Tech trends emerging from Silicon Valley are nothing new, but one of the latest could be a signpost for employers across industries about potential pitfalls of the rapid proliferation of AI use in workplaces.
The term “tokenmaxxing” has been gaining steam in recent months, as tech pros compete to spend the most “tokens,” or data units used by AI to process natural language. It’s a phenomenon whose implications go far beyond friendly office competition; rather, the trend highlights how quickly employee perceptions about the link between AI use and performance assessment are changing, all while standard expectations for AI use at work are still forming.
Several high-profile situations highlight that tokenmaxxing is likely widespread across—and beyond—Silicon Valley.
For instance, an employee at Meta created an internal dashboard to track AI token spend—revealing employees used 60 trillion tokens in one month—a platform that has since been taken down after the story went viral. In an interview with Yahoo News, Barney Hussey-Yeo, founder and CEO of financial assistant app Cleo, acknowledged that “everyone at Cleo is tokenmaxxing” and that he was strategizing for how to encourage even more uptake.
However, in the same story, Deloitte’s Jim Rowan points out the foundational problem of tokenmaxxing: The practice assumes greater token consumption equals greater business outcomes.
“For many organizations, this metric of consumption does not distinguish between AI usage and the actual value derived from AI,” Rowan says.
Rowan suggests tokenmaxxing is at risk of becoming a “vanity metric,” a sentiment shared by researchers at Atlassian. In a recent report on how organizations can drive real AI fluency—as opposed to “AI theater”—they write that token consumption, like the number of total prompts run, may “look impressive” on the surface, but the numbers may not ultimately connect to real value for stakeholders and the organization.
It’s important to focus on measurements that clarify, not police, in order to get a realistic look at how AI fluency is building, and how that materially impacts the business. And keep iterating.
“Treat metrics,” they say, “as conversation starters: a way to notice what to amplify or adjust.”
Rethinking how to encourage AI adoption—and measure its value
A report out this week in Axios found that tech giant Salesforce is taking a slightly different approach to measuring the value of AI usage, using agentic work units, which measure output and impacts, opposed to token spend.
According to Axios, Salesforce clients have used 2.4 billion AWUs as of the end of last year—triple-digit growth from 2024.
Salesforce joins other big-name tech giants like Meta, Google and Amazon that are formalizing expectations for AI usage at work. For instance, Google and Meta are both building AI usage metrics into performance reviews for engineers.
Such moves highlight the issue facing HR: How can leaders encourage and then measure AI adoption that drives real business outcomes, while avoiding employees using the tech in a performative way? It’s a particularly salient issue as employee fears about layoffs, especially those driven by AI,are rampant.
Cisco Chief People Officer Kelly Jones recently told HR Executive that AI usage rates are just a “sliver of the story” that HR needs to be thinking about, alongside employee trust, confidence and readiness.
Such metrics can tell HR “whether or not adoption is real or it’s just compliance theater,” Jones says.
There’s an acute risk for that, according to a recent survey of 1,000 full-time workers, which found that about 16% of employees admitted to pretending to use AI. Nearly a quarter say they feel pressured to use AI in situations they’re unsure about.
Similarly, recent research from Upwork reported that nearly half of workers surveyed said they “have no idea” how AI is supposed to improve their productivity, a prime motivator for AI integration work; however, a full 96% of executives expect AI to drive better productivity.
The disconnect could be connected to lagging training: Upwork found that only about one-quarter of leaders say their organizations have AI training programs.
Jones says building AI fluency is going to require HR to rethink its approach to learning, as standard, “old-school” training courses won’t cut it anymore. Employees need hands-on opportunities to try the tech and see its influence on their work, so they’re not just comfortable using it because of a company requirement, but rather they are truly bought into its value.
“If you want your workforce to embrace AI,” Jones says. “you’ve got to stop training them and start enabling them.”
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