When Zeb Evans announced that ClickUp was laying off 22% of its workforce last Thursday, he wasn’t presenting it as a cost-cutting move. Far from it. The CEO framed it as a radical embrace of artificial intelligence, a bet that AI agents will propel the company to what he called a “100x org.”
The announcement, made on X, came with a twist that has since sparked plenty of debate in tech circles. ClickUp plans to redirect the savings from these cuts back into the people who remain, introducing million-dollar salary bands for employees who use AI to create what Evans calls “outsized impact.”
“Most savings from this change will flow directly back into the people who stay,” Evans wrote. “If you create outsized impact using AI, you’ll be paid outside of traditional bands.”
This is quite the pitch. On one hand, you’re telling dozens or hundreds of employees their jobs are being eliminated. On the other, you’re dangling the promise of massive compensation for those who learn to work alongside AI agents rather than be replaced by them.
The company has already deployed roughly 3,000 internal AI agents to handle complex tasks across the organization, according to a Fortune article published earlier this week. Instead of doing the work themselves, remaining staff are now expected to direct these agents and review their output. It’s a fundamental shift in what it means to work at ClickUp, and it’s happening fast.
The timing matters here. ClickUp was last valued at $4 billion in 2021, back when software startups could do no wrong. Now, like many companies that rode the pandemic-era productivity boom, it’s recalibrating. But the framing is notably different from the typical “we had to make tough choices” layoff announcement. Evans is essentially saying: we’re not shrinking, we’re leveling up.
It’s a compelling narrative, especially for investors and customers who want to believe that AI adoption will translate into real value. But there’s a growing body of evidence suggesting that the reality is messier than the rhetoric.
According to a recent Gartner survey, about 80% of companies using autonomous tech have indeed cut jobs. That’s a striking figure and one that validates the fears of workers everywhere who worry that AI will make their roles obsolete. But here’s the catch: the same study found that these workforce reductions aren’t necessarily translating into meaningful financial returns. Companies are cutting headcount, but they’re not always seeing the productivity gains they预期ed.
This raises an uncomfortable question. Is ClickUp genuinely ahead of the curve, or is it using AI as a convenient justification for layoffs that would have happened regardless? Evans has repeatedly insisted that ClickUp is not one of those companies using “unproven AI as an excuse to downsize,” telling TechCrunch via email that the startup is indeed measuring productivity gains from its AI agents. The company is apparently so confident in these results that it plans to embed them into a forthcoming product for customers.
Still, it’s worth noting that lots of companies said similar things during previous waves of automation. The promise of doing more with less has always been seductive. The execution is where things get tricky.
There’s another layer to this story that deserves attention. In recent months, a growing number of companies have started monitoring what employees do with AI tools, tracking something called “token consumption” as a proxy for adoption. The idea is simple: if you’re using AI, you’re presumably being more productive, and tokens are the fuel that powers these systems.
Critics have a name for this approach: “tokenmaxxing.” And they’re not shy about calling it misguided. Tracking token usage tells you how much AI is being used, not whether it’s actually creating value. You can rack up enormous AI expenses while producing nothing useful. It’s the corporate equivalent of measuring effort instead of outcomes.
“The people that automate their jobs with AI will always have a job,” Evans claimed in his post. It’s a reassuring line, and one that fits neatly into the “AI won’t replace humans, but humans who use AI will replace those who don’t” argument that tech executives love to repeat.
But there’s a tension buried in that promise. If AI keeps吞 taking over more tasks, and if the goal is a “100x org” that can do vastly more with far fewer people, then eventually there simply won’t be enough work to go around. The logical endpoint of this trajectory is a company that needs fewer and fewer humans, full stop. Who “automates their functions well” enough to stay? And who gets left behind?
This isn’t just theoretical. There’s already a concrete example of a startup that has taken AI automation to its extreme. Polsia, a one-year-old company that claims to handle all software operations for solopreneurs, is run by a single person: founder and CEO Ben Broca. That’s it. No team, no staff, just one person and the AI systems that do everything else. The efficiency apparently works well enough that Polsia just raised $30 million at a $250 million valuation.
That’s a staggering benchmark, and you can bet it’s being discussed in boardrooms everywhere. If one person can run a company worth a quarter billion dollars, what does that mean for the rest of the industry?
ClickUp is betting that it can be the next Polsia, just with more people in the mix. The million-dollar salary bands are designed to attract and retain the kind of talent that can thrive in an AI-first environment. It’s a clever recruitment play, if nothing else. Who wouldn’t want to work at a place where your compensation is theoretically uncapped if you figure out how to use AI well?
But here’s what I’d really like to understand: what happens to the rest of the workers who aren’t in that top tier? The ones who stay but don’t hit those extraordinary benchmarks? Evans’s vision assumes that most employees can be retrained into AI orchestrators, but not everyone learns at the same pace, and not everyone has the same aptitude for directing autonomous systems.
The layoffs at ClickUp aren’t just a story about one company’s AI strategy. They’re a test case for an entire philosophy of work that is rapidly spreading across the tech industry. The question isn’t whether AI can increase productivity; plenty of evidence suggests it can. The question is what that productivity actually means for the people whose jobs are on the line, and whether the promise of new opportunities will materialize for anyone beyond a select few.
If the answer turns out to be “not really,” we might look back at this moment as the point where the AI productivity hype collided with some uncomfortable truths about what it means to build a company on the backs of both machines and the humans who feed them.
