要理解Anthropic所称其系统所做的事情，不妨想象你正在翻阅一个包含数百万个DNA序列的图书馆，这些序列随着科学家对活体世界的测序工作不断深入而逐渐积累。迈向突破的一步可能是发现一段编码有趣酶的奇特序列，或许如此。随后你需要弄清楚该酶的功能，并最终找到操纵它以发挥有用作用的方法。

Anthropic表示，其由950个智能体组成的系统在运行21小时后发现的并非全新的序列。这些智能体标记的是围绕已知酶的一个重复模式，Anthropic称这一特定模式此前从未被编目记录过。但如果你阅读Anthropic的公告，其中将该模式描述为“令人联想到”导致基因编辑技术CRISPR的发现，而该技术“已经彻底改变了科学和医学”，那么听起来这支智能体大军确实发现了值得注意的成果。

这些说法激怒了一些生物学家。一位生物学家发布的病毒式传播帖子随后得到了制药公司礼来（Eli Lilly）董事长兼首席执行官的背书，该帖子称：“发现奇怪的基因簇和重复序列往往是容易的部分。困难的部分，以及真正发现的来源，在于弄清楚该系统实际是做什么的。”换言之，这些智能体仅协助完成了一些实验室里的繁琐工作，但这并非一项科学发现。

这提醒我们，即使AI完成了令人印象深刻的任务——例如在海量生物数据中发现一个仅凭人眼难以察觉的模式——其结果本身可能并不构成科学的突破。对AI而言新颖的事物，对生物学家来说可能是常规、不足为奇，或者根本没那么重要。

进一步使问题复杂化的是，《纽约时报》报道，哥本哈根大学的生物学家Mario Rodríguez Mestre在周末表示，他的团队已经发现了这一特定模式。Mestre在工作中经常与Claude聊天，他怀疑Anthropic的团队是否从他的对话中汲取了经验。Anthropic否认了这一点，但Mestre表示无论如何他都将停止使用Claude。

这里的问题部分在于，AI公司并未仅仅将其系统呈现为科学家可以使用的工具，如显微镜或超级计算机。它们坚持认为AI系统本身正在做出发现。对一些人来说，这种方法与科学实际运作的方式不相容，因为新知识通常是通过协作和不断扩充的工具库涌现出来的。

To understand what Anthropic says its system did, imagine you’re flipping through a library of millions of DNA sequences, amassed as scientists sequence more and more of the living world. One step toward a breakthrough might be finding a peculiar sequence that encodes an interesting enzyme, perhaps. Then you’d need to figure out what that enzyme does and, eventually, how to manipulate it to do something useful.

What Anthropic says its system of 950 agents found after 21 hours was not a brand-new sequence. The agents instead flagged a repeating pattern surrounding a known enzyme, a particular pattern Anthropic said hadn’t been catalogued before. But if you read through Anthropic’s announcement, which calls this pattern “reminiscent” of what led to the gene-editing technology CRISPR that “has already transformed science and medicine,” it sounds as if this army of agents really found something of note.

These claims have angered some biologists. A viral post from one, subsequently endorsed by the chair and CEO of the drugmaker Eli Lilly, said that “finding a weird cluster of genes and repeats is often the easy part. The hard part, and where the real discoveries come from, is figuring out what the system actually does.” The agents helped with some laboratory grunt work, in other words. But a discovery it is not.

It’s a reminder that even if AI does something impressive—like finding a pattern in a mass of biological data that would be difficult to perceive with human eyes alone—the result itself may not constitute a breakthrough for science. What is novel for AI may be routine, unsurprising, or simply not that consequential to a biologist.

Muddying the issue further, Mario Rodríguez Mestre, a biologist at the University of Copenhagen, said over the weekend that his team had already discovered this particular pattern, the New York Times reported. Mestre, who regularly chatted with Claude in his work, wondered whether Anthropic’s team had learned from his conversations. Anthropic denies this, but Mestre says he’s stopping all use of Claude anyway.

Part of the problem here is that AI companies aren’t presenting their systems simply as tools scientists can use, like microscopes or supercomputers. They’re insisting that the AI systems are making discoveries themselves. To some, that approach is incompatible with how science actually works, with new knowledge more typically emerging from collaboration and an ever-growing arsenal of tools.