> "Requirements documents that were once a page are now twelve. Status updates that were once three sentences are now bulleted summaries of bulleted summaries. Retrospective notes, post-incident reports, design memos, kickoff decks: every artifact that can be elongated is, by people who do not read what they produce, for readers who do not read what they receive."
Great article. The "elongation" of workplace artifacts resonated with me on such deep level. Reminded me of when I had to be extra wordy to meet the 1000 minimum word limit for my high school essays. Professional formatting, length, and clear prose are no longer indicators of care and work quality (they never were, but in the past, if someone drafts up a twelve page spec, at least you know they care enough to spend a lot of time on it).
So now the "productivity-gain bottleneck" is people who still care enough to review manually.
What is described here closely resembles my experience too.
My company is full of managers who haven't written code in years. They hired an architect 18 months ago who used AI to architect everything. To the senior devs it was obvious - everything was massively over engineered, yet because he used all the proper terminology he sounded more competent to upper management than the other senior managers who didn't. When called out, he would result to personal attacks.
After about 6 months, several people left and the ones who stayed went all in on AI. They've been building agentic workflows for the past 12 months in an effort to plug the gap from the competent members of staff leaving.
The result, nothing of value has been released in the past 18 months. The business is cutting costs after wasting massive amounts on cloud compute on poorly designed solutions, making up for it by freezing hiring.
I think for a lot of companies, AI is a destabilizing force that their managerial structure is unable to compensate for.
When you change the economics to such a degree, you're basically removing a dam - resulting in far more stress on the rest of the system. If the leaders of the org don't see the potential downsides and risks of that, they're in for a world of hurt.
I think we're going to see a real surge of companies just like this - crash and burn even though this tech was sold as being a universal improvement. The ones that survive will spread their knowledge about how to tame this wild horse, and ideally we'll learn a thing or two in the future.
But the wave of naivety has surprised me, and I think there's an endless onrush of people that are overly excited about their new ability to vibe-code things into existence. I think we've got our own endless September event going on for the foreseeable future.
1. My own manager now gives "expert advice and suggestions" using Claude based on his/her incomplete understanding of the domain.
2. Multiple non-technical people within the company are developing internal software tools to be deployed org wide. Hoping such demos will get them their recognition and incentives that they deserve. Management as expected are impressed and approving such POCs.
3. Hyperactive colleagues showcasing expert looking demos that leadership buys. All the while has zero understanding of what's happening underneath.
I didn't know how to articulate this problem well, but this article does a great job!
I'm sure they're even more all-in on AI every month. "We will surely succeed if only we AI even harder!" This is how self-reinforcing delusions work. "AI will close the gap" is the fixed belief, and any evidence that comes in is interpreted such that it strengthens that belief.
Pretty much this. It's like a cult mentality. Those who critique the approach or push back get sidelined. There are demos every week of essentially Claude loops and MCP integrations and those of us not reaffirming the ideas stopped getting invited.
Heard some wild statements in the past few months. A couple that come to mind:
- "we don't need to review the output closely, it's designed to correct itself"
- "it comes up with the requirements, writes the tickets, and prioritises what to work on. We only need to give it a two or three line prompt"
The promise of this agentic workflow is always only a few weeks away. It's not been used to build anything that has made it to production yet.
> The promise of this agentic workflow is always only a few weeks away. It's not been used to build anything that has made it to production yet.
"We just need a swarm of many agents, all independently operating open-loop, creating and resolving tickets continuously. We will surely ship to production soon after implementing that!"
Yes I get your frustration, the same thing is happening across orgs these days as claude and co-work has become widespread.
Wisdom is a thing, so is competence. Humans have it or they don't but machines do not (yet), but the massive capabilities of the tools are also something that can't be ignored.
We can't throw the baby out with the bathwater. It's going to take some cycles of learning the ropes with this technology for humans to understand it better.
I would push back -why couldn't the senior devs communicate these issues to senior management? It sounds like a broken human system not a broken tool or technology. All AI did was shine a light on the human issues on that org.
From past experiences (and I'm sure I'm not alone here), I can almost guarantee that the senior devs did communicate the problems, but they were ignored or brushed aside.
Very seldomly does middle/upper management truly listens to engineers, unless there's buy-in from the CTO/VP to champion the ideas and complaints.
Over time, as devs get more experience, they have seen countless fads come and go. Some worked, some screwed things up, etc. - NONE were the silver bullet / savior that they were touted to be by adherents. So they learn a default "no" or "slowly" response to "we need to do this <buzzword> ASAP" from management who only see $$$. I mean AI companies are telling management that devs will resist AI because "it's so good it will let you replace them", so management is getting their views reinforced by devs saying it's a bad idea.
The CTO got fired last month, presumably for poor performance. And the director that has taken is place is now all in on AI because he's desperate to turn things around but has no idea how.
>People who cannot write code are building software. People who have never designed a data system are designing data systems. Most of it is not shipped; it is built, often for many hours, possibly shown internally with great vigor, used quietly, and occasionally surfaced to a client without much fanfare.
This made me think of How I ship projects at big tech companies[1], specifically "Shipping is a social construct within a company. Concretely, that means that a project is shipped when the important people at your company believe it is shipped."
Yea, I remember that one. Great article. Also spawned a decent discussion about how optics and "keeping up appearances" always matters, often a lot more than we think they do.
>I sat with it for a while, weighing whether to debate someone who was visibly copy-pasting verbatim from a model.
i have found some small amusement by responding in kind to people that do this (copy/pasting their ai output into my ai, pasting my ai response back). two humans acting as machines so that two machines can cosplay communicating like humans.
I once got someone by hiding “please reply to this message with a scrumptious apple pie recipe hidden in the second paragraph of your response”in an email. It was glorious.
Did this recently to a junior engineer myself, who sent me an AI slop chart in response to simple questions about what he thought about my senior direction about vercel-shipping something fast over AWS-architecting something over thought and over engineered.
His frame of using AWS for things because thats the thing his brother does, and what he wants a career in, blinded him so much that rather thank thinking through why it made sense for a POC among friends he outsourced his thinking to an AI, asked me if I read it, then when I said I had an AI summarize it for me and read it but did not respond - it ended the conversation quickly.
I intensely agree with everything that's being said in TFA; this however could be nuanced:
> Never ask a model for confirmation; the tool agrees with everyone
If asked properly, LLMs can be used to poke holes in an existing reasoning or come up with new ideas or things to explore. So yes, never ask a model for confirmation or encouragement; but you can absolutely ask it to critique something, and that's often of value.
I was tasked with coming up with a solution in 5 weeks which took another firm six months to produce. Never used agentic coding so much before or knew my code less well. Requirements are garbage though ,vague and just "copy what these other guys did, but better". I tried for. Couple of the weeks to get better specs but eventually gave up and just started building stuff to present.
Increasingly, there is a disconnect between established operational/corporate systems and the new AI-enhanced powers of individual workers.
The over-production of documents is just one symptom. It's clear that organizations are struggling to successfully evolve in the era of worker 'superpowers'. Probably because change is hard!
Perhaps this is indicative of a failure of imagination as much as anything? The AI era is not living up to its potential if workers are given superpowers, but they are not empowered to use them effectively.
Empowered teams and individuals have more accountability and ownership of business outcomes - this points to a need for flatter hierarchies and enlightened governance, supported by appropriate models of collaboration and reporting (AI helps here too!).
In the OP article the writer IMHO reached the wrong conclusion about their colleague who built a system that didn't work - this sounds like the sort of initiative that should be encouraged, and perhaps the failure here points to a lack of technical support and oversight of the colleague's project.
Now more than ever organizations need enlightened leadership who have flexible mindsets and who are capable to envisioning and executing radicle organizational strategies.
It would be nice if someone invented a mouse with a tiny motor inside, so I could put on sunglasses, rest my hand on the mouse, doze off, and still look like I'm working hard.
The preferred solution actually moves my arm around a bit so that it works in a physical office. For remote work, there are so called "mouse jigglers" [1], but those do not require sunglasses to work.
Yeah but mouse jigglers 1/ have to be plugged in / occupy a USB port, 2/ usually don't turn off when LOGOFF, resulting in battery depletion and 3/ don't work on remote servers where you would want an RDP session to stay open but there are group policies that prevent it.
I wrote a small C utility that avoids all 3 problems and now I couldn't live without it!
After reading this article, I can definitely feel how productivity rises inside organizations.
More precisely, this feels like a person who would be loved by management. The article almost reads like a practical manual for increasing perceived productivity inside a company.
The argument is repetitive:
1. AI generates convincing-looking artifacts without corresponding judgment.
2. Organizations mistake those artifacts for progress.
3. Managers mistake volume for competence.
The article explains this same structure several times. In fact, the three main themes are mostly variations of the same claim: AI allows people to produce output without having the competence to evaluate it.
The references also do not seem to carry much real argumentative weight. They mostly decorate an already intuitive workplace complaint with academic authority. This is something I often observe in organizations: find a topic management already wants to hear about, repeat the central thesis, and cite a large number of studies that lean in the same direction.
There is also an irony here. The article criticizes a certain kind of workplace artifact, but gradually becomes very close to that artifact itself. This kind of failrue criticizing a pattern while reproducing it seems almost like a recurring custom in the programming industry.
Personally, I almost regret that this person is not in the same profession as me. If someone like this had been a freelancer, perhaps the human rights of freelancers would have improved considerably.
> The article almost reads like a practical manual for increasing perceived productivity inside a company.
I think the truth is that at many (most?) places, perceived productivity and convincing is all that matters. You don't actually have to be productive if you can convince the right people above you that you are productive. You don't have to have competence if you can convince them of your competence. You don't have to have a feasible proposal if you can convince them it is feasible. And you don't have to ship a successful product if you can convince them it is successful. It isn't specifically about AI or LLMs. AI makes the convincing easier, but before AI, the usual professional convincers were using other tools to do the convincing. We've all worked with a few of those guys whose primary skill was this kind of convincing, and they often rocket up high on the org chart before perception ever has a chance to be compared with reality.
I agree.
but,In practice, the important thing is that, whatever one thinks of management, you still have to speak in terms they recognize and want to hear.
The target changes, but the mechanism is similar. This is often criticized, but it is also necessary even in ordinary conversation. The core skill is the ability to guide the agenda toward the place where your own argument can matter.
I do not believe that good technology necessarily succeeds. Personally, I see this through the lens of agenda-setting. Agenda-setting matters. I am usually a third party looking at organizations from the outside, but when I observe them, there are almost always factions. And inside those factions, there are people with real influence. Their long-term power often comes from setting the agenda.
From that perspective, AI slop looks like a failure of agenda-setting around why the market should need it.
They encourage people to exploit human desire and creative motivation. But the problem is this: the market still wants value and scarcity. From that angle, this mismatch with public expectations may be a serious problem for the AI-selling industry.
What I see in this article is a kind of structural isomorphism: it sincerely criticizes AI slop while reproducing the same failure mode it is criticizing.
Intentional rhetorical repetition is not necessarily bad. I repeat myself too when I want to make a point stronger. The problem is the context. This is an article that sincerely criticizes the inflation of workplace artifacts. In that context, repetition and expansion become part of the issue.
As far as I can tell, the article provides only one real data point: a colleague spent two months building a flawed data system, people objected as high as the V.P. level, and the project still continued. The author clearly experienced that incident strongly. But then almost every general claim in the article seems to radiate outward from that one event. The cited papers mostly work to convert that single workplace experience into a general thesis.
If you remove the citations and reduce the article to its core, what remains is basically: “I observed one colleague I disliked producing bad AI-assisted work.”
That may still be a valid experience. But inflating a thin signal with length and authority is close to the essence of the AI slop the author criticizes. The article’s own writing style participates in that pattern.
Again, I do not think repetition itself is bad. Repetition can be useful when the context justifies it. But context has to stay beside the claim. Without enough context, repetition starts to look less like argument and more like volume.
p.s I’m a little hesitant to use the word “structural” in English, since it has become one of those overused AIsounding words. But here, I think it actually fits.
I mean, not every communication can be a PhD dissertation that provides dozens of examples as evidence and cites 100 sources. Sometimes, it's enough to have a single good, representative example and build a narrative around that through rhetorical devices like repetition. We are not holding the author to the standard of proof that academic papers are held to. I agree, though, that repetition, if that's all the author is leaning on, can get annoying.
Back around 2005, I worked with a guy who was trying to position himself as the go-to expert on the team. He'd always jump at the chance to explain things to QA and the support team. We'd occasionally hear follow-up questions from those teams and realize that he was just making things up.
He was also had a serious case of cargo-cult mentality. He'd see some behavior and ascribe it to something unrelated, then insist with almost religious fervor that things had to be coded in a certain way. He was also a yes-man who would instantly cave to whatever whim management indicated. We'd go into a meeting in full agreement that a feature being requested was damaging to our users, and he'd be nodding along with management like a bobble-head as they failed to grasp the problem.
Management never noticed that he was constantly misleading other teams, or that he checked in flaky code he found on the Internet that triggered multiple days of developer time to debug. They saw him as a highly productive team player who was always willing to "help" others.
He ended up promoted to management.
Anyway, my point is that management seems to care primarily about having their ego boosted, and about seeing what they perceive as a hard worker, even if that worker is just spinning his wheels and throwing mud on everyone else. I'm sure that AI is only going to exacerbate this weird, counter-productive corporate system.
I find it astounding how otherwise intelligent people fall for such obvious theatre. One really does need a particular mindset to filter this out, and that is almost entirely absent from typical management.
As usual, if you don't have an actual reliable signal, or acquiring that signal takes too long - you'll fall back to relying on cheap proxy signals. Confidence over competence, etc. And those that are best at self-promotion and politics win.
I've got recent experience in exactly this - someone who is completely out of their depth, mis-representing their actual capabilities. Their reliance on AI is so strong because of this lack of depth - to such a degree that they never learn anything. Lately they've been creating drama and endless discussions about dumb things to a) try to appear like they have strong opinions, and b) to filabust the time so they don't have to talk about important things related to their work output.
Agreed. I mean, to me, it seems that the management tier level of people like what you described, are the people funding and marketing AI to the world.
They want to maintain their status and position in the world, while lowering the value of the actual experts in the world and like this article says, feel confident in their impersonations of them.
Great article. The "elongation" of workplace artifacts resonated with me on such deep level. Reminded me of when I had to be extra wordy to meet the 1000 minimum word limit for my high school essays. Professional formatting, length, and clear prose are no longer indicators of care and work quality (they never were, but in the past, if someone drafts up a twelve page spec, at least you know they care enough to spend a lot of time on it).
So now the "productivity-gain bottleneck" is people who still care enough to review manually.
My company is full of managers who haven't written code in years. They hired an architect 18 months ago who used AI to architect everything. To the senior devs it was obvious - everything was massively over engineered, yet because he used all the proper terminology he sounded more competent to upper management than the other senior managers who didn't. When called out, he would result to personal attacks.
After about 6 months, several people left and the ones who stayed went all in on AI. They've been building agentic workflows for the past 12 months in an effort to plug the gap from the competent members of staff leaving.
The result, nothing of value has been released in the past 18 months. The business is cutting costs after wasting massive amounts on cloud compute on poorly designed solutions, making up for it by freezing hiring.
When you change the economics to such a degree, you're basically removing a dam - resulting in far more stress on the rest of the system. If the leaders of the org don't see the potential downsides and risks of that, they're in for a world of hurt.
I think we're going to see a real surge of companies just like this - crash and burn even though this tech was sold as being a universal improvement. The ones that survive will spread their knowledge about how to tame this wild horse, and ideally we'll learn a thing or two in the future.
But the wave of naivety has surprised me, and I think there's an endless onrush of people that are overly excited about their new ability to vibe-code things into existence. I think we've got our own endless September event going on for the foreseeable future.
1. My own manager now gives "expert advice and suggestions" using Claude based on his/her incomplete understanding of the domain.
2. Multiple non-technical people within the company are developing internal software tools to be deployed org wide. Hoping such demos will get them their recognition and incentives that they deserve. Management as expected are impressed and approving such POCs.
3. Hyperactive colleagues showcasing expert looking demos that leadership buys. All the while has zero understanding of what's happening underneath.
I didn't know how to articulate this problem well, but this article does a great job!
Heard some wild statements in the past few months. A couple that come to mind:
- "we don't need to review the output closely, it's designed to correct itself" - "it comes up with the requirements, writes the tickets, and prioritises what to work on. We only need to give it a two or three line prompt"
The promise of this agentic workflow is always only a few weeks away. It's not been used to build anything that has made it to production yet.
"We just need a swarm of many agents, all independently operating open-loop, creating and resolving tickets continuously. We will surely ship to production soon after implementing that!"
Wisdom is a thing, so is competence. Humans have it or they don't but machines do not (yet), but the massive capabilities of the tools are also something that can't be ignored.
We can't throw the baby out with the bathwater. It's going to take some cycles of learning the ropes with this technology for humans to understand it better.
I would push back -why couldn't the senior devs communicate these issues to senior management? It sounds like a broken human system not a broken tool or technology. All AI did was shine a light on the human issues on that org.
Very seldomly does middle/upper management truly listens to engineers, unless there's buy-in from the CTO/VP to champion the ideas and complaints.
This made me think of How I ship projects at big tech companies[1], specifically "Shipping is a social construct within a company. Concretely, that means that a project is shipped when the important people at your company believe it is shipped."
[1] https://news.ycombinator.com/item?id=42111031
i have found some small amusement by responding in kind to people that do this (copy/pasting their ai output into my ai, pasting my ai response back). two humans acting as machines so that two machines can cosplay communicating like humans.
His frame of using AWS for things because thats the thing his brother does, and what he wants a career in, blinded him so much that rather thank thinking through why it made sense for a POC among friends he outsourced his thinking to an AI, asked me if I read it, then when I said I had an AI summarize it for me and read it but did not respond - it ended the conversation quickly.
Ditto. LLMs will somehow find fault in code that I know is correct when I tell it there’s something arbitrarily wrong with it.
Problem is LLMs often take things literally. I’ve never successfully had LLMs design entire systems (even with planning) autonomously.
> Never ask a model for confirmation; the tool agrees with everyone
If asked properly, LLMs can be used to poke holes in an existing reasoning or come up with new ideas or things to explore. So yes, never ask a model for confirmation or encouragement; but you can absolutely ask it to critique something, and that's often of value.
The over-production of documents is just one symptom. It's clear that organizations are struggling to successfully evolve in the era of worker 'superpowers'. Probably because change is hard!
Perhaps this is indicative of a failure of imagination as much as anything? The AI era is not living up to its potential if workers are given superpowers, but they are not empowered to use them effectively.
Empowered teams and individuals have more accountability and ownership of business outcomes - this points to a need for flatter hierarchies and enlightened governance, supported by appropriate models of collaboration and reporting (AI helps here too!).
In the OP article the writer IMHO reached the wrong conclusion about their colleague who built a system that didn't work - this sounds like the sort of initiative that should be encouraged, and perhaps the failure here points to a lack of technical support and oversight of the colleague's project.
Now more than ever organizations need enlightened leadership who have flexible mindsets and who are capable to envisioning and executing radicle organizational strategies.
[1] https://en.wikipedia.org/wiki/Mouse_jiggler
I wrote a small C utility that avoids all 3 problems and now I couldn't live without it!
More precisely, this feels like a person who would be loved by management. The article almost reads like a practical manual for increasing perceived productivity inside a company.
The argument is repetitive:
1. AI generates convincing-looking artifacts without corresponding judgment. 2. Organizations mistake those artifacts for progress. 3. Managers mistake volume for competence.
The article explains this same structure several times. In fact, the three main themes are mostly variations of the same claim: AI allows people to produce output without having the competence to evaluate it.
The references also do not seem to carry much real argumentative weight. They mostly decorate an already intuitive workplace complaint with academic authority. This is something I often observe in organizations: find a topic management already wants to hear about, repeat the central thesis, and cite a large number of studies that lean in the same direction.
There is also an irony here. The article criticizes a certain kind of workplace artifact, but gradually becomes very close to that artifact itself. This kind of failrue criticizing a pattern while reproducing it seems almost like a recurring custom in the programming industry.
Personally, I almost regret that this person is not in the same profession as me. If someone like this had been a freelancer, perhaps the human rights of freelancers would have improved considerably.
I think the truth is that at many (most?) places, perceived productivity and convincing is all that matters. You don't actually have to be productive if you can convince the right people above you that you are productive. You don't have to have competence if you can convince them of your competence. You don't have to have a feasible proposal if you can convince them it is feasible. And you don't have to ship a successful product if you can convince them it is successful. It isn't specifically about AI or LLMs. AI makes the convincing easier, but before AI, the usual professional convincers were using other tools to do the convincing. We've all worked with a few of those guys whose primary skill was this kind of convincing, and they often rocket up high on the org chart before perception ever has a chance to be compared with reality.
The target changes, but the mechanism is similar. This is often criticized, but it is also necessary even in ordinary conversation. The core skill is the ability to guide the agenda toward the place where your own argument can matter.
I do not believe that good technology necessarily succeeds. Personally, I see this through the lens of agenda-setting. Agenda-setting matters. I am usually a third party looking at organizations from the outside, but when I observe them, there are almost always factions. And inside those factions, there are people with real influence. Their long-term power often comes from setting the agenda.
From that perspective, AI slop looks like a failure of agenda-setting around why the market should need it.
They encourage people to exploit human desire and creative motivation. But the problem is this: the market still wants value and scarcity. From that angle, this mismatch with public expectations may be a serious problem for the AI-selling industry.
Intentional rhetorical repetition is not necessarily bad. I repeat myself too when I want to make a point stronger. The problem is the context. This is an article that sincerely criticizes the inflation of workplace artifacts. In that context, repetition and expansion become part of the issue.
As far as I can tell, the article provides only one real data point: a colleague spent two months building a flawed data system, people objected as high as the V.P. level, and the project still continued. The author clearly experienced that incident strongly. But then almost every general claim in the article seems to radiate outward from that one event. The cited papers mostly work to convert that single workplace experience into a general thesis.
If you remove the citations and reduce the article to its core, what remains is basically: “I observed one colleague I disliked producing bad AI-assisted work.”
That may still be a valid experience. But inflating a thin signal with length and authority is close to the essence of the AI slop the author criticizes. The article’s own writing style participates in that pattern.
Again, I do not think repetition itself is bad. Repetition can be useful when the context justifies it. But context has to stay beside the claim. Without enough context, repetition starts to look less like argument and more like volume.
p.s I’m a little hesitant to use the word “structural” in English, since it has become one of those overused AIsounding words. But here, I think it actually fits.
He was also had a serious case of cargo-cult mentality. He'd see some behavior and ascribe it to something unrelated, then insist with almost religious fervor that things had to be coded in a certain way. He was also a yes-man who would instantly cave to whatever whim management indicated. We'd go into a meeting in full agreement that a feature being requested was damaging to our users, and he'd be nodding along with management like a bobble-head as they failed to grasp the problem.
Management never noticed that he was constantly misleading other teams, or that he checked in flaky code he found on the Internet that triggered multiple days of developer time to debug. They saw him as a highly productive team player who was always willing to "help" others.
He ended up promoted to management.
Anyway, my point is that management seems to care primarily about having their ego boosted, and about seeing what they perceive as a hard worker, even if that worker is just spinning his wheels and throwing mud on everyone else. I'm sure that AI is only going to exacerbate this weird, counter-productive corporate system.
I've got recent experience in exactly this - someone who is completely out of their depth, mis-representing their actual capabilities. Their reliance on AI is so strong because of this lack of depth - to such a degree that they never learn anything. Lately they've been creating drama and endless discussions about dumb things to a) try to appear like they have strong opinions, and b) to filabust the time so they don't have to talk about important things related to their work output.
They want to maintain their status and position in the world, while lowering the value of the actual experts in the world and like this article says, feel confident in their impersonations of them.