I think it’s more that pushing frontier is extremely costly and there is no free lunches in same way as 2024.

Maybe, though that's another kind of progress in itself. Very impressive progress!

I have a pet project I have been working away on for some time that involves building GPU backends for various cards in Zig, lots of complex stuff in it. Lately I mostly use Opus 5, it can pretty reliably plug away at things but it does mess stuff up occasionally. For this codebase, Fable 5.1 was noticeably better at getting things right and doing things in a good reliable way. Of course, I can only use Fable for a bit before I hit the usage cap for the week, so I save it for the tougher things. That said, I absolutely abhor the way recent Anthropic models write prose, especially comments.

I recently tried doing a fairly normal task for this codebase with codex, as I have seen a lot of people talking it up on here. A single task running for ~1-2 hours burned through over half of my usage for the week on the $125/month plan, not on a top model (I don't remember which one specifically I used). It struggled to get the basics done, then got absolutely stuck on a follow up. Handed it over to Claude and it 1-shot it.

You don't need human input. Any coherent input will do the trick.

You can, for example, hook it up to a logging system and have it fix errors as they occur on your platform.

No idea what you are missing and yes, Opus is quite solid, but Fable is clearly way better for me.

I just did a direct comparison, big change in a quite complex codebase. Same prompt for Opus, same for Fable. Fable clearly won and delivered very good results, while Opus delivered mediocre, so I did not let it finish. I expected both to fail and was prepared to do lots of manual steering, but not necessary with Fable one shotting it, and all this with 35$ of credits for fable. I am still impressed. If I would have had to hire a human, it would have cost me thousands of dollar for the same task - and a way longer time. So maybe the valuations are overblown, but they clearly provide value for me.

If Fable doesn't add additional value in your workplace, it means you aren't being ambitious enough in how you integrate agents into your workstream.

Yes, it's probably comparable to 4.8 if you are just using it to write code and put up a couple pull requests. That's not where things are now.

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/2406.07515

> 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...

This is only the beginning, too... Look ahead a year or two.

There's a lot of deluland posts about.

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/

> 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-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.