The list of major AI models that missed their promised launch windows continues to grow.
Last summer, billionaire Elon Musk, the founder and CEO of AI company xAI, said that Grok 3, xAI’s next big AI model, would arrive by “the end of the year” 2024. Grok, xAI’s answer to models like OpenAI’s GPT-4o and Google Gemini, can analyze images and answer questions, and powers a number of features on X, Musk’s social network.
“Grok 3 at the end of the year after training on 100k H100 should be really something special,” Musk wrote in a July post on X, referring to xAI’s large Memphis-based group of GPUs. “Grok 3 will be a huge step forward,” he said in a follow-up post in mid-December.
However, it’s January 2nd and Grok 3 hasn’t arrived – nor are there any signs that its release is imminent.
In fact, some code on the xAI website, spotted by AI consultant Tibor Blaho, suggests that an intermediate model, “Grok 2.5,” may be deployed first.
Grok(.)com may be coming soon with Grok 2.5 (grok-2-latest – “Our smartest model”) – thanks for the tip, anon! pic.twitter.com/emsvmZyaf7
— Tibor Blaho (@btibor91) December 20, 2024
Granted, this isn’t the first time Musk has set a lofty goal and missed it. It’s well established that Musk’s statements about product launch times are often unrealistic at best.
But the Grok 3’s MIA status is interesting because it’s part of a growing trend.
Last year, AI startup Anthropic failed to deliver a successor to its flagship Claude 3 Opus model. Months after announcing that a next-generation model, the Claude 3.5 Opus, would be released by the end of 2024, Anthropic removed all mention of the model from the developer’s documentation. (According to one report, Anthropic finished training the Claude 3.5 Opus sometime last year, but decided that releasing it didn’t make economic sense.)
Google and OpenAI are said to have also suffered setbacks with their flagship models in recent months.
Blame all of this, in large part, on the limits of current AI scaling laws—the methods companies are using to increase the capabilities of their models. In the not-too-distant past, it was possible to achieve significant performance improvements using training models using massive amounts of computing power and ever-larger datasets. But the benefits have begun to shrink with each generation of models, prompting companies to pursue alternative techniques.
There may be other reasons for Grok 3’s delay. xAI has a much smaller team than many of its rivals, for one. However, the delayed launch timeframe adds to the evidence that conventional AI training methods are hitting a wall.
