There has always been pressure to do so, but there are fundamental bottlenecks in performance when it comes to model size.

What I can think of is that there may be a push toward training for exclusively search-based rewards so that the model isn't required to compress a large proportion of the internet into their weights. But this is likely to be much slower and come with initial performance costs that frontier model developers will not want to incur.

Of course and then watch those companies reined in.

That was why OpenAI went after the wafers, not the finished products. By buying up the supply of the raw materials they bottleneck everybody, even unrelated fields. It's the kind of move that requires a true asshole to pull off, knowing it will give your company an advantage but screw up life for literally billions of people at the same time.

TPUs use HBM, which are impacted.

Still susceptible, TPUs need DRAM dies just as much as anything else that needs to process data. I think they use some form of HBM, so they basically have to compete alongside the DDR supply chain.

Even their TPU based systems need RAM.

large language models are large and must be loaded into memory to train or to use for inference if we want to keep them fast. older models like gpt3 have around 175 billion parameters. at float32s that comes out to something like 700GB of memory. newer models are even larger. and openai wants to run them as consumer web services.

Micron has said they're not scaling up production. Presumably they're afraid of being left holding the bag when the bubble does pop

Chip factories need years of lead time, and manufacturers might be hesitant to take on new debt in a massive bubble that might pop before they ever see any returns.

Memory fabs take billions of dollars and years to build, also the memory business is a tough one where losses are common, so no such relief in sight.

With a bit of luck OpenAI collapses under its own weight sooner than later, otherwise we're screwed for several years.