7 ms·

On the slow death of scaling

123 points by sethbannon 9mo ago

pdf: https://download.ssrn.com/2026/1/6/5877662.pdf?response-content-disposition=inline&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEKT%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaCXVzLWVhc3QtMSJIMEYCIQCd%2FayXVPYy8C9vyG42DKPkHUfsAjYLKy5%2Fo6CHi3O%2ByAIhAKBZTUGATP5mw9%2FfP34xfHHs2nr%2FdPOWQTQapbiCX1kQKrwFCGwQBBoMMzA4NDc1MzAxMjU3IgxIVY59yJAG6BlhvUEqmQUqpEyEgN4QtLQL9FJQ8FOVff69YbuF22b08IDqrc4%2FQOQHIJeCPBstkKkCNlxzcq602HV%2BxxCsHYsRQG37L8zUAZza54xKvyyIeOBbEsQT4FzGLHPTGycr8M6W7Ug8SfxgARqUXYUAVKwRcCJuDxquVMsCqregvUAabVT3SGfv0jYpbarMbqEsuSXOzMV8AdDP2aG0KBwIztJ%2FxEnvTKu3GOmBA6E221b%2FjDga1PZROEP2UHWA6uPB835tRc8HkHBp%2B17jqjRKOiKxLSOmPry1uIupIgqjoWzV1a5sS3VOKLiZX0aJLM3ygfAUnwKRTN3y9qasw9P5P2Latp4X4mhXElFNNvC1m1E6xOaQH8LtdmmXltDJX0Aj4v1C1G7VAfwLmGfDmJvmcsCQs%2BAhR68BL1%2FGg65EV3w7yJnO77n8F1Q81QAvhlo%2Fyj%2B61tSj9E3bK3ydQojvvn4IqihtZwcPXzVkYfj0aruPRsNx9uemXQlLbYcSfap1cBCI6yr7z4rh9046Morgq5KFd977qKZiytu51alWD4kest4Isuze2FRSpVPZPp94yl78TABaGS3oUtivP3%2BKYrgHvUa%2FziriOerEZIhIwE%2BJ1YqUXbRmkWYPsec%2Bb2YS9I2KhcchW41HA0M4OcxkvR7ADq9gwDJPGrmbNN4qOE%2FtKMWFFhKZPkbtzB%2B2uMAtKka2vDjrL%2BOjp0%2BRCXbsZ3ynzkRKkyASq9R3hscJrWk9DXqy0z%2BAn0Vf7bQuCWzgOl5noyOUhjK1joLLOfEJLmUpRvZDWvCT1h%2FH1tvtT2ZiNqLpBVwnMK1pjo2pCp1zIjePJ%2BB%2B74u2%2FrOmcD%2FbOWohZzpXCs6rB9upw%2FAgg4BCOo8aW%2FRg9oSNixYnLarZRzCNovfKBjqwAXWrcRuixCgVYJGj%2BbngNx%2FR3yaOCvpfzyT%2FZUK2VvCz2sO5mi74oYk7ge1UeTpqovCVa5gqk0dXw8L0BUfCSi9BzewX4cSfzI%2FUW4p9MN1%2Bot1tjdA32HmbZ1nJfdjeCwTSyr6s92UoNv8TC8lvWZTusGE0UwzqAr0EseYh2TNvxUxHexsVpfLdq3x5SzuZ6RP0rFxJLg%2FqCLA1Msi7yuM95D4H6R2exUgQmtK9bjCj&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20260107T035015Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAUPUUPRWEROZGM7DD%2F20260107%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=d8b5d3278c4497cf2e8d6352666b667df8be9ae69817f8d8862496a11354bec3&abstractId=5877662 https://download.ssrn.com/2026/1/6/5877662.pdf?response-cont...

octoberfranklin 9mo ago
> Academia has been marginalized from meaningfully participating in AI progress and industry labs have stopped publishing Exactly like semiconductor wafer processing.
- random3 9mo ago
  If anyone believes they're close to a generalization end-game wrt to AI capabilities, it makes no sense to do anything that could impact their advantage, by enabling others to compete. Collaboration makes sense on timeframes that don't imply zero-sum games. Board games like the Settlers of Catan are good examples of the behavior— concretely the start of the game when everyone trades vs the end of the game when if you suspect someone wins it makes little sense to trade unless, you think it will help you win first.
  - SecretDreams 9mo ago
    > Collaboration makes sense on timeframes that don't imply zero-sum games. People are fooling themselves if they think AGI will be zero sum. Even if only one group somehow miraculously develops it, there will immediately be fast followers. And, the more likely scenario is more than one group would independently pull it off - if it's even possible.
    - pixl97 8mo ago
      >if it's even possible. Why do people keep repeating this. The only way artificial intelligence is impossible is if intelligence is impossible. And we're here so that pretty much removes that impediment.
    - random3 9mo ago
      Maybe, but at least Open AI, XAI and any Bostrom believer thinks this is the case. Ilya Sutskever (Sep 20, 2017) > The goal of OpenAI is to make the future good and to avoid an AGI dictatorship. You are concerned that Demis could create an AGI dictatorship. So do we. So it is a bad idea to create a structure where you could become a dictator if you chose to, especially given that we can create some other structure that avoids this possibility. Nick Bostrom - Decisive Strategic Advantage https://www.lesswrong.com/posts/vkjWGJrFWBnzHtxrw/superintelligence-7-decisive-strategic-advantage https://www.lesswrong.com/posts/vkjWGJrFWBnzHtxrw/superintel...
      - refulgentis 9mo ago
        Bostrom / Ilya are agreeing with the GPs argument AFAICT: it's not that AGI can create a dictatorship-of-the-first-AGI-owner, it's that only having one serious funded lab going at it creates a knowledge gap of N years that could give said lab escape velocity* * imagine if Google alone had LLMs. For an innocuous example, the only provider in my LLM client that regularly fails unit tests verifying they actually cache tokens and utilize them on a subsequent request is Gemini. I used to work at Google and it'd be horrible for that too-big-for-its-own-good institution regressing to the corporate mean to own LLMs all by itself
    - 9mo ago
ironbound 9mo ago
It's a mistake to publish papers without a code repo, with the majority of new ML paper's being noise at best.
- timy2shoes 9mo ago
  This is an opinion piece, similar to her infamous hardware lottery paper. We shouldn't expect a repo on an opinion piece.
- rvz 9mo ago
  So DeepMind (who almost always releases papers without code repositories) are mistakes then?
deleted 9mo ago
[deleted]
bicepjai 9mo ago
Hooker’s argument lands for me because it ties the technical scaling story to institutional incentives: as progress depends more on massive training runs, it becomes capital-intensive, less reproducible and more secretive; so you get a compute divide and less publication. I’m trying to turn that into something testable with a simple constraint: “one hobbyist GPU, one day.” If meaningful progress is still possible under tight constraints, it supports the idea that we should invest more in efficiency/architecture/data work, not just bigger runs. My favorite line >> Somewhat humorously, the acceptance that there are emergent properties which appear out of nowhere is another way of saying our scaling laws don’t actually equip us to know what is coming. Regarding this paragraph >> 3.3 New algorithmic techniques compensate for compute. Progress over the last few years has been as much due to algorithmic improvements as it has been due to compute. This includes extending pre-training with instruction finetuning to teach models instruction following ..., model distillation using synthetic data from larger more performant "teachers" to train highly capable, smaller "students" ..., chain-of-thought reasoning ..., increased context-length ..., retrieval augmented generation ... and preference training to align models with human feedback ... I would consider algorithmic improvements to be the following 1. architecture like ROPE, MLA 2. efficiency using custom kernels The errors in the paper 1. Transformers for language modeling (Vaswani et al., 2023). => this shd be 2017 Disclosure: my proposed experiments: https://ohgodmodels.xyz/ https://ohgodmodels.xyz/
- bigbadfeline 8mo ago
  > as progress depends more on massive training runs, it becomes capital-intensive, less reproducible and more secretive; so you get a compute divide and less publication. In the area of AI, secrecy and inability to reproduce/verify can become a huge systemic and social problem, the possible damage is literally unbounded. That's why I like open source AI, including training data and process, it solves the above problem as well as the problem of duplication of effort which leads to a huge waste of resources, waste that is economically significant on national and global scales.
gdiamos 9mo ago
It was an interesting read Sara, thanks for sharing it. I especially agree with your point that scaling laws really killed open research. That's a shame and I personally think we could benefit from more research. I originally didn't like calling them scaling laws. In addition to the law part seeming a bit much, I've found that researchers often overemphasize the scale part. If scaling is predictable, then you don't need to do most experiments at very large scale. However, that doesn't seem to stop researchers from starting there. Once you find something good, and you understand how it scales, then you can pour system resources into it. So I originally thought it would encourage research. I find it sad that it seems to have had the opposite effect.
charcircuit 9mo ago
>the acceptance that there are emergent properties which appear out of nowhere is another way of saying our scaling laws don’t actually equip us to know what is coming. Is this actually accepted? Ever since [0], I thought people recognized that they don't appear out of nowhere. [0] https://arxiv.org/pdf/2304.15004 https://arxiv.org/pdf/2304.15004
- red75prime 9mo ago
  "Appear out of nowhere" looks like a straw-man. Anyway, there are newer papers. For example "Emergent Abilities in Large Language Models: A Survey"[0] [0] https://arxiv.org/abs/2503.05788 https://arxiv.org/abs/2503.05788
  - Zigurd 9mo ago
    I was struck by this in the abstract: The scaling of these models, accomplished by increasing the number of parameters and the magnitude of the training datasets, has been linked to various so-called emergent abilities that were previously unobserved. These emergent abilities, ranging from advanced reasoning and in-context learning to coding and problem-solving... In my experience with agent assisted coding, how well it works seems very closely tied to the quantity and quality of training material. It also has some identifiable qualities like verifiability that make it a particularly good target for an LLM. I would not call that surprising or emergent.
- gwern 9mo ago
  > I thought people recognized that they don't appear out of nowhere. I don't think that paper is widely accepted. Have you seen the authors of that paper, or anyone else, use it to successfully predict (rather than postdict) anything?
  - charcircuit 8mo ago
    I haven't paid attention and the paper seems to be arguing against the existence of the phenomenon of emergence behavior and is not related to predicting what is possible with greater scale.
wiz21c 9mo ago
FTA: "One thing is certain, is the less reliable gains from compute makes our purview as computer scientists interesting again. We can now stray from the beaten path of boring, predictable gains from throwing compute at the problem." Isn't Ilya Sutskever who said some months ago that we were going back to research ?
darig 9mo ago
[dead]
officialchicken 9mo ago
Let's not forget about the myriad of basic problems that still remain - like deploying data, caching/distribution, and server resilience. There is absolutely NO reason why that PDF shouldn't load today.
Haaargio 9mo ago
Its not dying slowly right now at all. Compute is a massive driver for everything ML. From number of experiments you can run in paralle, to how much RL you can try out, how long stuff is running etc. ML is pushing scaling on dimensions we haven't had before (number of Datacenters, amount of energy we put into them) and ML is currently seen as the holy grail. But i'm definitly very very curious how this compute and current progress is playing out in the next few years. It could be that we hit a hard ceiling were every single % point becomes tremendesly costly before we hit a % point of benchmark archievements which makes all of that usable daily. OR we will se a significant change to our society. I do not think its something in between tbh because it def feels like in an expoential progress curve we are currently in.
drob518 9mo ago
I suspect scaling will not die a slow death, but rather slow for a while and then all at once. Further, I think we’re at the knee. We know scaling doesn’t work for resolving the fundamental issues we have with large models at this point. If it did, the latest models would have solved the issues. Now, we’re in the acceptance phase. That’s not technical, it’s human psychology. People who made bold claims and huge promises that things would get better if we just spent a few more billion dollars on data centers and GPUs need to unwind those claims and find a way to save face.
zerosizedweasle 9mo ago
https://am.jpmorgan.com/us/en/asset-management/institutional/insights/market-insights/eye-on-the-market/outlook-2026/ https://am.jpmorgan.com/us/en/asset-management/institutional...
- bicepjai 8mo ago
  This is my favorite line in the piece >> a "metaverse moment" for hyperscaler profits after $1.3 trillion of capex and R&D
FuriouslyAdrift 9mo ago
Iron law of efficiency: a system will always expand to use all resources available to it. You want to make an existing system more efficient, then take away resources.
tbrownaw 8mo ago
> A pervasive belief in scaling has resulted in a massive windfall in capital for industry labs and fundamentally reshaped the culture of conducting science in our field. People spend money on this because it works. It seems odd to call observable reality a "pervasive belief". > Academia has been marginalized from meaningfully participating in AI progress and industry labs have stopped publishing. Firstly, I still see news items about new models that are supposed to do more with less. If these are neither from academia nor industry, where are they coming from? Secondly, "has been marginalized"? Really? Nobody's going to be uninterested in getting better results with less compute spend, attempts have just had limited effectiveness. . > However, it is unclear why we need so many additional weights. What is particularly puzzling is that we also observe that we can get rid of most of these weights after we reach the end of training with minimal loss I thought the extra weights were because training takes advantage of high-dimensional bullshit to make the math tractable. And that there's some identifiable point where you have "enough" and more doesn't help. I hadn't heard that anyone had a workable way to remove the extra ones after training, so that's cool. . . The impression I had is that there's a somewhat-fuzzy "correct" number of weights and amount of training for any given architecture and data set / information content. And that when you reach that point is when you stop getting effort-free results by throwing hardware at the problem.
newsoftheday 8mo ago
I read scaling and assumed it would be about scaling but seems to be about AI possibly but didn't read further.
matusp 8mo ago
Scaling works, the problem is that it is practically impossible to scale much. There is only so much energy, text data, GPUs, etc. The folly of scaling is that we are living in a finite world. The huge investments in AI for the past few years are probably hitting the practical limits of scaling for now. I also feel like most insiders were fully aware of this fact, but it was a neat sales pitch.

123 points by sethbannon 9mo ago

octoberfranklin 9mo ago

> Academia has been marginalized from meaningfully participating in AI progress and industry labs have stopped publishing Exactly like semiconductor wafer processing.

random3 9mo ago
If anyone believes they're close to a generalization end-game wrt to AI capabilities, it makes no sense to do anything that could impact their advantage, by enabling others to compete. Collaboration makes sense on timeframes that don't imply zero-sum games. Board games like the Settlers of Catan are good examples of the behavior— concretely the start of the game when everyone trades vs the end of the game when if you suspect someone wins it makes little sense to trade unless, you think it will help you win first.
- SecretDreams 9mo ago
  > Collaboration makes sense on timeframes that don't imply zero-sum games. People are fooling themselves if they think AGI will be zero sum. Even if only one group somehow miraculously develops it, there will immediately be fast followers. And, the more likely scenario is more than one group would independently pull it off - if it's even possible.
  - pixl97 8mo ago
    >if it's even possible. Why do people keep repeating this. The only way artificial intelligence is impossible is if intelligence is impossible. And we're here so that pretty much removes that impediment.
  - random3 9mo ago
    Maybe, but at least Open AI, XAI and any Bostrom believer thinks this is the case. Ilya Sutskever (Sep 20, 2017) > The goal of OpenAI is to make the future good and to avoid an AGI dictatorship. You are concerned that Demis could create an AGI dictatorship. So do we. So it is a bad idea to create a structure where you could become a dictator if you chose to, especially given that we can create some other structure that avoids this possibility. Nick Bostrom - Decisive Strategic Advantage https://www.lesswrong.com/posts/vkjWGJrFWBnzHtxrw/superintelligence-7-decisive-strategic-advantage https://www.lesswrong.com/posts/vkjWGJrFWBnzHtxrw/superintel...
    - refulgentis 9mo ago
      Bostrom / Ilya are agreeing with the GPs argument AFAICT: it's not that AGI can create a dictatorship-of-the-first-AGI-owner, it's that only having one serious funded lab going at it creates a knowledge gap of N years that could give said lab escape velocity* * imagine if Google alone had LLMs. For an innocuous example, the only provider in my LLM client that regularly fails unit tests verifying they actually cache tokens and utilize them on a subsequent request is Gemini. I used to work at Google and it'd be horrible for that too-big-for-its-own-good institution regressing to the corporate mean to own LLMs all by itself
  - 9mo ago

ironbound 9mo ago

It's a mistake to publish papers without a code repo, with the majority of new ML paper's being noise at best.

timy2shoes 9mo ago
This is an opinion piece, similar to her infamous hardware lottery paper. We shouldn't expect a repo on an opinion piece.
rvz 9mo ago
So DeepMind (who almost always releases papers without code repositories) are mistakes then?

deleted 9mo ago

[deleted]

bicepjai 9mo ago

Hooker’s argument lands for me because it ties the technical scaling story to institutional incentives: as progress depends more on massive training runs, it becomes capital-intensive, less reproducible and more secretive; so you get a compute divide and less publication. I’m trying to turn that into something testable with a simple constraint: “one hobbyist GPU, one day.” If meaningful progress is still possible under tight constraints, it supports the idea that we should invest more in efficiency/architecture/data work, not just bigger runs. My favorite line >> Somewhat humorously, the acceptance that there are emergent properties which appear out of nowhere is another way of saying our scaling laws don’t actually equip us to know what is coming. Regarding this paragraph >> 3.3 New algorithmic techniques compensate for compute. Progress over the last few years has been as much due to algorithmic improvements as it has been due to compute. This includes extending pre-training with instruction finetuning to teach models instruction following ..., model distillation using synthetic data from larger more performant "teachers" to train highly capable, smaller "students" ..., chain-of-thought reasoning ..., increased context-length ..., retrieval augmented generation ... and preference training to align models with human feedback ... I would consider algorithmic improvements to be the following 1. architecture like ROPE, MLA 2. efficiency using custom kernels The errors in the paper 1. Transformers for language modeling (Vaswani et al., 2023). => this shd be 2017 Disclosure: my proposed experiments: https://ohgodmodels.xyz/ https://ohgodmodels.xyz/

bigbadfeline 8mo ago
> as progress depends more on massive training runs, it becomes capital-intensive, less reproducible and more secretive; so you get a compute divide and less publication. In the area of AI, secrecy and inability to reproduce/verify can become a huge systemic and social problem, the possible damage is literally unbounded. That's why I like open source AI, including training data and process, it solves the above problem as well as the problem of duplication of effort which leads to a huge waste of resources, waste that is economically significant on national and global scales.

gdiamos 9mo ago

It was an interesting read Sara, thanks for sharing it. I especially agree with your point that scaling laws really killed open research. That's a shame and I personally think we could benefit from more research. I originally didn't like calling them scaling laws. In addition to the law part seeming a bit much, I've found that researchers often overemphasize the scale part. If scaling is predictable, then you don't need to do most experiments at very large scale. However, that doesn't seem to stop researchers from starting there. Once you find something good, and you understand how it scales, then you can pour system resources into it. So I originally thought it would encourage research. I find it sad that it seems to have had the opposite effect.

charcircuit 9mo ago

>the acceptance that there are emergent properties which appear out of nowhere is another way of saying our scaling laws don’t actually equip us to know what is coming. Is this actually accepted? Ever since [0], I thought people recognized that they don't appear out of nowhere. [0] https://arxiv.org/pdf/2304.15004 https://arxiv.org/pdf/2304.15004

red75prime 9mo ago
"Appear out of nowhere" looks like a straw-man. Anyway, there are newer papers. For example "Emergent Abilities in Large Language Models: A Survey"[0] [0] https://arxiv.org/abs/2503.05788 https://arxiv.org/abs/2503.05788
- Zigurd 9mo ago
  I was struck by this in the abstract: The scaling of these models, accomplished by increasing the number of parameters and the magnitude of the training datasets, has been linked to various so-called emergent abilities that were previously unobserved. These emergent abilities, ranging from advanced reasoning and in-context learning to coding and problem-solving... In my experience with agent assisted coding, how well it works seems very closely tied to the quantity and quality of training material. It also has some identifiable qualities like verifiability that make it a particularly good target for an LLM. I would not call that surprising or emergent.
gwern 9mo ago
> I thought people recognized that they don't appear out of nowhere. I don't think that paper is widely accepted. Have you seen the authors of that paper, or anyone else, use it to successfully predict (rather than postdict) anything?
- charcircuit 8mo ago
  I haven't paid attention and the paper seems to be arguing against the existence of the phenomenon of emergence behavior and is not related to predicting what is possible with greater scale.

wiz21c 9mo ago

FTA: "One thing is certain, is the less reliable gains from compute makes our purview as computer scientists interesting again. We can now stray from the beaten path of boring, predictable gains from throwing compute at the problem." Isn't Ilya Sutskever who said some months ago that we were going back to research ?

darig 9mo ago

[dead]

officialchicken 9mo ago

Let's not forget about the myriad of basic problems that still remain - like deploying data, caching/distribution, and server resilience. There is absolutely NO reason why that PDF shouldn't load today.

Haaargio 9mo ago

Its not dying slowly right now at all. Compute is a massive driver for everything ML. From number of experiments you can run in paralle, to how much RL you can try out, how long stuff is running etc. ML is pushing scaling on dimensions we haven't had before (number of Datacenters, amount of energy we put into them) and ML is currently seen as the holy grail. But i'm definitly very very curious how this compute and current progress is playing out in the next few years. It could be that we hit a hard ceiling were every single % point becomes tremendesly costly before we hit a % point of benchmark archievements which makes all of that usable daily. OR we will se a significant change to our society. I do not think its something in between tbh because it def feels like in an expoential progress curve we are currently in.

drob518 9mo ago

I suspect scaling will not die a slow death, but rather slow for a while and then all at once. Further, I think we’re at the knee. We know scaling doesn’t work for resolving the fundamental issues we have with large models at this point. If it did, the latest models would have solved the issues. Now, we’re in the acceptance phase. That’s not technical, it’s human psychology. People who made bold claims and huge promises that things would get better if we just spent a few more billion dollars on data centers and GPUs need to unwind those claims and find a way to save face.

zerosizedweasle 9mo ago

https://am.jpmorgan.com/us/en/asset-management/institutional/insights/market-insights/eye-on-the-market/outlook-2026/ https://am.jpmorgan.com/us/en/asset-management/institutional...

bicepjai 8mo ago
This is my favorite line in the piece >> a "metaverse moment" for hyperscaler profits after $1.3 trillion of capex and R&D

FuriouslyAdrift 9mo ago

Iron law of efficiency: a system will always expand to use all resources available to it. You want to make an existing system more efficient, then take away resources.

tbrownaw 8mo ago

> A pervasive belief in scaling has resulted in a massive windfall in capital for industry labs and fundamentally reshaped the culture of conducting science in our field. People spend money on this because it works. It seems odd to call observable reality a "pervasive belief". > Academia has been marginalized from meaningfully participating in AI progress and industry labs have stopped publishing. Firstly, I still see news items about new models that are supposed to do more with less. If these are neither from academia nor industry, where are they coming from? Secondly, "has been marginalized"? Really? Nobody's going to be uninterested in getting better results with less compute spend, attempts have just had limited effectiveness. . > However, it is unclear why we need so many additional weights. What is particularly puzzling is that we also observe that we can get rid of most of these weights after we reach the end of training with minimal loss I thought the extra weights were because training takes advantage of high-dimensional bullshit to make the math tractable. And that there's some identifiable point where you have "enough" and more doesn't help. I hadn't heard that anyone had a workable way to remove the extra ones after training, so that's cool. . . The impression I had is that there's a somewhat-fuzzy "correct" number of weights and amount of training for any given architecture and data set / information content. And that when you reach that point is when you stop getting effort-free results by throwing hardware at the problem.

newsoftheday 8mo ago

I read scaling and assumed it would be about scaling but seems to be about AI possibly but didn't read further.

matusp 8mo ago

Scaling works, the problem is that it is practically impossible to scale much. There is only so much energy, text data, GPUs, etc. The folly of scaling is that we are living in a finite world. The huge investments in AI for the past few years are probably hitting the practical limits of scaling for now. I also feel like most insiders were fully aware of this fact, but it was a neat sales pitch.