7 ms·

> there's no reason to believe the progress of LLMs [...] will stop anytime soon Wrong. Every advancement has followed a s curve. Where we are on that curve i

by FrojoS 5mo ago

> there's no reason to believe the progress of LLMs [...] will stop anytime soon Wrong. Every advancement has followed a s curve. Where we are on that curve is anyones guess. Or maybe "this time its different".

Der_Einzige 5mo ago
This is FUD and extremely wrong. None of the advancements have followed an S curve. This time IS different and it should be obvious to you at this point.
aurareturn 5mo ago
He said "will stop anytime soon". He didn't say forever.
- Lionga 5mo ago
  Which still makes no sense. There is the same chance we are flatlining now as that we are flatlining in e.g. 3 years or 5 years.
  - squidbeak 5mo ago
    In what sense are the models flatlining?
    - nicoburns 5mo ago
      In the sense that the incremental improvements in capabilities that we've been seeing in recent models seem to taking exponentially growing amounts of compute to achieve.
      - nl 5mo ago
        But they don't? Mythos is a 10T model. Opus is a 5T model. That's not an exponentially growing amount of compute but it is achieving exponential improvements (eg from Mozilla: https://blog.mozilla.org/en/privacy-security/ai-security-zero-day-vulnerabilities/ https://blog.mozilla.org/en/privacy-security/ai-security-zer... )
        le-mark 5mo ago
        > but it is achieving exponential improvements “Exponential” used here is pure hyperbole. Can you justify it?
        coldtea 5mo ago
        Compute doesn't necessarily linerarly follow parameters. And with how many active parameters Mythos vs Opus gets its effectivenes from? Is it 1x or 2x? We don't know. We don't even know the parameters (it's more of rumor than confirmed 10T iirc). But even more so, who said the improvements are "exponential"? Mozilla's single metric, that doesn't even prove anything of the sort?
        nozzlegear 5mo ago
        > Mythos Ah yes, the marketing model that's ostensibly so powerful us mere mortals aren't allowed to use it. It's certainly led to exponential hype and speculation.
        _heimdall 5mo ago
        Wasn't 4.6 Sonnet a 1T model? Parameters and compute are quite the same thing, but going from 1T to 5T to 10T is quite a ramp up.
        minitech 5mo ago
        I know parameters don’t translate directly like that (and that linear and exponential aren’t the only types of growth) but a doubling as a go-to example of “not exponential growth” is pretty funny.
        crthpl 5mo ago
        where the heck did you get those parameter numbers from?
        nl 5mo ago
        Sonnet and Opus are from Elon Musk (given the people he's hired it seems likely it is approximately true). Mythos is quite widely spoken about.
gchamonlive 5mo ago
This could be right for the current architecture of LLMs, but you can come up with specialized large language models that can more efficiently use tokens for a specific subset of problems by encoding the information differently (https://www.nature.com/articles/d41586-024-03214-7 https://www.nature.com/articles/d41586-024-03214-7). So if instead of text we come up with a different representation for mathematical or physical problems, that could both improve the quality of the output while reducing the amount of transformers needed for decoding and encoding IO and for internal reasoning. There are also difference inference methods, like autoregressive and diffusion, and maybe others we haven't discovered yet. You combine those variables, along with the internal disposition of layers, parameter size and the actual dataset, and you have such a large search space for different models that no one can reliably tell if LLM performance is going to flatline or continue to improve exponentially.
- coldtea 5mo ago
  >This could be right for the current architecture of LLMs, but you can come up with specialized large language models that can more efficiently use tokens for a specific subset of problems by encoding the information differently. That's precisely what happens on the bad side of a S curve.
  - gchamonlive 5mo ago
    Progress don't stop however, and the S curve resets, because then you are optimizing a new architecture.
- ifdefdebug 5mo ago
  > So if instead of text we come up with a different representation for mathematical or physical problems, that could both improve But then, wouldn't we first have to translate all of our current math and physics knowledge into that new representation in order to be able to train a model on it? Looks like a tremendous amount of work to me.
  - gchamonlive 5mo ago
aspenmartin 5mo ago
It’s more of a guess if you don’t know about things like scaling laws and RL with verification. The onus of “we’re going to saturate” anytime soon is on that claim because every measurement points to that not being true.
- logicprog 5mo ago
  Yeah. People (Gary Marcus) have been claiming that AI will hit a wall or is hitting a wall or already has hit a wall since 2023, basically. And yet every time they proclaim that the AI industry found new ways of training their AI's, new ways of integrating them with external tools and feedback loops, new architectures and more to keep the exponential growing. And sure enough if you look at literally every attempt to objectively rate and verify the capability of these models, including things like the METR time horizon autonomy index or the artificial analysis intelligence index, you see exponential or even greater than exponential growth, continuing smoothly through each of the points people claimed that it would begin to slow down, with no sinus slowing down or stopping at all. So yeah, I think at some point the onus has to lie on the ones that are making the claim that keeps being wrong and the continues to be wrong and it completely goes against the current tangent of the curve that we're seeing in all objective metrics. Especially when they can't give specific new reasons for progress to stop beyond the ones they gave last time. It didn't stop and really can't give specific reasons at all besides vague general points about stochastic parrots and S curves. I really have to highlight the S-curve nonsense because, like, yes, I think this technology's improvement will follow an S-curve. It's absurd to think that it will just follow an exponential up towards infinity forever because nothing in the world really works like that. However, like everyone else in this thread is saying, we have no idea where on the S-curve we actually are, and it's impossible to know until it's already slowed down. So really all appeals to the S curve do are as function as a sort of non-specific, unfalsifiable prophecy that someday it will slow down, which doesn't really tell us anything useful, and also frees the person referencing the S curve from ever actually having to worry about being wrong. Just like the Singularity people, the slowdown of the S curve is always near. This is actually a known and well-established tactic of religions and other people that want to make prophecies without having to worry about turning out to be wrong — unfalseifiable vague prophecies with no actual timeline, and thus no clear import to the present so that they can never be shown to be wrong.
vessenes 5mo ago
There are advancements that do not follow s curves - consider for instance total data transmitted over all networks, or financial derivatives volumes. I think a better question for AI is “is it more like a network effect, liquidity effect, or a biological/physical effect”?
- 010101010101 5mo ago
  Those are measuring the utility of a technological advancement by looking at usage, not the pace of advancement of said technology.
  - eiieue 5mo ago
    [flagged]
  - vessenes 5mo ago
    Yes. But quantity has a quality all its own, as they say — derivatives have gone through at least a few step functions where they have become more important and more useful as their usage grows. I’d call that advancement. Maybe just to be clear I think that kneejerk “I hate this AI trend, and prefer to believe this will end soon, all exponential growth ends eventually” is intellectually lazy, and dangerous for younger engineers/hackers, a group I hope can benefit from being on HN. Bitcoin mining went through something like 13 10x growth periods, last I ran the numbers a few years ago. There are physical processes that do have very extended periods of doubling, and there are digital and financial processes that don’t show any signs of doing anything but continuing to keep growing over their multidecade lives. So, like I said, it’s worth thinking carefully, and risk mitigation for things like mental health, career decisions and investment decisions indicates we should be cautious assessing new dynamics.
- mirmor23 5mo ago
  [dead]
- camdenreslink 5mo ago
  Total volume of usage is not an advancement, it’s orthogonal.
gdhkgdhkvff 5mo ago
Great. You see a shape in graphs. And that shape tells you that _at some unknown point in the future_ progress will slow (but likely not stop). Now back to the point, what reason do you have to believe progress will stop soon? If you have no reason, then it sounds like you agree with OP. Which makes the patronizing sarcasm all that much more nauseating.
- le-mark 5mo ago
  Nausea aside, what evidence does anyone have that “super intelligence” of the sort your argument alludes to is even possible? Because that’s what we’re really talking about; greater than human intelligence on this sort of academic task. For example; When llms start contributing meaningfully to their own development, that would be a convincing indicator imo.
  - bdangubic 5mo ago
    > When llms start contributing meaningfully to their own development, that would be a convincing indicator imo. This has been the case for awhile now already… https://kersai.com/the-48-hours-that-changed-ai-forever-claude-opus-4-6s-million-token-agent-teams-gpt-5-3-codex-that-built-itself-and-geminis-750m-user-explosion/ https://kersai.com/the-48-hours-that-changed-ai-forever-clau...
    - eiieue 5mo ago
      And yet the world hasn’t changed all that much except people getting laid off in response to over-hiring prior to the diffusion of llm’s.
      - daishi55 5mo ago
        > over-hiring For how long should you be allowed to use this excuse? It’s nearly 5 years since the peak of COVID hiring. What’s an acceptable limit - 10 years? Of course at that point you can just switch over to outsourcing and “stupid MBAs”, the other two of Reddit’s favorite scapegoats. I find a lot of the AI skepticism to be totally unfalsifiable.
scotty79 5mo ago
It can be S curve (and it almost surely is), but on every chart you can plot, you don't see even of an inkling of the bend yet.
holoduke 5mo ago
Software and hardware have no limits. Theoretically would could bozons for computations and have the same amount of computation available on one cm3 of the current total computation in the entire world. Same with software. Never there was a stop on new algorithms. With LLMs there are so many parts that will get better and are not very far fetched.
- oblio 5mo ago
  > Software and hardware have no limits. Yeah, if time is infinite, R&D imagination is infinite, energy is infinite and material resources are infinite. Easy.
jeremyjh 5mo ago
What the fuck does that have to do with “soon”?
CuriouslyC 5mo ago
What people miss is that AI isn't one S curve, each capability we try to bake into a model has its own S curve. Model progress might not impact some capabilities at all, but other capabilities might get totally overhauled.
baq 5mo ago
you can tell where on the sigmoid we're currently sitting? frontier lab folks can't - chapeau bas good sir
- bigyabai 5mo ago
  > frontier lab folks can't Do you have a source for this that isn't marketing spiel? There's a fiscal incentive to lie about scaling research.
IanCal 5mo ago
Assuming it’ll stop soon is to wager that we’re at a very specific point on the curve. If it’s anyone’s guess then we’re much more likely to be left of that, unless you argue we’re already on the flat side.
dehrmann 5mo ago
I read an experiment someone wanted to try where they used pre-1900 content and tried to get relativity. Another version would be train an LLM on school curriculum up until calculus and see if it can invent calculus. Where we are on the curve depends on if it's remixing known things or genuinely inventing things. From the article, > ...LLMs have got to the point where if a problem has an easy argument that for one reason or another human mathematicians have missed (that reason sometimes, but not always, being that the problem has not received all that much attention), then there is a good chance that the LLMs will spot it. Conversely, for problems where one’s initial reaction is to be impressed that an LLM has come up with a clever argument, it often turns out on closer inspection that there are precedents for those arguments...
dang 5mo ago
> Wrong. Can you please edit out swipes/putdowns, as the guidelines ask (https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html)? I'm sure you didn't intend it, but it comes across that way, and your comment would be just fine without that bit. Edit: on closer look, it would be just fine without that bit and also without the snarky bit at the end. The rest is good.

by FrojoS 5mo ago

Der_Einzige 5mo ago

This is FUD and extremely wrong. None of the advancements have followed an S curve. This time IS different and it should be obvious to you at this point.

aurareturn 5mo ago

He said "will stop anytime soon". He didn't say forever.

Lionga 5mo ago
Which still makes no sense. There is the same chance we are flatlining now as that we are flatlining in e.g. 3 years or 5 years.
- squidbeak 5mo ago
  In what sense are the models flatlining?
  - nicoburns 5mo ago
    In the sense that the incremental improvements in capabilities that we've been seeing in recent models seem to taking exponentially growing amounts of compute to achieve.
    - nl 5mo ago
      But they don't? Mythos is a 10T model. Opus is a 5T model. That's not an exponentially growing amount of compute but it is achieving exponential improvements (eg from Mozilla: https://blog.mozilla.org/en/privacy-security/ai-security-zero-day-vulnerabilities/ https://blog.mozilla.org/en/privacy-security/ai-security-zer... )
      - le-mark 5mo ago
        > but it is achieving exponential improvements “Exponential” used here is pure hyperbole. Can you justify it?
      - coldtea 5mo ago
        Compute doesn't necessarily linerarly follow parameters. And with how many active parameters Mythos vs Opus gets its effectivenes from? Is it 1x or 2x? We don't know. We don't even know the parameters (it's more of rumor than confirmed 10T iirc). But even more so, who said the improvements are "exponential"? Mozilla's single metric, that doesn't even prove anything of the sort?
      - nozzlegear 5mo ago
        > Mythos Ah yes, the marketing model that's ostensibly so powerful us mere mortals aren't allowed to use it. It's certainly led to exponential hype and speculation.
      - _heimdall 5mo ago
        Wasn't 4.6 Sonnet a 1T model? Parameters and compute are quite the same thing, but going from 1T to 5T to 10T is quite a ramp up.
      - minitech 5mo ago
        I know parameters don’t translate directly like that (and that linear and exponential aren’t the only types of growth) but a doubling as a go-to example of “not exponential growth” is pretty funny.
      - crthpl 5mo ago
        where the heck did you get those parameter numbers from?
        nl 5mo ago
        Sonnet and Opus are from Elon Musk (given the people he's hired it seems likely it is approximately true). Mythos is quite widely spoken about.

gchamonlive 5mo ago

This could be right for the current architecture of LLMs, but you can come up with specialized large language models that can more efficiently use tokens for a specific subset of problems by encoding the information differently (https://www.nature.com/articles/d41586-024-03214-7 https://www.nature.com/articles/d41586-024-03214-7). So if instead of text we come up with a different representation for mathematical or physical problems, that could both improve the quality of the output while reducing the amount of transformers needed for decoding and encoding IO and for internal reasoning. There are also difference inference methods, like autoregressive and diffusion, and maybe others we haven't discovered yet. You combine those variables, along with the internal disposition of layers, parameter size and the actual dataset, and you have such a large search space for different models that no one can reliably tell if LLM performance is going to flatline or continue to improve exponentially.

coldtea 5mo ago
>This could be right for the current architecture of LLMs, but you can come up with specialized large language models that can more efficiently use tokens for a specific subset of problems by encoding the information differently. That's precisely what happens on the bad side of a S curve.
- gchamonlive 5mo ago
  Progress don't stop however, and the S curve resets, because then you are optimizing a new architecture.
ifdefdebug 5mo ago
> So if instead of text we come up with a different representation for mathematical or physical problems, that could both improve But then, wouldn't we first have to translate all of our current math and physics knowledge into that new representation in order to be able to train a model on it? Looks like a tremendous amount of work to me.
- gchamonlive 5mo ago

aspenmartin 5mo ago

It’s more of a guess if you don’t know about things like scaling laws and RL with verification. The onus of “we’re going to saturate” anytime soon is on that claim because every measurement points to that not being true.

logicprog 5mo ago
Yeah. People (Gary Marcus) have been claiming that AI will hit a wall or is hitting a wall or already has hit a wall since 2023, basically. And yet every time they proclaim that the AI industry found new ways of training their AI's, new ways of integrating them with external tools and feedback loops, new architectures and more to keep the exponential growing. And sure enough if you look at literally every attempt to objectively rate and verify the capability of these models, including things like the METR time horizon autonomy index or the artificial analysis intelligence index, you see exponential or even greater than exponential growth, continuing smoothly through each of the points people claimed that it would begin to slow down, with no sinus slowing down or stopping at all. So yeah, I think at some point the onus has to lie on the ones that are making the claim that keeps being wrong and the continues to be wrong and it completely goes against the current tangent of the curve that we're seeing in all objective metrics. Especially when they can't give specific new reasons for progress to stop beyond the ones they gave last time. It didn't stop and really can't give specific reasons at all besides vague general points about stochastic parrots and S curves. I really have to highlight the S-curve nonsense because, like, yes, I think this technology's improvement will follow an S-curve. It's absurd to think that it will just follow an exponential up towards infinity forever because nothing in the world really works like that. However, like everyone else in this thread is saying, we have no idea where on the S-curve we actually are, and it's impossible to know until it's already slowed down. So really all appeals to the S curve do are as function as a sort of non-specific, unfalsifiable prophecy that someday it will slow down, which doesn't really tell us anything useful, and also frees the person referencing the S curve from ever actually having to worry about being wrong. Just like the Singularity people, the slowdown of the S curve is always near. This is actually a known and well-established tactic of religions and other people that want to make prophecies without having to worry about turning out to be wrong — unfalseifiable vague prophecies with no actual timeline, and thus no clear import to the present so that they can never be shown to be wrong.

vessenes 5mo ago

There are advancements that do not follow s curves - consider for instance total data transmitted over all networks, or financial derivatives volumes. I think a better question for AI is “is it more like a network effect, liquidity effect, or a biological/physical effect”?

010101010101 5mo ago
Those are measuring the utility of a technological advancement by looking at usage, not the pace of advancement of said technology.
- eiieue 5mo ago
  [flagged]
- vessenes 5mo ago
  Yes. But quantity has a quality all its own, as they say — derivatives have gone through at least a few step functions where they have become more important and more useful as their usage grows. I’d call that advancement. Maybe just to be clear I think that kneejerk “I hate this AI trend, and prefer to believe this will end soon, all exponential growth ends eventually” is intellectually lazy, and dangerous for younger engineers/hackers, a group I hope can benefit from being on HN. Bitcoin mining went through something like 13 10x growth periods, last I ran the numbers a few years ago. There are physical processes that do have very extended periods of doubling, and there are digital and financial processes that don’t show any signs of doing anything but continuing to keep growing over their multidecade lives. So, like I said, it’s worth thinking carefully, and risk mitigation for things like mental health, career decisions and investment decisions indicates we should be cautious assessing new dynamics.
mirmor23 5mo ago
[dead]
camdenreslink 5mo ago
Total volume of usage is not an advancement, it’s orthogonal.

gdhkgdhkvff 5mo ago

Great. You see a shape in graphs. And that shape tells you that _at some unknown point in the future_ progress will slow (but likely not stop). Now back to the point, what reason do you have to believe progress will stop soon? If you have no reason, then it sounds like you agree with OP. Which makes the patronizing sarcasm all that much more nauseating.

le-mark 5mo ago
Nausea aside, what evidence does anyone have that “super intelligence” of the sort your argument alludes to is even possible? Because that’s what we’re really talking about; greater than human intelligence on this sort of academic task. For example; When llms start contributing meaningfully to their own development, that would be a convincing indicator imo.
- bdangubic 5mo ago
  > When llms start contributing meaningfully to their own development, that would be a convincing indicator imo. This has been the case for awhile now already… https://kersai.com/the-48-hours-that-changed-ai-forever-claude-opus-4-6s-million-token-agent-teams-gpt-5-3-codex-that-built-itself-and-geminis-750m-user-explosion/ https://kersai.com/the-48-hours-that-changed-ai-forever-clau...
  - eiieue 5mo ago
    And yet the world hasn’t changed all that much except people getting laid off in response to over-hiring prior to the diffusion of llm’s.
    - daishi55 5mo ago
      > over-hiring For how long should you be allowed to use this excuse? It’s nearly 5 years since the peak of COVID hiring. What’s an acceptable limit - 10 years? Of course at that point you can just switch over to outsourcing and “stupid MBAs”, the other two of Reddit’s favorite scapegoats. I find a lot of the AI skepticism to be totally unfalsifiable.

scotty79 5mo ago

It can be S curve (and it almost surely is), but on every chart you can plot, you don't see even of an inkling of the bend yet.

holoduke 5mo ago

Software and hardware have no limits. Theoretically would could bozons for computations and have the same amount of computation available on one cm3 of the current total computation in the entire world. Same with software. Never there was a stop on new algorithms. With LLMs there are so many parts that will get better and are not very far fetched.

oblio 5mo ago
> Software and hardware have no limits. Yeah, if time is infinite, R&D imagination is infinite, energy is infinite and material resources are infinite. Easy.

jeremyjh 5mo ago

What the fuck does that have to do with “soon”?

CuriouslyC 5mo ago

What people miss is that AI isn't one S curve, each capability we try to bake into a model has its own S curve. Model progress might not impact some capabilities at all, but other capabilities might get totally overhauled.

baq 5mo ago

you can tell where on the sigmoid we're currently sitting? frontier lab folks can't - chapeau bas good sir

bigyabai 5mo ago
> frontier lab folks can't Do you have a source for this that isn't marketing spiel? There's a fiscal incentive to lie about scaling research.

IanCal 5mo ago

Assuming it’ll stop soon is to wager that we’re at a very specific point on the curve. If it’s anyone’s guess then we’re much more likely to be left of that, unless you argue we’re already on the flat side.

dehrmann 5mo ago

I read an experiment someone wanted to try where they used pre-1900 content and tried to get relativity. Another version would be train an LLM on school curriculum up until calculus and see if it can invent calculus. Where we are on the curve depends on if it's remixing known things or genuinely inventing things. From the article, > ...LLMs have got to the point where if a problem has an easy argument that for one reason or another human mathematicians have missed (that reason sometimes, but not always, being that the problem has not received all that much attention), then there is a good chance that the LLMs will spot it. Conversely, for problems where one’s initial reaction is to be impressed that an LLM has come up with a clever argument, it often turns out on closer inspection that there are precedents for those arguments...

dang 5mo ago

> Wrong. Can you please edit out swipes/putdowns, as the guidelines ask (https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html)? I'm sure you didn't intend it, but it comes across that way, and your comment would be just fine without that bit. Edit: on closer look, it would be just fine without that bit and also without the snarky bit at the end. The rest is good.