Compute Verification on Short Timelines

Compute verification might be a very significant part of coordination with respect to intelligent AI systems. In short timelines (<2030), the kind espoused by a frontier lab leader, I'm suspicious of die-level verification hardware being tractable. If this is the case, we should then focus on auxilary verification methods, such as post-hoc modifications to chips or robust software verification methods. In any case, we should act now if we want this to happen. I'm going to be treating Nvidia as the only GPU manufacturer for the sake of simplicity, but at least in my conversations with semiconductor companies they all operate on similar manufacturing timelines (because they're all TSMC!), so this should be representative. Nvidia currently ships Blackwell architecture GPUs, sometimes paired with the Grace CPU, which are B200s, GB200s, and GB300s. Later this year, Rubin GPUs will be released, paired with Vera CPUs. There was around a year between the release of Blackwell chips and the first models trained on Blackwell chips, so we could expect similar for Rubin chips. I'd expect that this gap is slightly shorter for Rubin chips. The generation after Rubin is Feynman, expected to be released at some point in 2028. If we needed hardware verification at Die level, and we had the know-how to do so now, the earliest we could ship a verified chip would be with Feynman chips in 2028 if Nvidia could accomodate changes right now. Die-level verification is at least a few years away. Can we verify compute at higher levels, and how long do modifications at different levels take? My estimates from my own experience and from conversations with semiconductor companies: Die-level changes: >2 years Board Level changes: depends on what sorts of modifications you're making. you could feasibly add an MCU to a board with a few months, but for example changing memory configurations depends heavily on the supply of memory, which is very constrained and inflexible Software/Firmware changes: ca