George Cameron: belief

Transcript context

…Cause there are some efficiency questions along the way, but like you can make AI inference useful to that level in a bunch of ways that I can imagine. Right. Yeah. Um, I, I don’t think that’s that nuts. Um, but basically the, the reason we made this slide to answer the question, right. Is to show that the crazy thing is that it is actually true. We’ve had this hundred X to a thousand X decline in the cost of GPT four level intelligence on the left-hand side. And yet on the right-hand side, because the multipliers are so big for the fact that even though. Small models can do GPT four level. Now we still want to use big models and probably bigger than ever models to, um, do frontier level intelligence. We’ve got reasoning models using tokens, and then we’re throwing them in these, them in these agentic workflows where they’re consuming enormous numbers of input tokens and making enormous numbers of output tokens working for a really long time. Those two things taken together, get you back to, we can spend enormously more today than we could a couple of years ago. Yep. I think that’s right. There’s a number of drivers at play and we kind of outline kind of. Six key ones here. Um, but you know, as complex as changing quickly, all of these have changed very dramatically in the last, uh, in the last 12 months. Let’s pick on hardware efficiency since you also have, you also track hardware stuff. And I think the general assertion or the message is that the efficiency from next gen Nvidia chips is actually not 4X. So you have what? 3X or 4X? You have 3X in here and it’s, it’s like 2X maybe, or it’s more of like a. Power story rather than like a share sort of compute tokens efficiency story. But yeah, what, what’s going on in, in hardware. Okay.…

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