何时能说 AI 实现了一项科学发现？

要理解 Anthropic 所称其系统完成的工作，可以想象你正在翻阅一个收录数百万条 DNA 序列的数据库——随着科学家对越来越多的生物进行测序，这个数据库也在不断扩充。迈向突破的一步，或许是找到一段编码某种有趣酶的特殊序列。接下来，你还需要弄清这种酶的功能，并最终研究如何操控它来完成某些有用的工作。

Anthropic 称，其由 950 个 Agent 组成的系统耗时 21 小时发现的，并不是一个全新的序列。相反，这些 Agent 标记出了一种围绕已知酶出现的重复模式；Anthropic 表示，这一特定模式此前尚未被收录归类。但如果你阅读 Anthropic 的公告——该公告称这一模式“让人联想到”促成基因编辑技术 CRISPR 诞生的发现，而 CRISPR“已经改变了科学和医学”——听起来就像是这支 Agent 大军确实发现了某种值得关注的东西。

这些说法激怒了一些生物学家。其中一位生物学家发布的一则广泛传播的帖子，后来获得制药商礼来董事长兼 CEO 的认同。帖子称：“发现一组奇怪的基因簇和重复序列，往往是容易的部分。困难之处——也是真正发现的来源——在于弄清这个系统实际做了什么。”换言之，这些 Agent 协助完成了一些实验室中的基础性工作，但这并不构成一项发现。

这提醒人们，即使 AI 完成了令人印象深刻的工作——例如在海量生物数据中找出仅凭人眼难以察觉的模式——其结果本身也未必构成科学领域的突破。对 AI 而言具有新颖性的结果，对于生物学家来说可能是常规的、并不令人意外的，或者根本没有那么重要。

让问题变得更加复杂的是，据 New York Times 报道，哥本哈根大学生物学家 Mario Rodríguez Mestre 在周末表示，其团队此前已经发现了这一特定模式。Mestre 在工作中经常与 Claude 交流，因此他怀疑 Anthropic 的团队是否从这些对话中获知了相关信息。Anthropic 否认了这一点，但 Mestre 表示自己无论如何都会停止使用 Claude。

这里的问题之一在于，AI 公司并不只是将其系统呈现为科学家可以使用的工具，例如显微镜或超级计算机。它们坚持认为，这些 AI 系统本身正在作出发现。在一些人看来，这种说法与科学实际运作的方式并不相容：新知识通常是通过协作，以及不断扩展的工具库而产生的。

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.