I don't think this is true at all. It might feel like this because we are used to a very very fast release cycle but we are only in this topic for a few years.

We have so many ways of optimizing:

- continusly creating more and better training data

- increasing parameters to 20/50/100TB

- We still wait for Mythos access

- We still wait for Mythos distilation (i haven't heard any rumors or so that there is a distilled version of Mythos out)

- Reinforcment learning and evolutionary algortihm only started to appear

- If a small 30GB Model can do stuff, these models can also be used as teachers for the big ones

- We have not seen yet specialized models at all. Like a coding java german expert model. Why? Even with MoE architecture, you still need to have these layers around

- Research for Diffusion and other models is still in progress

- Nvidia just announced/showed a 7x speedup on inferencing for Nemotron

- Multitoken prediction became available just a few weeks ago

- Compute gets only in a range were they can do a lot more and cheaper experiments (see Google IO 2026 announcement)

- World models are showing great progress and we do not know yet what they will bring to the table

- They are probably not finetuning/fixing all areas in parallel. I would argue that Anthropic focuses most of its efforts into coding and agentic. Google for sure does subagent and agentic optimizations too. Plenty of areas are just not touched i would say because they don't have the capacity

- We see more and more mulit modal models (these also consume compute)

- N-Gram paper and co i have not seen all of these things in chinese open models

- We don't even know yet what Meta is doing, but we do know they restarted their efforts again

- Anthropics models got a lot better benchmark wise for dening non sense asks. They do learn how to get rid or reduce hallucinations

- We are in the middle of the biggest Reinforcement loop whith all the training data we give them day to day and its not clear at all if they already use these models in thir training and at what stage.

- We do expect bigger models to be able to comprehend deeper concepts / broader code bases. Big companies with huge code bases probably are waiting for this

- Thre will be also continues progress in harnesses which in it alone is not part of the LLM progress (fair) but these harnesses do get better when you finetune a model to be optimized for a harness

- ChatGPTs Image model 2.0 got relevant better and came out just a month ago

I suspect, based on hardware requirements and progress on hardware infrastructure alone, that the industry wants to go to 100t models and we do not know yet what this will mean. I could see that we might skip normal transformer and find relevant other architectures.

Just a week ago there was a research paper about parallel input and output streams which has not been explored enough.

There was also a research paper were they showed that a LLM can compute things. This will take time to see were this leads to.

I don't think the focus on GRAM and facts is so relevant. Its about context and context handling not just some facts.

Claude Opus 4.8