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AI output costs plummet 47% quarterly as price war intensifies

Researcher questions economics of AI spending

1 Sep 21 5:27 PM · 2d ago · 2 posts · 2 sources · development 1 of 2

Ross David Harthorn, with a background in neural networks since the mid-1990s, argues that unlike conventional software, neural networks have no point of saturation—they can always use more hardware, training data, and epochs. He suggests this structural incentive to buy ever more compute (combined with the ambition of reaching AGI rather than solving specific tasks) may explain why so much money flows into AI regardless of practical performance gains.

Researcher questions economics of AI spending
epoch.ai

“Neural networks, are not like that. They never say 'no thanks, I'm full'. You can always add more neurons, more hidden layers, more epochs of training, or more data to train on.”

Ross David Harthorn, Neural networks researcher · hn ↗

Ross David Harthorn Neural networks researcherEpoch Research organizationOpenAI AI model developer

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1Researcher questions economics of AI…2Epoch reports AI output costs fell 47%…
Sep 18Sep 19Sep 20Sep 21Sep 22yesterdaynow · 2:09 AM ET

Reported in the same hours no headline names this development itself — these 1 claim were published in its stretch

  1. 1 outlet AI Memory Costs Continue to Explode — And It’s About to Get a Lot Worse

    first by Yahoo Finance, 5d ago

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