This is a solved problem in the way Transformers were a solved problem 20 years before the paper came out. The ingredients seem to be out there. But the way no LLMs currently use anything like that in practice says that there is no clear roadmap to when frontier models will get there.
Steven Byrnes appears to read the gap as evidence that current algorithms are far from what the brain is doing, such that much better algorithms must be waiting to be found. His guess is that human-level, human-speed AGI will require not a datacenter but “one consumer gaming GPU,” even for training from scratch.
Yarrow Bouchard on the EA Forum, reads the same gap as evidence that AGI isn’t close at all, precisely because nobody knows how to close it.
Since there is no prototype close enough to the final thing that it only needs some scaling, it remains possible that superintelligence isn’t close at all. Though of course that prototype could be invented at any time, and then scaled to the frontier level within a year, the way RLVR became a central part of training for every frontier model over just the year 2025. I think AGI likely follows even without all that, just from the way LLMs are built now, at some point during their impending scaling from ten trillion params to a quadrillion params (with RLVR as the key ingredient that lets them usefully learn despite sample inefficiency everywhere else). But without that (still missing) breakthrough, it’ll remain a hobbled kind of AGI that’s slow at learning deep skills and so doesn’t quickly lead to a takeoff and superintelligence.
This is a solved problem in the way Transformers were a solved problem 20 years before the paper came out. The ingredients seem to be out there. But the way no LLMs currently use anything like that in practice says that there is no clear roadmap to when frontier models will get there.
Plausibly a lot more params than a gaming GPU can fit are necessary, and even if that wasn’t a problem, the compute of a gaming GPU might take years or decades to train an adult AGI. Also, the way GPUs work, you always want a batch of many AI instances to keep matmuls at a high arithmetic intensity. But that’s hardly timeline-relevant, since the wide availability of datacenter hardware capable of working with quadrillion parameter models is just 3-5 years away.
Since there is no prototype close enough to the final thing that it only needs some scaling, it remains possible that superintelligence isn’t close at all. Though of course that prototype could be invented at any time, and then scaled to the frontier level within a year, the way RLVR became a central part of training for every frontier model over just the year 2025. I think AGI likely follows even without all that, just from the way LLMs are built now, at some point during their impending scaling from ten trillion params to a quadrillion params (with RLVR as the key ingredient that lets them usefully learn despite sample inefficiency everywhere else). But without that (still missing) breakthrough, it’ll remain a hobbled kind of AGI that’s slow at learning deep skills and so doesn’t quickly lead to a takeoff and superintelligence.