Rippling has launched an AI Spend Console to track and control how its own staff use generative models.
The dashboard shows individual and team spending against output. It aims to identify engineers who spend heavily on tokens while peers frequently request code changes in reviews.
The tool emerged after the company spent millions on AI tokens at the start of the year. Chief Product Officer Matt MacInnis remembers the executive meeting in March when CFO Adam Swiecicki presented the figures.
At that time, Rippling was set to burn 40% of its R&D headcount budget on AI tokens. That figure matched the compensation paid to 40% of the engineering unit. Spending rose by 80% month-over-month. If the trend continued, the next year would see token costs reach 90% of the high-paid R&D unit’s salary budget.
“We were incredulous,” MacInnis told TechCrunch.
Management immediately started a project to understand the spending and return on investment. The launch ad features Swiecicki sitting on a stool while employees pick up wads of cash and dump them into a paper shredder.
The analysis revealed that roughly 10–15% of employees drove about 60% of total AI spend. One engineer spent $50,000 a month.
Rippling did not want to stop AI usage, just rein it in. It began by negotiating maximum spending caps with Cursor, OpenAI, and Anthropic. An obvious issue appeared: employees defaulted to 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 know they need multiple models from multiple AI labs at various price points. This includes a frontier open weight option, perhaps of Chinese origin.
Rippling founder and CEO Parker Conrad noted last month that his company’s internal benchmarks found SpaceX’s Grok to be the all-around leader. He added that “GLM 5.2 is 85% cheaper but [had] nearly identical performance” to the frontier models. SpaceX now owns Cursor, which offers access to Grok and dozens of other models. Z.ai’s GLM 5.2 has become a particular favorite Chinese model for coding tasks among tech companies these days. Databricks has also been championing it.
Second, enterprises now know they need an AI gateway that routes prompts to the best, most cost-effective model for the task. Rippling came to that conclusion too. So it built its own AI gateway that is also part of this product. MacInnis says it is possible for enterprises that already use another gateway to still use the AI Spend Console product, though if they want the features that govern spending, they would need to use Rippling’s gateway.
AI Spend Console produces dashboards that score attributes such as prompts per day combined with work output, including lines of code and pull requests, and spend.
With this tool in place, Rippling said it dropped its token spend from 40% of its headcount budget to about 15%. But it did not curtail AI usage. The company spent a peak of 605 billion tokens the month the CFO issued his warning. In July, internal usage hit 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 said, joking that “we’re not letting the sales team do grammar updates using Fable.”
But technology solutions are not enough, Rippling notes. The company found people using AI effectively and made them “AI captains” tasked with assisting the rest of the company.
Still, such efforts to use AI beyond engineering are a work in progress, MacInnis says, as software engineers have been the primary users so far. But Rippling is, for example, working on it for customer onboarding teams to automate some mailing data and data-reconciliation tasks. The dashboard will then measure productivity in terms of onboarding more customers.
“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 says.
So, if Rippling is an example, tokenmaxxing may have swung so far the other direction that employee AI access may no longer be like Slack or email. If the company can’t measure productivity, then all employees might not have access.
As for the product, AI Spend Console is included for Rippling’s HR subscribers, though there are additional AI usage-based costs. It can also be purchased as a stand-alone product and integrated with another HR system of record, MacInnis says.
What it means
Companies can now see exactly who burns money on AI tokens and whether that spending generates code or complaints. The shift from buying the most advanced models to routing tasks to cheaper, capable ones has cut costs while keeping usage high. Access to these tools now depends on proving value, not just asking for it.
