New AI models are losing their edge almost immediately |
In today’s AI race, breakthroughs are no longer measured in years—or even months—but in weeks.
The release of Opus 4.6 just over two weeks ago was a major moment for its maker, Anthropic, delivering state-of-the-art performance in a number of fields. But within a week, Chinese competitor Z.ai had released its own Opus-like model, GLM-5. (There’s no suggestion that GLM-5 uses or borrows from Opus in any way.) Many on social media called it a cut-price Opus alternative.
But Z.ai’s lead didn’t last long, either. Just as Anthropic had been undercut by GLM-5’s release, GLM-5 was quickly downloaded, compressed, and re-released in a version that could run locally without internet access.
Allegations have flown about the ways AI companies can match, then surpass, the performance of their competitors—particularly how Chinese AI firms can release models rivaling American ones within days or weeks. Google has long complained about the risks of distillation, where companies pepper models with prompts designed to extract internal reasoning patterns and logic by generating massive response datasets, which are then used to train cheaper clone models. One actor allegedly prompted Google’s Gemini AI model more than 100,000 times to try and unlock the secrets of what makes the model work so powerfully.
“I do think the moat is shrinking,” says Shayne Longpre, a PhD candidate at the Massachusetts Institute of Technology whose research focuses on AI policy.
The shift is happening both in the speed of releases and the nature of the improvements. Longpre argues that the frontier gap between the best closed models and open-weight alternatives is decreasing drastically. “The gap between that and fully open-source or open-weight models is about three to six months,” he explains, pointing to research from the nonprofit research organization Epoch AI tracking model development.
The reason for that dwindling gap is that much of the progress now arrives after a model ships. Longpre describes companies “doing different reinforcement learning or fine tuning of those systems, or giving them more test time reasoning, or enabling to have longer context windows”—all of which make the adaptation period much shorter, “rather than having to pre-train a new model from scratch,” he says.
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