Anthropic recorded an operating profit in the second quarter of 2026, with net revenue retention hitting 900% as spending from existing customers grew ninefold in the following period.
New visibility for enterprise spend
The company announced a new suite of reporting tools on Wednesday to help those customers understand the value of their token spend. Managers can access internal analytics and employee survey data to view a team’s work over a specific date range. The reports show what the team produced, the associated costs, and where workflows failed.
Anthropic states it will not use this data for model training by default. The reports will also offer recommendations on how to improve results.
This move addresses a major concern among large clients regarding the return on investment for AI tools. Anthropic is also introducing pooled group budgets to allow teams to manage token spending more effectively.
Engineering costs are rising fast
While usage among venture-backed firms drives token spend, most Fortune 500 companies are not seeing major gains from deploying AI. Jellyfish, a developer tools company, released a report yesterday based on the use of agentic coding tools by 276,000 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. Spending on those tools has grown 10x since the beginning of the year, with costs 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.”
Retention is the real challenge
Anthropic’s new reports address that challenge. The company leads among enterprise customers, but identifying what makes an agentic coding tool truly sticky remains difficult. The release of OpenAI’s Astra model this summer seemed like a potential threat to Anthropic, but so far it has not 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,” 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.”
Switching costs are high
It is 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 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.”
