A board member forwarded a memo to me from one of his portfolio CEOs last week, with three words in the subject line: “Did he write this?”
He hadn’t. Neither, as far as I could tell, had any human who understood what it was trying to argue. It was fluent. Formatted. Eight pages of plausible nothing.
That’s workslop - and if you’ve been feeling a low-grade dread every time an attachment lands in your inbox, you’ve been reading it for months without a name for it.
The term was coined by BetterUp Labs and Stanford’s Social Media Lab in HBR last September. But it didn’t catch on until Andrew McAfee put it on stage at the HBR Strategy Summit on April 2. Within a week, executives started passing it around. Not because it named something new. Because it named something they’d been noticing for months and trying to describe to each other in the hallway and couldn’t.
The idea is simple. AI lowered the cost of producing workplace output to approximately zero. It did not lower the cost of reading it, checking it, trusting it, or acting on it. Those costs are now higher than they’ve ever been - because every document arrives with an invisible asterisk: was this well considered and thought out, or was this generated?
The audience is starting to recognize it on sight
Faros AI published research this quarter on engineering teams with high AI adoption. The teams completed 21% more tasks. They merged 98% more pull requests. And the humans reviewing that code spent 91% more time doing it. The output went up. The bottleneck moved. It did not disappear — it relocated, and it landed on the most expensive people in the organization.
The signal is leaking outside the enterprise too. A post on r/ClaudeAI this month about Opus 4.7’s hallucination rate cleared roughly 1,700 upvotes — not because the model had suddenly regressed, but because enough senior developers had been burned by confidently-wrong output that one thread finally broke a dam of quiet frustration. Around the same time, designers started passing around a label — “Claude Designed” — for the uncanny sameness of AI-generated interfaces, the way a thousand new products now wear the same gradient, the same rounded card, the same face. Different surfaces, same signal: the audience for AI output has started recognizing it on sight. And recognition always erodes trust before it erodes volume.
Adoption is the wrong metric; accountability is the real one
Most leaders I talk to are still framing AI adoption as a productivity question. How do we get more of our people using it? How much can we automate? Those are the wrong questions.
The first-order question is quality governance. Who in your organization is accountable for the fact that a document is worth reading? Who decides an analysis is finished? Who carries the consequence when a decision gets made on an artifact nobody fully understood?
In the companies pulling ahead on AI right now, those answers exist. In the companies generating workslop, they don’t - there’s just more output, moving faster, with a diffused sense of authorship and a fragmented sense of review.
McAfee’s prescription, from the same HBR conversation, is worth consideration: commit decisively, learn in short cycles, and spread practices from your internal power users rather than mandating them from the top. I’d add one thing to that. The power users worth spreading from are the ones who’ve figured out where not to use AI - who treat it as a tool that earns its place on a specific task, not a default layer over everything.
That’s the operator’s move. It’s also the boring one. It doesn’t make the cut for a keynote slide.
The metric to focus on: signal-to-workslop ratio
Here’s what I’m monitoring, after watching this play out across a dozen organizations this year: the AI winners won’t be the ones with the highest adoption rates. They’ll be the ones with the highest signal-to-workslop ratio — and that’s an organization’s cultural metric, not a tooling metric.
You can’t buy it. You can’t roll it out. You build it the same way you build any high-trust team: by being specific about what good looks like, by naming quality failures when you see them, and by protecting the people who say “this isn’t ready yet” from the people who want more volume on the board.
The executives I work with who’ve figured this out describe it almost identically. They stopped measuring AI success by usage. They started measuring it by decisions made better. It’s a fuzzier number, a harder one, and the only one that matters.
The board member who forwarded that memo to me didn’t need an AI policy. He needed permission to say what everyone in his boardroom was already thinking: this document isn’t worth our time, and we need to build a culture where nobody sends one like it again.
The accelerator and the drag look identical from the outside. The difference is whether anyone in the room is willing to say “this isn’t ready yet” — and whether the culture rewards them for it.
If you’re building one of those cultures — or watching workslop pile up and trying to get ahead of it — hit reply. I’m collecting patterns for a longer piece on governance frameworks that actually hold, and the specifics from your organization are the part I can’t get from the research alone.