No but it would be nice if it would turn back in the tool it was. When it was called machine learning like it was for the last decade before the bubble started.
It’s not about hampering proliferation, it’s about breaking the hype bubble. Some of the western AI companies have been pitching to have hundreds of billions in federal dollars devoted to investing in new giant AI models and the gigawatts of power needed to run them. They’ve been pitching a Manhattan Project scale infrastructure build out to facilitate AI, all in the name of national security.
You can only justify that kind of federal intervention if it’s clear there’s no other way. And this story here shows that the existing AI models aren’t operating anywhere near where they could be in terms of efficiency. Before we pour hundreds of billions into giant data center and energy generation, it would behoove us to first extract all the gains we can from increased model efficiency. The big players like OpenAI haven’t even been pushing efficiency hard. They’ve just been vacuuming up ever greater amounts of money to solve the problem the big and stupid way - just build really huge data centers running big inefficient models.
What DeepSeek has done is to eliminate the threat of “exclusive” AI tools - ones that only a handful of mega-corps can dictate terms of use for.
Now you can have a Wikipedia-style AI (or a Wookiepedia AI, for that matter) that’s divorced from the C-levels looking to monopolize sectors of the service economy.
Cutting the cost by 97% will do the opposite of hampering proliferation.
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No but it would be nice if it would turn back in the tool it was. When it was called machine learning like it was for the last decade before the bubble started.
It’s not about hampering proliferation, it’s about breaking the hype bubble. Some of the western AI companies have been pitching to have hundreds of billions in federal dollars devoted to investing in new giant AI models and the gigawatts of power needed to run them. They’ve been pitching a Manhattan Project scale infrastructure build out to facilitate AI, all in the name of national security.
You can only justify that kind of federal intervention if it’s clear there’s no other way. And this story here shows that the existing AI models aren’t operating anywhere near where they could be in terms of efficiency. Before we pour hundreds of billions into giant data center and energy generation, it would behoove us to first extract all the gains we can from increased model efficiency. The big players like OpenAI haven’t even been pushing efficiency hard. They’ve just been vacuuming up ever greater amounts of money to solve the problem the big and stupid way - just build really huge data centers running big inefficient models.
What DeepSeek has done is to eliminate the threat of “exclusive” AI tools - ones that only a handful of mega-corps can dictate terms of use for.
Now you can have a Wikipedia-style AI (or a Wookiepedia AI, for that matter) that’s divorced from the C-levels looking to monopolize sectors of the service economy.
It’s been known for months that they were living on borrowed time: Google “We Have No Moat, And Neither Does OpenAI” Leaked Internal Google Document Claims Open Source AI Will Outcompete Google and OpenAI