Perhaps not in general, but if the barrier of entry is radically lowered - such as if a new technique that drastically lowers the compute required for training is discovered - then Nvidia and a whole host of startups will lose lot of value. A lot of the positions assumes LLMs will remain SotA in the foreseeable future, which is not a bet I'd take.
Even if that doesn't happen, there will be a lot of consolidation and bankruptcies when AI funding dries up when category winners become clear and investors cur their losses - the same happened suddenly when the dot-com bubble burst, and little more gently with "web 2.0" startups
If the barrier to entry is lowered I don't see Nvidia losing a dime... Jevons paradox tells us that further massive amounts of training will occur in multi-modality and grounding models to reality. That would then kick off the race to true reasoning and AGI/ASI, and if achieved, all bets are off after that point.
If no training breakthrough occurs, then your second option is much more likely.
> Jevons paradox tells us that further massive amounts of training will occur in multi-modality and grounding models to reality
I was thinking along the lines of how Sun Microsystems and big-iron were dethroned by good-enough commodity x86 servers + Linux. The two coexisted... for a short while.
All while x86 morphed to x64 and gained back many of the features of big iron.
Much of the problem with PC software at the time was one of scaling. Getting software to use multiple processors effectively took decades, but the cost difference and ability to actually get the hardware cheap allowed it to win the market... then grow back into systems with hundreds of cores and terabytes of memory because the needs for big iron didn't go away.
With AI/training/LLMs/NNs scale, at least appears, intrinsic. We see this in the animal world. We see insects with a very basic brain capable of interacting with the world and surviving. Then we see mammals with larger brains capable of far more as the scale/complexity/interconnectivity of their brains increases. At least from what we can tell, throwing more power at any given problem will give us a better solution.
Even if that doesn't happen, there will be a lot of consolidation and bankruptcies when AI funding dries up when category winners become clear and investors cur their losses - the same happened suddenly when the dot-com bubble burst, and little more gently with "web 2.0" startups