HR software company Rippling has introduced AI Spend Console, a new tool designed to combat "tokenmaxxing" by helping businesses monitor and control their artificial intelligence expenditures. One standout feature is its ability to break down spending by individual employees, teams, and roles, while also assessing whether that spending correlates with genuine productivity gains or simply generates more low-quality AI output. According to the company, the tool can identify "which engineers have high AI spend whose peers frequently ask them to redo work in code reviews."

The product emerged after Rippling fully embraced tokenmaxxing earlier this year—following a widespread industry trend—only to realize that employees were burning through cash at an alarming rate. Chief Product Officer Matt MacInnis still recalls the March executive meeting when CFO Adam Swiecicki presented a shocking figure: Rippling was on pace to spend 40% of its R&D headcount budget on AI tokens. In other words, the company was shelling out nearly as much on tokens as it was on compensation for 40% of its R&D staff—amounting to millions of dollars. (In most tech companies, the R&D organization encompasses engineering.)

Spending was climbing at 80% month-over-month, and if that trajectory held, the following year would see AI token costs reach 90% of what Rippling paid its highly compensated R&D employees. Management immediately launched an "urgent" initiative to understand where the money was going and what value it was delivering, MacInnis said. The launch advertisement for the new product even features Swiecicki perched on a stool while employees scoop up stacks of cash and feed them into a paper shredder.

When Rippling dug into the data, it uncovered striking patterns. "Roughly 10-15% of our employees were driving about 60% of total AI spend. One engineer was spending $50,000 a month," the company shared in its blog post. Rather than banning AI usage outright, Rippling aimed to rein it in significantly. The first step was negotiating maximum spending caps with each tool the company relied on: Cursor, OpenAI, and Anthropic. Almost immediately, a glaring issue surfaced: employees were defaulting to the newest and priciest frontier models for every task, regardless of complexity.

"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.

This was a common challenge in early 2026. But eight months into the year, enterprises have started to figure out a few key strategies. First, they recognize the need for multiple models from various AI labs at different price points, including a frontier open-weight option—potentially from a Chinese provider. Rippling founder and CEO Parker Conrad noted last month that when his company benchmarked models for its own internal use, it found SpaceX's Grok to be the overall leader, yet "GLM 5.2 is 85% cheaper but [had] nearly identical performance" to the frontier models. (SpaceX now owns Cursor, which provides access to Grok and dozens of other models.) Z.ai's GLM 5.2 has become a particular favorite among tech companies for coding tasks, and Databricks has also been a vocal advocate.

Second, enterprises now understand they need an AI gateway that routes prompts to the most suitable and cost-effective model for each task. Rippling reached this conclusion as well and built its own AI gateway, which is integrated into the new product. MacInnis says companies already using a different gateway can still adopt AI Spend Console, though to access spending governance features, they'd need to switch to Rippling's gateway.

AI Spend Console provides dashboards—formerly known as leaderboards during the tokenmaxxing era—that score metrics like prompts per day, combined with work output (such as lines of code or pull requests) and spending. With this system in place, Rippling reported that token spending dropped from 40% of its headcount budget to roughly 15%. Importantly, this didn't curtail AI usage. The company hit a peak of 605 billion tokens in the month the CFO issued his warning, MacInnis shared. In July, internal usage reached 600 billion tokens again, yet "the cost of July's token spend was 37% of the cost of April's token spend," he said. "That's just because now we're routing to the more effective models," he added, joking that "we're not letting the sales team do grammar updates using Fable."

However, Rippling acknowledges that technology alone isn't sufficient. The company identified employees who were using AI effectively and designated them as "AI captains" to assist the rest of the organization. Still, efforts to extend AI adoption beyond engineering are a work in progress, MacInnis says, since software engineers have been the primary users so far. Rippling is, for instance, exploring ways to use AI in customer onboarding teams to automate mailing data and data-reconciliation tasks. The dashboard would then measure productivity in terms of how many customers are onboarded. "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," MacInnis said.

If Rippling's experience is any indication, the pendulum may have swung so far in the opposite direction that employee AI access could become less like Slack or email—ubiquitous and assumed—and more conditional. If productivity can't be measured, then not all employees might get access. As for the product itself, AI Spend Console is included for Rippling's HR subscribers, though additional usage-based AI costs apply. It can also be purchased as a standalone tool and integrated with another HR system of record, MacInnis says.