I didn't ever believe that there was going to be a GPT-5 trained with exponentially more text and resources. Not only is there not enough text, but that's the path to ruin. Why?
Cycle time. Two years ago we had little idea of how those models work so I knew there was a huge room in improving performance. It gets the cost down, it lets you put the models on your device, and it speeds up development. If I can train 10 models in the time it takes you to train 1 model I can make much faster progress.
However even a GPT-15 trained with a Dyson sphere is going to struggle to sort things. (Structurally a pure LLM can't do that!) My #1 beef with Microsoft's Copilot is that you can ask it if it can sort a certain list of items (either a list you are discussing with it or say "states of the United States ordered by percent water area") it will say yes and if you ask it what it thinks the probability is that it will get it in the right order it will say "very high" but when you try it the list comes out totally wrong.
It is equally unable to "help me make an atom bomb" except in the bomb case it will say that it can't but in the sorting case it says it can.
The obvious answer is that it should use tools to sort. That's right but the problem of "knowing what you can really do with your tools" is philosophically challenged. (With problems so intractable it leads people like Roger Penrose to conclude "I couldn't do math if I wasn't a thetan")