That's hyperbolic. I use LLMs daily. They speed up tasks you'd normally use Google for and can extrapolate existing code into other languages. They boost productivity for professionals, but it's not like the discovery of the steam engine or electricity.
> And what limitations are obvious? Tell me? We have not reached any real ceiling yet.
Scaling parameters is the most obvious limitation of the current LLM architecture (transformers). That’s why what should have been called GPT-5 is instead named GPT 4.5, it isn’t significantly better than the previous model despite having far more parameters, a lot more cleaned up training data and optimizations.
The low-hanging fruit has already been picked, and most obvious optimizations have been implemented. As a result, almost all leading LLM companies are now operating at a similar level. There hasn’t been a real breakthrough in over two years. And the last huge architectural breakthrough was in 2017 (with paper "Attention is all you need").
Scaling at this point yields only diminishing returns. So no, what you’re saying isn’t accurate, the ceiling is clearly visible now.