Anthropic just launched tools to show companies what their AI spending is buying

The financials Anthropic shared with investors this week suggest that its AI coding tool, Claude Code, is every bit the business success the frontier lab wanted. The company recorded an operating profit in the second quarter, and 900% net revenue retention — that is, spending from the same customers in Q3 2025 grew nine times in Q3 2026.

Anthropic 本周与投资者分享的财务数据显示，其 AI 编程工具 Claude Code 正如这家前沿实验室所期望的那样，在商业上取得了巨大成功。该公司在第二季度实现了营业利润，净收入留存率达到 900%——也就是说，同一批客户在 2026 年第三季度的支出是 2025 年第三季度的九倍。

Anthropic has focused on getting enterprise users to stick around and Wednesday announced a new suite of reporting tools designed to give those customers a more granular understanding of the value they get from their token spend. Using internal analytics and employee surveys, managers can view a team’s work over a given date range, what the team produced, how much it cost, and where its workflows broke down. Anthropic says it won’t use this data for model training by default. The reports will also offer recommendations on how to improve the results.

Anthropic 一直致力于提高企业用户的留存率，并于周三宣布推出一套新的报告工具，旨在让这些客户更细致地了解其 Token 支出所带来的价值。通过内部分析和员工调查，管理者可以查看团队在特定日期范围内的产出、成本以及工作流程中出现的问题。Anthropic 表示，默认情况下不会将这些数据用于模型训练。报告还将提供关于如何改善结果的建议。

This is a clear attempt to demonstrate the return on investment of Anthropic’s tools and to show customers how to improve it — a major concern among large customers. Anthropic is also creating pooled group budgets to allow teams to better manage token spending. Businesses have been hunting for that kind of information as they figure out how best to deploy AI tools. While usage among venture-backed firms is driving token spend, most of the Fortune 500 are not seeing major gains from deploying AI.

这显然是为了展示 Anthropic 工具的投资回报率，并向客户展示如何提升这一回报——这是大型客户最关心的问题。Anthropic 还正在创建汇总的团队预算，以帮助团队更好地管理 Token 支出。企业一直在寻找此类信息，以确定部署 AI 工具的最佳方式。尽管风险投资支持的公司正在推动 Token 消费，但大多数财富 500 强企业尚未从 AI 部署中获得显著收益。

Jellyfish, a company that makes tools for software developers, released a new report yesterday based on the use of agentic coding tools by 276,000 of its customers. Claude Code and other tools built on Anthropic models remain the most widely used in the survey, responsible for nearly a third of all proposed code changes submitted for review. More importantly, spending on those tools has grown 10x since the beginning of the year, with their cost now equal to about 2% of total engineering cost.

为软件开发人员提供工具的公司 Jellyfish 昨日发布了一份新报告，基于其 276,000 名客户对智能体编程工具的使用情况。调查显示，Claude Code 及其他基于 Anthropic 模型构建的工具依然是最广泛使用的，占所有提交审查的代码变更请求的近三分之一。更重要的是，这些工具的支出自年初以来增长了 10 倍，其成本目前约占工程总成本的 2%。

“Even if you’re saying 2% is still small, that 10x growth is growth that most CFOs didn’t plan for,” Nicholas Arcolano, the chief scientist at Jellyfish, told TechCrunch. “We’re certainly getting to the point where people are realizing, okay, it’s not enough to just spend a bunch. You now have to optimize it, make it work.” Anthropic’s new reports speak to that challenge.

“即使你说 2% 仍然很小，但这种 10 倍的增长是大多数首席财务官没有预料到的，” Jellyfish 的首席科学家 Nicholas Arcolano 对 TechCrunch 表示。“我们确实已经到了人们意识到‘仅仅投入大量资金是不够的’这一阶段。你现在必须对其进行优化，让它真正发挥作用。” Anthropic 的新报告正是为了应对这一挑战。

Anthropic leads among enterprise customers, but what makes an agentic coding tool truly sticky is difficult to identify. The release of OpenAI’s Astra model this summer seemed like a potential threat to Anthropic, but so far it hasn’t slowed Anthropic’s adoption numbers. “It took a long time for us to see Codex really pop in our data at all, and it has come up with Astra [OpenAI’s latest model],” Nikolas Albarran, the AI product lead at Jellyfish, told TechCrunch.

Anthropic 在企业客户中处于领先地位，但究竟是什么让智能体编程工具具有真正的粘性，目前还很难界定。今年夏天 OpenAI 发布 Astra 模型似乎对 Anthropic 构成了潜在威胁，但到目前为止，这并没有减缓 Anthropic 的采用率。“我们花了很长时间才在数据中看到 Codex 的真正爆发，而 Astra [OpenAI 的最新模型] 的出现也带来了类似情况，” Jellyfish 的 AI 产品负责人 Nikolas Albarran 对 TechCrunch 说道。

Looking at X, he continued, you’d get the distinct impression that the competition is more heated. “Everyone in the Valley is like, ‘Oh, Codex is better.’” In reality, Albarran continued, “Anthropic was just there with great enterprise motions, and they had an incumbent weight of their own — this stuff just worked and worked well, and a lot of people just put a lot of effort into standardizing workflows around it. And there’s not a big enough reason to switch.”

他继续说道，如果你看 X（原 Twitter），会明显感觉到竞争更加激烈。“硅谷的每个人都在说，‘哦，Codex 更好。’” 但实际上，Albarran 补充道，“Anthropic 凭借出色的企业策略占据了市场，他们拥有自己的先发优势——这些工具确实好用，而且很多人已经投入了大量精力围绕它标准化工作流程。目前还没有足够的理由让他们更换工具。”

It’s not just a question of models and workflows, either. The realities of business mean that once a frontier lab has gotten its hooks into a customer, switching is harder than it is compared to an individual programmer or small organization. Albarran points to the investment all the major AI labs are making in forward-deployed engineering (FDE) organizations, teams that embed AI consultants inside customer organizations to help them get off the ground.

这不仅仅是模型和工作流程的问题。商业现实意味着，一旦前沿实验室锁定了客户，更换供应商的难度就比个人程序员或小型组织要大得多。Albarran 指出，所有主要的 AI 实验室都在投资“前沿部署工程”（FDE）组织，这些团队将 AI 顾问嵌入到客户组织内部，以帮助他们启动项目。

These new reporting tools can be seen as a kind of FDE-lite, a lighter version of that hands-on help, for Anthropic customers. “We’re at a point now where it’s mature enough, where once you’re in, it’s going to be harder and harder to switch because you’ve spent the effort,” he said. “Even if they’re all mostly the same, they’re all going to be slightly different, have their own slight quirks of how they handle agent.md files, etc., and so that cost to switch is not zero.”

这些新的报告工具可以被视为一种“轻量级 FDE”，即为 Anthropic 客户提供的一种更简便的实地协助。“我们现在处于一个足够成熟的阶段，一旦你投入其中，更换的难度就会越来越大，因为你已经付出了努力，” 他说。“即使它们大体相同，也会有细微差别，比如处理 agent.md 文件时各自的特性等，因此更换成本绝非为零。”