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@eestileib @webology @osma @raganwald I am not sure GPU value expires in 2 years, Google has already shown they have 5+ year old TPUs that are still fully utilized, I am not sure if Nvidia's chips can last as long as under load.
Without a doubt in the world of AI there is a lot "financial engineering" going on, which at some point will shake out, ala 2000s web.
@eestileib @webology @osma @raganwald The beauty of those models is that you can run them on own or cloud hardware, so you are not feeding training data to the AI labs and have full control.
They are admittedly a bit behind, but not far enough to be non-competitive or not useful for real work.
@eestileib @webology @osma @raganwald Not really, if you look @ token costs from Chinese models it is massively cheaper than what frontier labs charge, there is also the matter that there is little dedicated inference hardware optimized for inference and inference alone, which will reduce costs significantly.
That being, I do think over time number of engineers needed will only increase not decrease.
@eestileib @webology @osma @raganwald AI business could be profitable if the fees they charged better reflected costs, but right now they (AI companies) are still in price discovery mode and fighting to establish market/mind share.
Profitability is often sacrificed for growth, for example Amazon was unprofitable for 6 years after going IPO, uber 4 years, I am sure there many other similar examples