"Locally invalid" was a specific react for highlighting the part of a comment that makes a self-contained mistake, different from "Disagree". A faulty step is not centrally a "weak argument", as it's sometimes not any kind of argument. And discussion often gestures at a claim without providing any sort of evidence or giving any argument, the evidence or the argument is for the recipients to reconstruct for themselves.

Whether this thing in particular is a problem or not doesn't depend on the presence of other things in there, even those that would compensate for it.

I wouldn't know about what works in court, but not saying anything (in interviews or posts on their site and such) is probably even safer, unless the sky is already on fire or something. It seems to be a step in an obviously wrong direction, a friction that gets worse if the things AI company representative would've liked to say happen to be sufficiently contrary to prevailing discourse. Like with COVID-19.

Not make “any materially false or misleading statement” about catastrophic risk from its frontier models, its management of catastrophic risk, or its compliance with its frontier AI framework.

The risk of any statement being considered "materially false or misleading" is an incentive for AI companies to avoid talking about catastrophic risk.

In this framing the crux is whether there is an After at all (at any level of capability). The distinction between "failure doesn't kill the observer" (a perpetual Before) and "failure is successfully avoided" (managing to navigate the After).

There doesn't necessarily need to be algorithmic progress to get there, sufficient bandwidth enables traditional pretraining across multiple sites. But it might be difficult to ensure it's available across the geographically distributed sites on short notice, if you aren't already a well-established hyperscaler building near your older datacenter sites.

In 2028, targeting inference on Rubin Ultra NVL576 (150 TB of HBM in a scale-up world) might want a MoE model with 80 TB of total params (80T params if in FP8, 160T in FP4). If training uses the same precision for gradients, that's also 80 TB of gradients to exchange. If averaged gradients use more precision, this could be 2x-8x more data.

If training is done using 2 GW of some kind of Rubin GPUs, that's about 2e22-3e22 FP4 FLOP/s, and at 30% utilization for 4 months it produces 8e28 FP4 FLOPs. At 120 tokens/param (anchoring to 40 tokens/param for the dense Llama 3 405B and adjusting 3x for 1:8 sparsity), this system might want about 10T active params (so we get 1:16 sparsity, with 160T total FP4 params, or about 1:8 for FP8). This needs 1,200T tokens, maybe 250T unique, which is a problem, but not yet orders of magnitude beyond the pale, so probably something can still be done without needing bigger models.

With large scale-up worlds, processing sequences of 32K tokens with non-CPX Rubin NVL144 at 30% utilization would take just 2.7 seconds (for pretraining). A 2 GW system has 9K racks, so that's a batch of 300M tokens, which is already a lot (Llama 3 405B used 16M token batches in the main phase of pretraining), so that should be the target characteristic time for exchanging gradients.

Moving 80 TB in 2.7 seconds needs 240 Tbps, or 500-2,000 Tbps if averaged gradients use 2x-8x more precision bits (even more if not all-to-all, which is likely with more than 2 sites), and this already loses half of utilization or asks for even larger batches. A DWDM system might transmit 30-70 Tbps over a fiber optic pair, so this is 4-70 fiber optic pairs, which seems in principle feasible to secure for overland fiber cables (which hold hundreds of pairs), especially towards the lower end of the estimate.

Peter Wildeford: I’d expect the 10GW OpenAI cluster becomes operational around 2027-2028.

There are no 10 GW OpenAI clusters, there is a conditional investment by Nvidia for every additional 1 GW in total across all their datacenters, which won't be forming a single training system. Inference and smaller training experiments need a lot of compute, and OpenAI is now building their own even for inference, so most of what they are building is not for frontier model training.

Zvi Mowshowitz: these announcements plus the original cite in Abilene, Texas cover over $400 billion and 7-gigawatts over three years

Depending on OpenAI growth, this is more of a soft upper bound on what gets built. This is evidence that 5 GW training systems likely aren't going to be built by (end of) 2028. So there's going to be a slowdown compared to the trend of 12x in compute (and 6x in power) every 2 years for the largest frontier AI training systems, which held in 2022-2026^[1]. In 2027-2028, the largest training systems are likely going to be merely 2 GW instead of the on-trend 5 GW. Though for the 2024 systems, FP8 is likely relevant, and for 2026 systems maybe even FP4, which turns the 12x in compute every 2 years in 2022-2026 into 24x in compute every 2 years (5x per year in pretraining-relevant raw compute for a single training system).

This 24x every 2 years is even less plausible to remain on-trend in 2028, so 2027+ is going to be the time of scaling slowdown, at least for pretraining. Though the AIs trained on 2026 compute might only come out in 2027-2028, judging by how 2024 training compute is still held back by inference capabilities, and some AIs enabled by 2024 levels of compute might only come out in 2026. So the slowdown in the scale of frontier AI training systems after 2026 might only start being observable in scaling of deployed AIs starting in 2028-2029.

Perhaps some of these sites will be connected with sufficient bandwidth, and training with RLVR at multiple sites doesn't need a lot of bandwidth. Actual plans for larger training runs should urge them to build larger individual sites, as this ensures optionality for unusual training processes. So the fact that this isn't happening suggests that there are no such plans for now (except perhaps for RLVR-like things specifically).

I think the book is saying less that it's obvious that ASI will kill us all but that it is inevitable that ASI will kill us all, and so our only option is to make sure nobody builds it. I do think this is a pretty fair gloss

Crucial caveat that this is conditional on building it soon, rather than preparing to an unprecedented degree first. Probably you are tracking this, but when you say it like that someone without context might take the intended meaning as unconditional inevitable lethality of ASI, which is very different. Our only option is that nobody builds it soon, not that nobody builds it ever, is the claim.

it is trying to argue that future alignment techniques won't be adequate, because the problem is just too hard

This is still future alignment techniques that can become available soon. Reasonable counterarguments to inevitability of ASI-caused extinction or takeover if it's created soon seem to be mostly about AGIs developing meaningfully useful alignment techniques soon enough (and if not soon enough, an ASI Pause of some kind would help, but then AGIs themselves are almost as big of a problem).