Productivity Theater

On being watched while figuring it out

One of the loveliest and most talented designers I know walked into our team’s space the other day and said that they’d never experienced anxiety like they’re experiencing now. They said it so casually, like a weather report, and it absolutely killed me. I sat there for a second trying to figure out what to do.

There’s a specific flavor of dread that’s settled into AI-adjacent workplaces lately. It’s not fear of replacement, exactly, or not only that. It’s more like being handed a second job on top of your regular job, where the second job is learning how to use tools that are still very much under construction, and where your performance on this second job is now being watched, scored, and folded into evaluations of the first one. It’s the anxiety of a system that is both mandatory and broken, both the future and a genuine mess, both something you are supposed to master and something that nobody has actually mastered yet.

The tools are, to put it plainly, still kind of hacky. This is something people in the industry know but rarely say out loud, because saying it feels like criticizing the thing you’ve bet your career on, or that has been bet on you. But if you’ve spent real time working with these systems in an actual workflow under actual pressure, you know. The outputs are uneven, the context falls apart, the model forgets what you told it three messages ago. You spend twenty minutes wrestling something into shape that should have taken five. Sometimes it creates a new problem where there wasn’t one. The experience is frequently not chill. And yet, the question your employer is now asking is: how often are you using it?

The Dashboard Is Watching

Amazon, Meta, Microsoft, Google; they all have internal systems to track which AI tools employees use and how frequently. Inevitably this data will factor into performance reviews and promotion decisions. This is all framed as encouraging innovation. Amazon described it as “driving innovation by understanding how employees adopt new technologies,” with such a straight face that I have to admire it. But what is actually being measured is frequency, not whether, in a given case, it was simply the wrong tool for the job. They want to know: did you open it? Did you use it enough times this week to avoid being flagged as low-adoption?

There’s a term for what this creates: productivity theater. If you know your AI usage is being counted, you use AI, whether or not it’s helping. The dashboard looks a-okay. None of this necessarily means anything got better, or that you made a smarter decision, or that the work you produced was stronger, it just means you were seen using the thing.

This is not new behavior in workplaces. People have been gaming metrics since metrics were invented. What’s new is the strangeness of being asked to perform competency with a tool that is itself still working out its competencies. It’s not like being evaluated on your proficiency in Excel; Excel does what it says it does. No, rather, you’re being graded on your enthusiasm for a piece of software that is actively, visibly, sometimes hilariously figuring itself out in real time, while you are also figuring it out, while also trying to do your actual job.

The Hangover

Vibe coding was coined by Andrej Karpathy last year, the idea of describing what you want in plain language and letting AI write the code, relaxing into the process, and not getting too hung up on how the thing actually works underneath. “Fully giving in to the vibes,” as he put it. Collins made it the word of the year. For awhile there, everyone had a version of a story about a small miracle they’d built in an afternoon.

Mere months later, Fast Company was writing about the vibe coding hangover, with senior engineers citing development hell. Code that looked fine in testing and then brought production systems to their knees. A survey of 18 CTOs found 16 had dealt with production disasters caused by AI-generated code. Maintainers of major open-source projects started closing their doors to outside contributors because they were being flooded with low-quality AI-generated submissions, what one analyst called, with deserved bluntness, “AI slopageddon.” One developer described using AI tools so heavily at work that when he started a side project without access to them, tasks that used to be instinct felt cumbersome. He said he felt stupid, which is not great for him or for anyone.

This is not an argument that the tools are useless. The range of what they can do is impressive in the right circumstances, with bounded tasks, clear parameters, and a skilled person doing the steering. But the gap between what they can do at their best and what they reliably do under ordinary working conditions has not closed as fast as the marketing would suggest. And, the people who feel it most acutely are the ones for whom the tools were supposedly built. If engineers are having a hard time, assume everyone else is totally underwater.

Rest of World

There’s a particular condescension embedded in the phrase “non-technical users,” which is how the industry typically refers to people who work with anything other than code. As if the barrier is simply familiarity with technology, and once you learn the prompts it’ll click into place. It usually doesn’t, because the tools were designed with an implicit model of the user that doesn’t fit a lot of actual users, people who have spent years developing expertise and taste and judgment in their fields, and who now find themselves asked to describe that expertise to a machine that will approximate it back to them in a form they then have to extensively fix.

This is not relief from work! It’s a different kind of work, often slower, with the added psychological friction of the gap between what you know the output should be and what you’re actually getting. Being asked to prove your value through AI usage metrics, while simultaneously managing a tool that regularly produces work you have to catch and correct, puts you in the strange position of being evaluated on your relationship with something that would, in a fair accounting, sometimes be evaluated on its relationship with you.

It’s a daily interview you didn't know you were in, for a position you didn't realize was in question. And if this is the logic, where does it end? Are we going to tell a painter she needs to build an AI agent to take her work to market, and if she doesn't, she's no longer really a painter? These sound like absurd hypotheticals until you look at what's already happening in knowledge work, where competent people are being graded on their relationship to a tool as a proxy for their value.

To put some numbers to it: a survey from ManpowerGroup found that workers’ regular AI use increased 13% last year, while their confidence in the technology’s actual utility dropped 18%. Adoption in this case is just straight up compliance. And there’s a meaningful difference, even if the dashboard can’t see it.

Touching Grass, Briefly

So, if the people who build AI, work on AI, are incentivized to use AI, and are being monitored for their AI usage are still struggling years later, what exactly is the plan for everyone else?

The agentic future is sold with considerable confidence. AI that acts on your behalf while you focus on higher-order thinking is an appealing pitch (it’s not, but, that’s a different essay). It’s also a pitch that depends on users who are comfortable, fluent, and reasonably trusting of these systems, and most people are still rightly terrified of this AI future. The users who are imagined to be the incubators and early adopters on the frontier are simply not there yet, and may never care to be. The people with the most access, the most incentive, the most organizational pressure to get good at this are having a notably rough time of it. This is information.

There is a version of the agentic future story where it doesn’t matter, because the tools will improve fast enough that by the time they’re deployed broadly, the rough edges will be gone. Maybe. But the rough edges are not only technical, they’re about trust, which is slower to build than capability and faster to lose. Or it’s about the cognitive and emotional overhead of integrating these systems into real work, which isn’t a problem that better benchmarks solve. Or it’s about the basic reality that most people, encountering AI tools in their actual jobs, are going to have experiences that look a lot more like “I’ve never had anxiety until now.”

I want to be careful here not to argue that the technology is without value, or that everyone in the industry is performing distress (also want to be sure to say: we have it good and do not deserve sympathy). I do see genuine bright patches of utility, and I also see colleagues who are exhausted, who feel like they are failing, who are being asked to demonstrate enthusiasm for systems they don’t believe in, at least not every trackable minute of every monitored day.

The honest version of where we are is that this is hard, still, for the people most equipped to do it. The tools are both nifty and uneven, and require real skill to use well. The pressure to perform fluency before fluency is earned creates anxiety. And measuring adoption rather than judgment doesn’t tell you anything useful about either.