The ending is a really powerful point. Most people apparently agree on two things:
1. AI is a great boon for all tasks and specialties we don’t have the skills to do ourselves. Understandable, since (A) we’re ill equipped to see the flaws in its output because it isn’t our area of expertise, and (B) it often can unlock great gains because if we trust it, we then don’t have to pay and wait for humans to do that thing.
2. AI is a terrible replacement for me - my skills are at such a high level that it’s almost theoretical that it’ll ever be good enough to replace me for 90% of what I get paid to do. It’s a tool at best.
This is why I use AI for all my medical questions and doctors use AI to write software, and we both smirk at the quality the other person is getting from it.
> This is why I use AI for all my medical questions and doctors use AI to write software, and we both smirk at the quality the other person is getting from it.
There is an interesting third group emerging: People who acknowledge the quality problem, but think they can deal with it by applying more AI to the output.
This takes the form of people who spin up a lot of "agents" and give them personalities like security director or quality director (which are unnecessarily complex and maddeningly unpredictable ways to trigger an LLM session for doing a security review or a quality check pass).
It also includes the person who knows that their app is full of bugs, but thinks it's not a problem because they can have the AI fix the bugs as they show up. People in this class haven't encountered security breaches or data loss bugs yet. They think it's all about having Claude fix that div that isn't centered or handle that error code that shows up some times.
It does not have to be brushed away as "brute force" necessarily. We can, and do, build more reliable systems out of less reliable components. In fact, most industrial engineering accepts some defect rate and builds margins around it.
Software is no different. Even without AI, you already have buggy compilers and buggy OSes and buggy libraries. You just tend to accept the risk because you have some idea of what the failure modes are and can work around it or manage the risk in some other way (buy literal insurance.)
> you already have buggy compilers and buggy OSes and buggy libraries.
Which run, I must add, on effectively infallible hardware. Most of the software straight up assumes that the CPUs and the RAM will function perfectly and don't bother even trying to detect such failures (unless those failures manifest themselves in a catastrophic manner, the show will simply go on).
So in effect, we also can, and do, build less reliable systems out of more reliable components, and that's how software is different.
I am not sure if I correctly understood your point. On one dimension, you are basically hinting at another anecdote that proves my point: hardware failure (specifically bit flip in non-ECC memory) is pretty much guaranteed to happen at scale, but people are mostly okay with absorbing that risk. I feel you are overselling the hardware reliability story. For sure, we can build less reliable systems out of reliable components. That goes without saying, and no, that's definitely not software specific. Almost by default most composite systems are less reliable than their primitives (simple example would be nailing two pieces of wood) unless specific care is taken to build in those guardrails or redundancies. The point, however, is it is possible, and there is a vast precedent for it.
There are other places where some process has an error rate and you make up for that error rate by doing the work more than once and then comparing results. For example, I've heard in a video that satellites and other space craft often have 3 or 4 processors and compare the results to make sure there were no errors due to radiation. Similarly, we have RAID arrays that store data multiple times because disks can fail. So, even if AI has a failure rate of like 20%, maybe you can make up for that by running the same prompt multiple times with slight variations or with different models, comparing the results and choosing the best.
I think it also requires someone who knows just enough to be able to navigate between those ideas that will set you back and those which will propel you forward. At the end of the day, you still need some human filter.
> There is an interesting third group emerging: People who acknowledge the quality problem, but think they can deal with it by applying more AI to the output.
That's the entire big tech's business strategy right now.
I always imagine the model rolling its silicon eyes when it’s assigned a personality (“you are an expert growth hacker”) at the start of the prompt. Was that ever actually shown to be effective? Is it still?
> Was that ever actually shown to be effective? Is it still?
Yes! Personas demonstrated measurable improvement in a few different ways, with caveats of course. The common intuition is that personas influence token space in beneficial ways.
I'll come back here later on desktop and link a few (still) relevant papers on this topic.
I remember there were some studies that this kind of thing was effective a year or so ago, so essentially a lifetime in Model years.
However to me it seems completely reasonable that it would work, because my understanding of what happens is the model interprets what you said as:
Look for a group of people who are considered to be expert growth hackers by the world at large and answer my questions as though they were answering them.
So assuming that there are a set of questions that can best be answered by people that most other people identify as expert growth hackers then yes, I believe assigning a personality in this way should obviously work.
I imagined it as kind of a shorthand for "you should be spending my tokens on looking for / addressing issues like X, Y, and Z," where X, Y, and Z are the sorts of things that an expert in [insert domain here] would be likely to care most about.
At some point we have to just admit we're mass cargo-culting here and that these secret invocations people swear by have the same epistemic value as medieval superstitions.
It's been interesting to see how aggressively some reasoning models like to "reason" by analogy. They love to say things like "it's like a CPU" or "it's like a highway", and then they start to make logical leaps based off that rather than just using it for user explanation. Gemini 2.5 and 3.1 Pro have been particularly bad for this type of behavior. Telling models to "speak as though you are a physiologist considering the case with an expert colleague" gets them to "reason" using a more correct linguistic substrate.
The Opus models over the last year doesn't seem as vulnerable to this type of behavior and I've noticed the "identify as expert" prompt tricks aren't as meaningful there.
I propose we move away from the framing of "Model years" - they're standard human research years. Yes, likely more people are working on it, and also working harder, but ever since we acquired a certain amount of compute in the world, many people were able to independently find the same patterns and train models.
From what I've heard, personas give a greater chance that the LLM will answer confidently.. and also a greater chance it'll hallucinate something when the data is sparse. Supposedly "grounding" the personas on real documents/web searches is the best approach. Anecdotal though.
I did something different. Instead of describing the image, I described the artist. Made that artist in Ubermensch. Then asked AI to draw the image from his point of view. It worked fabulously.
There was a time when stuff like "Unreal Engine, trending on ArtStation, 8K resolution" actually worked when prompting image gen models because such labels actually correlated with higher-quality images in the web-crawled training datasets available back then.
Back with some papers. (Apologies in advance; I typically don't edit/format comments much here, please bear with me.)
Notable papers describing performance improvements with prescribed roles and personas:
- ExpertPrompting: Instructing Large Language Models to be Distinguished Experts (2023) https://arxiv.org/abs/2305.14688 (if you're going to only read one paper here, maybe read this one but know there has been a lot of follow up with more modern models.)
A TLDR of my *admittedly heavily biased* mental model (so take it with a grain of salt): personas do improve task alignment and precision to measurable effect but with observed negative impact to accuracy and knowledge grounding. Overall, this makes it quite suitable and preferred for code generation scenarios. (Don't over-index on 'accuracy' here as meaning "bad code", it's more about verbosity/jargon reducing clarity of higher order goals like business objectives and system architecture.)
Outside of code generation, personas have the interesting effect of increasing implicit biases and stereotypes. It's not hard to imagine something like "you are a left|right wing politician ..." or "you are a senior-citizen|teenager ..." influencing token space construction considerably.
I feel it helps for the personality aspect, how it handles answers and general vocabulary, but it doesn’t in any way improve skill level, at least that’s my take from building an assistant.
At least in the beginning of spicy autocomplete, this sort of role-play did work pretty dramatically at aligning a conversation to a task, though I don't think anyone ever tested it versus somewhat less cringe priming.
> though I don't think anyone ever tested it versus somewhat less cringe priming.
I really wonder if phrasing it differently would make a difference. In good faith conversations, it just doesn't happen that someone tells someone else who that person is.
The reason it seems suspicious is that it's phrased in a way that's oriented towards humans. I haven't tested this, but I suspect you'd get similar results if you said something like "orient your response to that of a growth hacker." Either one is likely to have the desired effect on the stochastic result.
> There is an interesting third group emerging: People who acknowledge the quality problem, but think they can deal with it by applying more AI to the output.
Ah yes, the known unknowns.
The discussion reminds me of a talk Zizek gave in which he discusses the speech Rumsfeld gave regarding the evidence Iraq supplying weapons to terrorist[0].
Zezik argues the unknown knowns are far more interesting (and the reason why USA was losing in Iraq). While Rumsfeld focused on the unknown unknowns.
I've noticed that domain experts who implicitly know the the known unknowns of their field distrust LLMs because they can identify their shortcomings. Those subtle mistakes LLMs make. I argue this is why domain experts using LLMs get such a boost. They can identify and avoid pitfalls sometimes before they happen. But in other fields the same people are in awe of LLM capabilities precisely because the known unknowns are a mystery.
The Unknown Unknowns of LLMs are the IMO the most interesting. The so called emergent capabilities of the technology. The use of LLMs in others fields such as biology, eg in protein language models, is really cool.
Everyone focuses on replacement of people workers when I think opening new fields of work for humans should be the goal of LLMs by leveraging the tech to discover.
The other interesting caregory is unknown knows. But that's another topic for another time.
As an aside, the mass mockery in response to Rumsfeld's statement always bothered me because it's the single most intelligent statement he ever made about the Iraq war, and if he had started out with that mindset things probably would not have gone nearly as pear-shaped as they did.
This is one of those classic "sounds dumb / doesn't play well on TV but is actually smarter than most of the other people babbling about it" things. Nassim Taleb has written for example about how maddening it is to watch world-class economists who are also just sort of awkward and a little nerdy go on TV and "lose" to blowhards who don't actually know what the hell they're talking about but appear confident and look good on camera. Thankfully in Rumsfeld's case I think as time has gone on it's become a pretty respected statement about risk even if people still occasionally find the phrasing a bit amusing.
They spin up agents, and then give them roles like commenter, and director of quality for the commenter. Although I'm unsure how the director helps since I've never seen one do actual work.
I have a few periods during my daily routine where I’m waiting somewhere away from the computer and need a break from email.
A lot of my comments have double digit upvotes and some get into the mid hundreds. I try to actually read articles and provide thoughtful comments, which gets upvoted a lot more than the throwaway.
> Are you averaging like 2000+ comments a month?
52000 / 3 years would be under 1500 points per month or 48 points per day. That could be done with 1-2 helpful comments per day on popular threads.
I browse HN a bit more than I should and I see you and simonw around a lot, like you said always providing thoughtful commentary.
When I write comments on here I tend to spend upwards of 15 minutes to draft and reformulate my comments. Sometimes double-checking what I'm about to say (sometimes not thoroughly enough as some of my recent comments show) and I was wondering if you have a similar experience in that regard or do you just manage to fire off a comment in a stream of thought fashion from start to end?
my skills are at such a high level that it’s almost theoretical that it’ll ever be good enough to replace me for 90% of what I get paid to do.
Is it really true for most people that they are using their core advanced skills 90% of the time? I'm curious about how people feel about this.
I'm a professor, which is supposed to be an intellectually demanding job. I do research in NLP/AI, and I don't think AI will replace my core intellectual tasks in the near future, but I don't think my core intellectual tasks represent even 10% of my time. Most of the time is taken by various things like writing bureaucratic reports, writing and polishing grant applications, grading exams and exercises, designing a poster, planning a course's calendar for a given year, creating a figure for slides, writing assignments and exams, attending teaching coordination meetings... which definitely are or should be automatable. Probably even teaching the same lesson for the umpteenth time also is from an objective point of view, we'll probably be kept doing it due to the human factors driving motivation but not because a lecture given by a human is intellectually superior.
> Confidence in AI output is inversely proportional to one's ability to verify it
I like this / generally agree. The only wrinkle is that - for some tasks - the verification _is_ "run the script, see if it worked, don't care how... just that it did" which is distinctly different from "not only did it do it correctly, it did so in the most direct and performant way possible".
For a _lot_ of what I use LLMs to build, the former is all I need.
And for as long that that runs on your computer, I don't care.
But the problem is that for many people they now believe it's ok to present a 10k line vibe-coded PR that only has been verified against external behavior, and some Senior Engineer needs to review it, in time, under pressure, without too much push-back, and lastly, it's the Senior Engineer that gets paged at 2am because something has fallen over.
Also, those scripts tend to start a life of their own, and because it looks good enough, people don't look at them again.
I recall a bug of someone vibe-coding a cleanup script for folders older than $x (on Windows).
Get the CreationDate, and sort. Delete older than $x. Except CreationDate can be null and null is always smaller than $x.
show me a prompt that is meaningfully expertly crafted beyond just providing Do's, Do not's, task context, and a goal.
> Correctly prompt, to steer it, to verify it, and to improve the harness.
I doubt this a lot. The average AI user is running claude code as the harness, or Codex etc. prompting has no secret incantations, and steer and verify is just knowing what the answer should roughly look like, which is a domain skill, not an AI skill.
I feel like you don't have any friends who make software but don't know how to code.
Yes, they do make software now - whereas it was impossible before. You may be absolutely shocked at how bad LLM code can be when prompted from a noncoder. How buggy, and how absolutely rife with security problems it can have. I honestly don't know how they can get LLMs to write such bad software - but somehow they can. This is from people who have been vibe coding for 3 years straight btw (huge amount of time p/day).
We must live in different realities, because I have the direct opposite experience.
Perhaps we are defining "job" differently? AI can, with much coaching, _perhaps_[1], do some _aspects_ of a programmer's job. But not all of it, or even the most important parts of it.
[1] given that we have spent the past many decades pointing out that developer productivity is possibly impossible to measure, or at least very hard; given "done" vs "done done"; given the history of "rock star" developers creating messes behind them, the difference between short and long term thinking and the external imperceptability of that difference; given all of that, we haven't really had enough time to form a valid opinion on what AI can do, in the long run.
are you saying that all of the programmers you’ve met in real life have automated their work away and are coasting while waiting for their bosses to fire them…?
…if not, they’ve found developer work that ai can’t do yet, no?
It seems to be a general principle: If AI is better than you at something, you use it. If AI is worse than you, you don't.
Each time the frontier models get better, I see another wave of AI doubters suddenly become believers. People say things like, "AI couldn't code last year, but now I use it for everything!" Interesting. Now we know how that the person who said this has the coding skills of a Claude Opus 4.5 or whenever the frontier was when they flipped.
Meanwhile, the rest of us keep using AI as simple tools, like the person in the article. I wonder how long it will take before computers can program better than me, and I flip too.
I’m not sure I agree with this but maybe I just lack self awareness?
There are large portions of my codebases that are essentially extremely verbose grunt work. My UI stack, IaC YAML, thin CRUD routes, etc.
I know what the code is supposed to look like when it’s done being written, but it’s going to take me for freaking ever to type it all out.
I can just few shot it now in an hour. Plan -> feedback loop -> build -> review loop.
Does it try to do weird stuff? Yeah. And then I’m just like “that’s weird, no, the components should be broken up like XYZ” and then it’s not weird anymore. Occasionally (1% of the time) I just do a quick refactor myself instead of trying to tell the agent harness what to do.
I can get something fairly close to the ballpark of what I would have done but in like single digit percentage of the time.
And the result is that I can spit out a bunch of purpose built tools (personal tools, internal tools for teams, etc.) that I never would have been able to justify building otherwise.
If AI is not better than you at a task, but it's good enough and saves you time, it also makes sense to use it. Many of my uses of AI fall in this category.
> the person who said this has the coding skills of a Claude Opus 4.5 or whenever the frontier was when they flipped
It's not about just skill. It's a matter of skill, time, and how critical the software you are writing is.
There is a lot of software that is not critical. That is not close to security mechanisms. And that even if the code quality is not the highest, it does not matter.
Even if you are the best coder in the world, you would already become more productive by using ai. Things that in the past you might have not coded yourself but delegated to an intern, or things that you wouldn't even delegate to an intern because they are just too boring to do like some refactorings.
Like I had this project at work that was written without typescript strict mode turned on. When I turned it on, it had over 700 errors. I might be better than AI to fix every single of one these errors. But my time is worth more than that in doing other things. But I can, and did, ask AI to fix every single one. And then I reviewed it batches, and something that my team wanted to do for multiple years and nobody had the time for, finally got done.
the sentiment "AI couldn't code last year, but now I use it for everything!" rings true for me... but I didn't flip cause AI is now better than me... I flipped cause now I am faster with AI than without it...
A year ago the AI output was so bad that getting it up to my standards took more than writing it myself from scratch. And nowadays it is faster for me to start with AI output and iterate from there to reach quality submission.
The ninety-ninety[0] rule was a thing talked about 40 years ago, long before anyone thought of AI coding. AI can nowadays make the first 90% of the task very fast and good enough. The last 10% is still the hardest part of coding by far.
I feel like I am the only one thinking AI is actually much better than me in the things I'm supposed to do well. I feel like that for years now, so it's not about the latest generation of models. I can't imagine a single thing I can really compete with an AI at this stage. I am not sure if I am under-skilled or others are overconfident. Maybe people who feel like me don't say this out laud.
agree. it's strange reading the loud voices that are counter to my lived experience. llms just have seemingly infinite depth - or can at least debug and execute without fatigue.
I was saying something like this a few years ago when people were getting first excited about ChatGPT. The gap has narrowed, but not by as much as people think.
AI produces output that is very convincing to a non-expert, and (dangerously), it's so good at looking like an expert, they might believe that it is an expert. But the moment you ask someone to use it for something they're an expert in themselves, the holes appear wide, consistent & obvious.
My favourite moment of seeing this in action was watching AI-worrier TV host/comedian Bill Maher. He has spent years talking about the dangers of AI taking everyone's jobs, destroying civilisation, ruining the economy, starting wars, "it's just getting better and better all the time", and so on. But one night he let slip a tell. "It's no good at writing jokes. Not yet, anyway". There you go, Bill... connect those dots...
There is real utility in it being a tool to help experts apply their expertise, as in this story where it speeds up some tasks to help the translator do part of the work, enhance their expertise, allow them to be more productive.
It's a better screwdriver, a better hammer, in the hands of somebody who knows what needs a screwdriver or a hammer. It doesn't replace them. It can't replace them. It's a tool that enhances the human, not an alternative.
I don't understand why this is not widely understood yet, but I'm sure it will in due course.
And I don't expect this to change. Even if the latest model scores 100% on every benchmark, all that really tells us is that it's now more productive/efficient than it was before at helping experts do that work, not that it can replace everyone in that category of work.
At what point does this become an issue for data quality and global epistemology?
It seems inevitable that we ask for more AI assistance on topics we don't understand. And therefore have the least context to correct. Result: a flood of poor quality information.
In areas we DO understand, we'll either not ask AI at all, or treat its results with a higher degree of skepticism. Result: a lack of high quality information.
Inevitably this means a higher volume of non-expert prompts gets translated into the next generation of internet content. AIs are pumping out more novice-level text and less expert guidance.
The result will be an internet full content written from the perspective of an ignoramus; not addressing any complex issues, staying surface level on every topic. Which will cascade into future models, etc.
> The result will be an internet full content written from the perspective of an ignoramus; not addressing any complex issues,
Not to be overly negative, but have you really looked at the vast majority of the content on the internet? There are good pockets of real, in depth content. But the absolute vast majority of it is surface level basics at best, and completely wrong hot takes at worst. Content farms and click spam have made up huge portions of the internet for a while, never mind the absolute hell holes that places like Facebook, Twitter and Tumblr were and have been. And that's before you consider how often news media gets stuff wrong and then everyone copies everyone else's homework. Knowledge propagation, and more specifically correct knowledge propagation has always been difficult, slow and rare. You have always needed to check primary sources, and AI is just the latest in a long line of reminders of that fact.
Yes, the first 80% of a subject is repeated everywhere (including all the misconceptions) and you cannot go deeper except if you got very lucky like found a 5 year old youtube video with 130 views or an old blog post or a downvoted reddit comment. This is what makes internet so addicting to me, the small chance of finding these hidden gems inside mountains of garbage.
Having 80% in a broad amount of subjects is basically worthless, it is the 90% and further that have value because it took luck and actual personal experience and effort to take it that far.
The fundamental issue imo is that LLMs are trained to make believable outputs. They can be complete horseshit, but because they look plausible they get treated like quality.
I swear that the intensity and time I've had to take with code reviews has gone up because LLMs are so good at making flawed code look good. I assume the same goes for everything else we use LLMs for.
> 2. AI is a terrible replacement for me - my skills are at such a high level that it’s almost theoretical that it’ll ever be good enough to replace me for 90% of what I get paid to do. It’s a tool at best.
Most? Perhaps it's depression, but I look back at my career and wonder if any code I've ever been paid to write is beyond what current AI can do.
Sure, this leaves me with the non-coding tasks of UX taste, and code review + a few other forms of QA (and, when self-employed, project management, game design, etc.), but man, I'm someone who actually learned to read in part on the Commodore 64 user manual (as in, trying to understand what PEAK and POKE meant concurrent with having "Jack and Jill go up the hill" picture books).
(And no, I'm not claiming LLMs make bug-free code, I see the bugs LLMs make during my code review of their output and some of them are awful, hence "this leaves me with …").
And? How valuable are individual lines of code? To the author's point, I'm sure AI can translate individual sentences perfectly, but miss the nuance of communication in a bigger project or body of text. In the same vein, when was the last time someone put an AI on a ralph loop, posted the result on r/vibecoding and ended up with actual users.
Don't care, only time I've measured them was personal curiosity about hand-written projects, and one time I was trying to work out how many blank comments a co-worker had put into their codebase*.
How valuable are features? Management kept giving me them, and I always just assumed they'd decided which ones were important. But I've seen git histories of apps where the same feature was added twice, 5 years apart, by the same developer.
> In the same vein, when was the last time someone put an AI on a ralph loop, posted the result on r/vibecoding and ended up with actual users.
How often do the megacorps currently boasting that 80% of their code is now vibed, post anything (other than adverts) to reddit?
* 20% of the whole project, or 24 thousand blank comments.
Reminded me of this post by EY. (You're making a different point about existing expertise, not LLM expertise, but I think it holds in general.)
Every month a new guy discovers LLMs; discovers a skill the current LLMs require to get good results; and writes about the future jobs that will always be available for smart people like HIM, that are SKILLED in using LLMs.
The next generation of AIs doesn't need his fancy prompt. The image model goes from needing to type in just the right set of weird words and cryptic sorcerous invocations, to most people being able to type in English what they want and get a pretty good result.
There are still tasks that require careful invocation. But they are a much smaller fraction of all the tasks people are trying to do, or you can get a bleh result without the elaborate invocation to get it really good. And to improve on the bleh result you need to be substantially more of an expert than back when the Guy was memorizing a rule about adding "trending on Artstation" to the image prompts, as would always require a human paid to do that.
Another generation of AIs comes out. The next generation of Clever Skills is obsolete. Image models just obey the instructions for compositing panels without mixing them up, and you don't need to be an expert to get them to do it right. Another human value-add is gone. A wider set of tasks require no human expert.
Now a new Guy notices LLMs have become useful in his field for the first time. He discovers they require SKILL to use CORRECTLY. He posts about how there will always be jobs for humans who are SKILLED in using LLMs like HIM.
But it is not an infinite cycle. It is not the same each time it repeats. Now the Guy is a highly paid programmer or a career mathematician in 2026, instead of a graphic artist in 2023.
In six months the models will no longer require his vaunted Skills.
And by then there will be another Guy.
But the process doesn't continue forever. The Guys are coming from fields that were harder and harder for AIs. The brief centaur eras are shorter and shorter.
Today it is writers who are laughing at how bad the LLMs are at their job, and who will perhaps soon be posting about how it takes Skill to get an LLM to do their job Correctly. But the models are coming faster, and the eras of kinds of human value-add in each field are shortening.
There is a point when you run out of Guys, either because the centaur eras are too short for people to develop SKILLs and post to Twitter about them; or because there are not lands left for AIs to conquer; or because ordinary people are not reassured by some Nobel laureate proclaiming there will always be jobs for Nobel laureates with the SKILLS to prompt robotized biology labs Correctly.
But we'll never run out of amateur economists who assert entirely without a brief contemporary example that there will always be jobs for humans skilled at operating AIs!
We'll run out of professional economists saying it when nobody is paid for that work anymore.
I guess we'll also run out of amateur economists when they're dead.
My fear is in the future it won't matter. People will accept slop because while they can be convinced it's not as good as it could be, it's good enough. To them it's good enough because it's fast and cheap not because it's actually good. There won't be any room in the economy for the value human output brings because the economy will rearrange itself around AI and become completely dependent on cheap output, good enough or not.
We should create a generalized version of "Gell-Mann Amnesia". This applies not just to fields of study. But also to time and space. We read history as if the person who wrote the history book has perfect knowledge.
Honestly, we're at a point where AI can write better software than some devs and answer medical questions with more knowledge than some doctors.
Likewise, AI is oblivious to it's own mistakes, much like said professionals can be at times.
Not that AI is actually thinking, but rather the collective corpus of text yields greater insights (knowledge of the crowd, not wisdom of the crowd) than a lower-average person in that same industry.
I read two translations of the book "The Master and Margarita". My first read was so boring I couldn't help but stop reading before the end of the first chapter. I can't find the copy and the name of the person who translated it, but this one had all the Russian nicknames translated. It kept talking about a guy called homeless. I thought it was just a bad book and dismissed it for years. I couldn't understand what all the fuss was about with this book.
But then, I stumbled upon the translation by Diana Burgin and Katherine Tiernan O'Connor. Although I don't speak Russian, I think this is as good as it gets. They did a phenomenal job.
You can see the same effect with the mechanical translation of the book "We" by Yevgeny Zamyatin, where the government is called "United State" easily confused with the "United States". The translation that called it "One State" was so much better.
I still clearly remember in my early twenties being stunned to discover that the Astérix comics are originally written in French and then translated. Coming up with names like Getafix in English for the druid — incroyable.
An honest to god article full of em dashes that's not because it was AI but because it was a human using them as a crutch to get around crafting sentences that flow naturally. Almost brings a tear to my eye.
> In my Ottawa life, every Tuesday evening, I take two gym classes back to back—boxing and the pompously named “body sculpt,” which makes me discover muscles I didn’t know I had.
The em-dash matches how you'd speak out loud.
You'd say "I take two classes every Tuesday back to back, boxing and 'body sculpt'. Weird name." (Parts of that sentence did flow oddly, but not because of the em-dash).
Grammarians say you can't make those separate sentences without adding some extra words, and because of blah-de-blah-blah-blah, someone might say you can't join them with a comma. So we have an em-dash.
Rewriting the sentence would make it flow less naturally, not more.
When I write like I talk, I use a lot of commas. Replacing some of my commas with em dashies, so long as it was done judiciously, would probably make things easier to chunk.
It’s become the exclamation mark of mid-sentence punctuation. It connotes fragmented or interrupted speech in my opinion. The problem is that writing is not speech, that’s why it is more often seen in written dialogue.
I wish more people had casual exposure to professional translators. Its a deeply important, vanishingly small segment of the human population and has been this way for at least many thousands of years. Also, it will continue to be!
They certainly have their place, but are massively overused in contemporary American prose. This might be slight more of an east coast thing, but that's just a subjective impression that I'm not willing to spend time measuring.
To me they come off as faddish, with many writers using them where commas and semicolons would have done just as well. I think their popularity stems from teh fact that provide the sense of a personal aside from the writer, allowing them to be more expressive while clearly delineating the personal or contextual remark from the main flow of the prose. No doubt this works for a lot of readers, but I find it tedious.
It's a fad that has been going strong for centuries in published literature, so I'd guess an awful lot of authors world disagree with you.
You can restructureany sentence to use fewer forms of punctuation -- but if you do that, you'll lose nuance. And nuance, in writing, is a very fine thing.
The em-dash has indeed been around for centuries, but the fad I refer to is its overuse in contemporary American prose. IF you look at Google Books n-gram viewer, you can see it went through a surge of popularity over a few decades that then fell off sharply.
It's also notable that the em-dash is approved in American Manuals of Style, while discouraged in British ones. I was unable to find longitudinal data for the em-dash's use in magazines, blogs etc., but AI summaries suggest it's 3-4 times more used in those contexts than in news reports.
Like strawberry ice cream or apple pie, nuance is certainly a fine thing; but a surfeit of it becomes cloying, and the antipathy toward the omnipresence of the em-dash in LLM-generated prose, along with other kinds of literary expression like contrast and comparison, suggests to me that people have had more than enough of it.
I use them because I know what I want to say out loud, but transcribing the pause with commas is incorrect because it's a comma splice, and I find that the semicolon often looks glaringly overly formal. So I've settled on the em-dash.
My writing used to be littered with them, but I now eschew the em in favour of en, as it has become too strong an anti-shibboleth.
I have also taken to being sloppier in my prose, as I’ve had stories rejected for being “written by AI” - when they’re shorts I wrote more than a decade ago. Reworked them to sound like a moron, accepted. Sigh.
I have a similar issue. I tend to have a very “structured” type of writing. Say on slack or Reddit for example. Using markdown formatting. Lists with bulletpoints etc. And I tend to write long detailed explanations, sometimes too long if I am being honest.
But now I find myself adding noise and imperfections to my writing (not that it was perfect) to make it more human, which is kinda silly.
Either it's LLM generated, or it's written by someone who wants to be ambiguous about using LLMs.
Either way, I'm not reading it, it's a clanker or a clanker collaborationist.
I mean, how would you even write an em dash? There's no button in the keyboard for em dashes, it's not in ascii, it's just not something we write in internet text with, it's a safety watermark put into LLMs by OpenAI to help making LLM generated content identifiable as such.
If for some reason you are an em dash lover that was hurt by the LLM debacle, I'm so sorry for your loss, but look who's on your side, give the em dash a funeral and let it go.
It's been seared into my muscle memory for more than a decade. I keep using it, too. It's present in the popular training sets – and then in LLM outputs – simply because it's proper punctuation.
You'd be laughed at if you said that ChatGPT could help you with graduate level mathematics in 2024, but this year, AI models on simple prompts are solving previously unsolved Erdos problems.
It seems silly to imagine that there is some fundamental barrier between human intelligence and AI, and that AI could never do many of the things that humans can do. Inferring intent, gauging sentiments, factoring in cultural values, etc. all the things cited as stuff humans can do but AI can't, AI can currently do if given enough context. But more importantly, all those things aren't magical tasks that can only occur inside a human skull, they are a product of information processing, its just the information processing that has been hard to make computers good at, but so far it appears AI keeps getting better.
I'm all for humans having special value that is not attached to their ability to perform useful work. However denying the abilities of AI models seems to be a common mistake many people are making, and sadly reality catches up to these people before they can emotionally prepare.
It's worth noting that you can substitute "dollars" for "context" in that sentence, which seems to be where many of these impressive achievements are coming from. As ever, it's unclear whether these models will get cheaper while remaining better, since all of the recent breakthroughs appear to be of the "think more" kind. For translation specifically, I'd be very surprised if the "think more" LLMs would help given the per-unit cost expected of the output.
> all those things aren't magical tasks that can only occur inside a human skull, they are a product of information processing
I agree but it's useful to remember that 1. brains and especially the human brain are enormous and 2. individual tokens carry significantly more meaning than individual tiny muscle twitches so even extremely primitive "cognition" can look like it's doing more work than it actually is.
> You'd be laughed at if you said that ChatGPT could help you with graduate level mathematics in 2024, but this year, AI models on simple prompts are solving previously unsolved Erdos problems.
I'm curious, do you have a graduate degree in mathematics?
I have a graduate degree in mathematics. AI models can absolutely help you do research math in 2026. I recently asked chatGPT to prove a result which I know to have been published in Advances in Mathematics (a pretty good, but not top tier, journal) this year, and it gave a correct proof which was completely distinct from the one that was published.
Yes. It's as if they think AI will forever be LLM only and won't develop world models that incorporate current state assessment, dynamic next-state prediction, cause-and-effect reasoning, object permanence, etc. I'm not in the AI industry but I assume there's got to be lots of research and work being done on this.
Fable has really spooked me, honestly. It's another big jump, but not in the actual coding. I was pretty comfortable with the "you do the implementation, I do the meta work and steering", and ... no steering required, no meta work required. Here's the backlog, let me know when it's complete, I guess I'm going to go touch grass until I have to review and refine... probably tomorrow?
Reminds me of the first time I saw a coding agent stumble through an issue in 2023 maybe? and went "this is a big deal", similarly when OG gpt started making jokes that actually kinda worked.
Updated modern version of the classic "make me a greentext", apologies for slop-posting, but it seems relevant:
> be me
> senior software engineer
> in charge of making sure the tickets get, in fact, implemented
> occasionally have to open the IDE and write some code myself
> one day i open the IDE and the ticket is already closed
> the agent did it overnight
> no steering, no review notes, nothing left for me to do
> distress.jpg
> ask my manager what to do
> he says "just focus on the high-level architecture stuff"
> i say "what high-level architecture stuff"
> he says "i don't know, you're the senior engineer"
> rage.jpg
> quit my job
> become a prompt engineer, nice and simple, just tell it what to build
> first day on the job, sit down to write the prompt
> AI already wrote it
Greentext is eh. Very formulaic, in fact very similar to the bottomless pit one, which I'd argue is better because of it's absurdity. I have to ask, did you mention the older GPT version to fable in the prompt?
As mentioned in the article, the point of language is to communicate with other humans, and you need a human to do that.
Mathematics is famously rigorously defined, it's roughly analog to AI beating humans at chess. Sure it's impressive, but it's also something you'd expect machines to be good at.
I have no doubt that the writer is better at translating than AI, but I have to say that AI translation has gotten so good that I'm not sure how much longer translation work will be there, or rather it might end up being more about auditing.
For example, I just read the Lawrence Ellsworth translation of The Three Musketeers, which I very thoroughly enjoyed. I don't speak or read French, but from my understanding Ellsworth's translation is considered one of the more accurate translations of the work.
Out of curiosity, I sic'd Claude Fable on the original French version of The Three Musketeers and told it to translate accurately, but also try and keep the same jovial tone as the original and do not censor anything. After it was done, I didn't read the entire output, but I did compare a few individual chapters between the Ellsworth translation and the Fable translation.
They were honestly remarkably similar. As far as I could tell, nothing was substantially different from the Ellsworth translation and the Fable translation. I do think that the prose for the Ellsworth translation was a bit better, but the prose for the Fable one was actually perfectly readable. Again, I don't speak French so I cannot say for sure, but I do not believe that I would have gotten a significantly different experience had I read the Fable version instead of the Ellsworth version.
Now, it's possible (and likely) that this is somewhat self-fulfilling; Fable might have been trained using Ellsworth's translation and as such it's very directly able to crib from it; sadly since I do not speak any language outside of English, there's sort of a catch-22: the only way I can compare the accuracy of a translation is to compare against other translations, but if other translations exist then that will likely influence the results, and if a translation doesn't already exist then I have no way of auditing it.
I'm still going to continue reading through Ellsworth's translations for the subsequent stories simply because that feels more canonical, and as I said I do think the prose was a bit better.
> I have to say that AI translation has gotten so good
> I do not speak any language outside of English
So you are entirely unqualified to judge this, and you acknowledge yourself that your test is flawed to the point of being completely useless. Yet you make grandiose statements about the quality.
> Out of curiosity, I sic'd Claude Fable on the original French version of The Three Musketeers and told it to translate accurately, but also try and keep the same jovial tone as the original and do not censor anything. After it was done, I didn't read the entire output, but I did compare a few individual chapters between the Ellsworth translation and the Fable translation.
This isn’t a great test, because Claude almost certainly has multiple translations of The Three Musketeers in its training data.
The things is, this is almost certainly what's happening.
You can (could, maybe they 'fixed' it by now) get sota LLMs to reproduce entire novels near verbatim.
The idea of giving it parallel texts of those novels in different languages, to train it on translation, is so obvious it'd just be strange if the AI labs didn't do it.
In fact DeepL was doing basically that more than 10 y ago.
Oops, I legitimately missed the second-to-last paragraph.
I still think there are better tests you could do. Ideally, you would choose a book that was published recently—after the model’s cut-off date—which is considered to be a good translation. But even something like The Girl With the Dragon Tattoo, which is not particularly new and by no means obscure, would be better than a famous work of literature like The Three Musketeers that has many translations.
Almost certainly correct, though I've noticed that these LLMs like to complain when you give it stuff that is still in copyright. The Three Musketeers is thoroughly public domain everywhere so in that sense it's a good test, but of course because it's public domain everywhere there are lots of translations to crib from so I acknowledge it's not a great test because the training data almost certainly contains a competent translation.
Even if Fable didn't have Ellsworth's translation, it certainly has the William Barrow translation, which would still get it like 80+% of the way there.
My wife speaks Spanish, I should get her to do some kind of comparison with a Spanish book that doesn't have English translations.
I tried this with the original comment in the thread. Guaranteed to not be in the corpus, references a few terms that also wouldn't be in the corpus (Claude Fable), and long enough to be more than a sentence or two while short enough to compare in a discussion like this.
I did this with entirely local models I have sitting around on my laptop. Minimax M2.7 at a 3 bit quant with 8 bit quantized KV cache for English -> French, Gemma 4 31B QAT (4 bit quant) MTP for French -> English.
It's perfectly readable, but there are a few places where the phrasing is a bit more awkward after the double translation ("auditing" to "revision" in particular is a bit off). Gemma did comment on not knowing what Claude Fable was in its thought process: "The author compares Ellsworth's translation with one produced by "Claude Fable" (likely a misspelling of "Claude" or a specific version of Claude)."
Here's the double translation:
"I have no doubt that a writer is better at translating than AI, but I must say that AI translation has become so good that I'm not sure how much longer the profession of translation will exist—or rather, it may become more a matter of revision.
"For example, I just read Lawrence Ellsworth's translation of The Three Musketeers, which I enjoyed immensely. I neither speak nor read French, but from what I understand, Ellsworth's translation is considered one of the most faithful translations of the work.
"Out of curiosity, I asked Claude Fable to translate the original French version of The Three Musketeers; I asked it to translate faithfully, but also to try to maintain the same playful tone as the original and to censor nothing.
"Once it was finished, I didn't read the entire result, but I compared a few individual chapters between Ellsworth's translation and Fable's.
"They were honestly remarkably similar. As far as I can tell, nothing was substantially different between Ellsworth's translation and Fable's. I think the prose in Ellsworth's translation was slightly better, but Fable's was actually perfectly readable. Again, I don't speak French, so I can't say for certain, but I don't believe I would have had a significantly different experience if I had read Fable's version instead of Ellsworth's.
"It is possible (and probable) that this is partly a self-fulfilling prophecy; Fable may have been trained using Ellsworth's translation and can therefore draw directly from it. Unfortunately, since I don't speak any language other than English, there is a sort of vicious circle: the only way to compare the fidelity of a translation is to compare it to other translations, but if other translations already exist, that will likely influence the results, and if a translation doesn't exist yet, I have no way of verifying it.
"I am going to continue reading Ellsworth's translations for the following stories simply because it feels more canonical to me, and as I said, I think the prose was slightly better."
As somebody who regularly reads translated works, including the occasional machine translation (MTL), they (MTL) suck. You got a hugely biased result, which you recognize.
Translation is hard. If you're familiar with reading translations from specific languages MTL works have a very specific smell to them, it's a bit hard to describe but it's there. A good translation is miles (kilometers, for those outside of the US) above MTL.
That's not to say that perhaps the latest LLMs will have better translation abilities, but that they are generally crap currently. Maybe they are fine for something very short, but absolutely not for longer content.
> As far as I could tell, nothing was substantially different from the Ellsworth translation and the Fable translation.
Crucially the full translation was part of ChatGPT’s training set. Recall is a pretty solved problem in machine learning.
How well does it translate a French novel published yesterday? Where neither the original novel nor any translations are in the training set yet? Or might not even exist!
I tried asking ChatGPT to translate a letter I wrote in Slovenian this weekend. It got the general gist but missed a lot of the nuance. Completely missed several of the little touches of tone where the right choice of synonym conveys a whole bunch of information.
Guess I have no way of proving it, but I pinky swear that I didn't edit it in later!
But yeah, I broadly do agree; if I read other languages I could find a book that hadn't been thoroughly translated to English and then I could give a proper analysis on how good the translation is, but since I'm a very stereotypical American I know exactly one language (and sometimes my comprehension of even that is questionable).
> I could find a book that hadn't been thoroughly translated to English and then I could give a proper analysis on how good the translation is, but since I'm a very stereotypical American I know exactly one language
> but I have to say that AI translation has gotten so good that I'm not sure how much longer translation work will be there, or rather it might end up being more about auditing
It's functional? I wouldn't say it's poetic, I wouldn't want any AI translator translating art, like say a book or poem, I'd be so uncertain that it would correctly bridge the concepts
A good translator can make stylistic choices that elevate the work and make it fit in their language
(Having read lots of well translated manga and anime, also from what I understand there's a few books I've been told by my bilingual friend's are just chef's kiss quality translations)
Considering translating meaningful art is of some value, on that score I don't think we're there yet
Honestly, translations of fiction are themselves creative works, and the translator needs to really understand both cultures and needs to write cohesively throughout the work. I'm not sure this is even really a question of "can it translate" so much as "can it create a good work of fiction" which is a much higher bar. So maybe the model can mimic the style (especially given that it was probably trained on existing translations) but could it really do so from scratch in a way that is actually compelling? I'm not so sure.
Of course as for the poor OP... is this a majority of what working translators are paid to do?
I suspect a lot of translation is just grunt work - technical and business documents. The lack of a cohesive voice with considered style is perhaps not really much of an issue in those. The expectations are just much lower; text that conveys the basic meaning is a much lower bar to clear.
She's probably better than a bot at that stuff, at least for now, but my concern is that it won't be "enough" better for businesses to justify her continued employment. And this is my general feeling about this stuff across society, in basically all domains.
Yeah, that's why I put the caveat in there. I have no real way to verify the result outside of checking against "known good" translations, though if the known-good translation exists then there's not exactly a lot of reason to do the AI translation in the first place.
I suspect if I knew another language I would be able to find errors in the translation.
Yes, it is another variation on the Gell-Mann Amnesia Effect. I have a number of non-developers in my circle of friends who think Claude is about to put me out of work. They think it is just a great tool for them, not a replacement. Of course!
I see the difficulties more in other areas, such as technical translations, specialist books, user manuals, and translating UIs, where contextual information and a back and forth with the client is needed to clarify details, and (for user manuals and UIs) the translator has to put themselves in the mind of the user and has to consider the possible contexts and use cases.
You're very likely to get a somewhat circular reference; the key (for me) is that for 90% of the usages, "standard translation LLMs" are just fine - I still recommend a translator but they're more of a proof-reader for both languages, catching where something slipped through.
This is sort of missing the point-- people who dont deal with linguistics dont understand that there are multiple types of translation. There's word for word (which is what you're talking about) and sense for sense. If you let an LLM do all of your translation you're letting it interpret huge amounts of intent and context it doesnt (and probably cant) access. The ways in which this impacts the translation will forever be unknown to you and in the worst case lost forever.
So i guess in the end it just matters how important the work is.
A raw "word for word" translation (which I also tried) made the story somewhat hard to follow and very dry, but just asking it to keep the same kind of jovial swashbuckling tone of the original made something pretty similar to Ellsworth's translation.
Again, before someone decides to "correct" me on this, I am aware that it's very likely that the Ellsworth translations are part of the training set so it's not directly a fair comparison.
LLMs are now being aggressively manipulated for propaganda purposes. Powerful people have realized that people believe LLMs, and treat them as authoritative sources of fact.
The number of lies, lies by omission, deceptive distortions, and fallacious argument tactics they generate is absurd, and increasing rapidly. Translation, when done as a service you are paid for, can't be relied on by propaganda bots.
I was asking about some allegations relating to the Epstein files, and it used the slogan "Satanic Panic" in a weird way that gave me a vibe of discrediting victims. I'm too young to know much about it, so I asked some things about it. It explained the McMartin case in a way that seemed too absurd to be real. I asked some follow-up questions about what the strongest evidence was, and how it was explained.
The first deception was omission. Initially, it didn't even mention what was arguably the most significant evidence in the case, which was the presence of tunnels under the school. ChatGPT mentioned the tunnels, and how an archaeologist named E. Gary Stickel found evidence of tunnels. Here's what it said about that:
> However, that conclusion has been repeatedly challenged and is not treated as settled fact in the academic or forensic archaeology literature.
> Other archaeologists and later reviewers reinterpreted the same physical findings differently. One major counter-analysis (W. Joseph Wyatt’s review) argued that what Stickel identified as tunnels was more plausibly explained as pre-existing trash pits and construction-related disturbance from before the school was built in the 1960s.
The first lie was by omission, it didn't even mention this when I asked about the most important evidence. The next misleading piece was the framing. Dr. Stickel is a PhD archaeologist, and doing this sort of analysis is his area of expertise. He used nine criteria as a basis for determining the presence of tunnels, and all nine were met. He found "conclusive" evidence of tunnels, and that they matched the expected locations described by the victims. Dr. Stickel was the only expert to review the site before significant construction made such an analysis impossible.
The "major counter-analysis (W. Joseph Wyatt’s review)" was done by psychologist Joseph Wyatt, who never physically visited the site, and who is not an expert in anything related even loosely to archaeology. ChatGPT presented this guy in a way that made it seem that Stickel had been debunked by a comparable expert.
> … considered one of the more accurate translations of the work.
I think you’re missing a big point of translating literary works. A purely “accurate”, phrase-by-phrase translation is often not very good; the actual literary style, the feeling and the allusions and references, often get lost that way. A good translation of literary work requires a lot of deliberate choices by the translator to deviate from literal translations in ways that convey the style of the original, or an extra layer of meaning that would be lost by an “accurate” translation of a phrase. Also, being consistent with these choices matters a lot, which OP claims LLMs are less good at.
why would it be different for other people if you already said senior level is not writing code but planning things out?
is there something about planning that LLMs cannot do being your crux of the argument?
what do you believe about your jobs or function that you think will be immune from AI replacing you?
If anything it seems your role is not that dissimilar to those translating languages or business requirements.
I am struggling to see what it is about this planning you do that cannot be done by AI because it seems to me thats not where the moat is rather I find the middle man jobs to be the most vulnerable to AI immediately much more than people writing code.
Because at least someone is watching the outputs from AI and understands the code and can communicate it easily back to the stakeholders without the middle man gate keeping and applying their "taste".
I have a feeling that anyone in your shoes is going to be working with code soon or they won't have much to offer anymore to the business. A stakeholder could easily replace the middle layer with AI and even as a business owner myself I do not see any need to add any more humans at the layer anymore unless they write code.
More specifically, it is coming for coders. If you make your living by banging out lines of code all day, then you may want to be looking at adjusting your career trajectory. But if that is your job, you are either very junior, or a bit foolish for getting into that situation.
so what is software developer doing if writing code is not part of their job
I don't see how not writing code is being offered as a moat, it seems like that is just translating business/stakeholder requirements to architecture/biz processes which is exactly the type of low hanging fruit that AI will capture first
or was it your point that the position sits closer to the stakeholders (relatively compared to those lifting) thus immune from replacement by AI
or is your argument that your taste is exquisite that no AI will be able to match it like it already has with software so far and it will not improve beyond the current state
I don't necessarily disagree, but there's gotta be a name for some kind of "infinite extrapolation" fallacy, where you assume that the current rate of progress will continue indefinitely.
That might happen, but I don't think it's implied, at least given literally every other bit of technology that has ever happened in history ever.
> I don't necessarily disagree, but there's gotta be a name for some kind of "infinite extrapolation" fallacy, where you assume that the current rate of progress will continue indefinitely.
I am not assuming they'll continue indefinitely, but it's a small step from writing code to planning out the code to write, and another small step from planning a coding project to planning a software project, etc.
These are all small steps, and because the act of specification + planning paid less than specification + planning + programming, what reason do you have for thinking that specification + planning is valuable enough to keep the salaries the same as specification + planning + programming?
Well--well look. I already told you: I deal with the god damn customers so the engineers don't have to. I have people skills; I am good at dealing with people. Can't you understand that? What the hell is wrong with you people?
I think this collapses a global, complex heirarchy of software engineering workers into a single monolith and serves only to advertise for frontier LLM providers. the point where you no longer need engineers is not going to be reached by making LLMs better and better.
I think there is going to be a long time before all of the obscure knowledge of a decent software developer can be completely replaced by AI. Though the job is going to change beyond recognition. It already has in many ways.
I just found out you were discussing my article, so obviously I had to come here. ;-)
I’ll take the time to read the thread properly because, hey, you took the time to read my article. Also, I genuinely enjoy reading and I’m curious to see what you think about AI.
The anecdote in the article is real, by the way. I only changed her title.
With AI, I went from “surely this won’t affect me” to “AI is dumb” — no, I was dumb, I just didn’t know how to prompt it — and now I’m at the “how can I make it work for me?” stage, while still hoping employers, clients and, well, the entire world realize it’s NOT a magic button.
It’s crazy how unreliable it can be. Sure, it can translate in the sense that it can give you an idea of what was said. But that doesn’t mean it’s good. I could give a million examples...
If you look at the trajectory of your stance, do you think you might reach "Uh-oh this is doing just as good a job as I can do in a much shorter time?" within the next year? I feel like that's what happened to pure coding for me, something I never ever thought would be possible.
The unreliability is something that seems like it might be a temporary early stage.
I think it's an interesting perspective, because translation is one of the jobs that I (a) hear is the first to lose work due to AI, and (b) often used as an example of "acceptable" AI by people who are skeptics of LLMs and AI-generated art.
> often used as an example of "acceptable" AI by people who are skeptics of LLMs and AI-generated art.
As one of such people, I think there is a nuance to it. AI is great when you’re translating something to yourself. But when translating things for others, more caution and human judgement is needed. Espesially when translating instruction manuals, where bad wording could cause someone to injure themself.
This. I put things through Google translate all the time and they're always unreliable. Sometimes they're correct, sometimes I need to know roughly what the original said. Infamously, Google used to say "geiler Typ" meant "horny guy" when it means "awesome guy". Google used to think "geil" meant "horny" in general, which it can but not usually
Google Translate is at the bottom of the barrel. All other AI translation tools are vastly superior. You'd want to evaluate those, and forget about Google Translate completely.
Googles machine translation team wrote the Attention is all you need paper that introduced transformers specifically to solve the problem that you can just model language by mapping one word to another. I'd be floored if they weren't using the tech they invented for intended purpose
Yeah. LLMs, machine translations, CJK keyboards, they are all the same technology; faster cars to each others, not cars vs horse drawn carriages. It'll be surprising if they didn't directly apply any applicable learnings back to Google Translate.
Language is incredibly complex. I remember a TikTok from a bilingual English-Korean speaker comparing the English subtitles from a Squid Game scene to what was actually being said by the characters. The nuance and info density lost in translation made the subtitles feel completely remedial. Americans were basically watching a different show altogether.
I was watching the Netflix show The Empress with Chinese subtitles that did a pretty good job translating the German. I switched to English subs for one episode and couldn't stop telling the people I was watching with "That's not what he said! That's completely different!"
There are translators and there are translators. Translating legal/business documents is a completely different thing from translating movies/books/games.
I can confidently say that LLMs do a better job than the average traditionally published fictions in my country, at least when the original works are in English. Every single time I watch a subbed movie there will be some lines noticeably wrong.
Yes, I've become very leery of artistic translation, in part because the paradigm of translators as adapters and localizers often ends up at odds with the job of faithfully and accurately representing the original material.
The most egregious example I came across recently was where a friend enthused about some manga he was reading and I agreed to read a few chapters, only to discover that the translator has decided to render the countryside accents of western Japan (engaging with a protagonist visiting from Tokyo) by having them say 'y'all' and 'bless your heart' and other Southern USA tropes. I get the aspiration of the translator, but it was excruciatingly unpleasant to read. At that point, why not just say the protagonist was from New York and on vacation in Florida, or draw in some meshback caps on some of the characters and add alligators here and there in the background?
Translators already started losing jobs due to machine translation a decade ago (e.g. DeepL), before LLMs. Remuneration going down made it more difficult to make a living as a translator already then, even if you still received offers.
Well it's more than acceptable to translate e.g. web pages for reading, but it's not something you'd want to professionally publish.
Kinda conceptually similar to how typos and grammatical mistakes aren't a big deal if you're shooting off a quick text or email, but publishing if you've got typos in your advertising copy, in your resume, on your medicine label, etc. it's a real bad look.
Not all translations are the same. Literary translations are often works of art in and of themselves, and automating them would be missing the point entirely, like automating homework or weightlifting at the gym. I don't really know what's the state of the art, but I do buy that, on the other hand, translating toaster manuals or generic copy could soon be automatic.
Yup. If you are bilingual, you quickly realize how some translations are very bad. How some translations are very good. And how hard it is to translate. With dry, simple text, it might be easy. But when it involves art?
Some jokes don't translate directly. There is pun. Sounds of words. Double meaning. Ambiguity. Cultural background. The creation of new words.
It can be reasonably argued that some poetry can be impossible to translate from some languages to others. A poem might be explained, but by a lenghty, dissecting explanation, that completely loses the point of it.
On the other hand, a lot of people become extremely put off by the smallest sign of ai slop. And the llms have a tendency to impart their style to any text they touch.
It'll be a similar theme for all facets of work involving any language, slowly - human or code. We'll parrot about humans in the loop this and that, but I think it'll be less humans in the loop over time and I think most people will even be willing to settle for a slightly more mediocre translation or coded project. It all comes back to our dopamine addiction, where we like fast feedback. And the oligarchs like tools to suppress wages. We will be our own demise for not advocating for either UBI or job protections, instead, happily using the technology while also rolling our eyes that it could never replace us.
Out of curiosity, I pasted an article in French I was reading a few minutes before coming across this thread into ChatGPT and asked for a translation into English. It was certainly passable from a functional perspective, and I wouldn't hesitate to use it to translate an article from a language I don't understand. But it was not professional-quality work. There were a couple instances where the French grammar was mistranslated, and the writing was perfunctory, not going into any effort to have the article flow like it was originally written in English instead of simply translating each sentence literally. Would I read an article written like this? A short one. A novel? Definitely not.
I think the issue is that a lot of professional work is being done when the commissioner would be perfectly fine with non professional work. There will always be a place for artful translation, theres a place for hasty translation as well.
I don't see LLMs being able to replace translators for less-spoken languages.
I know a translator between two Eastern European languages, and some jobs require use of specialized dictionaries. Using LLMs in such cases would be very unreliable and would require even more effort to check and correct than doing it correctly in the first place. Plus, I really doubt that US tech firms are training LLMs on language spoken by "only" 6 million people.
As for entertainment, anyone who grew up in Eastern Europe with pirated movies with nasal monotone translations, or machine-translated video games knows how much those take away from the experience. Sure, "AI could do better", but could it be consistent and capture cultural nuances and idioms, etc?
Despite the protests, he admits using AI and then charging his clients full price...
"But maybe I will ask Claude’s opinion, and if one of the suggestions is smart—cutting a paragraph, for instance, or clarifying a sentence—I might accept it.
When I started translating 15 years ago, we used to paste uncooperative sentences into Google Translate to see if it had interesting ways to phrase things differently. Then came DeepL—same idea."
I do admit testing AI. Hell, most of the time, I don't have the choice anymore—I don't use it but several of my clients send AI-translated documents. Do I just send back a CHatGPT version? Hell no. This is why and how I know it's not reliable or good.
It's not exactly taboo to use AI, is it? IT doesn't have to be all or nothing. AI is great for my glossaries. AI is shit to translate.
"we all more or less look the same in gym clothes"
Maybe my brain works differently than the author, but I'm surprised at this statement. Gym clothes don't change recognition for me, it's about the face, body, posture, clothes don't really enter into it. For me it is nonsensical enough to be suspicious.
And for a human centric perspective, not recognizing who someone is sad, it's knowing that you probably won't meet them again so it's not worth it, the community isn't there. Where community and interpersonal relationships between people are something we still hold dearly.
Is it that unfortunate? Tasks that don't require high-quality translation now don't need human labor. We should be celebrating.
The sad part is that we haven't figured out how to distribute our resources fairly to these people even thought their services aren't required as often. Instead we just take their wages and give them to the top 0.1%
It’s unfortunate because we are seeing more poor translations in all domains, and users suffer from it. It’s part of a general enshittification of things. There are few contexts where low-quality translations don’t constitute a degradation of user experience.
Just one amusing example I saw recently: On the Amazon website, a submit button labeled “Go” in English was translated to something which when translated back would be “Walking”. That’s the kind of thing that would be exceedingly unlikely to happen with a human translator.
"Switched to Opus 4.8 - Fable has safety measures that flag messages on most cybersecurity or biology topics. They may flag safe, normal content as well. These measures let us bring you Mythos-level capability in other areas sooner, and we're working to refine them."
This story is so great because it shows how robotic are so many jobs and tasks. Like, what happened in the reciepient mind to not consider whether the reply was appropriate or not? Did the almost instant response not hint at an automated email? Or the lack of any other content of the email (a greeting, something)? Or maybe people send so many emails or is doing so many thing they switch off certain parts of the brain?
I think you overestimate human translators. There is a lot of very poor quality human-translated text out there. English translated from Chinese is famous for this.
There will never be enough expert-level human translators, and they tend to be very expensive. Machine translation has raised the floor.
> I think you overestimate human translators. There is a lot of very poor quality human-translated text out there.
This.
There was even a big controversy recently with one of the games on Steam where human translators just completely botched and vandalized the translation, mistranslating huge chunks of it and injecting their own personal politics which are not present in the original text (only English was affected; other languages were translated fine apparently): https://store.steampowered.com/news/app/2914150/view/5028562...
If you'd get the AI to translate it, even without any editing, it would have done much better job. Just because something's done by a human it doesn't automatically make it good; you still need competent people at the helm, and recent machine translation advances certainly raise the floor on that.
I don’t agree that machine translation has raised the floor, because even LLM-based translation can get pretty bad when it isn’t provided with the necessary context. And the average quality level I’m encountering has dropped since machine translation became mainstream. Poor translations have become the norm, which wasn’t the case 20 years ago, despite the occasional “all your base are belong to us”.
That’s not how it works. Value for users doesn’t translate 1:1 into value for businesses, nor are either necessarily willing to pay for value. That’s why things enshittify.
if they are selling to a business, the biz will pay if it solves their problem. if the solution doesn't solve their problem, or something else solves their problem that is easier / cheaper / better, the business will not pay.
I recently saw a video showing the french to german translation of a french McDonald's terminal.
The translations were hilarious bad, like old school google translate bad.
Maybe McDonalds is big enough to not care about their reputation, maybe they are happy about the free clout from people making fun of them but they certainly chose to cheap out on translations.
Audio book narration. Human narrators are paid a seemingly ridiculous amount of money to literally read a book out loud. We have the tech to replace them, it’s actually pretty dang good, and it is substantially cheaper to do with computers. It’s pretty accurate too. In the audio book industry though, if you take your book seriously you have a real person read it. The best one you can find that you like. Readers enjoy hearing good narrators and the total value one narrator can bring is very high mostly because the value scales well.
Another real world example that doesn’t scale well, call centers. Customers want humans, but execs have tried to replace them with automation in every way possible. The margins of a business get squeezed because the value of the human touch doesn’t scale well in this case.
Translation falls a bit in the middle. I’m sure ChatGPT is good enough for some people. If you are a restaurant and need to understand what you are ordering at the local authentic Italian restaurant it’ll do the job. If you have a bad food allergy? Maybe not, you are willing to pay for accuracy because that’s what a human brings
So the answer to the question posed in the article, can’t you just upload it to ChatGPT? Maybe yea maybe no
I worked at large Japanese bank in New York and happened to sit near Chief US Economist next to his Japanese translator. She would occasionally ask about certain idioms. I remember explaining what a wildcat strike was for instance. But it must have been pretty tough because the guy was prolific in his commentary.
As a public service employee within the GOC, I feel the pain expressed by the author. I sat through a meeting today where somebody with no domain knowledge puffed up their chest to show off their gpt created master lesson plan for a four year long internal training plan that is being re-worked.
I could feel the heads of those around the table that had been teaching this material for a decade starting to explode as this was exactly what others in the thread have described: it looked good until vetted by experts, then it was easy to poke holes as it was just not right
The problem in the public service is that the experts who can review the output are leaving or being nudged out.
Is the assumption that the LLM did the translation? Or that it just understood your query and submitted, on your behalf, to a tool you could have just used directly?
I'm gonna sound a bit like the clueless gym hr lady: I assume most income generating translation jobs are either mandated by law or commercially high stakes enough to warrant a human to do, no? Were people really being paid to do the type off _low stakes_ translations implied that a automated system can replace?
Maybe a publisher will replace the translator of the next Dan Brown best seller with Mythos? Who cares other than those buying it, getting money out of it?
Presumably the people paying the author for translation services are aware of AI, but for whatever reason are choosing a humans services instead. IMO it would be a form fraud to heavily rely on AI and not disclose that to the customer.
Close. They will be unemployed because AI be "good enough" and companies won't care about it being better. Nothing they mentioned was really about principles. Everything was about quality output. Too bad companies dont care about quality.
TFA is a good little read - couple things come to mind
1) Knoll’s Law [0][1]
2) The ways I feel when I’m working on a hard problem in an area of my expertise and some person starts in with “Why don’t you just…”. Enough people have come to me in such a situation with such a comment that I think it mostly translates as a sort of shibboleth for “I have no real idea what I’m talking about.” Now to find out if this is a teachable moment, if I have to maintain a sense of humour, or find out if I’m actually one of the days lucky 10,000.[2]
AI isn’t replacing me. Like a toddler, it
needs to be constantly coached.
Like a toddler, it will grow up.
Humans are really bad at noticing trajectories. They see the current situation. They know what the situation was 5 years ago. But for some reason they do not believe that there is a trajectory. They view the present state as the final destination.
Your experiences must be much different from mine.
Three years ago, AI was barely able to provide sort-of reliable command completion.
Two years ago, it could extrapolate a single function from a docstring - but the docstring had to be so verbose that it wasn't practical to use in that way.
A year ago, I was tinkering with Devin to try to find a way to get it to reliably implement small, isolated features from verbose Jira tickets.
Six months ago, I started using AI to generate the majority of my code output. Most of my time was spent reviewing, and I was ecstatic to reach ~2x output because I could run the next task while reviewing the last.
Now, at work I'm managing a half dozen Claude Code instances, Devin sessions, and orchestrating a review loop between Claude, Devin, and CodeRabbit. It's not uncommon for me to be working on four or more discrete features at once. My output is approximately 15x my pre-AI baseline - and I've not sat down and written a line of code directly in six months.
At home I'm managing a Hermes agent that can spin up a whole fleet of purpose-tuned agents for whatever purpose I'd like. I've implemented spec-driven development a'la Acai, and extended it to the point that my agent creates specs from text or voice conversation, I review them, and it handles implementation end-to-end. The code itself is an almost disposable artifact - useful primarily to ensure no regressions have been introduced between rounds.
... I simply don't understand how you can assert that "it's been basically the same for 3 years". It absolutely has not.
It sounds like our experiences are different. My software work isn’t on products where code can be disposable, since it affects people’s lives in material ways. I’m not sure why you’re launching fleets of agents at home, either.
Cmon - cursor has been out for like 3.5 years at this point. AI was still in its infancy but it was definitely able to complete tasks, albeit smaller ones.
Not disputing the overall trajectory, yeah it’s gotten better. But it was definitely capable of more than just command completion 3 years ago.
I reach for it more frequently. But personally, it’s at the point of diminishing returns for my work. It’s capable enough now to handle most of the things I want to throw at it, sometimes it’s wrong, sometimes it’s right.
I’m not doing cutting edge deep tech work - and I also don’t have the motivation (or salary increase) to be 15X more productive, if that’s even measurable. We are so busy because the CEO hears these “15X” statements and then the pressure is on to match or exceed that, and I’m not playing that game.
> Should you pay your roofer less because he uses a hammer instead of his bare hands?
Yes. Effective tools increase the supply of roofs made. More supply means lower prices per roof. But because the same number of roofs need to get worked on, the increase in roofs per roofer means less roofers will be needed.
Yeah, I agree - I get what the author is saying, but I also don't expect "translator" to be a practical career path in the future.
Even small, dumb, local models are excellent at translation already. Frontier models are on par or better than the human translations we've tested them against at work.
Poor woman should really look into pivoting her career or finding a different way of making money. Truth be told, her industry/career is not going to get better. Consistent work will just not fall from the sky.
Being bitter will not improve her situation. Even organizations like UN/OECD are looking into implementing AI in various ways.
Really good blog though. I love life blogs like these! You can go back and live through so many interesting/pivotal moments.
Just to clarify, I am not saying this is a negative or offensive way. Just that she needs to proactively look into her options as I think its not going to get better, at least in the near term.
To answer your question, I think its happening as we speak, in small ways for some and in big ways for others.
Who knows really, either all this is a phase that pops with the AI bubble popping or something all of us will have to consider.
I can’t believe this article hasn’t been written by ChatGPT. The author claims to have written it but has clearly become completely captured by the awful generic style of AI writing.
As a former freelance translator (1986 to 2005, Japanese to English), I have much sympathy for the writer. But I wouldn’t be so confident that AI cannot do professional-level translation.
She writes: “I adapt, I localize, and I find the best way to convey the original message so it makes sense and feels natural. I research terminology. I make sure it’s consistent throughout.”
I’m sure she has other important insights into what enables her to do her job well. The problem is whether or not such insights can be incorporated into an AI-driven translation system, too.
Since early this year, I have been experimenting with a variety of agentic systems for language-related tasks, including dictionary-writing, research on topics in the philosophy of language, essay-writing, and translation. Other than the dictionary [1], I am keeping the results private, so they haven’t been evaluated by others. But my personal assessment is that agentic systems given suitable high-level guidance can be very good at such tasks now.
If I were still freelancing and I had a large translation job to do for a client, here is the outline of the prompt I would give to Claude to get it started:
“Use this private GitHub repository to build a system for translating [genre of text] from [Language1] to [Language2]. The directory samples/ contains examples of the type of document to be translated, high-quality human translations of those documents, and texts in [Language2] that are in writing styles that I believe to be appropriate for this genre of translation. The file guidelines.md contains my general instructions about the needs of my client and my preferences for how you should translate texts along various axes (natural vs. literal, informal vs. formal, preferred dialect in [Language2], consistency vs. variety in terminology translation, etc.). Begin building (1) a knowledge wiki for this project using Karpathy’s LLM-wiki framework and (2) a system inspired by Karpathy’s Autoresearch, AutoResearchClaw, etc. for testing and recursively improving both the functioning of the system and the quality of the translations. For the actual translation, editing, checking, etc., use not only your own ability and the knowledge assembled in (1) but also outsource such tasks to other frontier models through OpenRouter, and use adversarial evaluations among those models and yourself to check and recursively improve the system design, the prompt-writing for other models, and any translations created by the system. My OpenRouter API key is available in this environment. You may spend up to $xx per day in API calls until this project is ready to do real translations; before beginning a real job, give me an estimate for how much the API calls will cost for that job. The initial build-out of this project will take many sessions, so write a prompt called resume-prompt.md that I can point you to at the start of a scheduled Routine to have you work on this. Commit and squash-merge to main at the end of each session. I will be checking in occasionally to view your progress and to ask you to run translation tests, and I will offer guidance then on how to improve the pipeline further and make the translations closer to what my client needs. If you have any questions before you begin, please ask me.”
the version of this skillset that stays employed is "now I translate 10000x more than i could before by managing a fleet of agents. by encoding my experienced taste and judgement into robust evals, I've helped my ai translators be far better than chatgpt on its own, and much more cost effective compared to manual human translation"
"Expertise in one field does not carry over into other fields. But experts often think so. The narrower their field of knowledge the more likely they are to think so." - Robert Heinlein
In this case, the gym buddy doesn't think that she's an expert in the other field, but dismisses it as something ChatGPT can do with ease.
AI should be used for all the bullshit tasks that no one wants to do. There are garbage dumps full of stuff that can be reused and recycled. But it's not high enough ROI to pay someone $25/h to sort trash, so it isn't happening.
You don't even need to argue that you're better than the AI. The point is that the client could have uploaded it to ChatGPT too. Perhaps they even did, and they didn't like the answer they got. They are sending it to a human because they want a human to do the work. If you were to send back ChatGPT output, that would be fraud.
wrt. the end of the story, it will be interesting to see if people start noticing their Dunning-Kruger bias as a result of LLMs.
Specifically: LLMs make it really easy to misunderestimate the complexity of fields other than your own. (You can see this with a lot of vibecoded projects, for example – once they hit the wall of complexity, they stall out or start finding ugly patches for fundamental design issues, etc.)
I don't think this sort of cultural change will happen short-term, though.
> LLMs make it really easy to misunderestimate the complexity
In my experience this is a real problem. Just yesterday I asked my LLM to create a piece of software that could help me build an 'ambilight-like experience' through my home assistant. It did something that seems to work as I expected, but there is a lot of theory that I just brushed past. It would be pretty easy for me to assume that I would be able to replicate this feature from scratch 'now that I understand the problem'.
Agreed. LLMs are really terrific at sounding like they know exactly what they are talking about. Fable is the best yet. Beautiful, thorough explanations with absolute certainty, which under even light scrutiny turn out to be mostly bullshit.
I still love the tool, but remain as convinced as ever that AGI does not lie at the end of this particular path.
Translating is one thing that artificial intelligence undeniably excels at, and the value of this alone is enough to underpin the trillion dollar valuations of the gigantic AI companies.
Translation is a gigantic boon for business, but just as important for human connection, for culture, science, art, and entertainment. The value of automatic and cheap translation between all languages, this tower of Babylon, is immeasurable.
Human translators will always be better than any AI at their job. But they don't have unlimited time and energy, and they aren't cheap. AI makes good to great translations available to everybody.
Any expert in any field will gladly tell you that ML sucks for specifics of their field (and it does). But if you are not an expect in that field, it looks convincing enough to make you think that maybe it is OK for that field, and your field is somehow unique. It is not. Any expect in any field will confirm to you that ML produces plausible-looking slop which is occasionally completely wrong. This is the case for all fields.
This is all bullshit. I speak 4 languages, 3 fluently. Even chatGPT does a stellar job with translation. For most things people want translated- forms, administrative documents etc. I doubt you even need a human in the loop.
That being said, something with essence like a novel definitely still needs to be done by a human.
I think it can't be improved because it's measuring the wrong thing. A junior engineer becomes a senior when they stop being told what code to write and start solving business needs. Therefore often the highest paid engineers aren't the ones who would do the best on leetcode - or SWE bench pro verified.
Maybe AGI is possible and we'll have software defined human intelligence that's completely autonomous but that's not coming in the next slightly better RL trained LLM and if existed likely wouldn't be under our control anyway
This is just about the worst career you could be in right now. Of course people are just going to upload it to ChatGPT. Processing text is its forte.
This person is in the first stage of grief (denial); artists are several stages ahead. Most customers are not going to care about the difference in translation quality unless it's in a regulated sector.
All I got out of this article is that he should have went home and dumped it into chatgpt just to see what happened; then if it did as good a job as him, he should start looking for other places he can add value that AI can't.
The point of the comment was that models are improving a lot every release, so if your livelihood depends on something, you might want to check to see what the latest models are capable of before someone else (like your employer ) tells you.
The other person in the gym was right, did you you just dump it in the latest model?
The article does not say that. The author doesn't take the text the other person dumped into ChatGPT and evaluate its quality. That is what OP is referring to.
when someone says they have tried previously that makes me think once long ago when they first came out. If your employment could be replaced by this, I'd be testing all new models to see where they stand.
Just because you don't want to use AI/LLM to translate, that won't stop someone else who will, and they will end up doing it cheaper and faster (maybe not better, but most people don't really care about quality too much anymore.)
I agree with the take, but it's a temporary one, the sad reality is that we will be literally inferior soon, there will be a point where we will not trust human input without counter check by AI, we need to remember that we are kinda at the beginning of the AI era, in 5 to 10 years it's very unlikely that a human translator or software engineers will do better than the tooling we will have.
There is already a tipping point now in software engineering where we prefer to ask AI instead of humans because we believe accuracy will be better, see SO death as an example or just see the current state of online dev communities, it's getting deserted and between team members at work, we can also notice that people speak less and less.
This plague of misanthropic doom is itself pretty depressing. Why do so many people think LLMs are in any way on a path to compete with human brains? Why do you think so little of yourself? The brain is magnificent and complex in ways that we are unable to decipher anytime soon, and it does way more than an LLM. Way, way more.
I don't talk specifically about LLMs but AI in general, it's an important distinction because tooling is currently what make models useful and more performant.
When I say we, I mean the general population really. There0-'ll always be the super bright ones, sure, but we gotta be realistic here. Most people already struggle to make any meaningful contribution because it's so hard to compete, and that gap is just gonna get bigger and bigger.
I agree the brain is pretty magnificent, but when it comes to stuff like language, figuring out if an idea actually works, building the next LLM, or running business stuff, it's pretty obvious we'll be inferior. AI can already innovate and come up with new things way faster than any human could, so at some point (soon) => the majority of contributions are just gonna come from AI, not from us.
The thing is that AI is not some inevitable force of nature that must just be contended with and weathered. It is an active choice by our society to develop it and it is a choice by our society how we should use it, if at all.
We would all do well to remember that and remember that each and every advancement and use case regarding AI is the result of choices by people (or the groups of people we call corporations) and are oftentimes motivated by the profit motive, not the best interest of humanity.
We could make different choices up to and including our own Butlerian Jihad where we ban all forms of AI but we could also do everything we can to prevent the worst fallout short of that.
There are only two types of problems in the universe:
1) those posed by the laws of physics
2) those posed by human choices
This is anecdata, but in my experience with myself and my coworkers, it is not that we believe the AI will be more accurate in software engineering, but that the answer will come faster and be more tailored to our exact problems. If I have to search SO, I have to find the answer and then tweak it to fit my codebase, but with AI tooling, the AI is already basing its answer around my code.
I think we actually do believe it, do you believe Fable 5+GPT-5.5(+ the whole model zoo) in loop with adversarial (no budget limit) or a 10-year experienced SWE?
We are talking about "codebases" but realistically we won't even be checking the filetree of them soon, it will be all blind, containerized and verified with pseudo guarantees which are good enough to build serious things. We don't even write documentation for humans anymore, we need to look at the trends and the reality within companies, most developers became "callcenter agents" in a matter of only 2 years and literally most of them are not even using proper automated tooling yet as we can see the "vibe coding" trend with Claude Code which is weak, by far most work done daily by developers is already automatable entirely, but with exceptions, sure, but in a few years those exceptions will become rare.
There will be niche problems about legacy products, sure, but legacy products will all be replaced over time, if we think in depth, why do we even need that many languages, that many tools? Tomorrow AI will write 99% if not all code existing ("code" doesn't even matter anyway), so it's much better if it's specific to AI and not playing this dance where we think we are doing a meaningful human contribution on an "AI-made codebase".
For context, I have 2 decades of software dev behind me.
For personal projects that I don't plan to share widely, I'm making it a point to not look at the code at all. So far - and to my surprise - I've not only found that this has result in no more bugs than before, but it seems to result in fewer bugs over time. Every time I find a bug or a regression, I add it to the specification. My SDLC requires that every specification have at least one associated test. Not every function, or every line, or anything like that - every specified feature. The end result has been that my projects have matured over time much faster than if I'd been more closely involved.
I've already toyed with writing some projects in Nim and Haskell for token efficiency. At some point I plan to put together a simple test project, then do a comparison of token efficiency with every language I can think of to find the one that I'm able to generate most quickly, correctly, and cheaply.
> there will be a point where we will not trust human input without counter check by AI
That's nonsense. There is zero reason to believe that AI (with the current techniques) will ever become reliable enough to let it do its own thing, let alone better than a human. It's been years of development and you still can't trust it to get basic facts correct, not even "well it's better than it used to be". Saying it'll replace humans in 5-10 years is a fantasy (or a prediction that people are stupid enough to fall for hype, I guess).
You come from the principle that humans are reliable at first which is partly right but also wrong in so many scenarios, you can even see lately the CVE spree happening, which demonstrates that human-made codebases have serious vulnerabilities and without the help of AI, we probably won't even know about them which proves that humans are not that "reliable", the current societal structure is also built around the fact that humans can't really be trusted, nothing really different with AI, we can't fully trust them like we can't fully trust humans.
It's not a fantasy, I would bet that no serious engineer nowadays is putting in prod a codebase not AI reviewed meaning we already can't work on our own, we must factor in the on-going decline of human capabilities (at least developers) as well of course.
I'm not really saying this because of any sort of hype, but I can personally relate where I went from actually coding to NEVER CODE in less than 2 years, and everyone around me is the same thing, what it will be in 5 years?
Knowing that really, most developers aren't even using proper tooling yet so they are very slow compared to what they could be, I mean how many people we hear saying they can't even saturate an Anthropic Max 20 subscription? I saturated 7 accounts the last 2h alone, it's because they haven't entirely rethought their workflows yet, why do they even have "downtimes", it should be 24/7.
> It's been years of development and you still can't trust it to get basic facts correct
There's the rub: AI is not an oracle. It's neither designed nor intended to provide accurate recall of all facts. It's closer to a reasoning engine than anything IMO.
Oh, and for the record: I don't trust people to get basic facts correct, either. It's already far better than the average human at trivia.
The ending is a really powerful point. Most people apparently agree on two things:
1. AI is a great boon for all tasks and specialties we don’t have the skills to do ourselves. Understandable, since (A) we’re ill equipped to see the flaws in its output because it isn’t our area of expertise, and (B) it often can unlock great gains because if we trust it, we then don’t have to pay and wait for humans to do that thing.
2. AI is a terrible replacement for me - my skills are at such a high level that it’s almost theoretical that it’ll ever be good enough to replace me for 90% of what I get paid to do. It’s a tool at best.
This is why I use AI for all my medical questions and doctors use AI to write software, and we both smirk at the quality the other person is getting from it.
> This is why I use AI for all my medical questions and doctors use AI to write software, and we both smirk at the quality the other person is getting from it.
There is an interesting third group emerging: People who acknowledge the quality problem, but think they can deal with it by applying more AI to the output.
This takes the form of people who spin up a lot of "agents" and give them personalities like security director or quality director (which are unnecessarily complex and maddeningly unpredictable ways to trigger an LLM session for doing a security review or a quality check pass).
It also includes the person who knows that their app is full of bugs, but thinks it's not a problem because they can have the AI fix the bugs as they show up. People in this class haven't encountered security breaches or data loss bugs yet. They think it's all about having Claude fix that div that isn't centered or handle that error code that shows up some times.
> People who acknowledge the quality problem, but think they can deal with it by applying more AI to the output.
Brute Force: if it doesn't work, you're just not using enough.
What if they're right though?
It does not have to be brushed away as "brute force" necessarily. We can, and do, build more reliable systems out of less reliable components. In fact, most industrial engineering accepts some defect rate and builds margins around it.
Software is no different. Even without AI, you already have buggy compilers and buggy OSes and buggy libraries. You just tend to accept the risk because you have some idea of what the failure modes are and can work around it or manage the risk in some other way (buy literal insurance.)
> you already have buggy compilers and buggy OSes and buggy libraries.
Which run, I must add, on effectively infallible hardware. Most of the software straight up assumes that the CPUs and the RAM will function perfectly and don't bother even trying to detect such failures (unless those failures manifest themselves in a catastrophic manner, the show will simply go on).
So in effect, we also can, and do, build less reliable systems out of more reliable components, and that's how software is different.
I am not sure if I correctly understood your point. On one dimension, you are basically hinting at another anecdote that proves my point: hardware failure (specifically bit flip in non-ECC memory) is pretty much guaranteed to happen at scale, but people are mostly okay with absorbing that risk. I feel you are overselling the hardware reliability story. For sure, we can build less reliable systems out of reliable components. That goes without saying, and no, that's definitely not software specific. Almost by default most composite systems are less reliable than their primitives (simple example would be nailing two pieces of wood) unless specific care is taken to build in those guardrails or redundancies. The point, however, is it is possible, and there is a vast precedent for it.
There are other places where some process has an error rate and you make up for that error rate by doing the work more than once and then comparing results. For example, I've heard in a video that satellites and other space craft often have 3 or 4 processors and compare the results to make sure there were no errors due to radiation. Similarly, we have RAID arrays that store data multiple times because disks can fail. So, even if AI has a failure rate of like 20%, maybe you can make up for that by running the same prompt multiple times with slight variations or with different models, comparing the results and choosing the best.
I've seen it turn right in business contexts. Sometimes you can even lower your standard of "good enough" and find quantity has a quality all its own.
But it requires taste and engineering to do it right, and on the right things. It'll be an interesting few years.
I think it also requires someone who knows just enough to be able to navigate between those ideas that will set you back and those which will propel you forward. At the end of the day, you still need some human filter.
they are right. bad output is user error. there, am i suiting the role appropriately? i do like 65% believe that, fwiw.
They're right until they're not.
> There is an interesting third group emerging: People who acknowledge the quality problem, but think they can deal with it by applying more AI to the output.
That's the entire big tech's business strategy right now.
I always imagine the model rolling its silicon eyes when it’s assigned a personality (“you are an expert growth hacker”) at the start of the prompt. Was that ever actually shown to be effective? Is it still?
> Was that ever actually shown to be effective? Is it still?
Yes! Personas demonstrated measurable improvement in a few different ways, with caveats of course. The common intuition is that personas influence token space in beneficial ways.
I'll come back here later on desktop and link a few (still) relevant papers on this topic.
Please do, thank you! I have been similarly skeptical as your comment's parent
I added some brief commentary here: https://news.ycombinator.com/item?id=48507278#48511524 (or just refresh parent comment replies to see it)
It scratches the surface really but hopefully provides a helpful starting point.
I remember there were some studies that this kind of thing was effective a year or so ago, so essentially a lifetime in Model years.
However to me it seems completely reasonable that it would work, because my understanding of what happens is the model interprets what you said as:
Look for a group of people who are considered to be expert growth hackers by the world at large and answer my questions as though they were answering them.
So assuming that there are a set of questions that can best be answered by people that most other people identify as expert growth hackers then yes, I believe assigning a personality in this way should obviously work.
I imagined it as kind of a shorthand for "you should be spending my tokens on looking for / addressing issues like X, Y, and Z," where X, Y, and Z are the sorts of things that an expert in [insert domain here] would be likely to care most about.
At some point we have to just admit we're mass cargo-culting here and that these secret invocations people swear by have the same epistemic value as medieval superstitions.
right, but the thing is how do they know what an expect in [insert domain here] would care about? Obviously by finding content created by
people who claim to be experts in [domain] people who others claim to be experts in [domain]
hopefully valuing membership in group two over membership in group 1.
It's been interesting to see how aggressively some reasoning models like to "reason" by analogy. They love to say things like "it's like a CPU" or "it's like a highway", and then they start to make logical leaps based off that rather than just using it for user explanation. Gemini 2.5 and 3.1 Pro have been particularly bad for this type of behavior. Telling models to "speak as though you are a physiologist considering the case with an expert colleague" gets them to "reason" using a more correct linguistic substrate.
The Opus models over the last year doesn't seem as vulnerable to this type of behavior and I've noticed the "identify as expert" prompt tricks aren't as meaningful there.
I propose we move away from the framing of "Model years" - they're standard human research years. Yes, likely more people are working on it, and also working harder, but ever since we acquired a certain amount of compute in the world, many people were able to independently find the same patterns and train models.
From what I've heard, personas give a greater chance that the LLM will answer confidently.. and also a greater chance it'll hallucinate something when the data is sparse. Supposedly "grounding" the personas on real documents/web searches is the best approach. Anecdotal though.
It reminds me when people would stuff their image prompts with things like NO DEFORMED FINGERS.
I did something different. Instead of describing the image, I described the artist. Made that artist in Ubermensch. Then asked AI to draw the image from his point of view. It worked fabulously.
Instructions unclear, digitized subject into a mass of fingers.
Thanks for reigniting the PTSD of reading about SCP-4051.
You mean the 4051 from There's No Antimemetics Division and not the mainline 4051, right?
Yes. I'll confess that I started with the novel :)
Perfectly formed fingers.
I hope that pun was intended‽
SCP-48510055
"Don't think of an elephant"
I've always wondered if the go-to should have been prefilling its response with "I am an expert growth leader, and here are my thoughts:".
There was a time when stuff like "Unreal Engine, trending on ArtStation, 8K resolution" actually worked when prompting image gen models because such labels actually correlated with higher-quality images in the web-crawled training datasets available back then.
Back with some papers. (Apologies in advance; I typically don't edit/format comments much here, please bear with me.)
Notable papers describing performance improvements with prescribed roles and personas:
- ExpertPrompting: Instructing Large Language Models to be Distinguished Experts (2023) https://arxiv.org/abs/2305.14688 (if you're going to only read one paper here, maybe read this one but know there has been a lot of follow up with more modern models.)
- Expert Personas Improve LLM Alignment but Damage Accuracy (2026) https://arxiv.org/abs/2603.18507
- When Does Persona Prompting Actually Help? (2026) https://arxiv.org/abs/2605.29420
- Unveiling Power on Combining Prompt Engineering Techniques: An Experimental Evaluation on Code Generation (2025) https://doi.org/10.5753/sbbd.2025.247251
- A Pattern Language for Persona-based Interactions with LLMs (2025) https://www.dre.vanderbilt.edu/~schmidt/PDF/Persona-Pattern-...
A TLDR of my *admittedly heavily biased* mental model (so take it with a grain of salt): personas do improve task alignment and precision to measurable effect but with observed negative impact to accuracy and knowledge grounding. Overall, this makes it quite suitable and preferred for code generation scenarios. (Don't over-index on 'accuracy' here as meaning "bad code", it's more about verbosity/jargon reducing clarity of higher order goals like business objectives and system architecture.)
Outside of code generation, personas have the interesting effect of increasing implicit biases and stereotypes. It's not hard to imagine something like "you are a left|right wing politician ..." or "you are a senior-citizen|teenager ..." influencing token space construction considerably.
I feel it helps for the personality aspect, how it handles answers and general vocabulary, but it doesn’t in any way improve skill level, at least that’s my take from building an assistant.
At least in the beginning of spicy autocomplete, this sort of role-play did work pretty dramatically at aligning a conversation to a task, though I don't think anyone ever tested it versus somewhat less cringe priming.
After that, cargo cults do what they do best.
> though I don't think anyone ever tested it versus somewhat less cringe priming.
I really wonder if phrasing it differently would make a difference. In good faith conversations, it just doesn't happen that someone tells someone else who that person is.
The reason it seems suspicious is that it's phrased in a way that's oriented towards humans. I haven't tested this, but I suspect you'd get similar results if you said something like "orient your response to that of a growth hacker." Either one is likely to have the desired effect on the stochastic result.
> There is an interesting third group emerging: People who acknowledge the quality problem, but think they can deal with it by applying more AI to the output.
Ah yes, the known unknowns.
The discussion reminds me of a talk Zizek gave in which he discusses the speech Rumsfeld gave regarding the evidence Iraq supplying weapons to terrorist[0].
Zezik argues the unknown knowns are far more interesting (and the reason why USA was losing in Iraq). While Rumsfeld focused on the unknown unknowns.
I've noticed that domain experts who implicitly know the the known unknowns of their field distrust LLMs because they can identify their shortcomings. Those subtle mistakes LLMs make. I argue this is why domain experts using LLMs get such a boost. They can identify and avoid pitfalls sometimes before they happen. But in other fields the same people are in awe of LLM capabilities precisely because the known unknowns are a mystery.
The Unknown Unknowns of LLMs are the IMO the most interesting. The so called emergent capabilities of the technology. The use of LLMs in others fields such as biology, eg in protein language models, is really cool.
Everyone focuses on replacement of people workers when I think opening new fields of work for humans should be the goal of LLMs by leveraging the tech to discover.
The other interesting caregory is unknown knows. But that's another topic for another time.
[0] https://en.wikipedia.org/wiki/There_are_unknown_unknowns
As an aside, the mass mockery in response to Rumsfeld's statement always bothered me because it's the single most intelligent statement he ever made about the Iraq war, and if he had started out with that mindset things probably would not have gone nearly as pear-shaped as they did.
This is one of those classic "sounds dumb / doesn't play well on TV but is actually smarter than most of the other people babbling about it" things. Nassim Taleb has written for example about how maddening it is to watch world-class economists who are also just sort of awkward and a little nerdy go on TV and "lose" to blowhards who don't actually know what the hell they're talking about but appear confident and look good on camera. Thankfully in Rumsfeld's case I think as time has gone on it's become a pretty respected statement about risk even if people still occasionally find the phrasing a bit amusing.
Link for the curious: https://www.lacan.com/zizekrumsfeld.htm
How did you get over 52,000 karma in under 3 years with no submissions at all?
Are you averaging like 2000+ comments a month?
They spin up agents, and then give them roles like commenter, and director of quality for the commenter. Although I'm unsure how the director helps since I've never seen one do actual work.
Commenting more than I should, to be honest.
I have a few periods during my daily routine where I’m waiting somewhere away from the computer and need a break from email.
A lot of my comments have double digit upvotes and some get into the mid hundreds. I try to actually read articles and provide thoughtful comments, which gets upvoted a lot more than the throwaway.
> Are you averaging like 2000+ comments a month?
52000 / 3 years would be under 1500 points per month or 48 points per day. That could be done with 1-2 helpful comments per day on popular threads.
I browse HN a bit more than I should and I see you and simonw around a lot, like you said always providing thoughtful commentary.
When I write comments on here I tend to spend upwards of 15 minutes to draft and reformulate my comments. Sometimes double-checking what I'm about to say (sometimes not thoroughly enough as some of my recent comments show) and I was wondering if you have a similar experience in that regard or do you just manage to fire off a comment in a stream of thought fashion from start to end?
Serious, non-acusatory question. Your writing looks human. Do you use any writing assistants?
Where else, other than HN, do you post?
3 pages deep into their comment history only brings me to 5 days ago so probably yes.
my skills are at such a high level that it’s almost theoretical that it’ll ever be good enough to replace me for 90% of what I get paid to do.
Is it really true for most people that they are using their core advanced skills 90% of the time? I'm curious about how people feel about this.
I'm a professor, which is supposed to be an intellectually demanding job. I do research in NLP/AI, and I don't think AI will replace my core intellectual tasks in the near future, but I don't think my core intellectual tasks represent even 10% of my time. Most of the time is taken by various things like writing bureaucratic reports, writing and polishing grant applications, grading exams and exercises, designing a poster, planning a course's calendar for a given year, creating a figure for slides, writing assignments and exams, attending teaching coordination meetings... which definitely are or should be automatable. Probably even teaching the same lesson for the umpteenth time also is from an objective point of view, we'll probably be kept doing it due to the human factors driving motivation but not because a lecture given by a human is intellectually superior.
Well said. Everyone agrees AI can't do their job, so it ends up doing everyone else's.
I'm not sure how to formulate it yet but it seems there is some Peter Principle/Gell-Mann Effect corollary that is AI-related we can say here.
Perhaps: "AI rises to the level of its users' incompetence."
Or: "Confidence in AI output is inversely proportional to one's ability to verify it"
> Confidence in AI output is inversely proportional to one's ability to verify it
I like this / generally agree. The only wrinkle is that - for some tasks - the verification _is_ "run the script, see if it worked, don't care how... just that it did" which is distinctly different from "not only did it do it correctly, it did so in the most direct and performant way possible".
For a _lot_ of what I use LLMs to build, the former is all I need.
And for as long that that runs on your computer, I don't care.
But the problem is that for many people they now believe it's ok to present a 10k line vibe-coded PR that only has been verified against external behavior, and some Senior Engineer needs to review it, in time, under pressure, without too much push-back, and lastly, it's the Senior Engineer that gets paged at 2am because something has fallen over.
Also, those scripts tend to start a life of their own, and because it looks good enough, people don't look at them again.
I recall a bug of someone vibe-coding a cleanup script for folders older than $x (on Windows).
Get the CreationDate, and sort. Delete older than $x. Except CreationDate can be null and null is always smaller than $x.
Oops.
>Well said. Everyone agrees AI can't do their job, so it ends up doing everyone else's.
Its like basic income, everyone will stop working except from you.
It is not at all like universal basic income, except that both of those are misleadingly simple quips.
But using AI itself is a job too. It takes effort to correctly prompt, to steer it, to verify it, and to improve the harness.
show me a prompt that is meaningfully expertly crafted beyond just providing Do's, Do not's, task context, and a goal.
> Correctly prompt, to steer it, to verify it, and to improve the harness.
I doubt this a lot. The average AI user is running claude code as the harness, or Codex etc. prompting has no secret incantations, and steer and verify is just knowing what the answer should roughly look like, which is a domain skill, not an AI skill.
I feel like you don't have any friends who make software but don't know how to code.
Yes, they do make software now - whereas it was impossible before. You may be absolutely shocked at how bad LLM code can be when prompted from a noncoder. How buggy, and how absolutely rife with security problems it can have. I honestly don't know how they can get LLMs to write such bad software - but somehow they can. This is from people who have been vibe coding for 3 years straight btw (huge amount of time p/day).
> show me a prompt that is meaningfully expertly crafted beyond just providing Do's, Do not's, task context, and a goal.
The way that information is organised and formatted matters for compliance. It’s pretty similar to writing good procedural documentation for humans.
> Everyone agrees AI can't do their job, so it ends up doing everyone else's.
In real life I haven't met a single programmer who doesn't think AI can do their job.
If someone would actually say that I would immediately think they have hubris and overestimate their skills.
We must live in different realities, because I have the direct opposite experience.
Perhaps we are defining "job" differently? AI can, with much coaching, _perhaps_[1], do some _aspects_ of a programmer's job. But not all of it, or even the most important parts of it.
[1] given that we have spent the past many decades pointing out that developer productivity is possibly impossible to measure, or at least very hard; given "done" vs "done done"; given the history of "rock star" developers creating messes behind them, the difference between short and long term thinking and the external imperceptability of that difference; given all of that, we haven't really had enough time to form a valid opinion on what AI can do, in the long run.
are you saying that all of the programmers you’ve met in real life have automated their work away and are coasting while waiting for their bosses to fire them…?
…if not, they’ve found developer work that ai can’t do yet, no?
You mean theoretically in the future? Or right now?
It seems to be a general principle: If AI is better than you at something, you use it. If AI is worse than you, you don't.
Each time the frontier models get better, I see another wave of AI doubters suddenly become believers. People say things like, "AI couldn't code last year, but now I use it for everything!" Interesting. Now we know how that the person who said this has the coding skills of a Claude Opus 4.5 or whenever the frontier was when they flipped.
Meanwhile, the rest of us keep using AI as simple tools, like the person in the article. I wonder how long it will take before computers can program better than me, and I flip too.
I’m not sure I agree with this but maybe I just lack self awareness?
There are large portions of my codebases that are essentially extremely verbose grunt work. My UI stack, IaC YAML, thin CRUD routes, etc.
I know what the code is supposed to look like when it’s done being written, but it’s going to take me for freaking ever to type it all out.
I can just few shot it now in an hour. Plan -> feedback loop -> build -> review loop.
Does it try to do weird stuff? Yeah. And then I’m just like “that’s weird, no, the components should be broken up like XYZ” and then it’s not weird anymore. Occasionally (1% of the time) I just do a quick refactor myself instead of trying to tell the agent harness what to do.
I can get something fairly close to the ballpark of what I would have done but in like single digit percentage of the time.
And the result is that I can spit out a bunch of purpose built tools (personal tools, internal tools for teams, etc.) that I never would have been able to justify building otherwise.
If AI is not better than you at a task, but it's good enough and saves you time, it also makes sense to use it. Many of my uses of AI fall in this category.
> the person who said this has the coding skills of a Claude Opus 4.5 or whenever the frontier was when they flipped
It's not about just skill. It's a matter of skill, time, and how critical the software you are writing is. There is a lot of software that is not critical. That is not close to security mechanisms. And that even if the code quality is not the highest, it does not matter.
Even if you are the best coder in the world, you would already become more productive by using ai. Things that in the past you might have not coded yourself but delegated to an intern, or things that you wouldn't even delegate to an intern because they are just too boring to do like some refactorings.
Like I had this project at work that was written without typescript strict mode turned on. When I turned it on, it had over 700 errors. I might be better than AI to fix every single of one these errors. But my time is worth more than that in doing other things. But I can, and did, ask AI to fix every single one. And then I reviewed it batches, and something that my team wanted to do for multiple years and nobody had the time for, finally got done.
"Now we know how that the person who said this has the coding skills of a Claude Opus 4.5 or whenever the frontier was when they flipped."
Well, once folks like Linus Torvalds concede, this doesn't carry much sting.
the sentiment "AI couldn't code last year, but now I use it for everything!" rings true for me... but I didn't flip cause AI is now better than me... I flipped cause now I am faster with AI than without it...
A year ago the AI output was so bad that getting it up to my standards took more than writing it myself from scratch. And nowadays it is faster for me to start with AI output and iterate from there to reach quality submission.
The ninety-ninety[0] rule was a thing talked about 40 years ago, long before anyone thought of AI coding. AI can nowadays make the first 90% of the task very fast and good enough. The last 10% is still the hardest part of coding by far.
[0]: https://en.wikipedia.org/wiki/Ninety%E2%80%93ninety_rule
I feel like I am the only one thinking AI is actually much better than me in the things I'm supposed to do well. I feel like that for years now, so it's not about the latest generation of models. I can't imagine a single thing I can really compete with an AI at this stage. I am not sure if I am under-skilled or others are overconfident. Maybe people who feel like me don't say this out laud.
agree. it's strange reading the loud voices that are counter to my lived experience. llms just have seemingly infinite depth - or can at least debug and execute without fatigue.
I was saying something like this a few years ago when people were getting first excited about ChatGPT. The gap has narrowed, but not by as much as people think.
AI produces output that is very convincing to a non-expert, and (dangerously), it's so good at looking like an expert, they might believe that it is an expert. But the moment you ask someone to use it for something they're an expert in themselves, the holes appear wide, consistent & obvious.
My favourite moment of seeing this in action was watching AI-worrier TV host/comedian Bill Maher. He has spent years talking about the dangers of AI taking everyone's jobs, destroying civilisation, ruining the economy, starting wars, "it's just getting better and better all the time", and so on. But one night he let slip a tell. "It's no good at writing jokes. Not yet, anyway". There you go, Bill... connect those dots...
There is real utility in it being a tool to help experts apply their expertise, as in this story where it speeds up some tasks to help the translator do part of the work, enhance their expertise, allow them to be more productive.
It's a better screwdriver, a better hammer, in the hands of somebody who knows what needs a screwdriver or a hammer. It doesn't replace them. It can't replace them. It's a tool that enhances the human, not an alternative.
I don't understand why this is not widely understood yet, but I'm sure it will in due course.
And I don't expect this to change. Even if the latest model scores 100% on every benchmark, all that really tells us is that it's now more productive/efficient than it was before at helping experts do that work, not that it can replace everyone in that category of work.
At what point does this become an issue for data quality and global epistemology?
It seems inevitable that we ask for more AI assistance on topics we don't understand. And therefore have the least context to correct. Result: a flood of poor quality information.
In areas we DO understand, we'll either not ask AI at all, or treat its results with a higher degree of skepticism. Result: a lack of high quality information.
Inevitably this means a higher volume of non-expert prompts gets translated into the next generation of internet content. AIs are pumping out more novice-level text and less expert guidance.
The result will be an internet full content written from the perspective of an ignoramus; not addressing any complex issues, staying surface level on every topic. Which will cascade into future models, etc.
> The result will be an internet full content written from the perspective of an ignoramus; not addressing any complex issues,
Not to be overly negative, but have you really looked at the vast majority of the content on the internet? There are good pockets of real, in depth content. But the absolute vast majority of it is surface level basics at best, and completely wrong hot takes at worst. Content farms and click spam have made up huge portions of the internet for a while, never mind the absolute hell holes that places like Facebook, Twitter and Tumblr were and have been. And that's before you consider how often news media gets stuff wrong and then everyone copies everyone else's homework. Knowledge propagation, and more specifically correct knowledge propagation has always been difficult, slow and rare. You have always needed to check primary sources, and AI is just the latest in a long line of reminders of that fact.
Yes, the first 80% of a subject is repeated everywhere (including all the misconceptions) and you cannot go deeper except if you got very lucky like found a 5 year old youtube video with 130 views or an old blog post or a downvoted reddit comment. This is what makes internet so addicting to me, the small chance of finding these hidden gems inside mountains of garbage.
Having 80% in a broad amount of subjects is basically worthless, it is the 90% and further that have value because it took luck and actual personal experience and effort to take it that far.
The fundamental issue imo is that LLMs are trained to make believable outputs. They can be complete horseshit, but because they look plausible they get treated like quality.
I swear that the intensity and time I've had to take with code reviews has gone up because LLMs are so good at making flawed code look good. I assume the same goes for everything else we use LLMs for.
> 2. AI is a terrible replacement for me - my skills are at such a high level that it’s almost theoretical that it’ll ever be good enough to replace me for 90% of what I get paid to do. It’s a tool at best.
Most? Perhaps it's depression, but I look back at my career and wonder if any code I've ever been paid to write is beyond what current AI can do.
Sure, this leaves me with the non-coding tasks of UX taste, and code review + a few other forms of QA (and, when self-employed, project management, game design, etc.), but man, I'm someone who actually learned to read in part on the Commodore 64 user manual (as in, trying to understand what PEAK and POKE meant concurrent with having "Jack and Jill go up the hill" picture books).
(And no, I'm not claiming LLMs make bug-free code, I see the bugs LLMs make during my code review of their output and some of them are awful, hence "this leaves me with …").
And? How valuable are individual lines of code? To the author's point, I'm sure AI can translate individual sentences perfectly, but miss the nuance of communication in a bigger project or body of text. In the same vein, when was the last time someone put an AI on a ralph loop, posted the result on r/vibecoding and ended up with actual users.
> How valuable are individual lines of code?
Don't care, only time I've measured them was personal curiosity about hand-written projects, and one time I was trying to work out how many blank comments a co-worker had put into their codebase*.
How valuable are features? Management kept giving me them, and I always just assumed they'd decided which ones were important. But I've seen git histories of apps where the same feature was added twice, 5 years apart, by the same developer.
> In the same vein, when was the last time someone put an AI on a ralph loop, posted the result on r/vibecoding and ended up with actual users.
How often do the megacorps currently boasting that 80% of their code is now vibed, post anything (other than adverts) to reddit?
* 20% of the whole project, or 24 thousand blank comments.
Reminded me of this post by EY. (You're making a different point about existing expertise, not LLM expertise, but I think it holds in general.)
Every month a new guy discovers LLMs; discovers a skill the current LLMs require to get good results; and writes about the future jobs that will always be available for smart people like HIM, that are SKILLED in using LLMs.
The next generation of AIs doesn't need his fancy prompt. The image model goes from needing to type in just the right set of weird words and cryptic sorcerous invocations, to most people being able to type in English what they want and get a pretty good result.
There are still tasks that require careful invocation. But they are a much smaller fraction of all the tasks people are trying to do, or you can get a bleh result without the elaborate invocation to get it really good. And to improve on the bleh result you need to be substantially more of an expert than back when the Guy was memorizing a rule about adding "trending on Artstation" to the image prompts, as would always require a human paid to do that.
Another generation of AIs comes out. The next generation of Clever Skills is obsolete. Image models just obey the instructions for compositing panels without mixing them up, and you don't need to be an expert to get them to do it right. Another human value-add is gone. A wider set of tasks require no human expert.
Now a new Guy notices LLMs have become useful in his field for the first time. He discovers they require SKILL to use CORRECTLY. He posts about how there will always be jobs for humans who are SKILLED in using LLMs like HIM.
But it is not an infinite cycle. It is not the same each time it repeats. Now the Guy is a highly paid programmer or a career mathematician in 2026, instead of a graphic artist in 2023.
In six months the models will no longer require his vaunted Skills.
And by then there will be another Guy.
But the process doesn't continue forever. The Guys are coming from fields that were harder and harder for AIs. The brief centaur eras are shorter and shorter.
Today it is writers who are laughing at how bad the LLMs are at their job, and who will perhaps soon be posting about how it takes Skill to get an LLM to do their job Correctly. But the models are coming faster, and the eras of kinds of human value-add in each field are shortening.
There is a point when you run out of Guys, either because the centaur eras are too short for people to develop SKILLs and post to Twitter about them; or because there are not lands left for AIs to conquer; or because ordinary people are not reassured by some Nobel laureate proclaiming there will always be jobs for Nobel laureates with the SKILLS to prompt robotized biology labs Correctly.
But we'll never run out of amateur economists who assert entirely without a brief contemporary example that there will always be jobs for humans skilled at operating AIs!
We'll run out of professional economists saying it when nobody is paid for that work anymore.
I guess we'll also run out of amateur economists when they're dead.
Source: https://x.com/allTheYud/status/2057136382817231151
This is a new form of Gell-Mann Amnesia: https://en.wiktionary.org/wiki/Gell-Mann_Amnesia_effect
My fear is in the future it won't matter. People will accept slop because while they can be convinced it's not as good as it could be, it's good enough. To them it's good enough because it's fast and cheap not because it's actually good. There won't be any room in the economy for the value human output brings because the economy will rearrange itself around AI and become completely dependent on cheap output, good enough or not.
We should create a generalized version of "Gell-Mann Amnesia". This applies not just to fields of study. But also to time and space. We read history as if the person who wrote the history book has perfect knowledge.
Honestly, we're at a point where AI can write better software than some devs and answer medical questions with more knowledge than some doctors.
Likewise, AI is oblivious to it's own mistakes, much like said professionals can be at times.
Not that AI is actually thinking, but rather the collective corpus of text yields greater insights (knowledge of the crowd, not wisdom of the crowd) than a lower-average person in that same industry.
Except that it is also quite difficult to assess the quality of a doctor or a software developer if you don't work in the field.
I've heard numerous cases where AI solved medical issues that doctor couldn't.
Slight tangent into translations:
I read two translations of the book "The Master and Margarita". My first read was so boring I couldn't help but stop reading before the end of the first chapter. I can't find the copy and the name of the person who translated it, but this one had all the Russian nicknames translated. It kept talking about a guy called homeless. I thought it was just a bad book and dismissed it for years. I couldn't understand what all the fuss was about with this book.
But then, I stumbled upon the translation by Diana Burgin and Katherine Tiernan O'Connor. Although I don't speak Russian, I think this is as good as it gets. They did a phenomenal job.
You can see the same effect with the mechanical translation of the book "We" by Yevgeny Zamyatin, where the government is called "United State" easily confused with the "United States". The translation that called it "One State" was so much better.
I still clearly remember in my early twenties being stunned to discover that the Astérix comics are originally written in French and then translated. Coming up with names like Getafix in English for the druid — incroyable.
An honest to god article full of em dashes that's not because it was AI but because it was a human using them as a crutch to get around crafting sentences that flow naturally. Almost brings a tear to my eye.
First sentence:
> In my Ottawa life, every Tuesday evening, I take two gym classes back to back—boxing and the pompously named “body sculpt,” which makes me discover muscles I didn’t know I had.
The em-dash matches how you'd speak out loud.
You'd say "I take two classes every Tuesday back to back, boxing and 'body sculpt'. Weird name." (Parts of that sentence did flow oddly, but not because of the em-dash).
Grammarians say you can't make those separate sentences without adding some extra words, and because of blah-de-blah-blah-blah, someone might say you can't join them with a comma. So we have an em-dash.
Rewriting the sentence would make it flow less naturally, not more.
When I write like I talk, I use a lot of commas. Replacing some of my commas with em dashies, so long as it was done judiciously, would probably make things easier to chunk.
I’ve seen people use colons where em dashes are effective. I use em dashes. AI leans heavily on them for same reason
It’s become the exclamation mark of mid-sentence punctuation. It connotes fragmented or interrupted speech in my opinion. The problem is that writing is not speech, that’s why it is more often seen in written dialogue.
If I had a nickel for every em-dash I saw that could’ve been a colon…
You'd be full of shit.
Oh, sorry, I thought you said colon…
Good writing shouldn't just be how you talk out loud.
Good writing doesn’t exclude it.
I wish more people had casual exposure to professional translators. Its a deeply important, vanishingly small segment of the human population and has been this way for at least many thousands of years. Also, it will continue to be!
I’ve a friend who does simultaneous interpretation at the UN and she’s just… good god, how do you even do that. Oh, and she does it in six languages.
And here I am, brain the size of a galaxy, and I fumble my way through every language I speak other than English.
Serious respect for the linguists.
I guess I should have figured Marvin would be here on HN, feeling sorry for himself.
My first rule—before doing anything else—when writing a sentence, is to check whether I could have removed the em dashes by re-ordering the elements.
Update: in case it’s not obvious, I am sorry. I could not help it.
Em dashes are really good actually and a standard stylistic choice for non-technical writing, particularly outside the US.
They certainly have their place, but are massively overused in contemporary American prose. This might be slight more of an east coast thing, but that's just a subjective impression that I'm not willing to spend time measuring.
To me they come off as faddish, with many writers using them where commas and semicolons would have done just as well. I think their popularity stems from teh fact that provide the sense of a personal aside from the writer, allowing them to be more expressive while clearly delineating the personal or contextual remark from the main flow of the prose. No doubt this works for a lot of readers, but I find it tedious.
It's a fad that has been going strong for centuries in published literature, so I'd guess an awful lot of authors world disagree with you.
You can restructureany sentence to use fewer forms of punctuation -- but if you do that, you'll lose nuance. And nuance, in writing, is a very fine thing.
The em-dash has indeed been around for centuries, but the fad I refer to is its overuse in contemporary American prose. IF you look at Google Books n-gram viewer, you can see it went through a surge of popularity over a few decades that then fell off sharply.
https://books.google.com/ngrams/graph?content=%E2%80%93&year...
It's also notable that the em-dash is approved in American Manuals of Style, while discouraged in British ones. I was unable to find longitudinal data for the em-dash's use in magazines, blogs etc., but AI summaries suggest it's 3-4 times more used in those contexts than in news reports.
Like strawberry ice cream or apple pie, nuance is certainly a fine thing; but a surfeit of it becomes cloying, and the antipathy toward the omnipresence of the em-dash in LLM-generated prose, along with other kinds of literary expression like contrast and comparison, suggests to me that people have had more than enough of it.
I use them because I know what I want to say out loud, but transcribing the pause with commas is incorrect because it's a comma splice, and I find that the semicolon often looks glaringly overly formal. So I've settled on the em-dash.
English isn't my native language.
Also, I like em dashes.
And if this is my worst sin, so be it.
My writing used to be littered with them, but I now eschew the em in favour of en, as it has become too strong an anti-shibboleth.
I have also taken to being sloppier in my prose, as I’ve had stories rejected for being “written by AI” - when they’re shorts I wrote more than a decade ago. Reworked them to sound like a moron, accepted. Sigh.
> I now eschew the em in favour of en
They have different meaning and are not interchangeable.
I have a similar issue. I tend to have a very “structured” type of writing. Say on slack or Reddit for example. Using markdown formatting. Lists with bulletpoints etc. And I tend to write long detailed explanations, sometimes too long if I am being honest.
But now I find myself adding noise and imperfections to my writing (not that it was perfect) to make it more human, which is kinda silly.
The LLMs decided to use you as the model for the pinnacle of human communication style.
Either it's LLM generated, or it's written by someone who wants to be ambiguous about using LLMs.
Either way, I'm not reading it, it's a clanker or a clanker collaborationist.
I mean, how would you even write an em dash? There's no button in the keyboard for em dashes, it's not in ascii, it's just not something we write in internet text with, it's a safety watermark put into LLMs by OpenAI to help making LLM generated content identifiable as such.
If for some reason you are an em dash lover that was hurt by the LLM debacle, I'm so sorry for your loss, but look who's on your side, give the em dash a funeral and let it go.
> I mean, how would you even write an em dash?
With a keyboard shortcut. Just because you are incompetent, that does not mean everybody is.
Your argument goes as follows: “I’m incapable of it, therefore no-one is capable of it”.
Followed by, “You should abandon your preferences because I don’t share them”.
It's been seared into my muscle memory for more than a decade. I keep using it, too. It's present in the popular training sets – and then in LLM outputs – simply because it's proper punctuation.
I wrote the article.
Sorry if I like em dashes.
It's alt + 151 BTW.
"clanker"
Slang for an AI, used by a Blade Runner
You'd be laughed at if you said that ChatGPT could help you with graduate level mathematics in 2024, but this year, AI models on simple prompts are solving previously unsolved Erdos problems.
It seems silly to imagine that there is some fundamental barrier between human intelligence and AI, and that AI could never do many of the things that humans can do. Inferring intent, gauging sentiments, factoring in cultural values, etc. all the things cited as stuff humans can do but AI can't, AI can currently do if given enough context. But more importantly, all those things aren't magical tasks that can only occur inside a human skull, they are a product of information processing, its just the information processing that has been hard to make computers good at, but so far it appears AI keeps getting better.
I'm all for humans having special value that is not attached to their ability to perform useful work. However denying the abilities of AI models seems to be a common mistake many people are making, and sadly reality catches up to these people before they can emotionally prepare.
> AI can currently do if given enough context
It's worth noting that you can substitute "dollars" for "context" in that sentence, which seems to be where many of these impressive achievements are coming from. As ever, it's unclear whether these models will get cheaper while remaining better, since all of the recent breakthroughs appear to be of the "think more" kind. For translation specifically, I'd be very surprised if the "think more" LLMs would help given the per-unit cost expected of the output.
> all those things aren't magical tasks that can only occur inside a human skull, they are a product of information processing
I agree but it's useful to remember that 1. brains and especially the human brain are enormous and 2. individual tokens carry significantly more meaning than individual tiny muscle twitches so even extremely primitive "cognition" can look like it's doing more work than it actually is.
> You'd be laughed at if you said that ChatGPT could help you with graduate level mathematics in 2024, but this year, AI models on simple prompts are solving previously unsolved Erdos problems.
I'm curious, do you have a graduate degree in mathematics?
I have a graduate degree in mathematics. AI models can absolutely help you do research math in 2026. I recently asked chatGPT to prove a result which I know to have been published in Advances in Mathematics (a pretty good, but not top tier, journal) this year, and it gave a correct proof which was completely distinct from the one that was published.
Yes. It's as if they think AI will forever be LLM only and won't develop world models that incorporate current state assessment, dynamic next-state prediction, cause-and-effect reasoning, object permanence, etc. I'm not in the AI industry but I assume there's got to be lots of research and work being done on this.
Fable has really spooked me, honestly. It's another big jump, but not in the actual coding. I was pretty comfortable with the "you do the implementation, I do the meta work and steering", and ... no steering required, no meta work required. Here's the backlog, let me know when it's complete, I guess I'm going to go touch grass until I have to review and refine... probably tomorrow?
Reminds me of the first time I saw a coding agent stumble through an issue in 2023 maybe? and went "this is a big deal", similarly when OG gpt started making jokes that actually kinda worked.
Updated modern version of the classic "make me a greentext", apologies for slop-posting, but it seems relevant:
Spicy... I giggled.
Greentext is eh. Very formulaic, in fact very similar to the bottomless pit one, which I'd argue is better because of it's absurdity. I have to ask, did you mention the older GPT version to fable in the prompt?
Of course I did! Wouldn't be faithfully mediocre without the right context
As mentioned in the article, the point of language is to communicate with other humans, and you need a human to do that.
Mathematics is famously rigorously defined, it's roughly analog to AI beating humans at chess. Sure it's impressive, but it's also something you'd expect machines to be good at.
I have no doubt that the writer is better at translating than AI, but I have to say that AI translation has gotten so good that I'm not sure how much longer translation work will be there, or rather it might end up being more about auditing.
For example, I just read the Lawrence Ellsworth translation of The Three Musketeers, which I very thoroughly enjoyed. I don't speak or read French, but from my understanding Ellsworth's translation is considered one of the more accurate translations of the work.
Out of curiosity, I sic'd Claude Fable on the original French version of The Three Musketeers and told it to translate accurately, but also try and keep the same jovial tone as the original and do not censor anything. After it was done, I didn't read the entire output, but I did compare a few individual chapters between the Ellsworth translation and the Fable translation.
They were honestly remarkably similar. As far as I could tell, nothing was substantially different from the Ellsworth translation and the Fable translation. I do think that the prose for the Ellsworth translation was a bit better, but the prose for the Fable one was actually perfectly readable. Again, I don't speak French so I cannot say for sure, but I do not believe that I would have gotten a significantly different experience had I read the Fable version instead of the Ellsworth version.
Now, it's possible (and likely) that this is somewhat self-fulfilling; Fable might have been trained using Ellsworth's translation and as such it's very directly able to crib from it; sadly since I do not speak any language outside of English, there's sort of a catch-22: the only way I can compare the accuracy of a translation is to compare against other translations, but if other translations exist then that will likely influence the results, and if a translation doesn't already exist then I have no way of auditing it.
I'm still going to continue reading through Ellsworth's translations for the subsequent stories simply because that feels more canonical, and as I said I do think the prose was a bit better.
> I have to say that AI translation has gotten so good
> I do not speak any language outside of English
So you are entirely unqualified to judge this, and you acknowledge yourself that your test is flawed to the point of being completely useless. Yet you make grandiose statements about the quality.
> Out of curiosity, I sic'd Claude Fable on the original French version of The Three Musketeers and told it to translate accurately, but also try and keep the same jovial tone as the original and do not censor anything. After it was done, I didn't read the entire output, but I did compare a few individual chapters between the Ellsworth translation and the Fable translation.
This isn’t a great test, because Claude almost certainly has multiple translations of The Three Musketeers in its training data.
Read the last two paragraphs :)
The things is, this is almost certainly what's happening.
You can (could, maybe they 'fixed' it by now) get sota LLMs to reproduce entire novels near verbatim.
The idea of giving it parallel texts of those novels in different languages, to train it on translation, is so obvious it'd just be strange if the AI labs didn't do it.
In fact DeepL was doing basically that more than 10 y ago.
Oops, I legitimately missed the second-to-last paragraph.
I still think there are better tests you could do. Ideally, you would choose a book that was published recently—after the model’s cut-off date—which is considered to be a good translation. But even something like The Girl With the Dragon Tattoo, which is not particularly new and by no means obscure, would be better than a famous work of literature like The Three Musketeers that has many translations.
Almost certainly correct, though I've noticed that these LLMs like to complain when you give it stuff that is still in copyright. The Three Musketeers is thoroughly public domain everywhere so in that sense it's a good test, but of course because it's public domain everywhere there are lots of translations to crib from so I acknowledge it's not a great test because the training data almost certainly contains a competent translation.
Even if Fable didn't have Ellsworth's translation, it certainly has the William Barrow translation, which would still get it like 80+% of the way there.
My wife speaks Spanish, I should get her to do some kind of comparison with a Spanish book that doesn't have English translations.
They say "yes, I admit it, this is all invalid".
No, they are a disclaimer that it's possible that the data isn't conclusive. Not the same thing as saying "it's all invalid".
> I did compare a few individual chapters between the Ellsworth translation and the Fable translation.
I'm pretty sure the Ellsworth translation is in the corpus. You basically instructed claude to regurgitate it.
The llms all have the more famous books memorized. You can trick them to recite them more or less word for word.
I mentioned this specifically in my comment :)
... yet you still conclude "AI translation has gotten so good", so which is it?
I do think it's gotten pretty good. I'm just acknowledging my limitations in the matter. It's not a contradiction.
Try translating some prose from English to another language, then, in a different model, back to English
I tried this with the original comment in the thread. Guaranteed to not be in the corpus, references a few terms that also wouldn't be in the corpus (Claude Fable), and long enough to be more than a sentence or two while short enough to compare in a discussion like this.
I did this with entirely local models I have sitting around on my laptop. Minimax M2.7 at a 3 bit quant with 8 bit quantized KV cache for English -> French, Gemma 4 31B QAT (4 bit quant) MTP for French -> English.
It's perfectly readable, but there are a few places where the phrasing is a bit more awkward after the double translation ("auditing" to "revision" in particular is a bit off). Gemma did comment on not knowing what Claude Fable was in its thought process: "The author compares Ellsworth's translation with one produced by "Claude Fable" (likely a misspelling of "Claude" or a specific version of Claude)."
Here's the double translation:
"I have no doubt that a writer is better at translating than AI, but I must say that AI translation has become so good that I'm not sure how much longer the profession of translation will exist—or rather, it may become more a matter of revision.
"For example, I just read Lawrence Ellsworth's translation of The Three Musketeers, which I enjoyed immensely. I neither speak nor read French, but from what I understand, Ellsworth's translation is considered one of the most faithful translations of the work.
"Out of curiosity, I asked Claude Fable to translate the original French version of The Three Musketeers; I asked it to translate faithfully, but also to try to maintain the same playful tone as the original and to censor nothing.
"Once it was finished, I didn't read the entire result, but I compared a few individual chapters between Ellsworth's translation and Fable's.
"They were honestly remarkably similar. As far as I can tell, nothing was substantially different between Ellsworth's translation and Fable's. I think the prose in Ellsworth's translation was slightly better, but Fable's was actually perfectly readable. Again, I don't speak French, so I can't say for certain, but I don't believe I would have had a significantly different experience if I had read Fable's version instead of Ellsworth's.
"It is possible (and probable) that this is partly a self-fulfilling prophecy; Fable may have been trained using Ellsworth's translation and can therefore draw directly from it. Unfortunately, since I don't speak any language other than English, there is a sort of vicious circle: the only way to compare the fidelity of a translation is to compare it to other translations, but if other translations already exist, that will likely influence the results, and if a translation doesn't exist yet, I have no way of verifying it.
"I am going to continue reading Ellsworth's translations for the following stories simply because it feels more canonical to me, and as I said, I think the prose was slightly better."
This is terrible. I never use em dashes!
As somebody who regularly reads translated works, including the occasional machine translation (MTL), they (MTL) suck. You got a hugely biased result, which you recognize.
Translation is hard. If you're familiar with reading translations from specific languages MTL works have a very specific smell to them, it's a bit hard to describe but it's there. A good translation is miles (kilometers, for those outside of the US) above MTL.
That's not to say that perhaps the latest LLMs will have better translation abilities, but that they are generally crap currently. Maybe they are fine for something very short, but absolutely not for longer content.
> As far as I could tell, nothing was substantially different from the Ellsworth translation and the Fable translation.
Crucially the full translation was part of ChatGPT’s training set. Recall is a pretty solved problem in machine learning.
How well does it translate a French novel published yesterday? Where neither the original novel nor any translations are in the training set yet? Or might not even exist!
I tried asking ChatGPT to translate a letter I wrote in Slovenian this weekend. It got the general gist but missed a lot of the nuance. Completely missed several of the little touches of tone where the right choice of synonym conveys a whole bunch of information.
Did no one actually finish reading my comment?
I feel like that wasn’t there when I started writing my comment. I also have a bad habit of quickly posting and then adding over a few minutes.
Glad we agree :)
Guess I have no way of proving it, but I pinky swear that I didn't edit it in later!
But yeah, I broadly do agree; if I read other languages I could find a book that hadn't been thoroughly translated to English and then I could give a proper analysis on how good the translation is, but since I'm a very stereotypical American I know exactly one language (and sometimes my comprehension of even that is questionable).
> I could find a book that hadn't been thoroughly translated to English and then I could give a proper analysis on how good the translation is, but since I'm a very stereotypical American I know exactly one language
So you actually cannot give a proper analysis.
Welcome to the internet
> Fable might have been trained using Ellsworth's translation and as such it's very directly able to crib from it
The `cp` program on my computer also has the remarkable ability to produce a faithful translation of The Three Musketeers when provided one as input.
> but I have to say that AI translation has gotten so good that I'm not sure how much longer translation work will be there, or rather it might end up being more about auditing
It's functional? I wouldn't say it's poetic, I wouldn't want any AI translator translating art, like say a book or poem, I'd be so uncertain that it would correctly bridge the concepts
A good translator can make stylistic choices that elevate the work and make it fit in their language
(Having read lots of well translated manga and anime, also from what I understand there's a few books I've been told by my bilingual friend's are just chef's kiss quality translations)
Considering translating meaningful art is of some value, on that score I don't think we're there yet
Your example doesn't prove your point because you can't even tell it's translation, but also because you said it was not better and are not using it.
Honestly, translations of fiction are themselves creative works, and the translator needs to really understand both cultures and needs to write cohesively throughout the work. I'm not sure this is even really a question of "can it translate" so much as "can it create a good work of fiction" which is a much higher bar. So maybe the model can mimic the style (especially given that it was probably trained on existing translations) but could it really do so from scratch in a way that is actually compelling? I'm not so sure.
Of course as for the poor OP... is this a majority of what working translators are paid to do?
I suspect a lot of translation is just grunt work - technical and business documents. The lack of a cohesive voice with considered style is perhaps not really much of an issue in those. The expectations are just much lower; text that conveys the basic meaning is a much lower bar to clear.
She's probably better than a bot at that stuff, at least for now, but my concern is that it won't be "enough" better for businesses to justify her continued employment. And this is my general feeling about this stuff across society, in basically all domains.
> Again, I don't speak French so I cannot say for sure
This reminds me of the adage, that ChatGPT is really great at everything except my own work.
Yeah, that's why I put the caveat in there. I have no real way to verify the result outside of checking against "known good" translations, though if the known-good translation exists then there's not exactly a lot of reason to do the AI translation in the first place.
I suspect if I knew another language I would be able to find errors in the translation.
Yes, it is another variation on the Gell-Mann Amnesia Effect. I have a number of non-developers in my circle of friends who think Claude is about to put me out of work. They think it is just a great tool for them, not a replacement. Of course!
I see the difficulties more in other areas, such as technical translations, specialist books, user manuals, and translating UIs, where contextual information and a back and forth with the client is needed to clarify details, and (for user manuals and UIs) the translator has to put themselves in the mind of the user and has to consider the possible contexts and use cases.
You're very likely to get a somewhat circular reference; the key (for me) is that for 90% of the usages, "standard translation LLMs" are just fine - I still recommend a translator but they're more of a proof-reader for both languages, catching where something slipped through.
This is sort of missing the point-- people who dont deal with linguistics dont understand that there are multiple types of translation. There's word for word (which is what you're talking about) and sense for sense. If you let an LLM do all of your translation you're letting it interpret huge amounts of intent and context it doesnt (and probably cant) access. The ways in which this impacts the translation will forever be unknown to you and in the worst case lost forever.
So i guess in the end it just matters how important the work is.
Actually I was talking about tonally as well.
A raw "word for word" translation (which I also tried) made the story somewhat hard to follow and very dry, but just asking it to keep the same kind of jovial swashbuckling tone of the original made something pretty similar to Ellsworth's translation.
Again, before someone decides to "correct" me on this, I am aware that it's very likely that the Ellsworth translations are part of the training set so it's not directly a fair comparison.
If you let an LLM do all of your translation you're letting it interpret huge amounts of intent and context it doesnt (and probably cant) access.
What’s the intent and context that a human translator of a text is typically privy to that an LLM is not?
> If you let an LLM do all of your translation you're letting it interpret huge amounts of intent and context it doesnt (and probably cant) access.
Assuming lots of material local to the context one is wanting to translate is included, why couldn't it potentially access that additional context?
LLMs are now being aggressively manipulated for propaganda purposes. Powerful people have realized that people believe LLMs, and treat them as authoritative sources of fact.
The number of lies, lies by omission, deceptive distortions, and fallacious argument tactics they generate is absurd, and increasing rapidly. Translation, when done as a service you are paid for, can't be relied on by propaganda bots.
Do you have examples?
Tons. To pick the most recent example:
I was asking about some allegations relating to the Epstein files, and it used the slogan "Satanic Panic" in a weird way that gave me a vibe of discrediting victims. I'm too young to know much about it, so I asked some things about it. It explained the McMartin case in a way that seemed too absurd to be real. I asked some follow-up questions about what the strongest evidence was, and how it was explained.
The first deception was omission. Initially, it didn't even mention what was arguably the most significant evidence in the case, which was the presence of tunnels under the school. ChatGPT mentioned the tunnels, and how an archaeologist named E. Gary Stickel found evidence of tunnels. Here's what it said about that:
> However, that conclusion has been repeatedly challenged and is not treated as settled fact in the academic or forensic archaeology literature.
> Other archaeologists and later reviewers reinterpreted the same physical findings differently. One major counter-analysis (W. Joseph Wyatt’s review) argued that what Stickel identified as tunnels was more plausibly explained as pre-existing trash pits and construction-related disturbance from before the school was built in the 1960s.
The first lie was by omission, it didn't even mention this when I asked about the most important evidence. The next misleading piece was the framing. Dr. Stickel is a PhD archaeologist, and doing this sort of analysis is his area of expertise. He used nine criteria as a basis for determining the presence of tunnels, and all nine were met. He found "conclusive" evidence of tunnels, and that they matched the expected locations described by the victims. Dr. Stickel was the only expert to review the site before significant construction made such an analysis impossible.
The "major counter-analysis (W. Joseph Wyatt’s review)" was done by psychologist Joseph Wyatt, who never physically visited the site, and who is not an expert in anything related even loosely to archaeology. ChatGPT presented this guy in a way that made it seem that Stickel had been debunked by a comparable expert.
> … considered one of the more accurate translations of the work.
I think you’re missing a big point of translating literary works. A purely “accurate”, phrase-by-phrase translation is often not very good; the actual literary style, the feeling and the allusions and references, often get lost that way. A good translation of literary work requires a lot of deliberate choices by the translator to deviate from literal translations in ways that convey the style of the original, or an extra layer of meaning that would be lost by an “accurate” translation of a phrase. Also, being consistent with these choices matters a lot, which OP claims LLMs are less good at.
An interesting counter-example: https://xcancel.com/ValerioCapraro/status/206506665753442336...
I wonder if “Just 3 words: you’re not alone” would have been acceptable. :)
The Empire Strikes Back: "I'm your dad."
.
Already mentioned in the comment lol.
This moment is coming for software developers too
Yeah almost certainly, especially the ones who made a career out of "copypaste from StackOverflow", which is most engineers.
But even the good engineers should likely be a little worried.
why would it be different for other people if you already said senior level is not writing code but planning things out?
is there something about planning that LLMs cannot do being your crux of the argument?
what do you believe about your jobs or function that you think will be immune from AI replacing you?
If anything it seems your role is not that dissimilar to those translating languages or business requirements.
I am struggling to see what it is about this planning you do that cannot be done by AI because it seems to me thats not where the moat is rather I find the middle man jobs to be the most vulnerable to AI immediately much more than people writing code.
Because at least someone is watching the outputs from AI and understands the code and can communicate it easily back to the stakeholders without the middle man gate keeping and applying their "taste".
I have a feeling that anyone in your shoes is going to be working with code soon or they won't have much to offer anymore to the business. A stakeholder could easily replace the middle layer with AI and even as a business owner myself I do not see any need to add any more humans at the layer anymore unless they write code.
More specifically, it is coming for coders. If you make your living by banging out lines of code all day, then you may want to be looking at adjusting your career trajectory. But if that is your job, you are either very junior, or a bit foolish for getting into that situation.
so what is software developer doing if writing code is not part of their job
I don't see how not writing code is being offered as a moat, it seems like that is just translating business/stakeholder requirements to architecture/biz processes which is exactly the type of low hanging fruit that AI will capture first
or was it your point that the position sits closer to the stakeholders (relatively compared to those lifting) thus immune from replacement by AI
or is your argument that your taste is exquisite that no AI will be able to match it like it already has with software so far and it will not improve beyond the current state
If you get to senior level then most of your job probably is not writing code, but planning things out. The code is largely an implementation detail.
At least that's how it was for me, maybe other peoples' careers are different.
Yes, my career has been different. At my workplaces seniors still have to code because they dont want to hire juniors
The "planning things out" has moved to another layer, called "architects"
> If you get to senior level then most of your job probably is not writing code, but planning things out.
If they're so good at banging out code now, they're coming for that too, you know.
I don't necessarily disagree, but there's gotta be a name for some kind of "infinite extrapolation" fallacy, where you assume that the current rate of progress will continue indefinitely.
That might happen, but I don't think it's implied, at least given literally every other bit of technology that has ever happened in history ever.
> I don't necessarily disagree, but there's gotta be a name for some kind of "infinite extrapolation" fallacy, where you assume that the current rate of progress will continue indefinitely.
I am not assuming they'll continue indefinitely, but it's a small step from writing code to planning out the code to write, and another small step from planning a coding project to planning a software project, etc.
These are all small steps, and because the act of specification + planning paid less than specification + planning + programming, what reason do you have for thinking that specification + planning is valuable enough to keep the salaries the same as specification + planning + programming?
I think with a fixed size problem, no we wouldn't be able to demand the same salaries that we get now.
I dispute that the problem is fixed size. The people who are senior engineers now will learn how to think at a higher level with the AI models.
Same thing architects do if drawing lines gets automated: architecture.
Would you trust living in a high rise designed by AI?
Designing a system that survives production is the job.
So what a lab researcher doing if typing articles is not part of the job?
Well--well look. I already told you: I deal with the god damn customers so the engineers don't have to. I have people skills; I am good at dealing with people. Can't you understand that? What the hell is wrong with you people?
https://www.reddit.com/r/ProductManagement/comments/uy1ot1/w...
I think this collapses a global, complex heirarchy of software engineering workers into a single monolith and serves only to advertise for frontier LLM providers. the point where you no longer need engineers is not going to be reached by making LLMs better and better.
I think there is going to be a long time before all of the obscure knowledge of a decent software developer can be completely replaced by AI. Though the job is going to change beyond recognition. It already has in many ways.
But not before a huge crash in optimism about their capabilities. Specifically wrt accuracy, reliability, efficiency, and organization/architecture.
Hey guys,
I just found out you were discussing my article, so obviously I had to come here. ;-)
I’ll take the time to read the thread properly because, hey, you took the time to read my article. Also, I genuinely enjoy reading and I’m curious to see what you think about AI.
The anecdote in the article is real, by the way. I only changed her title.
With AI, I went from “surely this won’t affect me” to “AI is dumb” — no, I was dumb, I just didn’t know how to prompt it — and now I’m at the “how can I make it work for me?” stage, while still hoping employers, clients and, well, the entire world realize it’s NOT a magic button.
It’s crazy how unreliable it can be. Sure, it can translate in the sense that it can give you an idea of what was said. But that doesn’t mean it’s good. I could give a million examples...
If you look at the trajectory of your stance, do you think you might reach "Uh-oh this is doing just as good a job as I can do in a much shorter time?" within the next year? I feel like that's what happened to pure coding for me, something I never ever thought would be possible.
The unreliability is something that seems like it might be a temporary early stage.
I think it's an interesting perspective, because translation is one of the jobs that I (a) hear is the first to lose work due to AI, and (b) often used as an example of "acceptable" AI by people who are skeptics of LLMs and AI-generated art.
> often used as an example of "acceptable" AI by people who are skeptics of LLMs and AI-generated art.
As one of such people, I think there is a nuance to it. AI is great when you’re translating something to yourself. But when translating things for others, more caution and human judgement is needed. Espesially when translating instruction manuals, where bad wording could cause someone to injure themself.
Exactly, it's never about absolute results, it's always
Expected Value (Upside, given time/cost savings + Downside, given %reliability).
So, every task falls under a spectrum
This. I put things through Google translate all the time and they're always unreliable. Sometimes they're correct, sometimes I need to know roughly what the original said. Infamously, Google used to say "geiler Typ" meant "horny guy" when it means "awesome guy". Google used to think "geil" meant "horny" in general, which it can but not usually
Google translate is primitive compared to Claude at translations.
Google Translate is at the bottom of the barrel. All other AI translation tools are vastly superior. You'd want to evaluate those, and forget about Google Translate completely.
It's all the same, except LLMs are less precise with names.
Googles machine translation team wrote the Attention is all you need paper that introduced transformers specifically to solve the problem that you can just model language by mapping one word to another. I'd be floored if they weren't using the tech they invented for intended purpose
Yeah. LLMs, machine translations, CJK keyboards, they are all the same technology; faster cars to each others, not cars vs horse drawn carriages. It'll be surprising if they didn't directly apply any applicable learnings back to Google Translate.
Just like a car and a school bus are the same because both have four wheels?
Language is incredibly complex. I remember a TikTok from a bilingual English-Korean speaker comparing the English subtitles from a Squid Game scene to what was actually being said by the characters. The nuance and info density lost in translation made the subtitles feel completely remedial. Americans were basically watching a different show altogether.
I'm by no means a native level Japanese speaker but I'm frequently surprised at how off Japanese-English subtitles can be.
I was watching the Netflix show The Empress with Chinese subtitles that did a pretty good job translating the German. I switched to English subs for one episode and couldn't stop telling the people I was watching with "That's not what he said! That's completely different!"
There are translators and there are translators. Translating legal/business documents is a completely different thing from translating movies/books/games.
I can confidently say that LLMs do a better job than the average traditionally published fictions in my country, at least when the original works are in English. Every single time I watch a subbed movie there will be some lines noticeably wrong.
Yes, I've become very leery of artistic translation, in part because the paradigm of translators as adapters and localizers often ends up at odds with the job of faithfully and accurately representing the original material.
The most egregious example I came across recently was where a friend enthused about some manga he was reading and I agreed to read a few chapters, only to discover that the translator has decided to render the countryside accents of western Japan (engaging with a protagonist visiting from Tokyo) by having them say 'y'all' and 'bless your heart' and other Southern USA tropes. I get the aspiration of the translator, but it was excruciatingly unpleasant to read. At that point, why not just say the protagonist was from New York and on vacation in Florida, or draw in some meshback caps on some of the characters and add alligators here and there in the background?
Translators already started losing jobs due to machine translation a decade ago (e.g. DeepL), before LLMs. Remuneration going down made it more difficult to make a living as a translator already then, even if you still received offers.
Well it's more than acceptable to translate e.g. web pages for reading, but it's not something you'd want to professionally publish.
Kinda conceptually similar to how typos and grammatical mistakes aren't a big deal if you're shooting off a quick text or email, but publishing if you've got typos in your advertising copy, in your resume, on your medicine label, etc. it's a real bad look.
Not all translations are the same. Literary translations are often works of art in and of themselves, and automating them would be missing the point entirely, like automating homework or weightlifting at the gym. I don't really know what's the state of the art, but I do buy that, on the other hand, translating toaster manuals or generic copy could soon be automatic.
Yup. If you are bilingual, you quickly realize how some translations are very bad. How some translations are very good. And how hard it is to translate. With dry, simple text, it might be easy. But when it involves art? Some jokes don't translate directly. There is pun. Sounds of words. Double meaning. Ambiguity. Cultural background. The creation of new words.
It can be reasonably argued that some poetry can be impossible to translate from some languages to others. A poem might be explained, but by a lenghty, dissecting explanation, that completely loses the point of it.
Or if you compare a poetic translation to a literal one, of different translations of the same work to the same language to each other.
When it's one one-hundredth the cost, "good enough" is generally good enough.
"Could not connect to translation service" was apparently good enough for someone, so the bar must be extremely low.
https://www.reddit.com/r/funny/comments/3e786n/chinese_hair_...
On the other hand, a lot of people become extremely put off by the smallest sign of ai slop. And the llms have a tendency to impart their style to any text they touch.
I prefer to get my hair cut at 'Usage limits exceeded.'
It'll be a similar theme for all facets of work involving any language, slowly - human or code. We'll parrot about humans in the loop this and that, but I think it'll be less humans in the loop over time and I think most people will even be willing to settle for a slightly more mediocre translation or coded project. It all comes back to our dopamine addiction, where we like fast feedback. And the oligarchs like tools to suppress wages. We will be our own demise for not advocating for either UBI or job protections, instead, happily using the technology while also rolling our eyes that it could never replace us.
Out of curiosity, I pasted an article in French I was reading a few minutes before coming across this thread into ChatGPT and asked for a translation into English. It was certainly passable from a functional perspective, and I wouldn't hesitate to use it to translate an article from a language I don't understand. But it was not professional-quality work. There were a couple instances where the French grammar was mistranslated, and the writing was perfunctory, not going into any effort to have the article flow like it was originally written in English instead of simply translating each sentence literally. Would I read an article written like this? A short one. A novel? Definitely not.
I think the issue is that a lot of professional work is being done when the commissioner would be perfectly fine with non professional work. There will always be a place for artful translation, theres a place for hasty translation as well.
Especially when you get three assignments from 4 to 6 pm, all due for the day after. It's certainly literary translation they're after.
I don't see LLMs being able to replace translators for less-spoken languages.
I know a translator between two Eastern European languages, and some jobs require use of specialized dictionaries. Using LLMs in such cases would be very unreliable and would require even more effort to check and correct than doing it correctly in the first place. Plus, I really doubt that US tech firms are training LLMs on language spoken by "only" 6 million people.
As for entertainment, anyone who grew up in Eastern Europe with pirated movies with nasal monotone translations, or machine-translated video games knows how much those take away from the experience. Sure, "AI could do better", but could it be consistent and capture cultural nuances and idioms, etc?
Even more so for spoken-only languages.
Despite the protests, he admits using AI and then charging his clients full price...
"But maybe I will ask Claude’s opinion, and if one of the suggestions is smart—cutting a paragraph, for instance, or clarifying a sentence—I might accept it.
When I started translating 15 years ago, we used to paste uncooperative sentences into Google Translate to see if it had interesting ways to phrase things differently. Then came DeepL—same idea."
Hi, it's me, I wrote the article! I'm a she, BTW.
I do admit testing AI. Hell, most of the time, I don't have the choice anymore—I don't use it but several of my clients send AI-translated documents. Do I just send back a CHatGPT version? Hell no. This is why and how I know it's not reliable or good.
It's not exactly taboo to use AI, is it? IT doesn't have to be all or nothing. AI is great for my glossaries. AI is shit to translate.
"we all more or less look the same in gym clothes"
Maybe my brain works differently than the author, but I'm surprised at this statement. Gym clothes don't change recognition for me, it's about the face, body, posture, clothes don't really enter into it. For me it is nonsensical enough to be suspicious.
And for a human centric perspective, not recognizing who someone is sad, it's knowing that you probably won't meet them again so it's not worth it, the community isn't there. Where community and interpersonal relationships between people are something we still hold dearly.
I wrote the article.
I'm a real person.
And I'm shit at recognizing people I don't interact with. PIcture 50 of us in black legging in front of a mirror...!
What’s unfortunate is that the market that is willing to pay for high-quality human translation has shrunken considerably.
Is it that unfortunate? Tasks that don't require high-quality translation now don't need human labor. We should be celebrating.
The sad part is that we haven't figured out how to distribute our resources fairly to these people even thought their services aren't required as often. Instead we just take their wages and give them to the top 0.1%
It’s unfortunate because we are seeing more poor translations in all domains, and users suffer from it. It’s part of a general enshittification of things. There are few contexts where low-quality translations don’t constitute a degradation of user experience.
Just one amusing example I saw recently: On the Amazon website, a submit button labeled “Go” in English was translated to something which when translated back would be “Walking”. That’s the kind of thing that would be exceedingly unlikely to happen with a human translator.
On the other hand, a bridge sign that says "No entry for heavy vehicles" is unlikely to now read "I am out of office for the next 2 weeks" in Welsh: https://www.theguardian.com/theguardian/2008/nov/01/5
Now instead it will say, in Welsh
Hahaha that’s pretty funny. But in their defence perhaps if you didn’t want a tall tale you shouldn’t have asked for a Fable? ;)
This story is so great because it shows how robotic are so many jobs and tasks. Like, what happened in the reciepient mind to not consider whether the reply was appropriate or not? Did the almost instant response not hint at an automated email? Or the lack of any other content of the email (a greeting, something)? Or maybe people send so many emails or is doing so many thing they switch off certain parts of the brain?
I think you overestimate human translators. There is a lot of very poor quality human-translated text out there. English translated from Chinese is famous for this.
There will never be enough expert-level human translators, and they tend to be very expensive. Machine translation has raised the floor.
> I think you overestimate human translators. There is a lot of very poor quality human-translated text out there.
This.
There was even a big controversy recently with one of the games on Steam where human translators just completely botched and vandalized the translation, mistranslating huge chunks of it and injecting their own personal politics which are not present in the original text (only English was affected; other languages were translated fine apparently): https://store.steampowered.com/news/app/2914150/view/5028562...
If you'd get the AI to translate it, even without any editing, it would have done much better job. Just because something's done by a human it doesn't automatically make it good; you still need competent people at the helm, and recent machine translation advances certainly raise the floor on that.
I don’t agree that machine translation has raised the floor, because even LLM-based translation can get pretty bad when it isn’t provided with the necessary context. And the average quality level I’m encountering has dropped since machine translation became mainstream. Poor translations have become the norm, which wasn’t the case 20 years ago, despite the occasional “all your base are belong to us”.
if it was valuable, people would pay for it
That’s not how it works. Value for users doesn’t translate 1:1 into value for businesses, nor are either necessarily willing to pay for value. That’s why things enshittify.
if they are selling to a business, the biz will pay if it solves their problem. if the solution doesn't solve their problem, or something else solves their problem that is easier / cheaper / better, the business will not pay.
The most important thing a human translator does is certify that the translation is faithful.
Period.
You could do a machine translation if you want, but you better pore over every word in case you end up on the witness stand.
I recently saw a video showing the french to german translation of a french McDonald's terminal. The translations were hilarious bad, like old school google translate bad.
Maybe McDonalds is big enough to not care about their reputation, maybe they are happy about the free clout from people making fun of them but they certainly chose to cheap out on translations.
https://www.tiktok.com/@denneshow/video/7522160205501566230
I say it’s a simple value proposition.
A few examples
Audio book narration. Human narrators are paid a seemingly ridiculous amount of money to literally read a book out loud. We have the tech to replace them, it’s actually pretty dang good, and it is substantially cheaper to do with computers. It’s pretty accurate too. In the audio book industry though, if you take your book seriously you have a real person read it. The best one you can find that you like. Readers enjoy hearing good narrators and the total value one narrator can bring is very high mostly because the value scales well.
Another real world example that doesn’t scale well, call centers. Customers want humans, but execs have tried to replace them with automation in every way possible. The margins of a business get squeezed because the value of the human touch doesn’t scale well in this case.
Translation falls a bit in the middle. I’m sure ChatGPT is good enough for some people. If you are a restaurant and need to understand what you are ordering at the local authentic Italian restaurant it’ll do the job. If you have a bad food allergy? Maybe not, you are willing to pay for accuracy because that’s what a human brings
So the answer to the question posed in the article, can’t you just upload it to ChatGPT? Maybe yea maybe no
I worked at large Japanese bank in New York and happened to sit near Chief US Economist next to his Japanese translator. She would occasionally ask about certain idioms. I remember explaining what a wildcat strike was for instance. But it must have been pretty tough because the guy was prolific in his commentary.
So i assume this post is just a bit of writing out frustration, but i'm always hoping that "AI can't do it" posts to include examples.
A list of "Examples AI will silently fail at" would be a lot more interesting, and might just convince your next potential client to _not_ use AI.
Sounds a aweful lot like the kind of things we were all saying before realising that we had to change what our jobs meant.
> If you ask me, nothing can save downtown Ottawa or North American public transit.
Come to Montreal. Only 2H away and you can get by decently well without a car.
As a public service employee within the GOC, I feel the pain expressed by the author. I sat through a meeting today where somebody with no domain knowledge puffed up their chest to show off their gpt created master lesson plan for a four year long internal training plan that is being re-worked.
I could feel the heads of those around the table that had been teaching this material for a decade starting to explode as this was exactly what others in the thread have described: it looked good until vetted by experts, then it was easy to poke holes as it was just not right
The problem in the public service is that the experts who can review the output are leaving or being nudged out.
I had transliterated lyrics of a song * with stanzas in Urdu , Braj Basha, Persian and Arabic , that I wanted to understand better ..
Gemini did a pretty good job of translating this to English .
Sure a professional human translator would have done a more nuanced job if I was willing to invest the money and time . But ...
* tajdar e haram originally by Payam Saihalwi, later versions by the Sabri Brothers and recently by Asif Aslam
Is the assumption that the LLM did the translation? Or that it just understood your query and submitted, on your behalf, to a tool you could have just used directly?
I'm gonna sound a bit like the clueless gym hr lady: I assume most income generating translation jobs are either mandated by law or commercially high stakes enough to warrant a human to do, no? Were people really being paid to do the type off _low stakes_ translations implied that a automated system can replace?
Maybe a publisher will replace the translator of the next Dan Brown best seller with Mythos? Who cares other than those buying it, getting money out of it?
Presumably the people paying the author for translation services are aware of AI, but for whatever reason are choosing a humans services instead. IMO it would be a form fraud to heavily rely on AI and not disclose that to the customer.
unfortunately this person will soon be unemployed.
not because their skills are no longer relevant, but because they are taking a principled stance defending now irrelevant skills.
Close. They will be unemployed because AI be "good enough" and companies won't care about it being better. Nothing they mentioned was really about principles. Everything was about quality output. Too bad companies dont care about quality.
Exactly, companies and even NGOs/charities that might be past clients of hers will only look at costs, not her experience.
their number one priority is solving their problem so they can realize their organization's mission, and that's how it should be.
if the translation is good enough to solve their problem, then it doesn't need to be any better.
One of my parents tried this to beat a deadline for product packaging.
There are now bags being sold marked "Lawn Suits", when it was supposed to be Lawn Topdressing
Everybody else is a fungible cog.
TFA is a good little read - couple things come to mind
[0] https://effectiviology.com/knolls-law/[1] https://en.wikipedia.org/wiki/Erwin_Knoll
[2] https://xkcd.com/1053/
Humans are really bad at noticing trajectories. They see the current situation. They know what the situation was 5 years ago. But for some reason they do not believe that there is a trajectory. They view the present state as the final destination.
Sure, just like AI enthusiasts seem to be unfamiliar with the concept of local maxima...
It’s been basically the same for 3 years now. Are you sure we’re the ones who can’t see trends?
Your experiences must be much different from mine.
Three years ago, AI was barely able to provide sort-of reliable command completion.
Two years ago, it could extrapolate a single function from a docstring - but the docstring had to be so verbose that it wasn't practical to use in that way.
A year ago, I was tinkering with Devin to try to find a way to get it to reliably implement small, isolated features from verbose Jira tickets.
Six months ago, I started using AI to generate the majority of my code output. Most of my time was spent reviewing, and I was ecstatic to reach ~2x output because I could run the next task while reviewing the last.
Now, at work I'm managing a half dozen Claude Code instances, Devin sessions, and orchestrating a review loop between Claude, Devin, and CodeRabbit. It's not uncommon for me to be working on four or more discrete features at once. My output is approximately 15x my pre-AI baseline - and I've not sat down and written a line of code directly in six months.
At home I'm managing a Hermes agent that can spin up a whole fleet of purpose-tuned agents for whatever purpose I'd like. I've implemented spec-driven development a'la Acai, and extended it to the point that my agent creates specs from text or voice conversation, I review them, and it handles implementation end-to-end. The code itself is an almost disposable artifact - useful primarily to ensure no regressions have been introduced between rounds.
... I simply don't understand how you can assert that "it's been basically the same for 3 years". It absolutely has not.
It sounds like our experiences are different. My software work isn’t on products where code can be disposable, since it affects people’s lives in material ways. I’m not sure why you’re launching fleets of agents at home, either.
Cmon - cursor has been out for like 3.5 years at this point. AI was still in its infancy but it was definitely able to complete tasks, albeit smaller ones.
Not disputing the overall trajectory, yeah it’s gotten better. But it was definitely capable of more than just command completion 3 years ago.
I reach for it more frequently. But personally, it’s at the point of diminishing returns for my work. It’s capable enough now to handle most of the things I want to throw at it, sometimes it’s wrong, sometimes it’s right.
I’m not doing cutting edge deep tech work - and I also don’t have the motivation (or salary increase) to be 15X more productive, if that’s even measurable. We are so busy because the CEO hears these “15X” statements and then the pressure is on to match or exceed that, and I’m not playing that game.
> Like a toddler, it will grow up. Humans are really bad at noticing trajectories.
Yourself included??
head in the sand
> Should you pay your roofer less because he uses a hammer instead of his bare hands?
Yes. Effective tools increase the supply of roofs made. More supply means lower prices per roof. But because the same number of roofs need to get worked on, the increase in roofs per roofer means less roofers will be needed.
It's quite ironic as the transformer architecture that powers most generative AI was invented for language translation :)
LLM's are in fact very good at translation and transliteration.
Some would say that's exactly what they do best, learn a language and be able to transform across them. Hence, "language" model.
Yeah, I agree - I get what the author is saying, but I also don't expect "translator" to be a practical career path in the future.
Even small, dumb, local models are excellent at translation already. Frontier models are on par or better than the human translations we've tested them against at work.
Safe to say OP just does NOT like AI https://correresmidestino.com/sorry-i-was-busy-unfucking-my-...
Poor woman should really look into pivoting her career or finding a different way of making money. Truth be told, her industry/career is not going to get better. Consistent work will just not fall from the sky.
Being bitter will not improve her situation. Even organizations like UN/OECD are looking into implementing AI in various ways.
Really good blog though. I love life blogs like these! You can go back and live through so many interesting/pivotal moments.
I wonder when this is posted about your or my profession.
Just to clarify, I am not saying this is a negative or offensive way. Just that she needs to proactively look into her options as I think its not going to get better, at least in the near term.
To answer your question, I think its happening as we speak, in small ways for some and in big ways for others.
Who knows really, either all this is a phase that pops with the AI bubble popping or something all of us will have to consider.
I can’t believe this article hasn’t been written by ChatGPT. The author claims to have written it but has clearly become completely captured by the awful generic style of AI writing.
Marijuana ilegal
Ah, a fellow Manu Chao fan!
Denial is tangible
As a former freelance translator (1986 to 2005, Japanese to English), I have much sympathy for the writer. But I wouldn’t be so confident that AI cannot do professional-level translation.
She writes: “I adapt, I localize, and I find the best way to convey the original message so it makes sense and feels natural. I research terminology. I make sure it’s consistent throughout.”
I’m sure she has other important insights into what enables her to do her job well. The problem is whether or not such insights can be incorporated into an AI-driven translation system, too.
Since early this year, I have been experimenting with a variety of agentic systems for language-related tasks, including dictionary-writing, research on topics in the philosophy of language, essay-writing, and translation. Other than the dictionary [1], I am keeping the results private, so they haven’t been evaluated by others. But my personal assessment is that agentic systems given suitable high-level guidance can be very good at such tasks now.
If I were still freelancing and I had a large translation job to do for a client, here is the outline of the prompt I would give to Claude to get it started:
“Use this private GitHub repository to build a system for translating [genre of text] from [Language1] to [Language2]. The directory samples/ contains examples of the type of document to be translated, high-quality human translations of those documents, and texts in [Language2] that are in writing styles that I believe to be appropriate for this genre of translation. The file guidelines.md contains my general instructions about the needs of my client and my preferences for how you should translate texts along various axes (natural vs. literal, informal vs. formal, preferred dialect in [Language2], consistency vs. variety in terminology translation, etc.). Begin building (1) a knowledge wiki for this project using Karpathy’s LLM-wiki framework and (2) a system inspired by Karpathy’s Autoresearch, AutoResearchClaw, etc. for testing and recursively improving both the functioning of the system and the quality of the translations. For the actual translation, editing, checking, etc., use not only your own ability and the knowledge assembled in (1) but also outsource such tasks to other frontier models through OpenRouter, and use adversarial evaluations among those models and yourself to check and recursively improve the system design, the prompt-writing for other models, and any translations created by the system. My OpenRouter API key is available in this environment. You may spend up to $xx per day in API calls until this project is ready to do real translations; before beginning a real job, give me an estimate for how much the API calls will cost for that job. The initial build-out of this project will take many sessions, so write a prompt called resume-prompt.md that I can point you to at the start of a scheduled Routine to have you work on this. Commit and squash-merge to main at the end of each session. I will be checking in occasionally to view your progress and to ask you to run translation tests, and I will offer guidance then on how to improve the pipeline further and make the translations closer to what my client needs. If you have any questions before you begin, please ask me.”
[1] https://www.tkgje.jp
> “Great. So, do you use AI a lot at work?”
> “Oh, I can’t! It’s really not reliable enough.”
Gell-Mann Amnesia strikes again.
the version of this skillset that stays employed is "now I translate 10000x more than i could before by managing a fleet of agents. by encoding my experienced taste and judgement into robust evals, I've helped my ai translators be far better than chatgpt on its own, and much more cost effective compared to manual human translation"
Reminded me of this quote:
"Expertise in one field does not carry over into other fields. But experts often think so. The narrower their field of knowledge the more likely they are to think so." - Robert Heinlein
In this case, the gym buddy doesn't think that she's an expert in the other field, but dismisses it as something ChatGPT can do with ease.
Who is gonna tell her?
unfortunately this person will soon be unemployed.
Denial isn't just a river in Egypt
—
AI should be used for all the bullshit tasks that no one wants to do. There are garbage dumps full of stuff that can be reused and recycled. But it's not high enough ROI to pay someone $25/h to sort trash, so it isn't happening.
You don't even need to argue that you're better than the AI. The point is that the client could have uploaded it to ChatGPT too. Perhaps they even did, and they didn't like the answer they got. They are sending it to a human because they want a human to do the work. If you were to send back ChatGPT output, that would be fraud.
wrt. the end of the story, it will be interesting to see if people start noticing their Dunning-Kruger bias as a result of LLMs.
Specifically: LLMs make it really easy to misunderestimate the complexity of fields other than your own. (You can see this with a lot of vibecoded projects, for example – once they hit the wall of complexity, they stall out or start finding ugly patches for fundamental design issues, etc.)
I don't think this sort of cultural change will happen short-term, though.
> LLMs make it really easy to misunderestimate the complexity
In my experience this is a real problem. Just yesterday I asked my LLM to create a piece of software that could help me build an 'ambilight-like experience' through my home assistant. It did something that seems to work as I expected, but there is a lot of theory that I just brushed past. It would be pretty easy for me to assume that I would be able to replicate this feature from scratch 'now that I understand the problem'.
Agreed. LLMs are really terrific at sounding like they know exactly what they are talking about. Fable is the best yet. Beautiful, thorough explanations with absolute certainty, which under even light scrutiny turn out to be mostly bullshit.
I still love the tool, but remain as convinced as ever that AGI does not lie at the end of this particular path.
Translating is one thing that artificial intelligence undeniably excels at, and the value of this alone is enough to underpin the trillion dollar valuations of the gigantic AI companies.
Translation is a gigantic boon for business, but just as important for human connection, for culture, science, art, and entertainment. The value of automatic and cheap translation between all languages, this tower of Babylon, is immeasurable.
Human translators will always be better than any AI at their job. But they don't have unlimited time and energy, and they aren't cheap. AI makes good to great translations available to everybody.
Any expert in any field will gladly tell you that ML sucks for specifics of their field (and it does). But if you are not an expect in that field, it looks convincing enough to make you think that maybe it is OK for that field, and your field is somehow unique. It is not. Any expect in any field will confirm to you that ML produces plausible-looking slop which is occasionally completely wrong. This is the case for all fields.
This is all bullshit. I speak 4 languages, 3 fluently. Even chatGPT does a stellar job with translation. For most things people want translated- forms, administrative documents etc. I doubt you even need a human in the loop.
That being said, something with essence like a novel definitely still needs to be done by a human.
Jesus fuck, stop with the chatgpt written posts.
True, and relevant (I live with a professional editor)... yet I immediately think of Ximm's Law:
Every critique of AI assumes to some degree that contemporary implementations will not, or cannot, be improved upon.
Lemma: any statement about AI which uses the word "never" to preclude some feature from future realization is false.
Lemma: contemporary implementations have already improved; they're just unevenly distributed.
I think it can't be improved because it's measuring the wrong thing. A junior engineer becomes a senior when they stop being told what code to write and start solving business needs. Therefore often the highest paid engineers aren't the ones who would do the best on leetcode - or SWE bench pro verified.
Maybe AGI is possible and we'll have software defined human intelligence that's completely autonomous but that's not coming in the next slightly better RL trained LLM and if existed likely wouldn't be under our control anyway
No one assumes that AI systems won't be improved upon. What people don't assume is that progress will be infinite in every domain cheaply forever.
This is just about the worst career you could be in right now. Of course people are just going to upload it to ChatGPT. Processing text is its forte.
This person is in the first stage of grief (denial); artists are several stages ahead. Most customers are not going to care about the difference in translation quality unless it's in a regulated sector.
From the post:
> Ah, you can’t fire me, I’m self-employed!
I don't understand thinking like this. I think companies can certainly fire their contractors.
All I got out of this article is that he should have went home and dumped it into chatgpt just to see what happened; then if it did as good a job as him, he should start looking for other places he can add value that AI can't.
The point of the comment was that models are improving a lot every release, so if your livelihood depends on something, you might want to check to see what the latest models are capable of before someone else (like your employer ) tells you.
The other person in the gym was right, did you you just dump it in the latest model?
she did. Did you remember to read the article?
From the phrasing of the sentence, with the incorrect gender and the generic nature of the comment, obviously not.
The article does not say that. The author doesn't take the text the other person dumped into ChatGPT and evaluate its quality. That is what OP is referring to.
The article clearly implies she has tried so previously.
when someone says they have tried previously that makes me think once long ago when they first came out. If your employment could be replaced by this, I'd be testing all new models to see where they stand.
Just because you don't want to use AI/LLM to translate, that won't stop someone else who will, and they will end up doing it cheaper and faster (maybe not better, but most people don't really care about quality too much anymore.)
I agree with the take, but it's a temporary one, the sad reality is that we will be literally inferior soon, there will be a point where we will not trust human input without counter check by AI, we need to remember that we are kinda at the beginning of the AI era, in 5 to 10 years it's very unlikely that a human translator or software engineers will do better than the tooling we will have.
There is already a tipping point now in software engineering where we prefer to ask AI instead of humans because we believe accuracy will be better, see SO death as an example or just see the current state of online dev communities, it's getting deserted and between team members at work, we can also notice that people speak less and less.
Sad but I believe it.
> we will be literally inferior soon
This plague of misanthropic doom is itself pretty depressing. Why do so many people think LLMs are in any way on a path to compete with human brains? Why do you think so little of yourself? The brain is magnificent and complex in ways that we are unable to decipher anytime soon, and it does way more than an LLM. Way, way more.
I don't talk specifically about LLMs but AI in general, it's an important distinction because tooling is currently what make models useful and more performant.
When I say we, I mean the general population really. There0-'ll always be the super bright ones, sure, but we gotta be realistic here. Most people already struggle to make any meaningful contribution because it's so hard to compete, and that gap is just gonna get bigger and bigger.
I agree the brain is pretty magnificent, but when it comes to stuff like language, figuring out if an idea actually works, building the next LLM, or running business stuff, it's pretty obvious we'll be inferior. AI can already innovate and come up with new things way faster than any human could, so at some point (soon) => the majority of contributions are just gonna come from AI, not from us.
The thing is that AI is not some inevitable force of nature that must just be contended with and weathered. It is an active choice by our society to develop it and it is a choice by our society how we should use it, if at all.
We would all do well to remember that and remember that each and every advancement and use case regarding AI is the result of choices by people (or the groups of people we call corporations) and are oftentimes motivated by the profit motive, not the best interest of humanity.
We could make different choices up to and including our own Butlerian Jihad where we ban all forms of AI but we could also do everything we can to prevent the worst fallout short of that.
There are only two types of problems in the universe: 1) those posed by the laws of physics 2) those posed by human choices
The problem of AI is one of the latter.
This is anecdata, but in my experience with myself and my coworkers, it is not that we believe the AI will be more accurate in software engineering, but that the answer will come faster and be more tailored to our exact problems. If I have to search SO, I have to find the answer and then tweak it to fit my codebase, but with AI tooling, the AI is already basing its answer around my code.
I think we actually do believe it, do you believe Fable 5+GPT-5.5(+ the whole model zoo) in loop with adversarial (no budget limit) or a 10-year experienced SWE?
We are talking about "codebases" but realistically we won't even be checking the filetree of them soon, it will be all blind, containerized and verified with pseudo guarantees which are good enough to build serious things. We don't even write documentation for humans anymore, we need to look at the trends and the reality within companies, most developers became "callcenter agents" in a matter of only 2 years and literally most of them are not even using proper automated tooling yet as we can see the "vibe coding" trend with Claude Code which is weak, by far most work done daily by developers is already automatable entirely, but with exceptions, sure, but in a few years those exceptions will become rare.
There will be niche problems about legacy products, sure, but legacy products will all be replaced over time, if we think in depth, why do we even need that many languages, that many tools? Tomorrow AI will write 99% if not all code existing ("code" doesn't even matter anyway), so it's much better if it's specific to AI and not playing this dance where we think we are doing a meaningful human contribution on an "AI-made codebase".
For context, I have 2 decades of software dev behind me.
This is the direction I'm going.
For personal projects that I don't plan to share widely, I'm making it a point to not look at the code at all. So far - and to my surprise - I've not only found that this has result in no more bugs than before, but it seems to result in fewer bugs over time. Every time I find a bug or a regression, I add it to the specification. My SDLC requires that every specification have at least one associated test. Not every function, or every line, or anything like that - every specified feature. The end result has been that my projects have matured over time much faster than if I'd been more closely involved.
I've already toyed with writing some projects in Nim and Haskell for token efficiency. At some point I plan to put together a simple test project, then do a comparison of token efficiency with every language I can think of to find the one that I'm able to generate most quickly, correctly, and cheaply.
> there will be a point where we will not trust human input without counter check by AI
That's nonsense. There is zero reason to believe that AI (with the current techniques) will ever become reliable enough to let it do its own thing, let alone better than a human. It's been years of development and you still can't trust it to get basic facts correct, not even "well it's better than it used to be". Saying it'll replace humans in 5-10 years is a fantasy (or a prediction that people are stupid enough to fall for hype, I guess).
You come from the principle that humans are reliable at first which is partly right but also wrong in so many scenarios, you can even see lately the CVE spree happening, which demonstrates that human-made codebases have serious vulnerabilities and without the help of AI, we probably won't even know about them which proves that humans are not that "reliable", the current societal structure is also built around the fact that humans can't really be trusted, nothing really different with AI, we can't fully trust them like we can't fully trust humans.
It's not a fantasy, I would bet that no serious engineer nowadays is putting in prod a codebase not AI reviewed meaning we already can't work on our own, we must factor in the on-going decline of human capabilities (at least developers) as well of course.
I'm not really saying this because of any sort of hype, but I can personally relate where I went from actually coding to NEVER CODE in less than 2 years, and everyone around me is the same thing, what it will be in 5 years?
Knowing that really, most developers aren't even using proper tooling yet so they are very slow compared to what they could be, I mean how many people we hear saying they can't even saturate an Anthropic Max 20 subscription? I saturated 7 accounts the last 2h alone, it's because they haven't entirely rethought their workflows yet, why do they even have "downtimes", it should be 24/7.
> It's been years of development and you still can't trust it to get basic facts correct
There's the rub: AI is not an oracle. It's neither designed nor intended to provide accurate recall of all facts. It's closer to a reasoning engine than anything IMO.
Oh, and for the record: I don't trust people to get basic facts correct, either. It's already far better than the average human at trivia.
It can spot mistakes made by a human if asked to review code or write tests.
GP is is over the top ins saying humans will "be inferior soon" but AI can be a nice additional check so AI review might be come standard.