Among the most important ones:
-- the long-known Problem of Transparency, applied to the apparent emergent intelligence in NNs. Why does it happen - in detail?
-- then, a Theory of Apparent Intelligence through NNs. Transforming the results achieved into a Science. Which allows to do what we are doing - but in a lean and targeted way.
-- then, a General Theory of Intelligence, that includes the above to go beyond current architectures and get those features of Intelligence we expect and still not have.
The long-term direction we got into must lead to this.
(You note a ponderant detail of the above when you note the importance of explaining the emergence of a World Model from a Language Model.)
But also harnesses and more generally new insights on "the control flow problem" could end up squeezing a ton of performance out of small models.
Enough stuff can happen, software use itself might change, and that could really cause anything. "What will we do with all the gpus" might become a question if for a magnitude of tech and reasons leaked-opus-9 runs on a macbook m6 or 7
That's low hanging for you?
[1]: "FPGA-based CNN Acceleration using Pattern-Aware Pruning" https://inria.hal.science/hal-04689673/document
That wording screams "Taalas". Which, importantly, is not the only player trying to abate the distance between data and arithmetics...
An important part of the industry is studying that: it is built-up effort. Sooner or later, the fruits will be harvested. The targeted preparation has been there for years now.
This got everyone racing forward and right now there is not enough human attention left in the world to productionize this, or any of the other "side threads". When the race slows down, people will catch up, branch out, and loop back.
Assuming it won't get to full RSI, the current approach will burn out - most likely economically. The race slows down, people branch out, look back, start picking up the "untapped potential"/low-hanging fruits, and you have new S-curves launching in place of the one that just tapered off (hence a fallacy - a stack of S-curves adds up to continuing exponential growth).
In other words: it comes and goes. Hyperconcentrated capital will eventually deconcentrate.