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 â that is, spending from the same customers in Q3 2025 grew nine times in Q3 2026. Anthropic has focused on gettingenterprise users to stick aroundand Wednesdayannounceda 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 teamâs work over a given date range, what the team produced, how much it cost, and where its workflows broke down. Anthropic says it wonât 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 Anthropicâs tools and to show customers how to improve it â 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 anew report yesterdaybased 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. âEven if youâre saying 2% is still small, that 10x growth is growth that most CFOs didnât plan for,â Nicholas Arcolano, the chief scientist at Jellyfish, told TechCrunch. Weâre certainly getting to the point where people are realizing, okay, itâs not enough to just spend a bunch. You now have to optimize it, make it work.â Anthropicâs 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 OpenAIâs Astra model this summer seemed like a potential threat to Anthropic, but so far it hasnât slowed Anthropicâs adoption numbers. âIt took a long time for us to see Codex really pop in our data at all, and it has come up with Astra [OpenAIâs latest model],â Nikolas Albarran, the AI product lead at Jellyfish, told TechCrunch. Looking at X, he continued, youâd get the distinct impression that the competition is more heated. âEveryone in the Valley is like, âOh, Codex is better.ââ In reality, Albarran continued, âAnthropic was just there with great enterprise motions, and they had an incumbent weight of their own â this stuff just worked and worked well, and a lot of people just put a lot of effort into standardizing workflows around it. And thereâs not a big enough reason to switch.â Itâs 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. âWeâre at a point now where itâs mature enough, where once youâre in, itâs going to be harder and harder to switch because youâve spent the effort,â he said. âEven if theyâre all mostly the same, theyâre 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.â
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