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The models have not plateaued, and they are not even mildly close to any sort of ceiling. Right now the barrier is data and compute. Quality data can be creat

by nullbio 23d ago

The models have not plateaued, and they are not even mildly close to any sort of ceiling. Right now the barrier is data and compute. Quality data can be created synthetically at an exponential rate as models improve. Humans are actively feeding them with private IP. Compute advancements will begin to skyrocket as we unlock photonic computing and materials science advancements and scale up chip fabs. This is also compounding because the AI is accelerating the pace of research, testing, development, manufacturing, etc. It's a big self-accelerating feedback loop. There is no plateau.

konmok 23d ago
> Quality data can be created synthetically at an exponential rate as models improve No it can't? Every time the labs try this we see model collapse, e.g. shoving goblins into every conversation. And I have seen zero evidence that AI is accelerating materials science in any meaningful way, let alone photonic computing.
- f4dd 23d ago
  There's a lot of deluland posts about.
  - nullbio 22d ago
    You're not well informed. Helps to keep an open mind if you want to keep up to date.
- nullbio 22d ago
  > Every time the labs try this we see model collapse The latest studies demonstrate model collapse is not a given and synthetic data can be used just fine. The latest models are proof of that, they're all trained on large swathes of synthetic data. It can't be used as the -only- data source of course, but that's not how it is being used. This is an obvious conclusion, too, because there's no difference between synthetic data and the data people can create, the difference is whether that data is revealing new information about the thing the model is trying to learn. If the synthetic data is just teaching the model the same thing over and over again it results in overfitting, so it needs to be done intelligently. For example, if I have an example of a puzzle, I can generalize that example and create thousands of synthetic data examples, with different rotations/perspectives, rather than having to find the data naturally. It's not that the models are just generating data out of thin air, they're generating the synthetic data on top of real world data. The smarter the models get, the better they are at generating quality synthetic variations and finding valid synthetic variations. > And I have seen zero evidence that AI is accelerating materials science in any meaningful way, let alone photonic computing. It is accelerating how quickly researchers and engineers can do their jobs. https://news.mit.edu/2026/ai-helps-design-new-materials-that-work-in-real-world-0826 https://news.mit.edu/2026/ai-helps-design-new-materials-that... This is only the beginning, too... Look ahead a year or two.
  - konmok 22d ago
    > The latest studies demonstrate model collapse is not a given Which studies? [edit: I'll assume you mean these two given by @dorolow: https://arxiv.org/abs/2404.01413 https://arxiv.org/abs/2404.01413 https://arxiv.org/abs/2406.07515 https://arxiv.org/abs/2406.07515] > It can't be used as the -only- data source of course, but that's not how it is being used Right, so human data creation would also have to scale up exponentially, and that's not gonna happen. > because there's no difference between synthetic data and the data people can create I mean, that's obviously false, otherwise model collapse wouldn't exist. The difference is statistical, but it's there. > It is accelerating how quickly researchers and engineers can do their jobs. > https://news.mit.edu/2026/ai-helps-design-new-materials-that https://news.mit.edu/2026/ai-helps-design-new-materials-that... That's pretty clearly a hype article, the headline even says "The CrysVCD tool developed at MIT COULD cut the huge amounts of time and money spent". I'm asking for empirical measurements of timelines, not hypotheticals. > This is only the beginning, too... Look ahead a year or two. Lol that excuse is getting really old
    - nullbio 22d ago
      > Right, so human data creation would also have to scale up exponentially, and that's not gonna happen. It doesn't need to. We're not even close to exhausting the useful synthetic data within the human data we have, let alone all of the new data that is being created. > I mean, that's obviously false, otherwise model collapse wouldn't exist. The difference is statistical, but it's there. It's not. It's just bytes of information. A machine and a human can write the same bytes (and often do). Like I already said, model collapse happens when you are overfitting on data without useful, fresh training signals. That's the key difference between the data. The data itself isn't in some way "special", some unique configuration of bytes that imbues special powers, it's that the useful information in it has already been exhausted by the model. You can get the same phenomena by having a poor distribution of human training samples as well. I think you're confusing LLM generated data with synthetic data. Synthetic data doesn't need to be created by an LLM, although an LLM can assist in the creation. Wiki: > In early model collapse, the model begins losing information about the tails of the distribution – mostly affecting minority data. Later work highlighted that early model collapse is hard to notice, since overall performance may appear to improve, while the model loses performance on minority data.[11] In late model collapse, the model loses a significant proportion of its performance, confusing concepts and losing most of its variance.[10][12][13] As models retrain on outputs sampled disproportionately from the higher-probability center of the distribution, rare words and uncommon syntactic constructions are among the first features to disappear.[25] Statistical analysis of recursive next-token prediction training has shown that, when language models are trained recursively on synthetic data, the learned conditional distributions concentrate probability mass on a small subset of highly predictable continuations (a phenomenon characterized as "total collapse") > That's pretty clearly a hype article It was just the first article I saw on a quick google search, there are thousands of these stories. It's easy to dismiss anything that doesn't align with your worldview as hype, but you're the one lacking evidence now. > I'm asking for empirical measurements of timelines, not hypotheticals. Go and find it then? You haven't bothered looking. > Lol that excuse is getting really old You're doing the same thing people have been doing for years, comparing this very second in time and failing to extrapolate. HackerNews was full of developers who said that AI would never be useful for programming, it can't do x, y, z. Now these same people don't write code by hand anymore and haven't looked at their codebases in months. You had people in mathematics saying the same thing, now you have Terrence Tao posting articles about how AI is stealing their job. You had artists, designers and photographers saying the same thing, now they can't tell the difference between something human created or AI created.
- dorolow 22d ago
  We use large amounts of synthetic data for training at work and have not observed any sort of model collapse when done properly. Edit: https://arxiv.org/abs/2404.01413 https://arxiv.org/abs/2404.01413 https://arxiv.org/abs/2406.07515 https://arxiv.org/abs/2406.07515
- octoberfranklin 22d ago
  Goblins?
ranger_danger 22d ago
Can you provide sources for these claims?
- nullbio 22d ago
  What claim do you have a problem with? There are plenty of research papers on synthetic data that show its value, do a search on arxiv for "synthetic data". There are plenty of open-source post-training pipelines that incorporate synthetic data. As for the claim about accelerating the progress of hardware or materials science, I've seen quite a number of news articles from teams at universities using AI in their work with high quality outcomes, and they're becoming more frequent. https://openai.com/index/jalapeno-first-results/ https://openai.com/index/jalapeno-first-results/ > We used AI to design the chip, and designed the chip so AI could program it AI played a direct role in Jalapeño’s development, enabling the team to move from initial design to tapeout in nine months by exploring implementations, shortening design, measurement, and verification loops, and continuously iterating on model workloads. AI also helped optimize the chip’s arithmetic circuits, allowing the team to fit more compute performance into the chip on schedule. https://www.anl.gov/article/scientists-deploy-ai-agents-to-accelerate-discovery-of-new-materials https://www.anl.gov/article/scientists-deploy-ai-agents-to-a... > An AI-driven system automates a powerful simulation method used to discover new materials. The system can potentially reduce discovery time from months or years to just days.
  - visarga 22d ago
    It's not even synthetic data as such - often it is environments. So the models create their own data solving tasks in generated environments. I am making one such environment for computer use agents, 600 tasks, each of them a mini app.
Tanjreeve 22d ago
> Right now the barrier is data and compute. Those are pretty significant barriers seeing as we're closed to/have exhausted all the data on the internet and most of those compute bottlenecks are a castle of sand of dodgy finance deals that are getting blocked by community action. You say "synthetic data" but that's still vaporware right now in terms of being useful for model training. The good synthetic data uses are still grounded in real data and it's a coin flip on if it works well or not.
BoredomIsFun 22d ago
> The models have not plateaued, and they are not even mildly close to any sort of ceiling. Depends on defnition of "plateaued" and "ceiling". I am not impressed with 2026 consumer models at all. > This is also compounding because the AI is accelerating the pace of research, testing, development, manufacturing, etc. Yet it does accelerate - so is does Twitter. But does it to any substantial degree, esp. in AI theory? All the modern LLMs are the same old tired 2017 paper.

by nullbio 23d ago

konmok 23d ago

> Quality data can be created synthetically at an exponential rate as models improve No it can't? Every time the labs try this we see model collapse, e.g. shoving goblins into every conversation. And I have seen zero evidence that AI is accelerating materials science in any meaningful way, let alone photonic computing.

f4dd 23d ago
There's a lot of deluland posts about.
- nullbio 22d ago
  You're not well informed. Helps to keep an open mind if you want to keep up to date.
nullbio 22d ago
> Every time the labs try this we see model collapse The latest studies demonstrate model collapse is not a given and synthetic data can be used just fine. The latest models are proof of that, they're all trained on large swathes of synthetic data. It can't be used as the -only- data source of course, but that's not how it is being used. This is an obvious conclusion, too, because there's no difference between synthetic data and the data people can create, the difference is whether that data is revealing new information about the thing the model is trying to learn. If the synthetic data is just teaching the model the same thing over and over again it results in overfitting, so it needs to be done intelligently. For example, if I have an example of a puzzle, I can generalize that example and create thousands of synthetic data examples, with different rotations/perspectives, rather than having to find the data naturally. It's not that the models are just generating data out of thin air, they're generating the synthetic data on top of real world data. The smarter the models get, the better they are at generating quality synthetic variations and finding valid synthetic variations. > And I have seen zero evidence that AI is accelerating materials science in any meaningful way, let alone photonic computing. It is accelerating how quickly researchers and engineers can do their jobs. https://news.mit.edu/2026/ai-helps-design-new-materials-that-work-in-real-world-0826 https://news.mit.edu/2026/ai-helps-design-new-materials-that... This is only the beginning, too... Look ahead a year or two.
- konmok 22d ago
  > The latest studies demonstrate model collapse is not a given Which studies? [edit: I'll assume you mean these two given by @dorolow: https://arxiv.org/abs/2404.01413 https://arxiv.org/abs/2404.01413 https://arxiv.org/abs/2406.07515 https://arxiv.org/abs/2406.07515] > It can't be used as the -only- data source of course, but that's not how it is being used Right, so human data creation would also have to scale up exponentially, and that's not gonna happen. > because there's no difference between synthetic data and the data people can create I mean, that's obviously false, otherwise model collapse wouldn't exist. The difference is statistical, but it's there. > It is accelerating how quickly researchers and engineers can do their jobs. > https://news.mit.edu/2026/ai-helps-design-new-materials-that https://news.mit.edu/2026/ai-helps-design-new-materials-that... That's pretty clearly a hype article, the headline even says "The CrysVCD tool developed at MIT COULD cut the huge amounts of time and money spent". I'm asking for empirical measurements of timelines, not hypotheticals. > This is only the beginning, too... Look ahead a year or two. Lol that excuse is getting really old
  - nullbio 22d ago
    > Right, so human data creation would also have to scale up exponentially, and that's not gonna happen. It doesn't need to. We're not even close to exhausting the useful synthetic data within the human data we have, let alone all of the new data that is being created. > I mean, that's obviously false, otherwise model collapse wouldn't exist. The difference is statistical, but it's there. It's not. It's just bytes of information. A machine and a human can write the same bytes (and often do). Like I already said, model collapse happens when you are overfitting on data without useful, fresh training signals. That's the key difference between the data. The data itself isn't in some way "special", some unique configuration of bytes that imbues special powers, it's that the useful information in it has already been exhausted by the model. You can get the same phenomena by having a poor distribution of human training samples as well. I think you're confusing LLM generated data with synthetic data. Synthetic data doesn't need to be created by an LLM, although an LLM can assist in the creation. Wiki: > In early model collapse, the model begins losing information about the tails of the distribution – mostly affecting minority data. Later work highlighted that early model collapse is hard to notice, since overall performance may appear to improve, while the model loses performance on minority data.[11] In late model collapse, the model loses a significant proportion of its performance, confusing concepts and losing most of its variance.[10][12][13] As models retrain on outputs sampled disproportionately from the higher-probability center of the distribution, rare words and uncommon syntactic constructions are among the first features to disappear.[25] Statistical analysis of recursive next-token prediction training has shown that, when language models are trained recursively on synthetic data, the learned conditional distributions concentrate probability mass on a small subset of highly predictable continuations (a phenomenon characterized as "total collapse") > That's pretty clearly a hype article It was just the first article I saw on a quick google search, there are thousands of these stories. It's easy to dismiss anything that doesn't align with your worldview as hype, but you're the one lacking evidence now. > I'm asking for empirical measurements of timelines, not hypotheticals. Go and find it then? You haven't bothered looking. > Lol that excuse is getting really old You're doing the same thing people have been doing for years, comparing this very second in time and failing to extrapolate. HackerNews was full of developers who said that AI would never be useful for programming, it can't do x, y, z. Now these same people don't write code by hand anymore and haven't looked at their codebases in months. You had people in mathematics saying the same thing, now you have Terrence Tao posting articles about how AI is stealing their job. You had artists, designers and photographers saying the same thing, now they can't tell the difference between something human created or AI created.
dorolow 22d ago
We use large amounts of synthetic data for training at work and have not observed any sort of model collapse when done properly. Edit: https://arxiv.org/abs/2404.01413 https://arxiv.org/abs/2404.01413 https://arxiv.org/abs/2406.07515 https://arxiv.org/abs/2406.07515
octoberfranklin 22d ago
Goblins?

ranger_danger 22d ago

Can you provide sources for these claims?

nullbio 22d ago
What claim do you have a problem with? There are plenty of research papers on synthetic data that show its value, do a search on arxiv for "synthetic data". There are plenty of open-source post-training pipelines that incorporate synthetic data. As for the claim about accelerating the progress of hardware or materials science, I've seen quite a number of news articles from teams at universities using AI in their work with high quality outcomes, and they're becoming more frequent. https://openai.com/index/jalapeno-first-results/ https://openai.com/index/jalapeno-first-results/ > We used AI to design the chip, and designed the chip so AI could program it AI played a direct role in Jalapeño’s development, enabling the team to move from initial design to tapeout in nine months by exploring implementations, shortening design, measurement, and verification loops, and continuously iterating on model workloads. AI also helped optimize the chip’s arithmetic circuits, allowing the team to fit more compute performance into the chip on schedule. https://www.anl.gov/article/scientists-deploy-ai-agents-to-accelerate-discovery-of-new-materials https://www.anl.gov/article/scientists-deploy-ai-agents-to-a... > An AI-driven system automates a powerful simulation method used to discover new materials. The system can potentially reduce discovery time from months or years to just days.
- visarga 22d ago
  It's not even synthetic data as such - often it is environments. So the models create their own data solving tasks in generated environments. I am making one such environment for computer use agents, 600 tasks, each of them a mini app.

Tanjreeve 22d ago

> Right now the barrier is data and compute. Those are pretty significant barriers seeing as we're closed to/have exhausted all the data on the internet and most of those compute bottlenecks are a castle of sand of dodgy finance deals that are getting blocked by community action. You say "synthetic data" but that's still vaporware right now in terms of being useful for model training. The good synthetic data uses are still grounded in real data and it's a coin flip on if it works well or not.

BoredomIsFun 22d ago

> The models have not plateaued, and they are not even mildly close to any sort of ceiling. Depends on defnition of "plateaued" and "ceiling". I am not impressed with 2026 consumer models at all. > This is also compounding because the AI is accelerating the pace of research, testing, development, manufacturing, etc. Yet it does accelerate - so is does Twitter. But does it to any substantial degree, esp. in AI theory? All the modern LLMs are the same old tired 2017 paper.