There is no question LLMs are truly useful in some areas, and the LLM bubble will inevitably burst. Both can be simultaneously true, and we're just running up the big first slope on the hype curve [0].
As we learn more about the capabilities and limits of LLMs, I see no serious arguments scaling up LLMs with increasingly massive data centers and training will actually reach anything like breakthrough to AGI or even anything beyond the magnitude of usefulness already available. Quite the opposite — most experts argue fundamental breakthroughs will be needed in different areas to yield orders-of-magnitude greater utility, nevermind yielding AGI (not that much more refinement won't yield useful results, only that it won't break out).
So one question is timing — When will the crash come?
The next is, how can we collect in an open and preferable independently/distributed/locally-usable way the best usable models to retain access to the tech when the VC-funded data centers shut down?
As we learn more about the capabilities and limits of LLMs, I see no serious arguments scaling up LLMs with increasingly massive data centers and training will actually reach anything like breakthrough to AGI or even anything beyond the magnitude of usefulness already available. Quite the opposite — most experts argue fundamental breakthroughs will be needed in different areas to yield orders-of-magnitude greater utility, nevermind yielding AGI (not that much more refinement won't yield useful results, only that it won't break out).
So one question is timing — When will the crash come?
The next is, how can we collect in an open and preferable independently/distributed/locally-usable way the best usable models to retain access to the tech when the VC-funded data centers shut down?
[0] https://en.wikipedia.org/wiki/Gartner_hype_cycle