刚刚,诺贝尔奖得主成Anthropic新员工

marsbitPublicado em 2026-06-20Última atualização em 2026-06-20

Resumo

诺贝尔奖得主John Jumper宣布离开工作近9年的Google DeepMind,加入AI公司Anthropic。Jumper是AlphaFold项目的核心领导者,该AI模型彻底改变了结构生物学,曾因此获得诺贝尔化学奖。他的离职被视为谷歌的重大损失。 就在两天前,Transformer论文核心作者、Gemini联合负责人Noam Shazeer也宣布离开谷歌加入OpenAI。短短72小时内,谷歌连续流失两位顶尖AI人才。分析认为,新兴的AI实验室能提供“个人改变公司轨迹”的机遇,这对顶尖研究者颇具吸引力。 Jumper在2017年博士毕业后即加入DeepMind,并迅速领导AlphaFold团队。在他的带领下,AlphaFold系列模型成功预测了数亿个蛋白质结构,将生物学研究效率提升了上千倍。此次加入Anthropic,Jumper将助力该公司在生命科学领域的布局。Anthropic此前已收购生物科技公司Coefficient Bio,并推出专注于医疗和生命科学的AI工具。 目前,Anthropic、OpenAI和Google DeepMind三大AI实验室均已将生命科学视为下一个核心战场,竞相投入巨资,旨在用AI技术颠覆药物研发、基因组学等传统领域。Jumper的转会标志着这场顶级人才与技术竞赛进入新阶段。

诺贝尔奖得主,入职Anthropic了!

今天,AlphaFold核心领导者John Jumper官宣:离开工作近9年的Google DeepMind,加入Anthropic。

用一个AI模型改写了整个结构生物学的诺奖得主,转身走了。

Hassabis很快回应:「感谢John在过去9年里非凡的伙伴关系!我们凭借AlphaFold取得的成就改变了世界。」

合作9年、共享诺奖,这大概是科技圈最体面的一次告别了。

而就在两天前,Transformer论文的传奇共一、Gemini联合负责人Noam Shazeer刚宣布离开Google,去了OpenAI。

不到72小时,谷歌连丢两张王牌。

一个花27亿美元买回来也没留住,一个9年感情也没留住。

博士毕业6个月,直接领队AlphaFold

John Jumper在生命科学界,可以说就是「用AI改写整个学科」的代名词。

1985年,Jumper出生于阿肯色州小石城,一个普通的美国南方小城。

本科在Vanderbilt拿了数学和物理双学位,之后进入芝加哥大学一路读到博士,研究方向是理论化学。具体来说,就是用计算方法模拟蛋白质的动力学行为。

数学给他建模的直觉,物理给他对复杂系统的理解,理论化学让他比任何纯AI研究者都更懂蛋白质这个问题本身。

三个方向合在一起恰好是解蛋白质折叠问题最稀缺的知识组合。

2017年拿到博士学位后,Jumper直接就加入了DeepMind。

值得注意的是,彼时他几乎没有深度学习经验,简历上最突出的不是对神经网络的掌握,而是对蛋白质物理的理解。

但Hassabis看中的恰恰是这一点。

紧接着,他就做了一个谁都没想到的决定——让这个毕业仅6个月、连深度学习都要边干边学的年轻人,直接领导AlphaFold团队。

没有过渡期,没有「先做几年研究员攒攒资历」。

Hassabis赌的是,解蛋白质折叠这道题,懂蛋白质比懂AI更重要。而Jumper接下的,是整个计算生物学领域最大的一场豪赌。

一个人把生物学翻了1000倍

接下来几年发生的事,只能用「离谱」来形容——

2018年,AlphaFold在蛋白质结构预测竞赛CASP上首次亮相,碾压传统方法。

2020年,AlphaFold 2横空出世,困扰生物学家50年的蛋白质折叠问题,被一个AI模型直接「解了」。

2021年,Jumper带队算出了几乎所有5万多种人类蛋白质的3D结构。并在最终实现了约100万个物种、近2亿种已知蛋白质结构的生成。

在AlphaFold之前,人类花了几十年,用X射线晶体学、冷冻电镜等实验手段,一共解出了大约20万个蛋白质结构。

Jumper的团队,一次性翻了1000倍。

毫不夸张地说,生物学家过去一百年没干完的活,AlphaFold几个月就干完了。

2024年5月,AlphaFold 3发布——不只预测蛋白质了,DNA、RNA、小分子药物之间的相互作用,全能算。蛋白质-配体对接准确率76.4%,比前代方法提升1.8倍。

5个月后的斯德哥尔摩,John Jumper和Demis Hassabis一起站上了诺贝尔化学奖的领奖台。

那一年Jumper 39岁,是70年来最年轻的化学诺奖得主。

从一个连深度学习都要现学的博士毕业生,到站在斯德哥尔摩的聚光灯下,他只用了7年。

至此,Hassabis当年那个赌注的回报率,怕是人类科学史上都排得上号。

所以今天他走,Google DeepMind的痛,不只是少了一个Director那么简单。

谷歌到底怎么了?

消息炸开之后,X上的评论区直接沸腾了。

网友Chubby直呼:「这对Google来说是巨大的损失,对Anthropic来说简直是疯了!」

有网友感慨「Anthropic迎来了一位诺贝尔奖得主,人才正持续向OpenAI和Anthropic集中」,还有人直接喊出来:「先是Karpathy,现在又是AlphaFold背后的人,Anthropic这是在组AI复仇者联盟。」

Logan Kilpatrick开玩笑说期待Jumper「再拿一个诺贝尔奖」。语气是调侃,但仔细想想,还真不算夸张。

而震惊过后,所有人都在问同一个问题——谷歌到底怎么了?

Jumper没说,Anthropic没说,谷歌也没说。

或许,投资人Lior Alexander的一条评论,是目前最接近答案的——

「前沿AI实验室在兜售一种Google给不了的东西:一个人就能改变公司轨迹的感觉。」

27亿美元买回来的人,也没留住

就在Jumper官宣的两天前,Noam Shazeer宣布离开谷歌,加入OpenAI,任「架构研究负责人」。

2017年那篇现代AI的奠基之作「Attention Is All You Need」,他是核心作者之一。多头注意力是他设计的,第一个跑赢SOTA的可用实现是他一行一行敲出来的。

而谷歌为了把他从Character.AI请回来,砸了27亿美元。

回来后Shazeer出任Gemini联合负责人,成了谷歌大模型反攻的头号功臣。

结果不到两年,又走了。隔了两天,Jumper也走了。

他们既不是第一个,也不会是最后一个。

过去8年,超过20位在里程碑论文上署名的顶级研究者陆续离开DeepMind/Brain。

2025年一年,至少11名高管离职。DeepMind联合创始人Mustafa Suleyman本人,也在一轮6.5亿美元的acqui-hire中被微软挖走。

生命科学,AI三巨头的下一个战场

回到Anthropic这边。早在两个多月前,布局就已经开始了。

4月3日,Anthropic用4亿美元股票收购了生物科技公司Coefficient Bio。团队不到10人,但已经在AI驱动的抗体设计领域做出了业内顶尖的成果。

与此同时,Anthropic也在建自己的湿实验室,去年10月推出了帮助研究人员加速药物发现和生物实验设计的Claude for Life Sciences,今年1月又上线了面向医疗机构的Claude for Healthcare。

他们说,目标是把生命科学的研发周期压缩10倍。而现在,一个诺贝尔奖级别的蛋白质科学家来领这件事了。

事实上,不只是Anthropic在押注生命科学。

OpenAI今年4月发布了专门面向生物医学的推理模型GPT-Rosalind,主攻药物发现、基因组分析和蛋白质工程,已经和Amgen、Moderna、Thermo Fisher等头部药企达成合作。

OpenAI基金会更是直接表态:未来一年在生命科学方向的投入不低于10亿美元。加上刚刚挖来的Shazeer坐镇架构研究,OpenAI在这条赛道上同样来势凶猛。

而Google DeepMind这边,Hassabis旗下的Isomorphic Labs去年融了6亿美元,和礼来、诺华签下了总里程碑价值高达30亿美元的合作协议,AlphaFold的技术底座依然是行业标杆。

三家实验室,同时把筹码压向了同一个方向——用AI重写生命科学。

Jumper的选择,只是这场大棋的最新一步。

参考资料:

https://x.com/JohnJumperSci/status/2068001285173834106

编辑:摩西

本文来自微信公众号“新智元”,作者:ASI启示录

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Perguntas relacionadas

Q哪位诺贝尔奖得主新近入职了Anthropic,以及他此前在哪里工作?

A诺贝尔化学奖得主、AlphaFold的核心领导者John Jumper新近入职了Anthropic。他此前在Google DeepMind工作了近9年。

Q在加入Google DeepMind前,John Jumper的学术背景是什么?

AJohn Jumper在本科期间于范德比尔特大学获得了数学和物理双学位,之后在芝加哥大学攻读理论化学博士学位,研究方向是利用计算方法模拟蛋白质的动力学行为。

QJohn Jumper加入DeepMind后,Demis Hassabis做出了一个什么关键决策?

AJohn Jumper博士毕业仅6个月后,Demis Hassabis决定让他直接领导AlphaFold团队,这是一个大胆的赌注,因为他当时深度学习经验尚浅,但Hassabis认为解决蛋白质折叠问题,理解蛋白质本身比精通AI技术更为关键。

QAlphaFold系列模型在生命科学领域取得了哪些里程碑式的成就?

AAlphaFold系列模型取得了多项里程碑式成就:AlphaFold 2在2020年基本解决了困扰生物学界50年的蛋白质折叠问题;2021年,团队预测了约100万个物种、近2亿种蛋白质的结构,将已知结构数量提升了约1000倍;2024年发布的AlphaFold 3能预测蛋白质、DNA、RNA及小分子药物间的相互作用。

Q除了Anthropic,还有哪些AI巨头在重点布局生命科学领域?

A除了Anthropic(通过收购Coefficient Bio、建立湿实验室、发布Claude for Life Sciences),OpenAI也发布了生物医学推理模型GPT-Rosalind并计划投入巨额资金,而Google DeepMind则通过旗下的Isomorphic Labs与大型药企合作。这三家公司都在积极投入资源,旨在用AI技术重塑生命科学。

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