nice writeup, pointing out the tension between quick scaling and safety. I think a bigger problem with this is more simple: there's a pretty critical assumptio

nice writeup, pointing out the tension between quick scaling and safety. I think a bigger problem with this is more simple: there's a pretty critical assumption that the GPT-6 and GPT-7 models will need orders of magnitude more compute. On the one hand, that was true for GPT2 > 3 > 4. On the other hand, history is rife with fallacious predictions about technology development and resource usage, all the way back to Malthus. Typically, as the human race focuses on a problem, whether that be farm efficiency or AI or anything else, people invent new approaches that invalidate simplistic forecasting about resource usage. In this context specifically, I think there's a lot of promising research with different types of model architectures and multimodal models, to the extent that AGI may not be a path paved through brute forcing transformer training for LLMs. In fact, I believe Sam Altman said this himself last year!!! Also, from a pure business perspective: it's one thing to spend $7T and build a bunch of factories. It's quite another thing to beat Jensen at that game, who has a legendary history of beating the odds when his company was headed for failure by constant process innovation and strategic moves -- this podcast episode saga is a great listen: https://www.acquired.fm/episodes/nvidia-the-gpu-company-1993-2006 https://www.acquired.fm/episodes/nvidia-the-gpu-company-1993... I think Sam is thinking too much like a VC here -- raise as much as you can and take your % management fees -- and I would not be eager to invest so much into brute forcing a hardware business at scale. I'd much rather invest that into NVIDIA or AMD, who have the battle scars to know how to build hardware profitably.