At ICML 2026 in South Korea, DeepMind researcher and co-author of the original Kaplan et al. (2020) scaling-law paper, Sander Dieleman, publicly admitted on X: > "The original scaling laws were wrong due to a bug, which probably led to a lot of wasted compute on oversized undertrained models." This statement exploded in the LLM circle — Kaplan's 2020 paper is the theoretical cornerstone of the entire "bigger is better" route. Where the bug was: Tim Pearce and Jinyeop Song, in their TMLR 2024 paper "Reconciling Kaplan and Chinchilla Scaling Laws" (arXiv:2406.12907), point out: the optimal-size formula Kaplan gave is N_optimal ∝ C^0.73, while Chinchilla (Hoffmann et al. 2022) corrects it to N_optimal ∝ C^0.50. The difference stems from Kaplan counting parameters only as the non-embedding part, and the analysis is done only on small-scale data. The two biases stack, systematically pushing up the optimal model size. Using the Chinchilla protocol + Kaplan's "non-embedding" convention to re-run, it exactly reproduces 0.73 — proving the algorithm isn't wrong, but the denominator was selected wrong. Two years of tuition: Following Kaplan's configuration, from 2020-2022 the entire industry trained a batch of "big but undertrained" models: GPT-3 (175B trained on 300B tokens, ratio 1.7:1), Gopher (280B), PaLM (540B) all severely undertrained. The 175B model according to Chinchilla should have ~3.5T tokens, but actually only ate 1/12. After correction, the industry strategy flipped — "make small, train many": Llama 3 (8B / 15T tokens, tokens/param ratio near 1900:1), DeepSeek V3 (MoE 671B / 14.8T tokens) are products of this line. What it means today: First, the bug isn't a framework error. The mathematical framework of power law + (N, D, C) is still the default tool for foundation-model planning today, only the constants have been fixed. Second, the truly new axes of 2026 — test-time compute (o1/o3-style reasoning), agent deployment scaling (ByteDance EdgeBench's "3-month learning speed doubling") — Kaplan's set simply doesn't cover. Acknowledging the bug adds a footnote to "pretraining scaling has topped out", not equivalent to scaling-law as a whole failing. Karpathy, in his January nanochat miniseries, re-ran with total-params, reproducing clean 0.5/0.5 ratios, saying in person "I'm kind of glad to be able to use simple constants". Calibration: this admission isn't rewriting history, but putting a clean official seal on history. When we talk about scaling law today, the discussion is no longer "how big should the model be", but "how should test-time / agent / post-training, these new axes Kaplan never touched, be budgeted".