After Rippling blew millions on AI in months, it built an employee ROI tool
After Rippling blew millions on AI in months, it built an employee ROI tool
在数月内挥霍数百万美元于 AI 后，Rippling 开发了一款员工投资回报率（ROI）工具
HR 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. 人力资源软件提供商 Rippling 本周发布了 AI Spend Console，这是一款旨在遏制“代币最大化”（tokenmaxxing）的产品，旨在帮助企业追踪并控制其 AI 开支。该工具最有趣的功能之一是，它能映射出个人员工、团队和职位的具体支出情况，并判断他们是真的提高了生产力，还是仅仅在制造更多的“AI 垃圾”。Rippling 在其博客文章中承诺，该工具将能显示“哪些工程师的 AI 开支很高，但其代码在审查时却频繁被同事要求重做”。
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.) 这款工具的诞生源于 Rippling 在年初像许多公司一样全力投入“代币最大化”后，却发现员工正在疯狂烧钱。首席产品官 Matt MacInnis 至今仍记得 3 月份的那次高管会议，当时首席财务官 Adam Swiecicki 提出的一个数字令他们大为震惊：Rippling 当时的 AI 代币支出正以研发部门薪酬预算的 40% 的速度在消耗，这意味着公司在代币上的花费相当于该部门员工薪酬总额的 40%。这可是数百万美元的开支。（在大多数科技公司中，研发部门即工程部门。）
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. 支出以每月 80% 的速度增长，如果这种趋势持续下去，明年公司在 AI 代币上的支出将几乎达到其高薪研发人员薪酬的 90%。MacInnis 对 TechCrunch 表示：“我们简直不敢相信。”他说，管理层立即启动了一个“紧急”项目，以了解这些开支的去向以及公司从中获得了什么回报。事实上，该新产品的发布广告中，Swiecicki 坐在凳子上，而员工们正抓起一叠叠现金扔进碎纸机。
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. Rippling 在进行分析时发现了一些事实，例如“大约 10% 到 15% 的员工贡献了总 AI 支出的 60%。其中一名工程师每月花费 5 万美元，”其博客文章写道。Rippling 并不想停止使用 AI，只是想对其进行大幅度控制。它首先与公司使用的每种工具（Cursor、OpenAI 和 Anthropic）协商了最高支出上限。公司立即发现了一个明显的问题：员工在处理所有任务时，默认都会使用最新、最昂贵的前沿模型。
“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. “事实是，像 Anthropic 和 OpenAI 这样的推理提供商完全没有动力帮你控制开支。他们有充分的动力让支出失控，而这正是他们所做的。他们不提供有效的用量洞察，彼此之间也不协作，”MacInnis 说道。这是 2026 年初的一个普遍问题。现在，八个月过去了，企业已经弄明白了几件事。
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 benchmarks for its own internal uses, it discovered SpaceX’s Grok was the all-around leader but 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. 首先，他们知道需要来自不同 AI 实验室、处于不同价位的多种模型，包括前沿的开放权重选项，其中可能包括源自中国的模型。Rippling 创始人兼首席执行官 Parker Conrad 上个月指出，当公司为内部使用进行基准测试时，发现 SpaceX 的 Grok 是综合领先者，但“GLM 5.2 的价格便宜了 85%，但性能与前沿模型几乎相同”。（SpaceX 现在拥有 Cursor，该平台提供对 Grok 和其他数十种模型的访问权限。）Z.ai 的 GLM 5.2 已成为目前科技公司在编码任务中特别青睐的中国模型。Databricks 也一直在推崇它。
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. 其次，企业现在知道他们需要一个 AI 网关，将提示词（prompts）路由到最适合该任务且最具成本效益的模型。Rippling 也得出了这一结论，因此它构建了自己的 AI 网关，这也是该产品的一部分。MacInnis 表示，已经使用其他网关的企业仍然可以使用 AI Spend Console 产品，但如果他们想要使用控制支出的功能，则需要使用 Rippling 的网关。
AI Spend Console produces dashboards (once known as leaderboards in the tokenmaxxing days) that score attributes such as prompts per day combined with work output (lines of code/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 didn’t curtail AI usage. The company spent a peak of 605 billion tokens the month the CFO issued his warning, MacInnis shared. 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. AI Spend Console 会生成仪表板（在“代币最大化”时期曾被称为排行榜），对每日提示词数量、工作产出（代码行数/合并请求）和支出等属性进行评分。Rippling 表示，有了这个工具，其代币支出从占研发薪酬预算的 40% 降至约 15%。但这并没有削减 AI 的使用量。MacInnis 分享道，在首席财务官发出警告的那个月，公司代币使用量达到了 6050 亿的峰值。而在 7 月份，内部使用量再次达到 6000 亿代币，但“7 月份的代币支出成本仅为 4 月份的 37%，”他说。“这仅仅是因为我们现在正在将任务路由到更高效的模型上，”他开玩笑说，“我们不会让销售团队使用 Fable 来进行语法更新。”
“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 aren’t 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. “这仅仅是因为我们现在正在将任务路由到更高效的模型上，”他说道，并开玩笑说，“我们不会让销售团队使用 Fable 来进行语法更新。”但 Rippling 指出，技术解决方案是不够的。公司发现了那些能有效使用 AI 的员工，并将他们任命为“AI 队长”，负责协助公司其他部门。不过，MacInnis 表示，在工程领域之外推广 AI 的努力仍在进行中，因为到目前为止，软件工程师一直是主要用户。但 Rippling 正在尝试将其应用于客户入职团队，以自动化处理部分邮件数据和数据核对任务。
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. 仪表板随后将通过入职客户的数量来衡量生产力。“我们必须能够将行政管理（G&A）职能和面向客户职能中的代币消耗与生产力挂钩。如果我们做不到这一点，那么向更广泛的员工群体开放这些工具就无从谈起，”MacInnis 说道。因此，如果以 Rippling 为例，代币最大化的风潮可能已经向相反方向摆动得太远，以至于员工对 AI 的访问权限可能不再像 Slack 或电子邮件那样普及。如果公司无法衡量生产力，那么并非所有员工都能获得访问权限。
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. 至于产品本身，AI Spend Console 已包含在 Rippling 的人力资源订阅服务中，但会产生额外的基于 AI 使用量的费用。MacInnis 表示，它也可以作为独立产品购买，并与其他人事记录系统集成。