Anthropic本周与投资者分享的金融信息表明，它的AI编码工具Claude Code是前沿实验室所希望的商业成功。 公司在第二季度记录了营业利润，90%的净收入留存率——即2025年Q3年度同一客户的支出在2026年Q3年度增长了9倍。
Anthropic专注于让企业用户留在身边，星期三宣布是一套新的报告工具，旨在让这些客户更加细微地了解他们从象征性支出中获得的价值。
管理人员利用内部分析和雇员调查，可以查看一个团队在特定日期范围内的工作，团队制作什么，成本多少，工作流程在哪里破裂。 Anthropic 称默认不会使用此数据进行模型训练。 报告还将就如何改进成果提出建议。
这是一次明确尝试，以展示Anthropic工具的投资回报，并展示客户如何改进它——这是大型客户的主要关注。 Anthropic也正在创建集合组预算，让团队更好地管理象征性支出。
企业一直在寻找这种信息因为他们知道如何最好地部署AI工具虽然受风险支持的企业的使用情况正在推动象征性开支，但财富500强中的大多数人并没有看到应用AI的重大收益。
Jellyfish)，一家为软件开发者制作工具的公司，发布基于其客户的27.6万个使用代理编码工具，昨日发布新报告。 Claude Code和基于Anthropic模型的其他工具仍然是调查中使用最广泛的工具，占提交审查的所有拟议代码修改的近三分之一。 更重要的是，自年初以来，这些工具的支出增加了10x，其成本现在相当于工程总成本的约2%。
“即使你说2 % 仍然很小，10x增长是大多数CFO没有计划的增长，我们肯定正走到人们意识到的地步，你现在必须优化它，使之发挥作用。”
Anthropic的新报告谈到了这一挑战。 Anthropic领先于企业中的客户，但是什么使代理编码工具真正粘着，很难识别。 OpenAI的Astra型号今年夏天的发布似乎对Anthropic构成了潜在的威胁，但迄今为止并没有减缓Anthropic的收养编号。
“我们花了很长时间才看到Codex在数据中真正流行起来，他继续说，你会得到明显的印象，比赛更激烈。 " 山谷中的每一个人都说，`哦，Codex更好。 ' 在现实中，阿尔巴兰继续说，“Anthropic只是在那里有巨大的企业运动，他们有他们自己的力量——这些东西只是工作正常，工作正常，很多人只是为围绕它的工作流程标准化付出了很多努力。 也没有足够的理由改变。
这不仅仅是模型和工作流程的问题。 商业现实意味着，一旦前沿实验室把钩子装进客户身上，切换比个人程序员或小组织更难。 Albarran指出，AI实验室正在对前方部署的工程组织进行投资，这些小组在客户组织内安置AI顾问，帮助他们离开地面。 这些新的报告工具可以看作是一种FDE-lite，一种较轻的实用帮助版本，用于Anthropic 客户。
“我们现在正处于一个成熟的阶段，一旦你进入，“即使它们大多是相同的，它们也会略有不同，对如何处理agents到ND文件等有自己的微小疑问，因此切换成本不是零。”
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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 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.
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.
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.
“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. 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. 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.”
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. 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 agents to ND files, etc., and so that cost to switch is not zero.”
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