Anthropic’s Claude Science bets on workflow, not a new model, to win over scientists
Anthropic’s Claude Science bets on workflow, not a new model, to win over scientists
Anthropic 推出 Claude Science：押注工作流而非新模型，旨在赢得科研人员青睐
Anthropic introduced Claude Science on Tuesday, an AI workbench that gives scientists one environment to do computational research, sparing them the hassle of bouncing between databases, pipelines, and tools. Anthropic 周二推出了 Claude Science，这是一个人工智能工作台，为科学家提供了一个进行计算研究的统一环境，使他们免于在各种数据库、流程和工具之间来回切换的麻烦。
To be clear, Anthropic says Claude Science is “not a new AI model and not a more capable model for biology. It runs the same Claude models already available to everyone today (including Claude Opus 4.8), with no special access and no gating.” 需要明确的是，Anthropic 表示 Claude Science “并非一个新的 AI 模型，也不是一个在生物学领域能力更强的模型。它运行的是目前每个人都能使用的相同 Claude 模型（包括 Claude Opus 4.8），没有特殊访问权限，也没有任何门槛限制。”
The workbench builds on Anthropic’s October 2025 launch of Claude for Life Sciences, which essentially augmented the Claude chatbot by making it better at life sciences tasks. Claude Science is a dedicated place to do that work. 该工作台建立在 Anthropic 2025 年 10 月推出的“Claude for Life Sciences”基础之上，后者本质上是通过增强 Claude 聊天机器人在生命科学任务中的表现来对其进行升级。而 Claude Science 则是专门用于执行这些工作的平台。
The launch, announced Tuesday at an AI for Science briefing, fits into Anthropic’s broader push to be more than a model provider and to further own the operating layer for specific industries, the way Claude Code has become the operating layer for software development. 此次发布是在周二的“AI for Science”简报会上宣布的，这符合 Anthropic 更广泛的战略目标：即不仅仅做模型提供商，还要进一步掌控特定行业的操作系统层，正如 Claude Code 已经成为软件开发的操作系统层一样。
Anthropic is increasingly betting its growth on vertical, workflow-level products rather than just raw model capability (which could shape how it competes, and prices, against rivals). Anthropic 正日益将其增长押注在垂直领域的工作流产品上，而非仅仅依赖原始模型能力（这可能会影响其与竞争对手的竞争方式及定价策略）。
Here’s how it works: One main AI assistant acts as a kind of project manager for scientists. It connects to more than 60 scientific databases and comes with prebuilt toolkits for specific fields, like genomics, protein structure, and chemistry. 其工作原理如下：一个主要的人工智能助手充当科学家的“项目经理”。它连接了 60 多个科学数据库，并附带了针对基因组学、蛋白质结构和化学等特定领域的预构建工具包。
That assistant can then create sub-assistants to help split up the work, like a project lead delegating tasks to specialists, or hand work off to a custom “expert” assistant that the user has built for their own research. 该助手随后可以创建子助手来协助分担工作，就像项目负责人将任务委派给专家一样，或者将工作移交给用户为自己的研究构建的定制化“专家”助手。
A separate fact-checker AI then double-checks the citations and calculations before anything goes to publication. That fact-check step matters, as more AI-assisted writing leads to fabricated citations and unverifiable stats slipping into papers. That said, it’s still the same underlying model checking itself, not an independent source of truth. 一个独立的 AI 事实核查员会在论文发表前对引用和计算结果进行二次检查。这一事实核查步骤至关重要，因为越来越多的 AI 辅助写作导致虚假引用和无法验证的数据混入论文。话虽如此，这仍然是同一个底层模型在自我检查，而非一个独立的真理来源。
Claude Science has other ways of ensuring reproducibility, Anthropic says. For example, the workbench can generate figures like 3D protein structures and chemistry drawers alongside the code that made them. Each figure includes the “exact code and environment that produced it, a plain-language description of how it was created, and the full message history,” according to the company. The process also saves scientists time by allowing them to edit figures in plain language, prompting the agent to edit its own underlying code. Anthropic 表示，Claude Science 还有其他确保可重复性的方法。例如，该工作台可以生成 3D 蛋白质结构和化学绘图等图形，并附带生成这些图形的代码。据该公司称，每个图形都包含“生成它的确切代码和环境、关于其创建过程的通俗语言描述，以及完整的消息历史记录”。这一过程还允许科学家用通俗语言编辑图形，从而促使智能体修改其底层代码，从而节省了科研人员的时间。
Another way Claude Science can save scientists time is by running on the lab’s own infrastructure setup rather than sending data off to Anthropic’s servers. Claude Science 节省科学家时间的另一种方式是，它可以在实验室自己的基础设施上运行，而不是将数据发送到 Anthropic 的服务器。
Early users say they’re already putting this to work. Allen Institute neuroscientist Jérôme Lecoq used the tool to build a multi-agent computational review pipeline. Stephen Francis’s group at the UCSF Brain Tumor Center relied on Claude Science to speed up comprehensive germline analysis of glioma to a sliver of the time it previously required, with results independently validated. 早期用户表示他们已经开始应用该工具。艾伦研究所（Allen Institute）的神经科学家 Jérôme Lecoq 使用该工具构建了一个多智能体计算审查流程。加州大学旧金山分校（UCSF）脑肿瘤中心的 Stephen Francis 团队依靠 Claude Science 将胶质瘤的全面种系分析速度提升至仅需此前的一小部分时间，且结果已得到独立验证。
The Claude Science launch comes a couple of months after OpenAI approached the same problem from a different side. In April, OpenAI released GPT-Rosalind, a specialized model that is fine-tuned for biological reasoning. The difference between the two approaches isn’t only about whether a specialized model is necessary — it also comes down to who gets access, and how fast. Claude Science 的发布距离 OpenAI 从不同角度解决同一问题仅过去了几个月。今年 4 月，OpenAI 发布了 GPT-Rosalind，这是一个针对生物学推理进行微调的专用模型。这两种方法之间的区别不仅在于是否需要专用模型，还在于谁能获得访问权限以及获取速度如何。
Rosalind launched as a research preview limited to qualified enterprise customers in the U.S., gated behind a qualification and safety review. Partners like Amgen, Allen Institute, Moderna, Thermo Fisher, and Novo Nordisk got early access. Rosalind 作为研究预览版发布，仅限于美国符合条件的企业客户，并设有资格和安全审查门槛。安进（Amgen）、艾伦研究所、莫德纳（Moderna）、赛默飞世尔（Thermo Fisher）和诺和诺德（Novo Nordisk）等合作伙伴获得了早期访问权限。
And then there’s Google DeepMind, which is playing a different game entirely. DeepMind actually owns foundational science models like AlphaFold and AlphaGenome, which the other two can only call into as tools. Its Gemini for Science platform also bundles those plus more than 30 life science databases into one skill set. 此外还有 Google DeepMind，它玩的是完全不同的游戏。DeepMind 实际上拥有 AlphaFold 和 AlphaGenome 等基础科学模型，而另外两家公司只能将这些模型作为工具来调用。其 Gemini for Science 平台还将这些模型以及 30 多个生命科学数据库整合为一个技能集。
The net effect is that three very different distribution strategies are now competing for the same scientific research market: Anthropic is going wide with broad subscription access, OpenAI is going narrow and enterprise-gated, and Google is leaning on owned, proprietary models nobody else has. 最终的结果是，三种截然不同的分发策略正在争夺同一个科学研究市场：Anthropic 通过广泛的订阅访问走大众路线，OpenAI 走窄众且企业受限路线，而 Google 则依靠其独有的专有模型。
How that plays out could be an early signal for how AI vendors compete in other specialized verticals like law, finance, and engineering, down the line. 这一竞争格局的演变，可能成为未来 AI 供应商在法律、金融和工程等其他专业垂直领域竞争方式的早期信号。
Claude Science is available in beta to anyone on Pro, Max, Team, and Enterprise subscriptions. Anthropic also named Novo Nordisk and Allen Institute as customer case studies, suggesting pharma organizations are already working with multiple AI vendors. Claude Science 目前已向所有 Pro、Max、Team 和 Enterprise 订阅用户开放测试。Anthropic 还列举了诺和诺德和艾伦研究所作为客户案例，这表明制药机构已经在与多家 AI 供应商合作。
Anthropic will also support up to 50 Claude Science projects, providing up to $30,000 in credits: “We are looking for postdoctoral and graduate projects that span domains and explore the boundaries of science, with an early focus on fields across biomedical research. Applications are open through July 15, 2026, with award notifications sent out by July 31. Projects will run from September 1 to December 1, 2026.” Anthropic 还将支持多达 50 个 Claude Science 项目，提供最高 30,000 美元的积分：“我们正在寻找跨领域并探索科学边界的博士后和研究生项目，初期重点关注生物医学研究领域。申请截止日期为 2026 年 7 月 15 日，获奖通知将于 7 月 31 日发出。项目执行期为 2026 年 9 月 1 日至 12 月 1 日。”