>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."
>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.
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.
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.
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.
> Never ask a model for confirmation; the tool agrees with everyone.
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.
So essentially, AI is exacerbating the Dunning-Kruger effect in society.
>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."
[1] https://news.ycombinator.com/item?id=42111031
>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.
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.
Please explain what you would have preferred instead, I'm failing to understand your criticism here.
AI can be (and often is) a confident incompetence amplifier.
Sidenote: why is the post dated in the future? (May 28, 2026)
So artificially productive you que up the crap you do and slowly release it?
Queue not que.
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.
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.