smaller audiences. And with fewer audiences, there are fewer plays.
Maybe I should have been clearer that I'm not including film and video production here. Yes, there are definitely opportunities there, though acting for a camera is not at all the same experience as acting for a live audience.
No, but AI did.
It changed the skill set but it didn’t “kill the graphic arts”
Rotoscoping in photoshop is rotoscoping. Superimposing an image on another in photoshop is the same as with film, it’s just faster and cheaper to try again. Digital painting is painting.
AI doesn’t require an artist to make “art”. It doesn’t require skill. It’s different than other tools
Whatever can be replaced by AI will, cause it is easier for business people to deal with than real people.
That's a bit beside the point, which is that AI will not be just another tool, it will take ALL the jobs, one after another.
I do agree it's absolutely great though, and being against it is dumb, unless you want to actually ban it- which is impossible.
Quite the opposite, I'd say that it's what it has most. What are "hallucinations" if not just a display of immense creativity and intuition? "Here, I'll make up this API call that's I haven't read about anywhere but sounds right".
I'm an engineer through and through. I can ask an LLM to generate images just fine, but for a given target audience for a certain purpose? I would have no clue. None what so ever. Ask me to generate an image to use in advertisement for Nuka Cola, targeting tired parents? I genuinely have no idea of where to even start. I have absolutely no understanding of the advertisement domain, and I don't know what tired parents find visually pleasing, or what they would "vibe" with.
My feeble attempts would be absolute trash compared to a professional artist who uses AI to express their vision. The artist would be able to prompt so much more effectively and correct the things that they know from experience will not work.
It's the exact same as with coding with an AI - it will be trash unless you understand the hows and the whys.
I believe you, did you try asking ChatGPT or Claude though?
You can ask them a list of highest-level themes and requirements and further refine from there.
Although I've seen a little American TV ads before, that shit's basically radioactively coloured, same as your fizzy drinks.
The models are trained primarily on copyrighted material and code written by the very professionals who now must "upskill" to remain relevant. This raises complex questions about compensation and ownership that didn't exist with traditional tools. Even if current laws permit it, the ethical implications are different from Photoshop-like tools.
Previous innovations created new mediums and opportunities. Photoshop didn't replace artists, because it enabled new art forms. Film reduced theater jobs but created an entirely new industry where skills could mostly transfer. Manufacturing automation made products like cars accessible to everyone.
AI is fundamentally different. It's designed to produce identical output to human workers, just more cheaply and/or faster. Instead of creating new possibilities, it's primarily focused on substitution. Say AI could eliminate 20% of coding jobs and reduce wages by 30%:
* Unlike previous innovations, this won't make software more accessible
* Software already scales essentially for free (build once, used by many)
* Most consumer software is already free (ad-supported)
This isn't an argument against progress, but we should be clear-eyed about how this transition differs from historical parallels, and why it might not repeat the same historical outcomes. I'm not claiming this will be the case, but that you can see some pretty significant differences for why you might be skeptical that the same creation of new jobs, or improvement to human lifestyle/capabilities will emerge as with say Film or Photoshop.
AI can also be used to achieve things we could not do without, that's the good use of AI, things like Cancer detection, self-driving cars, and so on. I'm speaking specifically of the use of AI to automate and reduce the cost/speed of white collar work like software development.
Its why it is impacting so many people, but also having very small changes to everyday "quality of life" kind of metrics (e.g. ability to eat, communicate, live somewhere, etc). It arguably is more about enabling greater inequality and gatekeeping of wealth to capital - where intelligence and merit matters less in the future world. For most people its hard to see where the positives are for them long term in this story; most everyday folks don't believe the utopia story is in anyway probable.
This is the thing that worries me the most about AI.
The author's ramblings dovetails with this a bit in their "but the craft" section. They vaguely attack the idea of code-golfing and focusing on coding for the craft as essentially incompatible with the corporate model of programming work. And perhaps they're right. If they are, though, this AI wave/hype being mostly about process-streamlining and such seems to be a distillation of that fact.
This might be how one looks at it in the beginning, when having no experience or no idea about coding. With time one will realize it's more about creating the correct mental model of the problem at hand, rather than the activity of coding itself.
Once this realized, AI can't "save" you days of work, as coding is the least time consuming part of creating software.
e.g: MUI, typescript:
// make the checkbox label appear before the checkbox.
vs. about 2 minutes wading through the perfectly excellent but very verbose online documentation to find that I need to set the "labelPlacement" attribute to "start".
Or the tedious minutia that I am perfectly capable of doing, but it's time consuming and error-prone:
// execute a SQL update
I think these days coding is 20% of my job, maybe less. But HN is a diverse audience. You have the full range of web programmers and data scientists all the way to systems engineers and people writing for bare metal. Someone cranking out one-off Python and Javascript is going to have a different opinion on AI coding vs a C/C++ systems engineer and they're going to yell at each other in comments until they realize they don't have the same job, the same goals or the same experiences.
C++, Linux: write an audio processing loop for ALSA
reading audio input, processing it, and then outputting
audio on ALSA devices. Include code to open and close
the ALSA devices. Wrap the code up in a class. Use
Camelcase naming for C++ methods.
Skip the explanations. https://grok.com/
Typically, I'll edit code like this from top to bottom in order to get it to conform to my preferred coding idioms. And I will, of course, submit the code to the same sort of review that I would give my own first-cut code. And the way initialization parameters are passed in needs work. (A follow-on prompt would probably fix that). This is not a fire and forget sort of activity. Hard to say whether that code is right or not; but even if it's not, it would have saved me at least 12 days of effort.
Why did I choose that prompt? Because I have learned through use that AIs do will well with these sorts of coding tasks. I'm still learning, and making new discoveries every day. Today's discovery: it is SO easy to implement SQLLite database in C++ using an AI when you go at it the right way!
Saas just seems very much like a terminator seed situation in the end.
Having plenty of initial discussion and distilling that into requirements documents aimed for modularized components which can all be easily tackled separately is key.
I’d add that Excel didn’t kill the engineering field. It made them more effective and maybe companies will need less of them. But it also means more startups and smaller shops can make use of an engineer. The change is hard and an equilibrium will be reached.
This is how I use it mostly. I also use it for boilerplate, like "What would a database model look like that handles the following" you never want it to do everything, though there are tools that can and will and they're impressive, but then when you have a true production issue, your inability to quickly respond will be a barrier.
- Split things into small files, today’s model harnesses struggle with massive files
- Write lots of tests. When the language model messes up the code (it will), it can use the tests to climb out. Tests are the best way to communicate behavior.
- Write guides and documentation for complex tasks in complex codebases. Use a language model for the first pass if you’re too lazy. Useful for both humans and LLMs
It’s really: make your codebase welcoming for junior engineers
Or not. I watched Copilot's agent mode get stuck in a loop for most of an hour (to be fair, I was letting it continue to see how it handles this failure case) trying to make a test pass.
I’m impressed with this latest generation of models: they reward hack a lot less. Previously they’d change a failing unit test, but now they just look for reasonable but easy ways out in the code.
I call it reward hacking, and laziness is not the right word, but “knowing what needs to be done and not doing it” is the general issue here. I see it in junior engineers occasionally, too.
Desktop publication software killed many jobs. I worked for a publication where I had colleagues that used to typeset, place images, and use a camera to build pages by hand. That required a team of people. Once Quark Xpress and the like hit the scene, one person could do it all, faster.
In terms of illustration, the tools moved from pen and paper to Adobe Illustrator and Aldus / Macromedia Freehand. Which I'd argue was more of a sideways move. You still needed an illustrators skillset to use these tools.
The difference between what I just described and LLM image generation is the tooling changed to streamline an existing skillset. LLM's replace all of it. Just type something and here's your picture. No art / design skill necessary. Obviously, there's no guarantee that the LLM generated image will be any good. So, I'm not sure the Photoshop analogy works here.
Relatively speaking, I would say that film and TV did kill theater
One thing I wish he would have talked about though is maintenance. My only real qualm with my LLM agent buddy is the tendency to just keep adding code if the first pass didn't work. Eventually, it works, sometimes with my manual help. But the resulting code is harder to read and reason about, which makes maintenance and adding features or behavior changes harder. Until you're ready to just hand off the code to the LLM and not do your own changes to it, it's definitely something to keep in mind at minimum.
My obligatory comment how analogies are not good for arguments: there is already discussion here that film (etc.) may have killed theatre.
AI has helped me pick up my pencil and paper again and realize my flawed knowledge, skills, and even flawed approach to AI.
Now i instructed it to never give me code :). not because the code is bad, but my attempts to extract code from it are more based in laziness than efficiency. they are easy to confuse afterall ;(....
I have tons of fun learning with AI, exploring. going on adventures into new topics. Then when i want to really do something, i try to use it for the things i know i am bad at due to laziness, not lack of knowledge. the thing i fell for first...
it helps me explore a space, then i think or am inspired for some creation, and it helps me structure and plan. when i ask it from laziness to give me the code, it helps me overcome my laziness by explaining what i need to do to be able to see why asking for the code was the wrong approach in the first place.
now, that might be different for you. but i have learned i am not some god tier hacker from the spawl, so i realized i need to learn and get better. perhaps you are at the level you can ask it for code and it just works. hats off in that case ;k (i do hope you tested well!)
If this is true, can you share your initial draft that you asked the AI to rewrite. Am I not right that the initial draft is more concise and better conveys your actual thought, even though it's not as much convincing.
I can only imagine what this technology will be like in 10 years. But I do know that it's not going anywhere and it's best to get familiar with it now.
Just stop with this, it's bulshitty. There's nothing related between LLMs and the migration from punch cards to terminals, nor to photoshop compared to film theatre, literally [nothing]. This is a pretty underwelming way of trying to say people that are critiques of this are akin to nostalgic people that "miss the old good days", when there are more than enough pertinent reasons to disagree with this tech in this case. Basically calling opposing people irrational.
I'm not talking about doom or software dev dying or any bullshit like that, I'm just saying this kind of point you make in the end is not reasonable.
Most importantly, I'll embrace the change and hope for the possible abundance.
"Garbage in, garbage out", is still the rule for LLM's. If you don't spend billions training them or if you let them feed on their own tail too much they produce nonsense. e.g. Some LLM's currently produce better general search results than google. This is mainly a product of many billions being spent on expert trainers for those LLM's, while google neglects (or actively enshitifies) their search algorithms shamefully. It's humans, not LLM's, producing these results. How good will LLM's be at search once the money has moved somewhere else and neglect sets in?
LLM's aren't going to take everyone's jobs and trigger a singularity precisely because they fall apart if they try to feed on their own output. They need human input at every stage. They are going to take some people's jobs and create new ones for others, although it will probably be more of the former than the latter, or billionaires wouldn't be betting on them.
This is my experience in general. People seem to be impressed by the LLM output until they actually comprehend it.
The fastest way to have someone break out of this illusion is tell them to chat with the LLM about their own expertise. They will quickly start to notice errors in the output.
If I can reduce this even by 10% for 20 dollars it’s a bargain.