Pure inference would have to compete with other service providers and even local hardware that becomes capable of running models for a price if hardware demand by openAI and Anthropic dwindles.

That is a very commodify market and as such I expect power cost per query ends up setting the pricing.

I certainly won't be. The US Government that allocates my tax dollars might though.

I expect them to sell everything if they go bust to recoup what little they can for investors. That would double whack hardware manufacturers are their final consumers are now both leaving the markets as buyers and entering as competitors.

Well, but to who? And for pennies on the dollar, most likely. Also not all ram is made equal. HBM for AI cards isn't LPDDR5 in iPhones.

The catchup models are all basically distillations of the sota models. Without the next training cycle things are going to stagnate. And as all those companies you mentioned are all linked to OpenAI and Anthropic and relying on hosting deals and things they are all going to be in the ringer when things to go south.

So on the one hand you have all the sota model makers doing investor expectation management in saying wet need a slowdown for safety (may or may not be true, but also means they don’t spend on the next training cycle before IPO? Could be making their books look better too?) and on the other we have a sense that the models aren’t yet at the stopping place where we can just not train another cycle and use distillations of the current generation?

Also gpu speedup. If vera Rubin is 7x faster then to serve same number of tokens you need 1/7 the memory. Did I get that right?

Only last year Microsoft admitted to having GPUs in inventory that they couldn't power, due to a lack of electricity. So much of this production capacity could very well go into making memory chips for GPUs sitting in a warehouse.

The amount of money spend on the AI hype train is crazy. Meanwhile we're wasting fab time making chips that might never be powered on. It's absolutely insane that there are more money to be made on hardware for AI that may never be used, rather than producing a product that consumers and businesses need right now.