I started my career writing software 3D renderers before switching to Direct3D in the later 90s

You can't really do a whole lot of inference in 16ms on consumer hardware. Not to say that inference isn't useful in realtime graphics, DLSS has proven itself well enough, but that's a very small model laser-targetted at one specific problem and even that takes a few milliseconds to do its thing. Fitting behemoth generative models into those time constraints seems like an uphill battle.

I just assumed hallucinated rendering was a stepping stone to training AGIs or something. No one is actually seriously trying to build games that way, are they? Seems horribly inefficient at best, and incoherent at worst.

reminds me of this remark made by Carmack on hidden surface removal https://www.youtube.com/watch?v=P6UKhR0T6cs&t=2315s > "research from the 70s especially, there was tons of work going on on hidden surface removal, these clever different algorithmic ways - today we just kill it with a depth buffer. We just throw megabytes and megabytes of memory and the problem gets solved much much easier." ofcourse "megabytes" of memory was unthinkiable in the 70s. but for us, its unthinkable to have real-time frame inferencing. I cant help but draw the parallels between our current-day "clever algorithmic ways" of drawing pixels to the screen. I definitely agree with the take that in the grand scheme of things, all this pixel rasterizing business will be a transient moment that will be washed away with a much simpler petaflop/exaflop local TPU that runs at 60W under load, and it simply 'dreams' fra…

It is more likely that machine learning models will be used by the game artists for asset generation, but not for rendering those assets at the client side, which would be extremely expensive. But another upcoming use case of ML on the client side is neural texture compression, which somehow needs not just less storage but also less RAM. Though it comes at a computational (frame time) cost on the client side, though not as bad as generative AI. Neural mesh compression could be another potential thing we get in the future. (All lossy compression seems to go in the ML direction: currently there is a lot of work going on with next generation neural audio and video codecs. E.g. https://arxiv.org/abs/2502.20762 )