> unlike what I am seeing in AI and deep neural networks for decades which that still has not changed with the way on how models continue to get larger and req

> unlike what I am seeing in AI and deep neural networks for decades which that still has not changed with the way on how models continue to get larger and require more planet incinerating power and will currently require millions and in the future, billions. As long as they stay inefficient, Nvidia will continue smiling knowing that. What are you talking about? The inaccessibility of the models and the heavy gatekeeping by the big players is leading to more and more open source models that can run on an average gaming GPU or a Mac M1. It seems like every day on HN there is another post on another super-optimized version of something that was once only accessible to those with huge server farms or cloud budgets. You're seeing announcements like Stanford's Alpaca that specifically mention how it can run on low-budget commodity hardware or a few hundred dollars in the cloud. I'd say efficiency and optimization is one of the hottest areas in large language models right now, although the motivation is less for climate reasons and more for 'I want to do this on my own hardware and have no safety censors' reasons.