As someone who is aggressively pro AI, I've also noticed how awful everything is to read lately. It's always so hard to put my finger on the exact thing, but it's a mixture of everything sounding identical and everything being a rambling unconcise mess. I find I routinely start reading interesting posts and just .. stop part way, abandoning it. A site I read stories on has been flooded with AI generated content, all of it terrible, and it just makes me retreat to things created before 2023 as a way of filtering it all out.
A model trained to minimize statistical prediction errors is going to sound ... predictable (aka boring). A (good) human author tries to keep their writing interesting, while an LLM strives for predictable instead.
It's not just predicting content but also the form/shape of the response, highly shaped by RLHF preferences, which seems to encourage longer form responses which need padding with filler to generate the "preferred" amount of verbiage.
It seems most of these models have been trained to interpret questions as wanting a long-form authoritative/comprehensive-sounding response (even when midwit filler is all it has to offer), rather than initiating a short-turn conversation which would better allow you to steer the conversation. Apparently Muse is much better in this regard, but so far I've resisted signing up for any Meta/FaceBook service, and not inclined to break this winning streak!
Human writers also use specific constructions for definite (though not always consciously known) reasons, and mapping those correlations onto writing models is only getting done on accident. This is a big part of why LLMs doing pointless negations (it's not X, it's Y) is so jarring: people use that construction in order to preempt an expected misconception, whereas LLMs try to use linear algebra over the learning corpus to generate it in appropriate quantities and places according to RLHF/fine-tuning/etc.
In other words, it isn't just verbiage that has a "preferred" quantity, but also higher-level language features, and the appropriate contexts for those features feel like the sort of thing that will be extraordinarily difficult to replicate without a theory of mind.
edit after coming back and re-reading: the "in other words," and rephrase is because I'm trying to explain something complex enough that I'm not even sure I fully understand it, so I try typing out multiple explanations and leave the ones that make the most sense to me. This might be idiosyncratic to my writing and not a significant feature of training corpuses, but even if it wasn't it's a second example of the sort of thing I'd suspect LLMs to be bad at using appropriately.
Another LLM weirdness is to explain what code is not doing. My theory: such negations were somehow encouraged in training because they are very valuable where used properly (and sparely) by humans. "Not doing the obvious thing due to xyz happening two modules over" is a very useful comment. "Not using bubblesort because quicksort is faster" is garbage.
I think a lot of the time it's prompt leakage. Forgive the personification, but it's like they don't have a mental model of the reader of the thing that they're authoring being separate from the person who's prompting them to author it, so it often seems like the main conversation kind of spills into the prose.
Not to pick on GenHTTP Lambda, especially because I think it's someone's fun side project, but I was just trying to read though their docs and I was finding it super difficult. I kept getting distracted thinking "why the hell did it write it _that_ way?"
Like, the first sentence of "The control center" section [1] is the exact sort of weird negation that you're describing:
> The editor link opens a control center rather than a text box: most of the code here is written by agents, so the first thing on the screen is how your lambda is doing.
I can't help but think it generated the first round of the page with a text box to paste the code into, got told "no, I want a control center," and then this ended up in the docs.
I also think the phrasing of that sentence (and many others in those docs) is just super weird and unnatural - e.g. "so the first thing on the screen is how your lambda is doing." It also feels like it mirrors a prompt - like, "no, it should be a control center, not a text box, and the first thing the user should see is how their lambda is doing." But in docs I'd never expect it to be written "the status of your lambda" or "the health of your lambda." And what does the code being written by agents have to do with it?
1: https://genhttp.dev/en/docs#editor
1000% this. I encounter this all the time (with Claude) and it drives me nuts!
I vaguely remember a study that I was able to find when fact-checking this comment before publishing it [1], which found that music needs periods of high predictability to nurture listener expectations for the sake of later subverting those expectations, and the most enjoyable parts of music are those moments where expectations are subverted. This sounds like a similar effect.
1: PDF warning https://www.cell.com/current-biology/pdfExtended/S0960-9822(...
uh huh. so music is like a joke - the payoff is unexpected, while simultaneously adhering to scenario expectations.
"A model trained to minimize statistical prediction errors is going to sound ... predictable (aka boring)." -- what if the goal is to be a (good) human author.
In that case, model will in fact strive to keep their writing as close to interesting writing as possible.
What if? But this certainly doesn't describe LLMs.
That doesn't follow. Predictability is your brain reacting to the lack of novelty. What an LLM produces is a chain of words following some kind of conditional distribution, whose output could vary significantly from your understanding of novelty depending on its training and priors.
With a single, generalized prompt ("produce something unpredictable"), ChatGPT responded with:
> 00:— / ANOMALOUS RESIDUE > Vekh. > Ìssara. > Nnnn—tq—èl. > > A temporary thickness enters the meat of the signal. > > Not rain. Not a substance with permission to fall. > > UL / 9 / ferrous / ∂ > The measurements are eating each other. > > Someone has left an entire childhood in the wrong unit of pressure.
That is not predictable to me, at least. It could be boring, but I think that is mainly a product of me knowing where the text is from. LLM-generated text is inherently boring, to me, but that has nothing to do with predictability.
Gemini models are so, so much better in this regard. Their outputs are actually readable.
Gemini tends to glaze you though. I now believe I’m a genius at pretty much everything after trying it a little.
Every model has done this. And sometimes the makers go the other way to compensate, and then you get Opus 5 who answered like it was possessed by the ghost of stack overflow.
Interesting, I was thinking the walls of text are better for the watermarks.
I'd be shocked if it wasn't a shotgun strategy learned during RLHF or RLVR post-training.
A short response that's wrong is going to be punished, whereas a long answer that contains the correct information, and then a whole bunch of other stuff surrounding that info, is more likely to be rewarded. Even if you discount the reward by answer length, when there's ambiguity involved it's still going to be a better strategy on average to hedge by saying too much vs risking saying too little.
Don't these sort of conflict with being "aggressively pro AI"?
> how awful everything is to read lately > flooded with AI generated content, all of it terrible > everything sounding identical and everything being a rambling unconcise mess.
I can see the benefits of AI and enjoy using the technology - but, like many have said, it's wreaking havoc on the "content" that's out there nowadays. Gotta take some bad with the good, I suppose.
> Gotta take some bad with the good, I suppose.
Well, it's quite simple if you think about it. Intelligence is all about awareness of the imminence of death and the finite amount of time given to any of us. So, we humans intrinsically evolved to make the most of it. The majority of the tools we create have the ultimate purpose - to save time. Even money is nothing but a way to cut some time. It's not really about time but experiences - we want to have as many and as rich as we can get, and we innately try to cut time somewhere else, so we can use what we saved for better experiences.
Therefore, inventing cars was an easily predictable event. There's not a single hypothetical timeline or dimension where humans don't end up with cars - to transport things and to travel faster. Similarly, it was always simply inevitable - we would have invented something like LLMs, sooner or later.
Cars required significant sacrifices - they are absolutely deadly machines, they kill thousands of people every single day, kill wildlife, endager our own existence, and we're kind of okay with that. I can absolutely assure you, if someone tomorrow invents a gravitational jump travel that allows moving things between continents in minutes, even with a success rate as low as 10%, we would still be using it.
So, yes. We can't un-invent cars and AI, the only way to move forward is to adjust to this new reality and extract the best possible experiences, preferably by not robbing other people of their - i.e., enjoy life while it lasts, it still remains finite.
> Intelligence is all about awareness of the imminence of death and the finite amount of time given to any of us.
You lost me there. Children are pretty intelligent and have no concept of time. I don't see the relation you find obvious and the rest of your points similarly don't follow.
> Therefore, inventing cars was an easily predictable event.
This feels like a benefit of hindsight unless you can call out other obvious and imminent (but not yet existing) inventions.
Yeah, I guess, "intelligence is all about awareness of death" is kind of a bomb of a premise. I admit, not the best thought I spat out through my keyboard. I suppose I mean some narrower sense of intelligence here. On one hand, you're absolutely right - crows, octopuses and toddlers solve problems without any hint of understanding mortality. Yet "children have no concept of time" is incorrect: not only do they have one, but most kids also grasp that death is irreversible and happens to everyone by about age 5-7.
I don't blame you for immediately rejecting the rest of it. But do hear me out: the point about the inevitability of invention does have real support. Historians call it multiple discovery - tons of things have been invented independently - calculus, the telephone, the jet engine, to name a few.
Neural nets, GPUs, and the scale of Internet text made something like LLMs close to inevitable. Of course, we could debate forever whether any of these on its own was ever inevitable. The point is - society tolerates any cost of a useful invention for the benefit of saving time. So it's reasonable to assume this will be the case with AI as well, which is really happening as we speak.
I'm kind of pro AI while also being pro LESS AI.
These are ultimately all software systems where updates can be pushed out with the press of a button. We just found an amazing new tool in the LLM and people are trying out all kinds of ways of using it, many of them dumb or stupid, this is discovery that has to be done, we'll keep doing it and pushing updates.
It doesn't take rose colored glasses really to infer that over time we'll cull the dumbest uses and extrapolate on the best ones. There's a huge difference between using a generic chatbot, even one driven by a SOTA model, vs targeting a specific business task with good guardrails, skills, and tool interfaces that call out to real business logic when the model isn't the right thing. The latter will emerge more and more over time.
You can be pro-"thinking AI is going to get really good and solve many problems" (i.e. be the sort of person who would "go long" on AI), while also being anti-"applying AI right now to domains outside of the few it's been specifically optimized for [e.g. coding]" (because in these other domains, AI is yet to approach, let alone surpass, "authentic quality" equivalent to that of a skilled junior professional.)
Only a programmer or a business guy would think just because it becomes capable we would want to see AI produce "content". The effect is the erasure of human culture.
This is somewhat of a dramatic statement. What, exactly, is the mechanism by which human culture is erased because LLMs write some stories or compose some music?
The flood of LLM-produced stories and music. Spam killed email.
I think there could be room for something like how holodeck programs are shown to be authored in Star Trek, i.e.: the author does all the primary work, and then the system, if a particular work's design allows it, extrapolates from that to fill in the blanks when somebody decides to make Moriarty super-intelligent or put a hundred androids in a Wild West scenario or take part in a go-karting death race with Frankenstein's monster.
But, that comes with with the same kind of cultural acknowledgement, as seen with the characters in Star Trek, that such algorithmically generated material is an experimental diversion, and inherently not worth the same respect or focus as experiencing something authored with intentionality by a person.
I can like the concept of the engine, and the leverage it provides, but I hate traffic and get anxious about having an accident on the freeway. And the people behind the industrial revolution have destroyed the climate.
I love computers and the internet, and the internet has ruined a lot of people and things around me.
It's possible to be interested in a new technology and enjoy its leverage while still being extremely apprehensive about how it intersects with game theory and politics. The race to the bottom of the barrel keeps coming for the jugular of every cool new thing.
Well, we have a good understanding of the benefits that the industrial revolution and the computing revolution have given us.
The AI situation is different. The technology is obviously extraordinarily powerful but the benefits to humanity are still nebulous and there are many very obvious harms like the one we're discussing. So to describe yourself as "aggressively pro AI" you must believe that future, as-yet unrealized benefits will outweigh the harms. I don't think that's a crazy thing to believe but it deserves scrutiny and demands elaboration.
> it deserves scrutiny and demands elaboration.
These challenges never go anywhere because it’s essentially a religious conviction. We’re likely in sort of uncharted territory on a macro scale and I reckon people a) either acknowledge that or refuse, and b), either trust or distrust the tech world’s stewardship of this technology. There’s a hundred bazillion data points, and I’m pretty pessimistic about the tech scene at large right now, but I think reasonable people can disagree on this without being factually or logically wrong. But true zealots in the midst of such uncertainty are driven by faith, which can certainly be deftly swayed over time, but definitely can’t be conquered in a debate.
Eh, I think many of the harms of AI are currently also nebulous. It's not as though the harms are all concrete and massive and the benefits are vague and "in the future"
I’ve been starting to think that a lot of the variance in how people see the world can be explained by whether their attention is drawn to first-order localized effects, or nth-order distributed effects. This is not the same thing as optimism vs. pessimism, but it sure is correlated!
This sounds intriguing. Can you expand on what your thinking is or link to somewhere I could read more?
Or just short term gains versus long term pains (or vice versa, depending on how you view it)
I feel like in my life all I ever see is people mortgaging the future for today though. Hey how about that record storm in California?
It's a multi-mandate to focus on what's best for your yourself and society such as your mind, your body, your chosen family, your friends, your neighborhood/community, your city, state, country, the global population, and more abstract things such as the human condition, when evaluating things such as privilege or fairness or duty.
You often have to make compromises somewhere in the hierarchy, especially in late-stage capitalism where even access to basic needs comes at a moral cost, and as such those compromises sometimes come with a moral duty. I am writing this via a computer built from limited natural resources, some of which have been obtained through what I see as slave labor. I don't eat meat and still have to acknowledge the harm that clearing and maintaining farmland does to wildlife, even if the result could theoretically be a net gain.
When you start viewing situations through the lens of game theory, you realise some compromises are inevitable for the rational individual. But you must acknowledge the individual is not the only concern. As such, I use AI as part of my work, but I opposed the local buildout of a large data center in my community and tried in vain to make the person responsible for the deal understand the issues it could introduce. This isn't NIMBYism, we need a halt on all buildouts until fundamental environmental and accountability concerns are addressed.
And many other aspects of my existence and quality of life, poor and underprivileged as I was growing up, have depended upon US imperialism. I have also produced a ton of waste just existing and owning pets. It's abhorrent.
I continue to ask myself how I can fulfill the moral duty which I have accrued and reduce my dependence on exploitative economic systems, but I also recognize that AI is the new social leverage and we only have so much time to use it to our advantage before it becomes institutionalized and wielded against the people. I also might only find future income stability by evolving with my industry.
Life is complicated.
This resonates with me and I suspect a lot of others here. As always life is more nuanced that many would have argue.
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Agree but also want to point out that this started with the SEO boom of the last decade where sites started rambling so they would keep you on the site longer and get more SEO "points", they were articles meant for a robot to catch humans.
AI has made it even worse and lots of content on the web, for both these reasons, is not worth you time (now wonder users don't read most of the text on our apps!)
In a prior evolution of this, if you look into the Get Rich Quick marketing copy of the early 2000s (before SEO had taken over content formatting), you can see a parallel of sorts in those marketers doing human sales optimization Impact with bold and underline. It derives from research into the human response to ads from decades of print ads and centuries of printed matter prior, and I think there is a direct lineage from ‘how can we bias humans through restructuring text’ to ‘how can we bias machines through restructured text’. This isn’t to disagree, but to call out how rapidly humanity (merely twenty years!) has switched from marketing to people, to marketing to algorithms, in search of a favorable bias.
Ah yes I remember meeting this end of spontaneous expression to bend toward whatever might please some algorithm.
Soulless Enshitificatoon Oblivion now exercises a huge weight on societies to such an extent, it's ridiculous.
My model is about gaps - all ideas start with gaps. LLMs can fill in these gaps quickly - but some ideas are just bad - so the filling in does not improve them. Maybe it would be useful to treat that filling in as a kind of hallucination - the LLM trying to meed impossible requirements. But on other cases the ideas are good and the LLMs fill in the gaps in the right way. It is a kind of skill to see when LLMs are hallucinating in the gaps. I use LLMs very extensively and I am adding checks to that should catch when the filling in is weak - but it is still not fully autonomous.
I also try to keep the llm-enhanced (or even fully llm-generated) content separate from my human writing - because it is true that the failure modes are different. But think LLM writing might be useful even if it is not consumed directly - it can enhance LLM answering your questions.
And apparently I am repeating here what I already have in my KB: https://zby.github.io/commonplace/theory/llm-generation-rela... - I borrowed the model from Boretti. The agent says that the note is much more careful and better developed than my comment above. You can judge yourself - but I think it does have many more details and hopefully most of these details are the good kind of filling in - but I guess it might be harder to read.
I suspect it’s partially an average / regression to mean.
If you built a model only based on long form journalism and The New Yorker voice, including both the template structure of the article and the grammar and syntax, the model would replicate the patterns it is built upon. You could do the same for high vocabulary rap.
When you stuff all of English or all of human thought into it with somewhat equal weight, including very technical writing the sum is a lack of style and elegance and personality.
The second problem is that writing often doesn’t happen linearly. You write bookends and fill in, edit and revise. You sort of know where you’re going and can then make decisions how to get there. Language models may benefit from being redesigned to perform passes. Select a template of some sort, fill in the mad lib, then run a detail pass, then run a persuasion / emotion pass, then run a style pass. Current model writing very much sounds like I would expect autocomplete to.
It’s heavily RL’d and it’s gotten worse because they’re using LLM judges, that’s why it’s gotten worse. The last good writing model was GPT 4.5.
I suspect they’re leaning heavily on some kind of auto verifier, and that’s why the writing sucks, because good writing is so subjective.
And they’re probably nerfing what it can get from training as they’ve lost lawsuits for scraping copyrighted work, so they’ll purposefully avoid anything that references a specific writers style, etc.
Isn't this easy for frontier 'labs' to fix? If the user requests lots of writing and prose, then use an auxiliary model which has been trained on 'award-winning' writing instead of all the crap on the internet?
Award-winning writing has all kinds of different styles. The average of them will probably get you some boring median soup once again, though maybe a bit less bland than the LLMs trained on oceans of LinkedIn and Reddit slop.
Even if it doesn't, the effect of so much writing getting pushed out the door through default LLM settings is going to make it feel same-y and bland in short order.
Oh boy, more IP theft and copyright infringement!
The "regression to the mean" or "it's just the training data" hypothesis doesn't work. Pretrained models do not have these weird writing styles and sound much more human. It's only after instruction tuning -- SFT, DPO, etc. -- that LLMs develop weird writing styles.
That is, the original training on massive text corpora seems to produce an average of human writing, but that average looks pretty average. It doesn't feel like LLM slop. But the instruction tuning on specific tasks (answer this prompt, summarize this text, do this math problem), which turns text predictors into chatbots, seems to create strange style features as a side effect. And because that tuning step depends on company-specific training data (rather than just a crawl of the entire Internet), each company's LLM has a different style.
It's not that difficult to measure this. Linguists have had measures of style variation for a long time, though NLP and AI researchers seem to have forgotten. We've generated parallel human and AI corpora to measure it, and can show specifically which writing features change: https://arxiv.org/abs/2410.16107
Currently we're working on tweaking the instruction tuning process to see if we can pin down which steps and what types of tasks tend to produce the weird style features.
The part I find grating is the attempts to punch everything up. ("It's not X. It's Y"). Like, its a react badge component, it doesn't need punching up. You didn't find the smoking gun in a murder mystery, just a misplaced CSS rule.
I've noticed this as well, I've been comparing it to 4Chan Greentext memes. Everything is a short punchy sentence, and every sentence has the same "weight" to it. There's no flow or rhythm, it's like reading off bullet points.
Sorry, meant to reply to your comment, but my post above...
"You can be pro AI productivity but anti-AI creativity. Automating creativity is the death of art."
Meaning you reject the creative value of AI but values it's ability to drive a car safely, or write code for your job, or turn off the lights when you leave the room. objective goal oriented task completion great.
But AI draws your a picture? What's the point. AI writes you a song? No feeling. AI creativity isn't interesting.
> sounding identical
In AI-generated entertainment, the names "Marcus", "Chloe", "Sterling", "Meridian", and "Apex" keep appearing. Those aren't common names, but LLMs seem to prefer them.
Elias the lighthouse keeper
Do you have more info on this?
I am willing to believe it happened the same way goblins crept in.
I’ve got the same bright red line for 2023. E.g. I treat a handful of youtube channels with long electronic jams almost like oldies at this point - keep listening to the same stuff from at least two years ago because it’s the authentically good stuff.
Maybe stop being pro ai then.
That’s taking the wrong lesson from this. The fault here lies entirely with people who are unable to recognize when AI output is subpar, or else don’t care. “AI” here is just a scapegoat for people’s intellectual laziness, lack of discernment, and/or inability to reflect on their actions.
There isn’t any doubt that AI can be a useful tool. So what you’re essentially saying is that we should avoid using a useful tool because many people, without any relevant training, use it badly.
By that standard, you should not be pro-car, pro-airplane, pro-boat, etc. etc.
Do you have some personal financial or other incentive to be “aggressively pro AI”?
They will never admit it, but they give clues.
It makes no sense on being "aggressively pro AI" without any payment. It is like being an unpaid shill.
There are plenty of people that genuinely believe it’s going to cure cancer, solve all our problems, and end work.
> it's a mixture of everything sounding identical and everything being a rambling unconcise mess.
For me it's mostly the latter, which is a natural consequence of LLM writing. When the (time) cost-per-word goes down, there is less incentive to keep signal-to-noise ratio high, so it ends up verbose and bloated. I also believe that this is (at least partially) upstream from everything sounding identical: take two articles on two different subjects - when SNR is high, there isn't really much room for them to become similar (The ingredient list for a risotto will never read the same as the ingredient list for pizza), but when 80% of them is fluff, they can blend together more easily (To take the recipe comparison, this would be the background/story part above the recipe), especially when written by the same LLM
It's (at least) two things I think; one is that thanks to the internet, the ability to focus on things for long is shot - I struggle to read long form content on the internet, generally speaking. I also did my whole career with Google and skim reading documentation looking for solutions for an immediate problem more than getting a wholesale end-to-end understanding.
(this isn't a new observation / thing, see https://randsinrepose.com/archives/nadd/ originally from somewhere in the 2000s)
But now with AI it's easy to generate a lot of prose. I'm thinking for example of the merge request descriptions my colleague submits. And here's the thing, it's not even bad or incorrect - I read it thinking, if I were to really sit down for it, get into The Zone etc, I could crank this out myself.
But I don't because honestly the MR is relatively trivial and can be described in just a sentence or two. Style and accuracy aside, my current take is that LLMs write things that humans wouldn't bother with, because it's too much effort and there's not enough value.
I mean it's an MR for an enterprise library project, the chances that anyone will ever read that MR description again once it's merged is close to zero.
That said: LLMs themselves don't get that, they will read everything you tell them to with the same amount of attention. Granted, their context windows are limited (and performance declines the more you put in it), so they too will use some tools to skim and pick out the important parts, but I think that's a temporary thing. The huge amount of LLM prose will end up being consumed by other LLMs. I don't know enough about the subject whether that's a good or a bad thing though, right now I think it just adds to the noise until it becomes noise in, noise out.
LLMs have made the internet unreadable. It used to be just Google search results were SEO-gamed empty prose, but now it's every blog post, news article, product announcement ... everything. I've stopped reading basically everything online except user comments on a few websites. It's the last place remaining to actually connect with other human minds, but even that seems like it won't last forever.
My guess is that tokenization is typically not (strictly) syllabic.
OP’s linked article mentions the importance for them of reading their writing aloud. Humans construct sentences with a (mostly unconscious) sonic influence. How things sound really matters.
My theory is that LLM prose sounds slop-like because the base unit for an LLM is not a single sound.
Thus, perhaps, you get the uncanny, meandering, hollow-feeling outputs, which feel vapid to human readers, without the reader really knowing quite why.
I think this is also part of the reason why LLMs are rewarded for and thus more strongly exhibit their faux-punchy constructions, such as the infamous “it’s not [X], it’s [Y].” The transformer model can, with current tokenization, articulate some features of “good writing”, but not quite all.
I think there are a number of fairly simple SOLNs to this (or ways to test this hypothesis) without making tokenizers purely syllabic across the board, which would probably be a mistake.
Whether prose output quality even matters for models which are not explicitly trained for creative writing is highly debatable, though.
This applies to code btw, it's so much nicer to read a human made pull requests. It's also much faster as people tend to be lazy and produce denser code.
Honestly I think it's a good thing it's so hard to get AI to talk normal and I hope they keep making the voice more and more distinct. Agents should run as background tasks and automations. They should be understandable so they can communicate with you about what they did, and for observability/ai safety. Im perfectly fine reading llm speak when it's an ai telling me what work it did. When in reading a blog post with a human listed as the author though, I do not want to read an AIs thoughts that the human is laundering. Honestly I'd probably tolerate it if it was framed as "I asked my Claude to do a cool thing here is its report on what it did." But in general it's really nice to have an instant filter of - this post is a waste of time - ah this post has an actual human's thoughts
This is true across every topic. Anything at all that someone can put into an LLM and get out an emoji-laiden wikipedia article sized post to slap on a heavily SEO-ed website is absolutely flooded with it.
My parents live on a rural property, and I'll help them do research on things like livestock practices or occasionally "prepper" related things to give them a sanity check before they spend money. I have to weed through a dozen or more obvious ChatGPT-created pages that are the first page or two of results on every search engine before I get to anything a human has touched. The vast majority of the LLM-created pages are either massively verbose and vague, or often wrong. But they're getting clicks. Very often, when asking an LLM the same question and to give citations, I'll get a combination of useless, irrelevant sites with no connection to the material, maybe one of these LLM-generated sites, and maybe one online forum.
I'm not aggressively pro-AI, I'm in the middle, but stuff like this has me worried that we're looking at the equivalent of the Dark Ages all over again. But instead of simply no one bothering to write anything down as was the case in the early Middle Ages, it's going to be the task of searching for real human work and thought in a sea of aggressively spammed LLM-generated slop pages.
Sounds like it was trained on Reddit. Reddit has this effect where catchy snippets get upvoted training people to write more catchy snippets... While reality has nothing to do with it.
to AI verbosity and weird use of diction is natural, to humans it makes it hard to parse. The number of time i start reading long winded AI written design docs, and i just can't get into the flow after reading a few sentences and have to keep re reading certain sentences.
How is someone "aggressively pro AI"? This is the first time I read such concept.
Is typically called ai booster. The opposite is ai doomer.
Importantly, both boosters and doomers are believers (or perhaps cultists).
A doomer isn't skeptical; they're nearly as credulous as boosters, or maybe more so in some ways.
Not necessarily -- I am extremely anti-AI; I think the danger is not that it's good, it's that it's convincing. Turns out most people don't care about the quality of art or prose or code that they interact with, and it's very depressing.
That's not exactly what I've heard called a doomer. Doomers are the ones who think we're building the wrong AI god.
(But I agree with you fwiw)
Most gods have never been observed to have any real world impact. Only their worshippers cause real world effects. So god is not a bad analogy even for doomers.
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My take is that most art, prose, and code was really bad before AI too. Now there's just more of it good and bad. It's like hating that YouTube exists because cable TV was so good. Now anyone with a camera can make and upload whatever nonsense they want but in the end lots of very good content was made on top of the huge pile of rubbish.
https://en.wikipedia.org/wiki/Effective_accelerationism
Leans into AI adoption.
Personally, for blog posts, documentation (unless I flat out vibe code the entire thing) etc are all written by me by hand. If the model can save me hours of "oops that was really dumb of me" why can't I take time to read and document things by hand? I think everyone's embracing AI in the wrong ways. Maintain your knowledge of things, and keep the documents nice. There's still room for letting your agent manage agent specific docs, but for the humans, just write it yourself, ask the model for feedback and tweak it by hand. Let the machine talk to the machines.
Gentle reminder that most AI token spend is used for TPS report type writing not Cutting edge fiction writing. Creative writing, or general human-to-human communication was never really the target. It's not surprising the first majority use of this was code.
Endless headlines like, "aArON SORkIn SAYs hE WiLL Never UsE ai to WRite" doesn't change that base fact. No one asked him to, other than clickbait journalists.
If you find this hard to believe, just think about relative market sizes.
100 percent agree. It's like the consensus for tone and vocabulary has been "midlevel digital marketer from 2015" and now everything is starting to sound like a lazy sales pitch. I'm with you on AI but man, this is such an annoying side effect.
> I've also noticed how awful everything is to read lately.
Yeah, thanks dude. We appreciate your efforts to enshittify everything.
Edit: Sure, downvote, but the parent comment is really not very different from "arsonist puzzled and upset that so many buildings have burned down".
And yet you remain aggressively pro AI? Why?
So, you're still aggressively pro AI even though you recognize how quickly it is destroying the quality of online content?
I've noticed this a lot in conservative spaces especially, when they say "I love Trump, but..." -- why the need to say how much you love AI before criticizing it? Who are you trying to convince?
I use LLMs constantly for work and they have genuinely improved my life. But I absolutely despise generated "content". Text, audio, video, images -- for the reasons you describe.
It's ok to have a nuanced and multi-faceted opinion and approach to this and not have to call yourself a raving fanboy.