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Rippling launches AI Spend Console after burning millions on tokens

Rippling unveiled AI Spend Console after discovering it was on track to spend 40% of its R&D headcount budget on AI tokens. The tool tracks employee AI spending and productivity.

  • Rippling's AI Spend Console maps individual, team, and role AI spending against productivity.
  • Rippling was on track to burn 40% of its R&D headcount budget on AI tokens, with spending growing 80% month-over-month.
  • After using the tool, Rippling cut token spend from 40% to about 15% of headcount budget.

HR software provider Rippling has launched AI Spend Console, a tool that helps companies track and control AI spending. It maps how much individual employees, teams, and roles spend on AI and whether that spending correlates with genuine productivity gains.

The product follows Rippling's own AI spending wake-up call. Chief Product Officer Matt MacInnis recalled an executive meeting in March when CFO Adam Swiecicki presented figures showing Rippling was on track to spend 40% of its R&D headcount budget on AI tokens — millions of dollars. Spending was growing 80% month-over-month, and if that trend continued, the next year it would spend almost as much on AI tokens as on its R&D employees.

Rippling's analysis found that roughly 10–15% of employees drove about 60% of total AI spend, with one engineer spending $50,000 a month. The company negotiated spending caps with AI providers including Cursor, OpenAI, and Anthropic, and built its own AI gateway to route prompts to the most cost-effective models.

MacInnis said inference providers have no incentive to help control spend, and that they don't provide great usage insight or collaborate with each other. Rippling also found that employees defaulted to the most expensive frontier models for all tasks.

With the tool in place, Rippling reduced its token spend from 40% of headcount budget to about 15%, while maintaining similar usage levels. In July, internal usage hit 600 billion tokens, but the cost was 37% of April's cost, according to MacInnis.

Rippling also appointed "AI captains" — employees who use AI effectively — to assist others. The company is working on extending AI use beyond engineering, for example in customer onboarding teams, but MacInnis said such efforts are a work in progress.

Why this matters: Rippling's experience highlights the financial risks of unmanaged enterprise AI adoption and the growing need for tools that link AI spending to productivity.

What this means for you: Businesses using AI tools may need to monitor spending and productivity closely to avoid runaway costs, as Rippling discovered.

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