It depends if the goal is a more accurate simulation, or if the goal is a tool for creatives to build games or content.

If it's the latter, I don't see a 'hole'.

My point was that there is also the possibility of AI intelligence improving to the point that there isn't a difference anymore. That whatever beats the next set of benchmarks (which of course won't be the simulator or world model) is so smart it is both a better solution to the problem the world simulator poses and a better world simulator all in one and a better tool for creatives to build games or content.

This has already happened to other ML fields. For example, there's not much point anymore to ASR models (just feed audio into LLMs) and the space that TTS models occupy is fast disappearing. Oh and it's not just that Qwen 3.6 is a good ASR in addition to being good at coding mario. Doing things this way, not using an ASR model is fundamentally more expressive. A whole bunch of companies started up on ASR + TTS "towers" (lots of models, Pipecat style) as they're called, and they're dead companies walking. Their tech has been surpassed by just bog-standard generic LLMs in a way that they will never get the upper hand again.

A chain of models approach, a separate ASR, will never do this: "you said you agree but I can hear you're sarcastic, so I'll explain again". Also getting a conversational tower to switch languages when the user does that (or when it makes sense in a language course) is not quite impossible but very, very hard. And yet feeding audio directly into Qwen will almost magically solve these problems better than I will ever do it (lightly finetuned). It's fundamentally better in every way (except perhaps speed, of course. Although arguably such a tower needs an LLM too, it'll just have way less tokens)

This point has long been passed for image recognition. Feeding a photo or screenshot into a full LLM along with a request on what to do beats the crap out of any OCR, image recognition, ... model every time these days.

And now there's also hints of world models just being bypassed. Just giving a finetuned model the last 50 video frames (surprisingly, that fits in context, especially at reduced resolution), movement statistics, motor power, status, ... of a robot and asking what to do actually works (with heavy finetuning, but I think it'll start working without). Well ... AND then distilling that knowledge into a much smaller model because B200 racks are more than a bit to heavy for a logistics robot to carry around. But we'll get over that.