Grok 3: Another win for the bitter lesson

The creation of a model which is "co-state-of-the-art" (assuming it wasn't trained on the benchmarks directly) is not a win for scaling laws. I could just as easily claim out that xAI's failure to significantly outperform existing models despite "throwing more compute at Grok 3 than even OpenAI could" is further evidence that hyper-scaling is a dead end which will only yield incremental improvements.

Obviously more computing power makes the computer better. That is a completely banal observation. The rest of this 2000-word article is groping around for a way to take an insight based on the difference between '70s symbolic AI and the neural networks of the 2010s and apply it to the difference between GPT-4 and Grok 3 off the back of a single set of benchmarks. It's a bad article.

> The creation of a model which is "co-state-of-the-art" (assuming it wasn't trained on the benchmarks directly) is not a win for scaling laws.

Just based on the comparisons linked in the article, it's not "co-state-of-the-art", it's the clear leader. You might argue those numbers are wrong or not representative, but you can't accept them then claim it's not outperforming existing models.