[CamperBob2]

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.

[weatherlite]

> 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.

[windexh8er]

> 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.

[ordersofmag]

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".

[windexh8er]

> 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.

[0] https://archive.is/FM4y8 [0] https://www.theverge.com/ai-artificial-intelligence/827820/l... [1] https://www.raspberrypi.org/blog/secondary-school-maths-show...

[CamperBob2]

Mastery of words is thinking?

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.

ELIZA, ROFL. How'd ELIZA do at the IMO last year?

[svieira]

> Yes, in fact, it turns out that language encodes and embodies reasoning ... No one had the faintest clue

Funnily enough, they did, if you go back far enough. It's only the deconstructionists and the solipsists who had the audacity to think otherwise.

[meindnoch]

So people without language cannot reason? I don't think so.

[CamperBob2]

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.

[windexh8er]

> ELIZA, ROFL. How'd ELIZA do at the IMO last year?

What's funny is the failure to grasp any contextual framing of ELIZA. When it came out people were impressed by it's reasoning, it's responses. And in your line of defense it could think because it had mastery of words!

But fast forward the current timeline 30 years. You will have been of the same camp that argued on behalf of ELIZA when the rest of the world was asking, confusingly: how did people think ChatGPT could think?

[CamperBob2]

No one was impressed with ELIZA's "reasoning" except for a few non-specialist test subjects recruited from the general population. Admittedly it was disturbing to see how strongly some of those people latched onto it.

Meanwhile, you didn't answer my question. How'd ELIZA do on the IMO? If you know a way to achieve gold-medal performance at top-level math and programming competitions without thinking, I for one am all ears.

[arcatech]

> 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'.

This is the definition of the word ‘novel’.

[Kerrick]

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.

[windexh8er]

> 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".

[threethirtytwo]

Pedantic and not true. The LLM has stochastic processes involved. Randomness. That’s not old information. That’s newly generated stuff.

[jama211]

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”.

[nl]

What does "think" mean?

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).

[closewith]

By that definition, nearly all commercial software development (and nearly all human output in general) is derived output.

[windexh8er]

Wow.

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.

[CamperBob2]

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.

[windexh8er]

> 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.

The mental gymnastics here is entertainment at best. Of course the thinking LLM would give feedback on how it's actually just a pattern model over text - well, we shouldn't believe that! The LLM was trained to lie about it's true capabilities in your own admission?

How about these...

What observable capability would you expect from "true cognitive thought" that a next-token predictor couldn’t fake?

Where are the system’s goals coming from—does it originate them, or only reflect the user/prompt?

How does it know when it’s wrong without an external verifier? If the training data says X and the answer is Y - how will it ever know it was wrong and reach the correct conclusion?

[CamperBob2]

How does it know when it’s wrong without an external verifier? If the training data says X and the answer is Y - how will it ever know it was wrong and reach the correct conclusion?

You need to read a few papers with publication dates after 2023.

[closewith]

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.

[windexh8er]

Oh yes, they are.

And beyond people claiming that LLMs are basically sentient you have people like CamperBob2 who made this wild claim:

"""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."""

Let that sink. They literally think that there's no such thing as people without language. Talk about a wild and ignorant take on life in general!

[CamperBob2]

How'd they communicate with the test subjects?

That's "language."

[zingar]

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?

[techpression]

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.

[threethirtytwo]

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.

[ahtihn]

> 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.

[threethirtytwo]

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.

[crystal_revenge]

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.

[PeterHolzwarth]

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.

[signatoremo]

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

[aoeusnth1]

> 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.

[oedemis]

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...

[throwaway7783]

Would you say that LLMs can discover patterns hitherto unknown? It would still be generating from the past, but patterns/connections not made before.

[uxcolumbo]

How do human brains create something novel and what will it take for AIs to do the same?

[threethirtytwo]

> 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.

[jheez3]

"You don’t have a solid background.

If you want to go around huffing and puffing your chest about a subject area, you kinda do fella. Credibility.

[threethirtytwo]

Not only is what he saying in direct contradiction to what people with credibility have said, but his claimed credentials can be utter bullshit.

This is the internet bro. Credibility is irrelevant because identities can never be verified. So the only thing that matters is the strength and rationality of an argument.

That’s the point of hacker news substantive content not some battle of comparison of credentials or useless quips (like yours) with zero substance. Say something worth reading if you have anything to say at all, otherwise nobody cares.