GoKawiilHR software provider Rippling this week unveiled AI Spend Console, an anti-tokenmaxxing product that helps a company track and contain its AI spending. One of the most interesting features is that it maps how much individual employees, teams, and roles are spending and if they are genuinely more productive, or generally producing more AI slop.
The company promises the tool will show “which engineers have high AI spend whose peers frequently ask them to redo work in code reviews,” the company says in its blog post.
The tool was born after Rippling went all in on tokenmaxxing at the start of the year — as so many did — only to discover employees were wildly burning cash. Chief Product Officer Matt MacInnis still recalls the executive team meeting in March when CFO Adam Swiecicki presented a number that shocked them.
Rippling was on track to burn 40% of its R&D headcount budget on AI tokens, meaning it was spending as much on tokens as 40% of all the compensation it paid employees in that unit. Millions of dollars. (The R&D org is home to engineering at most tech companies.)
Spending was growing by 80% month-over-month, and if that trend continued, the next year it would spend almost as much on AI tokens — 90% — as it spent on its high-paid R&D unit employees.
“We were incredulous,” MacInnis told TechCrunch.
Management immediately undertook an “urgent” project to understand the spending and what they were getting for that money, he said. In fact, the launch ad for this new product features Swiecicki sitting on a stool while employees are picking up wads of cash and dumping them into a paper shredder.
When Rippling conducted an analysis, it discovered facts like “roughly 10–15% of our employees were driving about 60% of total AI spend. One engineer was spending $50,000 a month,” its blog post shared.
Rippling didn’t want to stop AI usage, just rein it in — a lot. It started by negotiating a max spending cap with each of the tools its company used: Cursor, OpenAI, and Anthropic. It immediately found an obvious issue: Employees defaulted to using the most recent, and most expensive, frontier models for all tasks.
“The truth is that the inference providers, like Anthropic and OpenAI, have absolutely no incentives to help you control your spend. They have every incentive for it to be a runaway expense, and that’s exactly what they do. They don’t provide you with great usage insight, and they don’t collaborate with one another,” MacInnis said.
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