Denise runs marketing operations at a mid-sized software company, and on a Tuesday morning in late 2025 she got a Slack message from a junior analyst on her team: “competitive landscape deck attached, ready for the leadership review tomorrow!” The deck was seventeen slides, cleanly formatted, with a colour-coded matrix comparing eleven competitors across nine dimensions, complete with a five-bullet “strategic implications” summary on the final slide. At a glance, in the ninety seconds she had before her next meeting, it looked like exactly what she’d asked for.
It took her until nearly midnight to discover it wasn’t. Three of the eleven competitors had been discontinued or acquired more than a year earlier: information that was publicly available and trivially searchable, but which nobody had bothered to check because the deck itself looked too polished to need checking. Two of the “differentiators” listed for her own company’s product were features that had been deprecated in the last release. The “strategic implications” bullets were fluent, confident, and completely disconnected from the actual data in the matrix above them, in the specific way that happens when a document is generated end-to-end rather than built up from verified pieces. She spent that night rebuilding the deck from scratch, because by the time she’d finished checking it, fixing it would have taken longer than starting over.
The analyst hadn’t lied to her, exactly. He’d fed a prompt into a chatbot, gotten back something that looked like a finished competitive analysis, formatted it nicely, and sent it along in genuine good faith, under real time pressure, believing he’d done the job. In his mind, the deck was done. In Denise’s, it had just become a different job entirely: verifying and rebuilding something that presented itself as already trustworthy. Nobody in that exchange thinks of themselves as having produced or received a failure. That’s precisely what makes it so hard to see, and why it took researchers naming the pattern before most organisations noticed they were drowning in it.
Naming the thing
In September 2025, researchers at BetterUp Labs and the Stanford Social Media Lab surveyed 1,150 full-time U.S. desk workers and gave this pattern a name that stuck almost immediately across the business press:
workslop. Their definition is precise, and worth sitting with, because it’s narrower than “bad AI content”: workslop is AI-generated work that looks complete and polished on the surface, but lacks the substance to actually accomplish the task it was meant to accomplish, and in doing so, quietly offloads the real cognitive labour onto whoever receives it next.
The numbers were larger than almost anyone in the study expected going in. Forty percent of employees said they’d received workslop from a colleague in just the past month. Workers estimated that 15.4% of everything they receive from teammates now falls into this category. More than half of managers, 54%, report receiving it regularly, compared with 38.5% of individual contributors, a gap that makes sense once you realise managers sit at the checkpoint where unfinished thinking either gets caught or ships to a client. And more than half of respondents, 53%, admitted to having sent workslop themselves at least once, which is the detail that turns this from a story about a few careless employees into a story about an entire incentive structure.
Stanford’s Jeff Hancock, who co-led the research, described the underlying shift in a way that cuts closer to the mechanism than most coverage of “AI at work” usually gets:
“Now that [the effort] piece is gone, I can generate a lot of useless or unproductive content very easily.”
That sentence is worth reading twice. It isn’t a complaint about AI quality. It’s a description of what happens when the cost of producing the appearance of finished work drops to nearly zero, while the cost of verifying whether it’s actually finished stays exactly where it always was: squarely on another human being’s afternoon.
How we got here: a familiar shape wearing a new name
Every technology that lowers the cost of producing plausible-looking output eventually produces some version of this problem, and it’s worth being honest that workslop isn’t entirely unprecedented. Desktop publishing software in the 1990s produced a wave of beautifully formatted internal memos that said almost nothing: the joke at the time was that PowerPoint had made it possible to look prepared without being prepared. Stock photography and template decks did something similar a decade later: professional-looking scaffolding standing in for actual thinking. What’s different this time is the scale, the speed, and the specific way the deception operates. A 1990s memo took real time to format even if it said nothing. Workslop takes twelve minutes to generate something that would have taken three hours to produce honestly, and it does so with a fluency and internal consistency that makes the absence of substance far harder to spot on a first read than a sloppy old-fashioned memo ever was.
There’s a useful contrast with an adjacent, better-known failure mode: AI hallucination. Hallucination is usually a factual problem: the model states something false with confidence, and once you catch it, the fix is straightforward: correct the fact. Workslop is structurally different and, in a specific sense, more corrosive: nothing in it may be technically false. The competitor names in Denise’s deck were real companies. The formatting was accurate. What was missing wasn’t correctness: it was judgement about what actually mattered, what needed verifying, and what the document was supposed to accomplish for the person receiving it. You can fact-check a hallucination. It’s much harder to fact-check the absence of thought.
The MIT Media Lab’s NANDA initiative added a data point in August 2025 that reads, at first, like a completely separate finding, but fits the workslop pattern almost exactly: 95% of organisations piloting generative AI in business processes report no measurable return on that investment. Adoption metrics across the same organisations were climbing fast: more licences issued, more tools rolled out, more employees “using AI” by any usage-log definition. The value those adoption charts were supposed to represent simply wasn’t showing up in the numbers that mattered. Workslop is a large part of the missing link between those two graphs: usage goes up and to the right, while a meaningful share of what that usage produces creates net-negative work for whoever downstream has to deal with it.
The mechanism: fluency severed from labour
Here is the mechanism in its simplest form. Human communication has always used fluency (clean structure, confident tone, professional formatting) as a rough, imperfect but genuinely useful proxy for effort and thought. A well-organised report was reasonable evidence that someone had done the underlying work, not because fluency and substance are the same thing, but because, historically, producing fluent output without substance took almost as much effort as just doing the work properly. That coupling has now snapped, cleanly and almost overnight, for a huge share of everyday knowledge work.
A generative model can produce fluency (clean structure, confident tone, polished formatting) for close to zero marginal effort, entirely independent of whether any actual thinking happened underneath it. That means the signal receivers have relied on for decades to triage their attention, “does this look put-together?”, has stopped correlating reliably with the thing it was always a proxy for. And because most people haven’t consciously updated their triage instincts to reflect that, workslop routinely sails through the first-glance filter that would have caught a genuinely sloppy, honestly-unfinished piece of work in the pre-AI era. Ironically, the worse and more obviously rushed a document looks, the more scrutiny it tends to get. Workslop is dangerous specifically because it looks finished, which is exactly when people’s guard comes down.
The direction of flow matters too, and the study’s data on it is telling. Workslop moves sideways between peers 40% of the time, up to managers 19% of the time, and down from leaders to teams 16% of the time. It isn’t concentrated at any single level of seniority: it’s a property of the incentive structure itself, appearing wherever someone faces a task, a time constraint, and a tool that can manufacture the appearance of having met the deadline faster than actually meeting it.
There’s also a straightforward asymmetry hiding inside the mechanism, and it’s the part that makes workslop so durable once it starts. Generating fluent, plausible-looking output costs the sender almost nothing: a prompt, a few seconds of model latency, a quick skim for typos. Verifying that same output properly, checking the numbers, confirming the named facts, testing whether the logic actually holds together, costs the recipient real time and real attention, often more than it would have taken to produce the thing honestly from scratch. Any system where the cost of producing a claim is a rounding error compared with the cost of checking it will, predictably, fill up with unverified claims. That isn’t a statement about anyone’s character. It’s closer to a law of incentives, and it would apply to workslop’s human-only predecessors too, if humans could generate boilerplate as cheaply as a model can.
What gets lost
The most visible cost is the time. The average piece of workslop costs its recipient one hour and fifty-one minutes to identify, unpick, and either fix or redo: about twenty minutes longer, on average, than it would have taken the original sender to simply do the work properly the first time. Multiply that across a large organisation and BetterUp’s researchers arrive at roughly $186 in wasted time per affected employee per month, or north of $9 million a year in a company of 10,000 people. That number exists nowhere on any organisation’s financial statements, because there is no line item for “hours spent decoding a colleague’s chatbot output,” even though the hours are entirely real and entirely lost.
The second cost is relational, and the study’s data here is sharper than most coverage of it acknowledges. When people receive workslop, 54% report feeling annoyed, 46% frustrated, 38% confused, and 22% describe themselves as outright offended: a striking word to use about a piece of office correspondence, and a sign of how personally people take being handed something that wastes their time while pretending not to. More consequentially, more than half of recipients say the experience made them view the sender as less creative, less capable, and less reliable. Over 40% see the sender as less trustworthy going forward. Nearly a third say they’re less willing to collaborate with that person again. The person who sent the workslop, in other words, thought they were being efficient. Their colleague now quietly trusts them less: a cost that never shows up in any productivity metric, but shows up very clearly the next time that person needs a favour, a reference, or the benefit of the doubt.
The third cost is the hardest to quantify and, in the long run, probably the most important: an erosion of the norm that finished work should actually be finished before it leaves your hands. Once workslop becomes common enough inside a team, the entire distribution of trust shifts. People start reflexively re-verifying things that used to be safe to take at face value, which is its own tax, levied on everyone, including the honest senders who never produced a slop deck in their lives.
Four ways this shows up
Not everyone producing or receiving workslop is doing the same thing for the same reason, and collapsing all of it into “people being lazy with AI” misses most of what’s actually happening. Four recognisable patterns show up across the research and across most teams once you start looking for them.
The Overwhelmed Corner-Cutter. Genuinely underwater (too many deliverables, not enough hours) and reaching for AI as the only way to hit a deadline that was unrealistic before AI ever entered the picture. This person isn’t being dishonest so much as making a rational trade under bad constraints: something delivered on time beats something correct delivered late, in a culture that measures the former far more visibly than the latter.
The Mandate Follower. Was told, often vaguely, to “use AI more” by leadership eager to show adoption numbers, with no accompanying guidance on where AI output still needs human judgement layered on top. This person is complying with an instruction as literally as it was given. The resulting workslop isn’t a personal failure so much as a predictable outcome of an unclear directive handed down without the quality guardrails to go with it.
The Confident Skimmer. Uses AI output as a first draft with every intention of reviewing it properly, then runs out of time, attention, or diligence before the review actually happens, and ships the draft anyway, genuinely believing it’s closer to finished than it is. This is arguably the largest and most sympathetic category, and the hardest to fix with policy alone, because the failure happens silently, inside someone’s own judgement about their own remaining time.
The Downstream Absorber. The recipient-side archetype: the manager, teammate, or client-facing employee who catches workslop before it does real damage, at direct personal cost: the extra two hours, the late night, the quiet erosion of trust in a colleague they still have to work with tomorrow. This is the person actually paying the tax the other three archetypes generate, and the one whose cost is least visible in any usage dashboard, because absorbing workslop successfully looks, from the outside, exactly like normal work getting done.
Most teams contain some blend of all four simultaneously, which is exactly why a single fix (a training session, a new AI usage policy, a stern all-hands email) rarely solves the whole problem on its own. Solve for the Mandate Follower with clearer guidance and you’ll still have Overwhelmed Corner-Cutters reaching for the fastest available option under deadline pressure the guidance never addressed, and Confident Skimmers whose failure happens entirely inside their own private judgement about how much review time they actually have left. A policy aimed at one archetype is, at best, a partial fix for the other three.
What actually holds up
The researchers behind the original study were careful to frame their findings as a management problem rather than an individual-behaviour problem, and the practical implications follow from taking that framing seriously.
For individual contributors: treat AI-generated first drafts exactly like drafts, not like finished deliverables, regardless of how polished they look on screen. The specific discipline that helps most is verifying the parts of any AI output that are cheapest to fake convincingly (named facts, comparative claims, anything presented with confident specificity) because those are precisely the details that read as most credible while requiring the least underlying effort to fabricate.
For managers: the instinct to reward speed and visible AI adoption needs a counterweight that explicitly values verified, load-bearing work over merely fast-looking work. A few managers interviewed alongside the original study started asking a simple diagnostic question before accepting any AI-assisted deliverable: “walk me through the one part of this you’d bet your reputation on.” Workslop tends to fail that question immediately, because there usually isn’t a specific part the sender actually stands behind: the whole document was generated with roughly uniform, unverified confidence, cover to cover.
For leaders: an AI adoption mandate that measures itself purely by usage (seats activated, queries run, tools rolled out) is optimising for exactly the wrong variable, and workslop is the fully predictable output of that specific measurement choice. The fix isn’t slowing AI adoption down. It’s pairing every mandate to “use AI” with explicit, concrete guidance on where human judgement still has to sit on top of the output, and building review capacity into timelines rather than assuming AI adoption makes that step optional. Leadership research on this consistently points to the same root causes: unclear mandates from the top, and overwhelmed teams with no safe way to admit they don’t have time to do the verification properly. Both of those are organisational design problems, not individual character problems, and both are entirely within a leadership team’s power to fix, which is, depending on how you look at it, either the most hopeful or the most damning part of the entire finding.
The closing thought
Denise never told her analyst how much of that night she spent rebuilding his deck from scratch. She didn’t think it would land the way she needed it to: he genuinely believed he’d delivered on the ask, and in a narrow sense, measured against the instruction he was actually given, he had. What she quietly changed instead was her own definition of “done” for anything AI-assisted crossing her desk: not delivered, not polished, not confident-sounding, but specifically verified, on the parts that would actually cost something if they turned out to be wrong.
That’s a small, individual-level fix to a problem the research is clear is mostly structural, and it won’t scale to an entire organisation on its own. But it points at the actual shape of what’s being lost underneath the adoption charts and the usage dashboards, all trending confidently up and to the right: not effort, exactly, which AI has always been honest about reducing, but the quiet, unglamorous labour of deciding what’s actually true and what actually matters before you hand something to somebody else. That labour hasn’t gotten any cheaper. It’s just gotten a great deal easier to skip, and, for now, almost entirely someone else’s problem to notice.
The organisations that get ahead of this won’t be the ones that slow down AI adoption or ban the tools outright; the numbers make clear that ship has sailed and isn’t coming back. They’ll be the ones that figure out, deliberately and early, how to keep paying for judgement even as the cost of fluency collapses to zero, because judgement was always the scarce, valuable half of the work. Fluency was just the part that used to be a reasonably reliable clue that judgement had happened underneath it. It no longer is, and pretending otherwise is how a $9 million-a-year tax quietly becomes a permanent line in the budget nobody ever agreed to add.