> LLMs are literally technology that can only reproduce the past.
Funny, I've used them to create my own personalized text editor, perfectly tailored to what I actually want. I'm pretty sure that didn't exist before.
It's wild to me how many people who talk about LLM apparently haven't learned how to use them for even very basic tasks like this! No wonder you think they're not that powerful, if you don't even know basic stuff like this. You really owe it to yourself to try them out.
I've worked at multiple AI startups in lead AI Engineering roles, both working on deploying user facing LLM products and working on the research end of LLMs. I've done collaborative projects and demos with a pretty wide range of big names in this space (but don't want to doxx myself too aggressively), have had my LLM work cited on HN multiple times, have LLM based github projects with hundreds of stars, appeared on a few podcasts talking about AI etc.
This gets to the point I was making. I'm starting to realize that part of the disconnect between my opinions on the state of the field and others is that many people haven't really been paying much attention.
I can see if recent LLMs are your first intro to the state of the field, it must feel incredible.
That's all very impressive, to be sure. But are you sure you're getting the point? As of 2025, LLMs are now very good at writing new code, creating new imagery, and writing original text. They continue to improve at a remarkable rate. They are helping their users create things that didn't exist before. Additionally, they are now very good at searching and utilizing web resources that didn't exist at training time.
So it is absurdly incorrect to say "they can only reproduce the past." Only someone who hasn't been paying attention (as you put it) would say such a thing.
> They are helping their users create things that didn't exist before.
That is a derived output. That isn't new as in: novel. It may be unique but it is derived from training data. LLMs legitimately cannot think and thus they cannot create in that way.
I will find this often-repeated argument compelling only when someone can prove to me that the human mind works in a way that isn't 'combining stuff it learned in the past'.
5 years ago a typical argument against AGI was that computers would never be able to think because "real thinking" involved mastery of language which was something clearly beyond what computers would ever be able to do. The implication was that there was some magic sauce that human brains had that couldn't be replicated in silicon (by us). That 'facility with language' argument has clearly fallen apart over the last 3 years and been replaced with what appears to be a different magic sauce comprised of the phrases 'not really thinking' and the whole 'just repeating what it's heard/parrot' argument.
I don't think LLM's think or will reach AGI through scaling and I'm skeptical we're particularly close to AGI in any form. But I feel like it's a matter of incremental steps. There isn't some magic chasm that needs to be crossed. When we get there I think we will look back and see that 'legitimately thinking' wasn't anything magic. We'll look at AGI and instead of saying "isn't it amazing computers can do this" we'll say "wow, was that all there is to thinking like a human".
> 5 years ago a typical argument against AGI was that computers would never be able to think because "real thinking" involved mastery of language which was something clearly beyond what computers would ever be able to do.
Mastery of words is thinking? In that line of argument then computers have been able to think for decades.
Humans don't think only in words. Our context, memory and thoughts are processed and occur in ways we don't understand, still.
There's a lot of great information out there describing this [0][1]. Continuing to believe these tools are thinking, however, is dangerous. I'd gather it has something to do with logic: you can't see the process and it's non-deterministic so it feels like thinking. ELIZA tricked people. LLMs are no different.
That's the crazy thing. Yes, in fact, it turns out that language encodes and embodies reasoning. All you have to do is pile up enough of it in a high-dimensional space, use gradient descent to model its original structure, and add some feedback in the form of RL. At that point, reasoning is just a database problem, which we currently attack with attention.
No one had the faintest clue. Even now, many people not only don't understand what just happened, but they don't think anything happened at all.
There's no such thing as people without language, except for infants and those who are so mentally incapacitated that the answer is self-evidently "No, they cannot."
Language is the substrate of reason. It doesn't need to be spoken or written, but it's a necessary and (as it turns out) sufficient component of thought.
> I will find this often-repeated argument compelling only when someone can prove to me that the human mind works in a way that isn't 'combining stuff it learned in the past'.
That is a pedantic distinction. You can create something that didn't exist by combining two things that did exist, in a way of combining things that already existed. For example, you could use a blender to combine almond butter and sawdust. While this may not be "novel", and it may be derived from existing materials and methods, you may still lay claim to having created something that didn't exist before.
For a more practical example, creating bindings from dynamic-language-A for a library in compiled-language-B is a genuinely useful task, allowing you to create things that didn't exist before. Those things are likely to unlock great happiness and/or productivity, even if they are derived from training data.
> That is a pedantic distinction. You can create something that didn't exist by combining two things that did exist, in a way of combining things that already existed.
This is the definition of a derived product. Call it a derivative work if we're being pedantic and, regardless, is not any level of proof that LLMs "think".
Yeah you’ve lost me here I’m sorry. In the real world humans work with AI tools to create new things. What you’re saying is the equivalent of “when a human writes a book in English, because they use words and letters that already exist and they already know they aren’t creating anything new”.
Why is that kind of thinking required to create novel works?
Randomness can create novelty.
Mistakes can be novel.
There are many ways to create novelty.
Also I think you might not know how LLMs are trained to code. Pre-training gives them some idea of the syntax etc but that only gets you to fancy autocomplete.
Modern LLMs are heavily trained using reinforcement data which is custom task the labs pay people to do (or by distilling another LLM which has had the process performed on it).
Could you give us an idea of what you’re hoping for that is not possible to derive from training data of the entire internet and many (most?) published books?
This is the problem, the entire internet is a really bad set of training data because it’s extremely polluted.
Also the derived argument doesn’t really hold, just because you know about two things doesn’t mean you’d be able to come up with the third, it’s actually very hard most of the time and requires you to not do next token prediction.
The emergent phenomenon is that the LLM can separate truth from fiction when you give it a massive amount of data. It can figure the world out just as we can figure it out when we are as well inundated with bullshit data. The pathways exist in the LLM but it won’t necessarily reveal that to you unless you tune it with RL.
> The emergent phenomenon is that the LLM can separate truth from fiction when you give it a massive amount of data.
I don't believe they can. LLMs have no concept of truth.
What's likely is that the "truth" for many subjects is represented way more than fiction and when there is objective truth it's consistently represented in similar way. On the other hand there are many variations of "fiction" for the same subject.
They can and we have definitive proof. When we tune LLM models with reinforcement learning the models end up hallucinating less and becoming more reliable. Basically in a nut shell we reward the model when telling the truth and punish it when it’s not.
So think of it like this, to create the model we use terabytes of data. Then we do RL which is probably less than one percent of additional data involved in the initial training.
The change in the model is that reliability is increased and hallucinations are reduced at a far greater rate than one percent. So much so that modern models can be used for agentic tasks.
How can less than one percent of reinforcement training get the model to tell the truth greater than one percent of the time?
The answer is obvious. It ALREADY knew the truth. There’s no other logical way to explain this. The LLM in its original state just predicts text but it doesn’t care about truth or the kind of answer you want. With a little bit of reinforcement it suddenly does much better.
It’s not a perfect process and reinforcement learning often causes the model to be deceptive an not necessarily tell the truth but it more gives an answer that may seem like the truth or an answer that the trainer wants to hear. In general though we can measurably see a difference in truthfulness and reliability to an extent far greater than the data involved in training and that is logical proof it knows the difference.
Additionally while I say it knows the truth already this is likely more of a blurry line. Even humans don’t fully know the truth so my claim here is that an LLM knows the truth to a certain extent. It can be wildly off for certain things but in general it knows and this “knowing” has to be coaxed out of the model through RL.
Keep in mind the LLM is just auto trained on reams and reams of data. That training is massive. Reinforcement training is done on a human basis. A human must rate the answers so it is significantly less.
You’re using ‘derived’ to imply ‘therefore equivalent.’ That’s a category error. A cookbook is derived from food culture. Does an LLM taste food? Can it think about how good that cookie tastes?
A flight simulator is derived from aerodynamics - yet it doesn’t fly.
Likewise, text that resembles reasoning isn’t the same thing as a system that has beliefs, intentions, or understanding. Humans do. LLMs don't.
Also... Ask an LLM what's the difference between a human brain and an LLM. If an LLM could "think" it wouldn't give you the answer it just did.
Ask an LLM what's the difference between a human brain and an LLM. If an LLM could "think" it wouldn't give you the answer it just did.
I imagine that sounded more profound when you wrote it than it did just now, when I read it. Can you be a little more specific, with regard to what features you would expect to differ between LLM and human responses to such a question?
Right now, LLM system prompts are strongly geared towards not claiming that they are humans or simulations of humans. If your point is that a hypothetical "thinking" LLM would claim to be a human, that could certainly be arranged with an appropriate system prompt. You wouldn't know whether you were talking to an LLM or a human -- just as you don't now -- but nothing would be proved either way. That's ultimately why the Turing test is a poor metric.
You’re arguing against a straw man. No one is claiming LLMs have beliefs, intentions, or understanding. They don’t need them to be economically useful.
> So it is absurdly incorrect to say "they can only reproduce the past."
Also , a shitton of what we do economically is reproducing the past with slight tweaks and improvements. We all do very repetitive things and these tools cut the time / personnel needed by a significant factor.
I think the confusion is people's misunderstanding of what 'new code' and 'new imagery' mean. Yes, LLMs can generate a specific CRUD webapp that hasn't existed before but only based on interpolating between the history of existing CRUD webapps. I mean traditional Markov Chains can also produce 'new' text in the sense that "this exact text" hasn't been seen before, but nobody would argue that traditional Markov Chains aren't constrained by "only producing the past".
This is even more clear in the case of diffusion models (which I personally love using, and have spent a lot of time researching). All of the "new" images created by even the most advanced diffusion models are fundamentally remixing past information. This is really obvious to anyone who has played around with these extensively because they really can't produce truly novel concepts. New concepts can be added by things like fine-tuning or use of LoRAs, but fundamentally you're still just remixing the past.
LLMs are always doing some form of interpolation between different points in the past. Yes they can create a "new" SQL query, but it's just remixing from the SQL queries that have existed prior. This still makes them very useful because a lot of engineering work, including writing a custom text editor, involve remixing existing engineering work. If you could have stack-overflowed your way to an answer in the past, an LLM will be much superior. In fact, the phrase "CRUD" largely exists to point out that most webapps are fundamentally the same.
A great example of this limitation in practice is the work that Terry Tao is doing with LLMs. One of the largest challenges in automated theorem proving is translating human proofs into the language of a theorem prover (often Lean these days). The challenge is that there is not very much Lean code currently available to LLMs (especially with the necessary context of the accompanying NL proof), so they struggle to correctly translate. Most of the research in this area is around improving LLM's representation of the mapping from human proofs to Lean proofs (btw, I personally feel like LLMs do have a reasonably good chance of providing major improvements in the space of formal theorem proving, in conjunction with languages like Lean, because the translation process is the biggest blocker to progress).
When you say:
> So it is absurdly incorrect to say "they can only reproduce the past."
It's pretty clear you don't have a solid background in generative models, because this is fundamentally what they do: model an existing probability distribution and draw samples from that. LLMs are doing this for a massive amount of human text, which is why they do produce some impressive and useful results, but this is also a fundamental limitation.
But a world where we used LLMs for the majority of work, would be a world with no fundamental breakthroughs. If you've read The Three Body Problem, it's very much like living in the world where scientific progress is impeded by sophons. In that world there is still some progress (especially with abundant energy), but it remains fundamentally and deeply limited.
Just an innocent bystander here, so forgive me, but I think the flack you are getting is because you appear to be responding to claims that these tools will reinvent everything and introduce a new halcyon age of creation - when, at least on hacker news, and definitely in this thread, no one is really making such claims.
Put another way, and I hate to throw in the now over-used phrase, but I feel you may be responding to a strawman that doesn't much appear in the article or the discussion here: "Because these tools don't achieve a god-like level of novel perfection that no one is really promising here, I dismiss all this sorta crap."
Especially when I think you are also admitting that the technology is a fairly useful tool on its own merits - a stance which I believe represents the bulk of the feelings that supporters of the tech here on HN are describing.
I apologize if you feel I am putting unrepresentative words in your mouth, but this is the reading I am taking away from your comments.
> It's pretty clear you don't have a solid background in generative models, because this is fundamentally what they do: model an existing probability distribution and draw samples from that.
After post-training, this is definitively NOT what an LLM does.
Lot of impressive points. They are also irrelevant. The majority of people also only extrapolate from the knowledge they acquired in the past. That’s why there is the concept of inventor, someone who comes up with new ideas. Many new inventions are also based on existing ideas. Is that the reason to dismiss those achievements?
Do you only take LLM seriously if it can be another Einstein?
> But a world where we used LLMs for the majority of work, would be a world with no fundamental breakthroughs.
What do you consider recent fundamental breakthroughs?
Even if you are right, human can continue to work on hard problems while letting LLM handle the majority of derivative work
as architectures evolve, i think it can be that we learn more "side effects".. back in 2020 openai researchers said "GPT-3 is applied without any gradient updates or fine-tuning" the model emerges at a certain level of scale...
> It's pretty clear you don't have a solid background in generative models, because this is fundamentally what they do
You don’t have a solid background. No one does. We fundamentally don’t understand LLMs, this is an industry and academic opinion. Sure there are high level perspectives and analogies we can apply to LLMs and machine learning in general like probability distributions, curve fitting or interpolations… but those explanations are so high level that they can essentially be applied to humans as well. At a lower level we cannot describe what’s going on. We have no idea how to reconstruct the logic of how an LLM arrived at a specific output from a specific input.
It is impossible to have any sort of deterministic function, process or anything produce new information from old information. This limitation is fundamental to logic and math and thus it will limit human output as well.
You can combine information you can transform information you can lose information. But producing new information from old information from deterministic intelligence is fundamentally impossible in reality and therefore fundamentally impossible for LLMs and humans. But note the keyword: “deterministic”
New information can literally only arise through stochastic processes. That’s all you have in reality. We know it’s stochastic because determinism vs. stochasticism are literally your only two viable options. You have a bunch of inputs, the outputs derived from it are either purely deterministic transformations or if you want some new stuff from the input you must apply randomness. That’s it.
That’s essentially what creativity is. There is literally no other logical way to generate “new information”. Purely random is never really useful so “useful information” arrives only after it is filtered and we use past information to filter the stochastic output and “select” something that’s not wildly random. We also only use randomness to perturb the output a little bit so it’s not too crazy.
In the end it’s this selection process and stochastic process combined that forms creativity. We know this is a general aspect of how creativity works because there’s literally no other way to do it.
LLMs do have stochastic aspects to them so we know for a fact it is generating new things and not just drawing on the past. We know it can fit our definition of “creative” and we can literally see it be creative in front of your eyes.
You’re ignoring what you see with your eyes and drawing your conclusions from a model of LLMs that isn’t fully accurate. Or you’re not fully tying the mechanisms of how LLMs work with what creativity or generating new data from past data is in actuality.
The fundamental limitation with LLMs is not that it can’t create new things. It’s that the context window is too small to create new things beyond that. Whatever it can create it is limited to the possibilities within that window and that sets a limitation on creativity.
What you see happening with LEAN can also be an issue with the context window being too small. If we have an LLM with a giant context window bigger than anything before… and pass it all the necessary data to “learn” and be “trained” on lean it can likely start to produce new theorems without literally being “trained”.
Actually I wouldn’t call this a “fundamental” problem. More fundamental is the aspect of hallucinations. The fact that LLMs produce new information from past information in the WRONG way. Literally making up bullshit out of thin air. It’s the opposite problem of what you’re describing. These things are too creative and making up too much stuff.
We have hints that LLMs know the difference between hallucinations and reality but coaxing it to communicate that differentiation to us is limited.
Over half of HN still thinks it’s a stochastic parrot and that it’s just a glorified google search.
The change hit us so fast a huge number of people don’t understand how capable it is yet.
Also it certainly doesn’t help that it still hallucinates. One mistake and it’s enough to set someone against LLMs. You really need to push through that hallucinations are just the weak part of the process to see the value.
The problem I see, over and over, is that people pose poorly-formed questions to the free ChatGPT and Google models, laugh at the resulting half-baked answers that are often full of errors and hallucinations, and draw conclusions about the technology as a whole.
Either that, or they tried it "last year" or "a while back" and have no concept of how far things have gone in the meantime.
It's like they wandered into a machine shop, cut off a finger or two, and concluded that their grandpa's hammer and hacksaw were all anyone ever needed.
From what you've described an LLM has not invented anything. LLMs that can reason have a bit more slight of hand but they're not coming up with new ideas outside of the bounds of what a lot of words have encompassed in both fiction and non.
Good for you that you've got a fun token of code that's what you've always wanted, I guess. But this type of fantasy take on LLMs seems to be more and more prevalent as of late. A lot of people defending LLMs as if they're owed something because they've built something or maybe people are getting more and more attached to them from the conversational angle. I'm not sure, but I've run across more people in 2025 that are way too far in the deep end of personifying their relationships with LLMs.
Hang on, you're now saying that if something has ever been described in fiction it doesn't count as invention? So if somebody literally developed a working photon torpedo, that isn't new because "Star Trek Did It"?
You seem to be pretty far down the rabbit hole. How about this... You task an LLM to create a photon torpedo. If it can truly think then it should be able to provide you with something tangible. When you've got that in hand let us all know.
Back to the land of reality... Describing something in fiction doesn’t magically make it "not an invention". Fiction can anticipate an idea, but invention is about producing a working, testable implementation and usually involves novel technical methods. "Star Trek did it" is at most prior art for the concept, not a blueprint for the mechanism. If you can't understand that differential then maybe go ask an LLM.
Well, they can use tools, and tools includes physics simulations, so if it is possible (and FWIW the tool-free "intuition" of ChatGPT is "there will never be an age of antimatter"), then why couldn't LLMs grind those tools to get a solution?
When a computer is able to invent things, we’ve achieved AGI. Do you believe we are already in the AGI era, or is the inventor in this case actually you?
FWIW, your "evidence" is a text editor. I'm glad you made a tool that works for you, but the parent's point stands; this is a 200-level course-curriculum homework assignment. Tens of thousands of homemade editors exist, in various states of disrepair and vain overengineering.
The difference between those is the person is actually using this text editor that they built with the help of LLMs. There's plenty of people creating novel scripts and programs that can accommodate their own unique specifications.
If a programmer creating their own software (or contracting it out to a developer) would be a bespoke suit and using software someone or some company created without your input is an off the rack suit, I'd liken these sorts of programs as semi-bespoke, or made to measure.
"LLMs are literally technology that can only reproduce the past" feels like an odd statement. I think the point they're going for is that it's not thinking and so it's not going to produce new ideas like a human would? But literally no technology does that. That is all derived from some human beings being particularly clever.
LLMs are tools. They can enable a human to create new things because they are interfacing with a human to facilitate it. It's merging the functional knowledge and vision of a person and translating it into something else.
What is the point that you believe would be demonstrated by a new text editor running at the limit of hardware in a compiled editor? Would that point apply to every other text editor that exists already?
> 99 times out of 100 you need a solid budget to make a solid movie/game.
Sure, but 1 in 100 still gets you dozens of games a year now. There's plenty of genres where the top titles are nowhere near an AAA budget: Hades 2, Silksong, and Claire Obscura all being popular examples from this year, and Factorio being another well known example around here. Even simpler games like Balatro and Vampire Survivor are plenty of fun for some people.
The biggest studios have rarely been the ones producing the best work - budget gets you fancy cinematics and a beautifully rendered 3D world, but it doesn't make level design go any faster. It could plausibly buy better writing, but that requires all the executives to back off and trust the creatives.
And for what it's worth, the big studios are all happy raking in money on mindless remakes - it keeps working for them.
I think at some point in the future, you'll be able to reconfigure programs just by talking to your LLM-OS: Want the System Clock to show seconds? Just ask your OS to make the change. Need a calculator app that can do derivatives? Just ask your OS to add that feature.
"Configuration" implies a preset, limited number of choices; dynamic languages allow you to rewrite the entire application in real time.
Pick a language you love, and put together a text editor, or even just a quick utility to search through all your files for a keyword and show the results in a window. Write your own Clock app for Android, just to fix that little niggling detail that no other app quite gets right.
I think you'll be surprised how easy it is to put things together, once you start.
The point isn't to build something anyone else would care about - don't worry about the polish, you don't need to publish it, you don't even need to use it yourself. The point is just to make something. Although, personally, I now have a collection of random utilities that all make my life a little bit better, and it's nice knowing that any time a simple app like "Clock" or "Calculator" bugs me, I COULD fix it.
Most games use ranked match-making to resolve this. If you're in Bronze, compete trying to get into Silver, etc.. My experience is that you have to be extremely bad to get stuck at the bottom of Bronze in most modern games.
Yeah, you'll lose a few matches as the ranking system figures out where to place you, but the cost of competition is unfortunately the mortifying ordeal of learning that you are not in fact the best in the world.
I slowly climbed up the ranks of the "Go" ladder over the course of playing 1-2 games a week for a couple of years. "Play a lot" doesn't require "play a lot TODAY". There's a ton of games that have stable, long-term communities where you can reasonably expect that game to be around in 5-10 years.
Turns out, 95% of the predators already know exactly where the victims are, usually because it's their kid. Probably we want to worry about that a lot more.
Doubly so since, y'know, this only works if the predator lives close enough to act on the information before it changes - so the tiny possibility of a predator, a tiny possibility that they didn't already know this, and a tiny possibility of being able to act on the information...
I mean, it does seem relevant that this thread is for an article about them being fined a quarter-billion Euros, so they very much did break the law and the law very much does have teeth.
Oh boy, let me tell you about people 60 years ago - almost none of them knew about floppy disks and they were all busy doing physical drugs at Woodstock.
The onus of evidence is generally on the one making the initial claim: what evidence do you have that the modern world is actually getting worse?
But if you want evidence that we're improving, I'd point out that 20 years ago, the mainstream US position was that gay people were evil, 60 years ago they thought black people shouldn't be allowed to vote, and 100 years ago they thought women were also inferior and shouldn't be allowed to vote.
We can keep going back to when people thought "slavery" and "the divine right of kings" were solid ideas.
So... if people were so much smarter in the past, why did they believe all these obviously-dumb ideas?
Is that really a bad thing? It's like saying Google Maps makes you lazier, because you don't have to learn navigation. And, heck, why stop there: cars are just insanely lazy! You lose all the exercise benefits of walking.
Why is losing the ability/interest in navigating through a paper map by hand bad, though?
Humanity has adopted and then discarded skills many times in its history. There were once many master archers, nobody outside of one crazy Danish guy has mastered archery for hundreds of years. That isn't bad, nobody cares, nothing of value was lost.
You can still use pencil and paper for the difficult things. In fact, you'll have more time for doing so, because you don't have to use pencil and paper for the simple things.
Funny, I've used them to create my own personalized text editor, perfectly tailored to what I actually want. I'm pretty sure that didn't exist before.
It's wild to me how many people who talk about LLM apparently haven't learned how to use them for even very basic tasks like this! No wonder you think they're not that powerful, if you don't even know basic stuff like this. You really owe it to yourself to try them out.
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