6 ms·

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 imager

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.

crystal_revenge 10mo ago
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.
- throwaway7783 10mo ago
  Would you say that LLMs can discover patterns hitherto unknown? It would still be generating from the past, but patterns/connections not made before.
- PeterHolzwarth 10mo ago
  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 10mo ago
  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
- uxcolumbo 10mo ago
  How do human brains create something novel and what will it take for AIs to do the same?
- deleted 10mo ago
weatherlite 10mo ago
> 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 10mo ago
> 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.
- Kerrick 10mo ago
  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 10mo ago
    > 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 10mo ago
    Pedantic and not true. The LLM has stochastic processes involved. Randomness. That’s not old information. That’s newly generated stuff.
- zingar 10mo ago
  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?

crystal_revenge 10mo ago

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.

throwaway7783 10mo ago
Would you say that LLMs can discover patterns hitherto unknown? It would still be generating from the past, but patterns/connections not made before.
PeterHolzwarth 10mo ago
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 10mo ago
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
uxcolumbo 10mo ago
How do human brains create something novel and what will it take for AIs to do the same?
deleted 10mo ago

weatherlite 10mo ago

> 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 10mo ago

> 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.

Kerrick 10mo ago
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 10mo ago
  > 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 10mo ago
  Pedantic and not true. The LLM has stochastic processes involved. Randomness. That’s not old information. That’s newly generated stuff.
zingar 10mo ago
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?