From Tokenmaxxing to ROI: Rippling’s Journey to Tame AI Spending

The artificial intelligence gold rush of 2026 has created a new corporate problem: runaway spending on AI tokens. Human resources software provider Rippling experienced this firsthand when it discovered its employees were burning through cash at an alarming rate. The company’s response offers valuable lessons for any organization navigating the complex landscape of enterprise AI adoption.

The Wake Up Call

In March 2026, Rippling’s executive team received a shocking financial report. Chief Financial Officer Adam Swiecicki revealed that the company was on track to spend 40% of its entire research and development headcount budget on AI tokens. This meant the company was spending as much on AI usage as it was paying 40% of its R&D employees.

Even more concerning, the spending was growing by 80% month over month. If that trajectory continued, the company would soon spend 90% of its R&D budget on AI tokens, nearly matching what it paid its highly compensated engineering staff.

Chief Product Officer Matt MacInnis described the moment as one of disbelief. “We were incredulous,” he told TechCrunch.

Discovering the Problem

When Rippling analyzed its AI usage patterns, it uncovered startling facts. Approximately 10 to 15% of employees were responsible for about 60% of total AI spending. One engineer alone was spending $50,000 per month on AI tokens.

The root cause was simple but pervasive. Employees defaulted to using the most recent and most expensive frontier models for every task, regardless of whether the task required that level of capability. They were using premium AI models to perform tasks that could have been handled by more affordable alternatives.

MacInnis noted that AI providers have no incentive to help customers control spending. “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,” he said.

Building a Solution

Rather than restricting AI usage, Rippling took a strategic approach. The company built an AI gateway that routes prompts to the most cost effective model for each specific task. This gateway became a core component of a new product called AI Spend Console.

The tool provides dashboards that show exactly how much individual employees, teams, and roles are spending on AI. More importantly, it measures whether that spending translates to genuine productivity gains. The console tracks metrics like prompts per day combined with work output such as lines of code or pull requests, and compares those figures to spending.

The company also established spending caps with each AI provider it used, including Cursor, OpenAI, and Anthropic. This created guardrails while still allowing employees to leverage AI tools.

Benchmarking for Efficiency

Rippling conducted internal benchmarks to identify which AI models performed best for specific use cases. Founder and CEO Parker Conrad shared a revealing finding: SpaceX’s Grok was the overall leader for Rippling’s needs, but GLM 5.2 from Chinese company Z.ai delivered nearly identical performance at 85% lower cost.

This discovery highlights the importance of evaluating multiple models from different providers. Organizations need access to various price points, including frontier models and more affordable open weight options.

Measurable Results

The approach delivered dramatic results. Rippling reduced its token spending from 40% of its R&D headcount budget to approximately 15%. Yet the company’s AI usage remained robust. After peaking at 605 billion tokens in the month the CFO issued his warning, internal usage reached 600 billion tokens again in July. However, the cost of July’s token spending was just 37% of what it had been in April.

MacInnis explained the reduction came from routing to more effective models for specific tasks. He joked that “we’re not letting the sales team do grammar updates using Fable,” referencing the premium AI tool.

Beyond Technology

Rippling discovered that technology solutions alone were insufficient. The company identified employees who were using AI effectively and designated them as “AI captains.” These individuals assist the rest of the organization in using AI tools more efficiently.

The company acknowledges that extending AI usage beyond engineering remains a work in progress. Software engineers have been the primary users so far, but Rippling is exploring applications for customer onboarding teams to automate mailing data and data reconciliation tasks.

MacInnis emphasized the importance of linking AI consumption to productivity. “We have to be able to link token consumption in G&A functions and in customer-facing functions back to productivity. If we can’t do that, all bets are off on any of this stuff being available to the broader employee base,” he said.

A New Paradigm

Rippling’s experience suggests that enterprise AI access may not become as ubiquitous as Slack or email. If companies cannot measure productivity gains from AI usage, they may restrict access to only those employees who can demonstrate clear returns.

The AI Spend Console product is now available to Rippling’s HR subscribers, with additional usage based costs. It can also be purchased as a standalone product and integrated with other HR systems.

Key Takeaways

For organizations looking to avoid similar spending pitfalls, several lessons emerge from Rippling’s experience.

First, implement an AI gateway that routes requests to the most cost effective model for each task. This simple intervention can dramatically reduce costs without limiting AI usage.

Second, conduct internal benchmarks to identify which models perform best for your specific use cases. The most expensive option is not always the best choice.

Third, measure productivity alongside spending. Understanding whether AI usage translates to genuine output is essential for justifying continued investment.

Fourth, designate internal experts to help colleagues use AI tools more effectively. Human guidance complements technological solutions.

Finally, negotiate spending caps with AI providers. Establishing boundaries prevents uncontrolled growth in token consumption.

As organizations continue to integrate AI into their operations, Rippling’s experience provides a roadmap for balancing innovation with financial discipline. The goal is not to restrict AI usage but to ensure that every token spent delivers measurable value.

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