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> 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 pr

> 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.

CamperBob2 10mo ago
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?
handoflixue 10mo ago
Seriously, all that familiarity and you think an LLM "literally" can't invent anything that didn't already exist? Like, I'm sorry, but you're just flat-out wrong and I've got the proof sitting on my hard drive. I use this supposedly impossible program daily.
- bigyabai 10mo ago
  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.
  - least 10mo ago
    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.
    - resize2996 10mo ago
      compilers can only produce machine code. so unorginal.
- windexh8er 10mo ago
  Do you also think LLMs "think"? 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.
threethirtytwo 10mo ago
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.
- CamperBob2 10mo ago
  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.
  - habinero 10mo ago
    No, frankly it's the difference between actual engineers and hobbyists/amateurs/non-SWEs. SWEs are trained to discard surface-level observations and be adversarial. You can't just look at the happy path, how does the system behave for edge cases? Where does it break down and how? What are the failure modes? The actual analogy to a machine shop would be to look at whether the machines were adequate for their use case, the building had enough reliable power to run and if there were any safety issues. It's easy to Clever Hans yourself and get snowed by what looks like sophisticated effort or flat out bullshit. I had to gently tell a junior engineer that just because the marketing claims something will work a certain way, that doesn't mean it will.
    - CamperBob2 10mo ago
      You sound pretty certain. There's often good money to be made in taking the contrarian view, where you have insights that the so-called "smart money" lacks. What are some good investments to make in the extreme-bear case, in which we're all just Clever Hans-ing ourselves as you put it? Do you have skin in the game?

CamperBob2 10mo ago

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?

handoflixue 10mo ago

Seriously, all that familiarity and you think an LLM "literally" can't invent anything that didn't already exist? Like, I'm sorry, but you're just flat-out wrong and I've got the proof sitting on my hard drive. I use this supposedly impossible program daily.

bigyabai 10mo ago
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.
- least 10mo ago
  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.
  - resize2996 10mo ago
    compilers can only produce machine code. so unorginal.
windexh8er 10mo ago
Do you also think LLMs "think"? 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.

threethirtytwo 10mo ago

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.

CamperBob2 10mo ago
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.
- habinero 10mo ago
  No, frankly it's the difference between actual engineers and hobbyists/amateurs/non-SWEs. SWEs are trained to discard surface-level observations and be adversarial. You can't just look at the happy path, how does the system behave for edge cases? Where does it break down and how? What are the failure modes? The actual analogy to a machine shop would be to look at whether the machines were adequate for their use case, the building had enough reliable power to run and if there were any safety issues. It's easy to Clever Hans yourself and get snowed by what looks like sophisticated effort or flat out bullshit. I had to gently tell a junior engineer that just because the marketing claims something will work a certain way, that doesn't mean it will.
  - CamperBob2 10mo ago
    You sound pretty certain. There's often good money to be made in taking the contrarian view, where you have insights that the so-called "smart money" lacks. What are some good investments to make in the extreme-bear case, in which we're all just Clever Hans-ing ourselves as you put it? Do you have skin in the game?