Not convinced. That is a very static view. You would think that the output of AI will be better AI, better energy sources and that will make AI way cheaper in the long run...
It will end up a cheap commodity that is basically free to produce.
Over the long run it is absolutely one of the best investments in projections.
Why is no one talking about open source models being burned direct to chip and running inference at 10k-15k a second?
OS models close the gap (via distillation) with frontier models, then get burned to chip, then offer commoditized inference via data farms or local plugins.
With thought loops this fast even if the models are less smart they can be self correcting to level them selves up.
There are some glaring local errors that make this analysis less than trustworthy. For instance, an assumption that corporate income tax applies directly to revenue, or a supposedly generous assumption that GPUs will fully depreciate after 3 years (6-year-old A100s are still in very high demand!). I would love to read a really well thought through investigation of inference costs and how they relate to token pricing, but I have low confidence that this is it.
The analysis seems iffy. As with most industries it's like:
Cost of producing service X
Revenue coming Y
Whether X>Y or not is mostly down to how much competition drives the price down. At the moment prices are down due to an investment fueled land grab but that could change.
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[ 4.1 ms ] story [ 25.5 ms ] threadOS models close the gap (via distillation) with frontier models, then get burned to chip, then offer commoditized inference via data farms or local plugins.
With thought loops this fast even if the models are less smart they can be self correcting to level them selves up.
Cost of producing service X
Revenue coming Y
Whether X>Y or not is mostly down to how much competition drives the price down. At the moment prices are down due to an investment fueled land grab but that could change.