Grok 3 was in fact trained on xAI’s Colossus supercomputer at the ~100,000–200,000‑GPU scale (far beyond the ~20–32k‑GPU clusters that previous frontier models used), providing precisely the kind of new high‑compute data point Gavin Baker and Friedberg were talking about. (rdworldonline.com) Multiple analysts explicitly framed Grok 3 as an experiment on scaling laws: Tencent/WallstreetCN’s post‑mortem describes Grok 3’s 100k+ H100 run as showing that pre‑training scaling laws “have not hit a ceiling” and still improve performance, albeit with poor cost‑effectiveness, concluding that “Scaling Law did not hit a wall.” (longbridge.com) Technical explainers and blogs similarly note that Grok 3’s performance gains over Grok 2, given roughly 10x more compute, are largely what standard scaling‑law curves predict—clear improvement but not a dramatic new “generation” jump—indicating continued validity of scaling rather than a breakdown. (uxtigers.com) Ethan Mollick characterized Grok 3 as the first release we know used an order of magnitude more training compute than GPT‑4‑class models and said it would help test whether the first (pre‑training) scaling law still holds; after the results, he wrote that Grok 3 landed right at expectations, with no need to revise the consensus that more compute still buys more capability. (ethanbholland.com) While some online discussions still debate whether we are near a “wall,” the empirical outcome from Grok 3 is broadly read as evidence that scaling laws remain intact at this larger scale rather than having already failed, which is exactly the kind of resolution Friedberg predicted Grok 3 would provide.