via TechCrunch AI
After Burning Millions on AI in Months, Rippling Built an Employee ROI Tool
HR software provider Rippling this week unveiled AI Spend Console, an anti-"tokenmaxxing" product that helps companies track and contain their AI spending. One of its most compelling features is the ability to map how much individual employees, teams, and roles spend on AI — and whether that spend translates into genuine productivity gains or simply produces more "AI slop."
The company promises the tool will reveal, for example, "which engineers have high AI spend whose peers frequently ask them to redo work in code reviews," according to its blog post.
The tool was born after Rippling went all in on tokenmaxxing at the start of the year — as many companies did — only to discover that employees were burning through cash at an alarming rate. 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 compensation paid to employees in that unit. That amounted to millions of dollars. (The R&D org is home to engineering at most tech companies.)
Spending was growing by 80% month-over-month. If that trend continued, the company would spend nearly as much on AI tokens — 90% — as it did on its highly compensated 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 the money, he said. In fact, the launch ad for this new product features Swiecicki sitting on a stool while employees pick up wads of cash and dump them into a paper shredder.
When Rippling conducted an analysis, it uncovered facts like "roughly 10–15% of our employees were driving about 60% of total AI spend. One engineer was spending $50,000 a month," the blog post shared.
Rippling didn't want to stop AI usage — just rein it in, and significantly. It started by negotiating maximum spending caps with each of the tools it used: Cursor, OpenAI, and Anthropic. It immediately identified 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.
That was a common early-2026 problem. Now, eight months into the year, enterprises have figured out a couple of things. First, they know they need multiple models from multiple AI labs at various price points, including a frontier open-weight option, perhaps of Chinese origin.
Rippling founder and CEO Parker Conrad noted last month that when his company conducted its own analysis, it found similar patterns across the industry. The AI Spend Console aims to solve this by providing granular visibility into token usage, cost per employee, and productivity correlation. The tool integrates with major AI providers and offers real-time dashboards, alerts for unusual spending, and recommendations for model optimization — such as routing simple tasks to cheaper models.
Looking ahead to the rest of 2026, Rippling plans to expand the tool's capabilities to include predictive budgeting and automated policy enforcement, helping enterprises not only monitor but also proactively manage AI costs. As AI adoption continues to surge, tools like AI Spend Console are becoming essential for CFOs and CIOs seeking to balance innovation with fiscal responsibility.