I think the implications for our industry are truly fundamental. I work at a large European IT company and am transforming existing product teams into an agentic SDLC pipeline. Typically, I need five days to set things up, followed by one or two days of coaching per month. We were able to reduce the size of the team I’m currently working with from 37 to 9 members. Colleagues whose sole value lies in their ability to code and who cannot contribute any domain expertise are no longer necessary. The impact on our profession and the next generation is terrible, revolutionary. In my opinion, no one is really giving much thought to what is currently happening socially within our industry. I try to write down my thoughts on this from time to time. https://ajagara.com/en/blog/agile-wie-wir-es-kennen-verschwi...
> The cost of building has collapsed, but the cost of aligning organisationally has not. If anything, it's gone up. When three different teams can each produce a working solution to the same problem in the time it used to take to write a proposal, the bottleneck moves from engineering to coordination.
We're still figuring out how to productively use coding agents as individuals, the next challenge is figuring out how to productively use them within teams. Coding agents reduce one bottleneck - producing working code - but that just moves the bottlenecks elsewhere.
(Note I said "working" code and not "good" code, that's a whole other thing.)
I still don't understand why coding agents are silo'd, and chat history is treated as disposable. Everyone on the team should be able to see all conversations and drop in and steer agents at any time, and chat history should be part of organizational memory.
I thought labs would have pushed collaborative steering by now, but I guess people got so TUI pilled they haven't even considered the possibility.
I built this at my work and it didn’t turn out too well.
It’s a distributed agentic system on Temporal where all inputs are signals to the workflow. And then each agent has its own GNOME desktop either in a K8S pod or KubeVirt VM.
The biggest problem was context and ownership. The more we kept steering the model the worse it got until eventually it couldn’t complete its task. And on the ownership side, no one clearly owned the outcomes so it was just there… generating slop.
One mitigating factor is the increased productivity leads to consolidation, aka layoffs, meaning fewer people to align with. (Leading to further increased productivity, more consolidation, and so on ... Whether this is a virtuous cycle or a vicious cycle depends on perspective).
The vibe I got from the article was that now that technical work is faster, bosses expect everything else to be proportionally equally accelerated, even though LLMs either don’t help at all at those workloads or actively make things worse.
Different teams could already step on each others' toes before AI. If there is confusion over which team ought to develop something it might indicate an organizational problem. Crucially, if you ship something you must be willing to maintain it.
> The result, in my case, is that I code more than I have in years. Three years ago I coded maybe once a fortnight, mostly throwaway PoCs to demonstrate concepts. Now I code most days of the week, in between other work.
This kind of senior engineering role really depends on the type/size of an org.
I've had jobs like this on & off and generally don't stick around for long. There have always been high-impact hands-on-keyboard senior roles that involve coding most/every day..
> The other thing that gave way was thinking time. There's very little of it in my working day now. The productivity gains from AI got captured by output volume rather than output quality.
I actually see this externally from b2b vendors I am a client of. Companies that used to churn out X new products/month are now pushing 4X products but they all suck. The quantity over quality market is going to produce new opportunities for others.
> Three years ago [...], the process was familiar: write a proposal, get feedback, iterate, build a small PoC to demonstrate value, get a team assigned to take it to MVP, ship something fully featured and integrated with the rest of the platform six to twelve months later.
This to me smells of large, slow, very political organisation where actual work gets done at glacial pace. The increase in speed is probably not due to LLMs, rather to the fact that this person now has an excuse to present working products while before, by their own admission, they were mostly dedicated to producing corporate slop.
It’s not corporate slop, its risk avoidance. Now leaders are so enchanted with productivity they are willing to lower the risk avoidance bar. “Move fast and break things” is meta now. It was before too, but AI is giving it a fresh breath.
there's no anti-ai hype train. It doesn't exist. There'll merely be more and more bots crowding into every space that the only people left will not care.
not yet. but given the growing backlash my bet is that at some point linkedin monkeys will eventually all begin posting about the importance of “human centric design” or whatever.
at that point there’ll be some money to be made for the supposedly “replaceable” software engineers they’ve been shitting on this whole time.
i’m hoping my bet pays off, cos i would like nothing more than to rinse these people for everything they’ve got. it’ll be payback time baby.
> When I returned from five months of paternity leave in early 2024, the org needed someone at my level to lead GenAI work in developer experience and I was the available person. It wasn't a bet so much as an opportunity I recognised when it showed up. I took enough time to satisfy myself that this wave was different from previous hype cycles before I committed, but once I did, I had to drop most of the rest of my work on developer experience to make space for it. For a while I was the only engineer at any seniority dedicated to this, and the depth I built up happened by necessity as much as by design.
Followed by....
> Senior engineers in AI-forward orgs are doing more leveraged, more hands-on, more meeting-heavy work simultaneously, with the human-focused parts of the role paying for it. The build cost collapsed, the alignment cost rose, the thinking time disappeared, and the productivity gains got captured by output volume rather than output quality. I
What is the job of a senior/lead engineer if not to take the uninformed hype chasing of the senior business, and deploy it in a way that makes things better?
I can't help but feel this senior engineer is talking far too casually about how - under their watch as senior AI engineer chap - engineers spend more time on throwaway code, have less personal development/1-to-1s, and didn't improve code quality. They haven't even mentioned the added financial token cost.
The only thing standing in the way of greedy hype chasing CEOs and a post apocolyptic wasteland is engineers taking their crazy requests and not making the world worse, and it sounds like the author has failed here. I think it's very positive and frank to share their experience, but I'm surprised they don't seem to see their role in it.
> It's not an engineer's job to fix upper management delusions, and engineering is poorly equipped for that in any case.
What is the job of engineering leadership in your mind then, if not to take requirements from the business and convert them into technical solutions which improve things for the business? The requirement here was "AI to improve DX" the outcome was developers lives are worse in many creative and future impacting ways.
I sincerely would love to know if the author or their employer consider what they've done as successful or not.
All right… So, the cost of technical work has gone down, surely leaving more time for things like coordination, leadership, and thinking on a more operational level, right?
> The cost of building has collapsed, but the cost of aligning organisationally has not. If anything, it's gone up.
…Oh.
> What gave way is the human-focused work. Mentoring is the clearest example. I have less time for 1-2-1s than I did three years ago, and that isn't an accident, it's a choice I've made under pressure.
Ohh.
> The other thing that gave way was thinking time. There's very little of it in my working day now.
When I get the sense that something might be generated I ctrl+f "honest" and "framing".
These are words that humans use, but that Claude loves to use in a particular way, the kind of way used in this article. It particularly likes the phrase "The honest version".
I do not mind at all that someone creates an AI Image to decorate a blog article.
I'm also not directly offended if someone is using an LLM to write text.
And i find it a valid argument, that people who post on HN and get enough votes for content, people read and liked, that this can be enough as a filter or quality gate.
I was listening to some obscure band you wouldn't know with a few chicks I met roller blading last week, and I can't help but read your comment while imagining the first few notes of the second song playing. After that we watched The Simpsons and my advice is to not eat a cow, man.
But on second consideration, I also want to apologize to you. It's not that your comment in a vacuum is so terribly, incredibly self-absorbed. It's the cumulative effect of many little such comments over time, which I pay way too much attention to, and you could say your comment "triggered" me and I let all that frustration out in one solid chunk of snark. "Gobsmacked" is really the wrong word for that. I sometimes am, but your comment isn't the one.
The truth is, I still don't know how to put it more nicely, but please have the generosity to take my lampooning of your comment as the time honored tradition of whatever you call it when you point out the flaw in something by driving it to an absurd extreme. It does have a place, but my eye-rolling "being gobsmacked" was a bit too much indignation. tbh when I wrote that I thought I ought to get flak for that, but fuck it, I'm annoyed, I'll say it anyway. But I didn't get flak so now I feel the need to calibrate it myself a little.
In short, I think you're wrong, that comment was bleh, but so was mine. Have a great weekend and much fun showing your kid stuff you both enjoy.
> ...this shift advantages people who can build fast with AI tools and disadvantages people who can't. The bias to action is genuine, but it isn't neutral. The engineers who've adopted these tools effectively get heard more often, get their proposals taken seriously more often, and shape direction more than those who haven't.
If only this was limited to engineers.
I'm seeing this applied to every role across organizations. The designer that gets heard is the one who can vibe code the best. Same with strategists, writers (seriously), and other people for whom "coding" is not remotely their expertise or function. This exponentially accelerates the "cost of aligning organisationally" when you have not only several eng groups but every single person in your team regardless of role proposing their own solutions, that as an engineer it is now your responsibility to babysit (or as we are terming it, "harden").
Meanwhile, the quality of creative work suffers because instead of working in a surface that's designed for their trade and process (e.g. Figma) they jump straight to Claude or Cursor or v0 or whatever, where they're bounded by (and graded on) their ability to manipulate a bot, rather than their actual skillset.
>> I'm seeing this applied to every role across organizations. The designer that gets heard is the one who can vibe code the best. Same with strategists, writers (seriously), and other people for whom "coding" is not remotely their expertise or function.
It's not about vibe coding the best, it's about delivering value the fastest. And the value in this case is bringing clarity to the conversation when there are a lot of unknowns. As the article notes, prototypes are great for that. It's much easier to poke and prod at a prototype and use it to explore the problem space than to sit in on a presentation where the slides are just heavily distilled versions of an engineering design doc that is dozens of pages long (and most people will just skim anyway).
I know a few guys here who were doing sysadmin, devops, frontend jobs for a few years in India and now they are driving a taxi in India.
AI took their job. There have been mass layoffs by foreign companies in India; fewer outsourcing contracts are flowing to India.
As a result, many service companies are moving to product businesses.
will it also happen to developers in other countries? I've no idea.
When I tell this to others, they go in disbelief, but people don't care till their own job is gone.
There are plenty of use cases were you can easily replace a person with AI today.
AI is very good in creating a working website. We do not have the issue of internet explorer 6 anymore, frontend doesn't host security relevant code and it shouuld also not have the business logic. So its easy to let ai create a website which talks to a secure api endpoint.
Plenty of basic android apps are just the same thing: Some API Endpoint calling Frontend.
I know plenty of people i worked with, which I would replace tomorrow if i got the choice of more tokens vs. having to work with them, putting in a lot of effort in Code Review and not seeing real benefits.
If the industry thinks we don't want to grow anymore, than the market gets saturated -> issue 1. Issue 2 -> if the market is saturated and tokens costs a lot of money budget wise, you will have to reduce your team size to compensate for the budget -> further reduction of market capacity.
No, it isn't sustainable. There is a paper called "The AI Layoff Trap"[1], it says that it is a prisoner's dilemma and this is why this dude feels like he's in an arms race.
On the other front, people are saying that NVidia can't deliver stable drivers for like 15 months and they don't want to take software updates at all, they are more happy with last year's drivers.
I think this is a black swan event in the industry. A lot of people already suffered and more people will suffer still. Industry is going to change for sure, but probably not in a way that you would expect. Black swan simply doesn't work that way, it doesn't change industry in a good way, hence black swan.
black swan is not a reference to the badness of blackness but the unlikelihood of something contradicting volumes of previous data.
Thus it can change industry in a good way or a bad way, because the black swan is unprecedented and unpredicted, its consequences and their nature is unsettled.
Traditionally in economics black swan is an unpredictable negative event.
The only thing that is unsettled here still is how many more people will lose their jobs and how much cumulative loss prisoner's dilemma will generate.
I saw random people on Internet suggesting to piggyback this disaster and dip into the crazy money that it is "generating", but in a zero-sum game somebody has to lose.
As a general rule in human history unpredictable events seem negative when they happen, the greater the unpredictability the greater the negativeness seems to people when it happens because unpredictability tends to destabilize systems set up to deal with predictable things, this however does not mean that the later consequences will remain negative for ever and ever.
All that said however, probably the greatest black swan in terms of unpredictability in human history was that there were two freaking big continents in between Europe and Asia when you try to circumnavigate the globe.
on edit: probably should be "the greater the unpredictability and the more things the unpredictable event connects to the greater the negativeness seems to be", because obviously the platypus while completely unexpected affected basically nothing at all.
Unpredictable positive events usually just don't get attention. Something good just happened, okay, that's good. People just don't pay attention.
Technically black swan might not be negative, but for all practical reasons, "black swan event" is what people call impactful unpredictable negative events and they expect those events to be negative. In other words, it is a synonym for "a disaster" of sorts, only "economical".
they do get attention, but generally people don't dwell on how unpredictable they were. Operation Warp Speed got covid vaccines out within ~1 year. Initial predictions were more in the >= 5 years time range. Fast covid vaccines were very much a "white swan" event, but reporting around it wasn't "this is so unlikely to have worked out" etc.
The description I remember is the idea of holding the belief “all swans are white” until one encounters a black swan, and having to update their beliefs accordingly. What does this mean about swans?
But maybe that’s not the intended meaning either? It’s an interesting expression.
I agree with the content, but... "1-2-1"? From context, this seems to be "1 to 1" or "1:1", but the numeral 2 would never make sense. Am I missing something?
I’m curious where you’re running into limits there. Intuitively, it feels like stuff like change lead time and MTTR is rooted enough in “what is the customer experiencing” that it shouldn’t have been made outdated by AI. But what are you finding?
It makes me wonder if large engineering organizations are going to splinter. The coordination costs are getting, proportionally, much larger than they used to.
When I left my corporate engineering job wayyyyy back in March, there were engineers and engineering leaders going off and getting a lot done, individually or in small teams. But project management and QA couldn't keep up with it. Managers resorted to turning their tokens loose on Jira just to try to make sense of it all (which, ironically made them the first to hit their token goals on the dashboard every week, and brought Jira to it's knees).
And, even worse, the junior engineers had no idea what was going on or how to get involved in anything.
My take on this, which is almost entirely pulled out of my rear end because I last worked in a large company before the rise of agents, is that we’ll see a move from vertical teams of specialists who get pulled into projects to build a mobile app or handle infrastructure. Instead there’ll be a much stronger focus on teams of generalists, or combined teams of specialists from different fields, working on a feature or product end to end.
Coordination has in my experience always been the big bottleneck in getting anything done, it’s just not hurt so much because everyone expected a feature that could have been done in a fortnight to take months.
> Instead there’ll be a much stronger focus on teams of generalists, or combined teams of specialists from different fields, working on a feature or product end to end.
> Coordination has in my experience always been the big bottleneck in getting anything done
I work at a large enterprise you've heard of. They're currently re-organizing the product area to remove currently-static two pizza teams into an amorphous blob of feature-oriented teams. Once the feature is complete, the team is dissolved and the engineers re-enter the pool, tasked with new features.
All that to say, I think you're right on the money with your assessment.
Where does the feature go for long term ownership? That throws you build it you own it out the window. We are going to get more time for documentation and handover right, right? Engineers are famous for generating good documentation.
Some places I've worked in the past had a rotating support team who were tasked with dealing with bugs/small feature requests/incidents for a period. I'm not sure that's the right answer because nobody wants to be on the team that's just doing scutwork even if its just for a month or two but it is an option.
Claude with Jira is the first time I've applied AI and felt like it was truly saving me time. The UI and search tools are so clunky, it feels much better to say "Find jira tickets like xyz" and read through their titles/summaries in the command prompt.
I am not sure if that's a good thing for Claude, or an indictment of Jira.
I've always been frustrated by charlatans who do a great job advancing their own goals at the cost of those foolish enough to actually care about doing a good job. AI is a very empowering tool for these kinds as it is super effective at creating facades of productivity and thoughtfulness.
I think AI has a lot of value when used with thoughtful care. I think a lot of the conversations around it are really about frustrations with charlatans.
In the past bullshitting would only get you so far, but now bullshitting is both pervasive and revered. Contributors who specialize in taking things all the way, getting not just features but entire projects complete and fully functional aren't even in the conversation. What matters is the appearance of speed, not proof of completeness.
Sustainability will be most stretched where the bottom line actually matters. Medical devices and data processing, commercial aviation operations, precision manufacturing, and such. Bullshitting only takes you so far until actually doing the thing and showing that with high confidence becomes the only criterion.
AI exerts pressure to flatten roles; everybody feels the false confidence to do everything, so companies need to ensure expert oversight, and that shipping means assuming responsibility. If the latter can't be realistically accomplished, shipping should not be allowed. I think a healthy approach is to encourage everybody to create their own POCs with the understanding that only domain experts ship production code.
I still believe in ensuring that contributors understand every line of shipped code. This creates a natural ceiling on the width of their domain; though AI can help them learn more quickly, everyone can't know everything about engineering.
AI does not increase co-ordination costs per se; it increases productivity. You can achieve the same result with fewer people using AI, so it should decrease co-ordination costs. Only when you add AI while keeping the organization constant should co-ordination costs increase.
The author could have written a rather incisive 800 words on this if he'd really tried.
But I will not read 2500 words of redundant, repetitive slop. It's really bad writing.
There is no pacing or conclusion to speak of. It's sort of just a loose list alternating between upsides and downsides, punctuated by the usual bullshit list-y ad-copy summations:
The build cost collapsed, the alignment cost rose, the thinking time disappeared, and the productivity gains got captured by output volume rather than output quality.
It did not. The author decided against spending time thinking. Nobody is following him around whacking him on the head if he blocks off tomorrow morning to go heads down on something. At least, I don't think they are?
A tiny bit of project management discipline can go a long way.
Thinking that memorizing insane code rules is being skilled in making software is like thinking that memorizing all the generals' birth days is being skilled in warfare.
Before AI, trying to program even a simple thing was an exercise in frustration from rules that had only been put in place by programmers to protect their own jobs and make it as difficult as possible for a normal person to develop. Oh! You mixed tabs and spaces, now your code will not compile and you're stuck another day. Oh! You forgot a semicolon, now the code won't run, even though the software points out your missed semicolon and thus knows how to fix it.
AI takes care of all that bagage and now I and others can make fully functional software that solves real world problem for real people.
> memorizing insane code rules is being skilled in making software
That’s on the level of complaining about having to learn music theory to play the piano, or to learn grammar to write a report. Or having to learn the road rules to drive a car on the street.
Have you ever seen a police report? They are the people who write most reports of any profession, and usually they are full of grammar and spelling mistakes.
But I understand that the majority of hackers here think that spelling mistakes in a report means that somebody is bad at their job as a police officer. And I know that a good portion of hackers here think that a suspect should have all charges dismissed if a police officer has mixed tabs and spaces when typing his final report, or forgot a semicolon. Or used ' where he should have used ".
Nope. Doing a syntax error in a programming language is the equivalent of filing the wrong form or filing the form incorrectly. Like if the question was “Suspect Height” and you put “green”.
Indentation rule are about scoping, You need a delimiter to mark end of a statements, and quoting often have to do with value type and interpolation. They’re not merely visual markers. Messing them up is the equivalent of answering “400 miles” when asked “what’s the color of the sky?”.
ADDENDUM: Yep writing code is filing a form. And just like any form, it’s easy to validate basic errors like syntax and type of values. The hard thing is to validate what happens after the form is processed. i.e, the intent of filing that specific form.
I've never had a problem mixing tabs or spaces in any language that supported this, which is basically most languages.
I've spent the last three weeks working out a spec and didn't even start the development process yet.
The idea that the syntax of the language would ever be a bottleneck sounds ridiculous to me.
The gate keeping allegations are also incomprehensible. The vast majority of developers are working on making their jobs easier. There wouldn't be an endless stream of new programming languages, libraries and frameworks, if there was a software guild that you needed approval from to work on software. Even if such a guild existed, it would get obsoleted by the competition.
There are cases of people maximizing their own job security by writing terrible and incomprehensible code, but most experienced developers have gotten bitten by their own cleverness and try to make their code as easy to understand and as accessible as possible.
Things like Java Server Faces and Java Enterprise Edition died out a long time ago. The XML craze is over. Roy Fielding style REST/HATEOAS is dead and everything is an HTTP API with OpenAPI docs nowadays. People understand by now that micro service architectures only make sense for organisational purposes but not for technical reasons. NoSQL also waned and everyone is basically putting their JSON into PostgreSQL if they need to store complex hierarchical data.
Why do you even care about irrelevant things like semicolons? Like, any reasonable editor gives you squiggly lines so you can't miss them, meanwhile in practice having a line delimiter helps disambiguate hairy expressions and produce more readable error messages. For me they are an imperceptible cost that I couldn't care less about.
If you talked about null pointers, which are basically a landmine in every line you've ever written, waiting for a chance to explode, maybe you'd have a point but even nullable pointers are an idea that is being relegated to the history books.
> The idea that the syntax of the language would ever be a bottleneck sounds ridiculous to me.
Great! Then you can pick any man of the street and show him some code, and he will understand the syntax intuitively and start coding? Dollar signs, semicolons, brackets and === and the difference between "" and ''. It's all self explanatory.
Driving a motorized vehicle was a highly specialized task in the beginning. You had to prime fuel, adjust carb needles, maybe tighten a chain after a day of driving. Manufacturers did all they could to make vehicles as easy as possible for the users, so that they can focus on actually driving, and not fighting against the machine. Look at where cars are today - anybody can drive without needing any skills relating to the machine. AI is doing the same for programming, which is great.
Now a common man can make software without learning thousands of different arbitrary rules.
Can you pick any man of the street and show him some text in a foreign language and get him to translate it? Especially with a foreign script? Can you write in japanese? or Persian? You had to go to school to learn how to write, you were not born with that knowledge.
A programming language is way easier than learning a foreign natural language. I believe the issue you struggle with is formal logic, not the syntax. Not everyone is trained to think formally (and some may find it arduous).
> The engineers who've adopted these tools effectively get heard more often, get their proposals taken seriously more often, and shape direction more than those who haven't.
I want to point out if the organizational model or your team's engineers are resistant to change, it doesn't matter how good of an engineer you are, or how good at proposal writing you are. With or without AI.
I think the implications for our industry are truly fundamental. I work at a large European IT company and am transforming existing product teams into an agentic SDLC pipeline. Typically, I need five days to set things up, followed by one or two days of coaching per month. We were able to reduce the size of the team I’m currently working with from 37 to 9 members. Colleagues whose sole value lies in their ability to code and who cannot contribute any domain expertise are no longer necessary. The impact on our profession and the next generation is terrible, revolutionary. In my opinion, no one is really giving much thought to what is currently happening socially within our industry. I try to write down my thoughts on this from time to time. https://ajagara.com/en/blog/agile-wie-wir-es-kennen-verschwi...
This piece is really good:
> The cost of building has collapsed, but the cost of aligning organisationally has not. If anything, it's gone up. When three different teams can each produce a working solution to the same problem in the time it used to take to write a proposal, the bottleneck moves from engineering to coordination.
We're still figuring out how to productively use coding agents as individuals, the next challenge is figuring out how to productively use them within teams. Coding agents reduce one bottleneck - producing working code - but that just moves the bottlenecks elsewhere.
(Note I said "working" code and not "good" code, that's a whole other thing.)
I still don't understand why coding agents are silo'd, and chat history is treated as disposable. Everyone on the team should be able to see all conversations and drop in and steer agents at any time, and chat history should be part of organizational memory.
I thought labs would have pushed collaborative steering by now, but I guess people got so TUI pilled they haven't even considered the possibility.
Yeah, I see the chat transcript as an important part of the work, and worth recording.
I've taken to linking to mine in commit messages, e.g. this one: https://github.com/simonw/simonwillisonblog/commit/e781e4eef...
(My favourite feature of the new Codex desktop app is that it has a comprehensive "Copy as Markdown" feature, which I can then paste into a Gist.)
I built this at my work and it didn’t turn out too well.
It’s a distributed agentic system on Temporal where all inputs are signals to the workflow. And then each agent has its own GNOME desktop either in a K8S pod or KubeVirt VM.
The biggest problem was context and ownership. The more we kept steering the model the worse it got until eventually it couldn’t complete its task. And on the ownership side, no one clearly owned the outcomes so it was just there… generating slop.
What will chat history give you that the output of that chat won't?
Context? Without it, it would be like starting a new session for each follow-up prompt
One mitigating factor is the increased productivity leads to consolidation, aka layoffs, meaning fewer people to align with. (Leading to further increased productivity, more consolidation, and so on ... Whether this is a virtuous cycle or a vicious cycle depends on perspective).
The vibe I got from the article was that now that technical work is faster, bosses expect everything else to be proportionally equally accelerated, even though LLMs either don’t help at all at those workloads or actively make things worse.
Different teams could already step on each others' toes before AI. If there is confusion over which team ought to develop something it might indicate an organizational problem. Crucially, if you ship something you must be willing to maintain it.
I don't think the author would dispute that. But the problem is the foot is bigger and the stepping is faster.
> The result, in my case, is that I code more than I have in years. Three years ago I coded maybe once a fortnight, mostly throwaway PoCs to demonstrate concepts. Now I code most days of the week, in between other work.
This kind of senior engineering role really depends on the type/size of an org. I've had jobs like this on & off and generally don't stick around for long. There have always been high-impact hands-on-keyboard senior roles that involve coding most/every day..
> The other thing that gave way was thinking time. There's very little of it in my working day now. The productivity gains from AI got captured by output volume rather than output quality.
I actually see this externally from b2b vendors I am a client of. Companies that used to churn out X new products/month are now pushing 4X products but they all suck. The quantity over quality market is going to produce new opportunities for others.
There's also this part:
> Three years ago [...], the process was familiar: write a proposal, get feedback, iterate, build a small PoC to demonstrate value, get a team assigned to take it to MVP, ship something fully featured and integrated with the rest of the platform six to twelve months later.
This to me smells of large, slow, very political organisation where actual work gets done at glacial pace. The increase in speed is probably not due to LLMs, rather to the fact that this person now has an excuse to present working products while before, by their own admission, they were mostly dedicated to producing corporate slop.
It’s not corporate slop, its risk avoidance. Now leaders are so enchanted with productivity they are willing to lower the risk avoidance bar. “Move fast and break things” is meta now. It was before too, but AI is giving it a fresh breath.
> The quantity over quality market is going to produce new opportunities for others
once the linkedin anti-ai hype train starts in earnest that’ll be when there’ll be money to be made.
monkey see, monkey do.
there's no anti-ai hype train. It doesn't exist. There'll merely be more and more bots crowding into every space that the only people left will not care.
The rest will be out touching grass.
not yet. but given the growing backlash my bet is that at some point linkedin monkeys will eventually all begin posting about the importance of “human centric design” or whatever.
at that point there’ll be some money to be made for the supposedly “replaceable” software engineers they’ve been shitting on this whole time.
i’m hoping my bet pays off, cos i would like nothing more than to rinse these people for everything they’ve got. it’ll be payback time baby.
its a existential pzombie problem, not a movement against.
> When I returned from five months of paternity leave in early 2024, the org needed someone at my level to lead GenAI work in developer experience and I was the available person. It wasn't a bet so much as an opportunity I recognised when it showed up. I took enough time to satisfy myself that this wave was different from previous hype cycles before I committed, but once I did, I had to drop most of the rest of my work on developer experience to make space for it. For a while I was the only engineer at any seniority dedicated to this, and the depth I built up happened by necessity as much as by design.
Followed by....
> Senior engineers in AI-forward orgs are doing more leveraged, more hands-on, more meeting-heavy work simultaneously, with the human-focused parts of the role paying for it. The build cost collapsed, the alignment cost rose, the thinking time disappeared, and the productivity gains got captured by output volume rather than output quality. I
What is the job of a senior/lead engineer if not to take the uninformed hype chasing of the senior business, and deploy it in a way that makes things better?
I can't help but feel this senior engineer is talking far too casually about how - under their watch as senior AI engineer chap - engineers spend more time on throwaway code, have less personal development/1-to-1s, and didn't improve code quality. They haven't even mentioned the added financial token cost.
The only thing standing in the way of greedy hype chasing CEOs and a post apocolyptic wasteland is engineers taking their crazy requests and not making the world worse, and it sounds like the author has failed here. I think it's very positive and frank to share their experience, but I'm surprised they don't seem to see their role in it.
> The only thing standing in the way of greedy hype chasing CEOs and a post apocolyptic wasteland is ...
Nothing.
Either you quit outright, or ride the flames to the bottom, depending on personal ethics to cash-out potential ratio.
It's not an engineer's job to fix upper management delusions, and engineering is poorly equipped for that in any case.
> It's not an engineer's job to fix upper management delusions, and engineering is poorly equipped for that in any case.
What is the job of engineering leadership in your mind then, if not to take requirements from the business and convert them into technical solutions which improve things for the business? The requirement here was "AI to improve DX" the outcome was developers lives are worse in many creative and future impacting ways.
I sincerely would love to know if the author or their employer consider what they've done as successful or not.
If the requirements are self-contradicting while at the same time being non-negotiable their job is to quit.
So we agree on what their job is, but you're saying that if their job becomes impossible, they should quit, and I agree.
Do you think the author of the original article should have quit, rather than tried?
Early 2024 being assigned to "GenAI DX"? I would have interpreted that as being politely shown the door and quit at the spot.
All right… So, the cost of technical work has gone down, surely leaving more time for things like coordination, leadership, and thinking on a more operational level, right?
> The cost of building has collapsed, but the cost of aligning organisationally has not. If anything, it's gone up.
…Oh.
> What gave way is the human-focused work. Mentoring is the clearest example. I have less time for 1-2-1s than I did three years ago, and that isn't an accident, it's a choice I've made under pressure.
Ohh.
> The other thing that gave way was thinking time. There's very little of it in my working day now.
Ohhh.
When a long article is topped by an AI-generated image, it makes me wonder if I should bother reading. Did a human write this?
When I get the sense that something might be generated I ctrl+f "honest" and "framing".
These are words that humans use, but that Claude loves to use in a particular way, the kind of way used in this article. It particularly likes the phrase "The honest version".
> But the trade is real, and I don't think the industry is being honest about it.
Add "X is real".
I got about halfway through before the AI cliches drove me away.
It appears to be a mix. I sense that a human wrote it, or at least parts of it, and AI was used for polish. But the LLM-isms are definitely obvious.
It's the same thing that's been AI-rehashed 10,000 times already - did you read one of those?
Yes of course.
Why? You are already on hn which filters content before you read it
that is just simply wrong
What is wrong?
I do not mind at all that someone creates an AI Image to decorate a blog article.
I'm also not directly offended if someone is using an LLM to write text.
And i find it a valid argument, that people who post on HN and get enough votes for content, people read and liked, that this can be enough as a filter or quality gate.
Just a funny observation, apologies in advance:
I am rewatching Stargate SG-1 with my children right now and cannot read this comment except in the voice of Teal’c.
In the previous episode the team was in 1969 on a hippy bus.
My advice is to chill out a little…
I'm just gobsmacked at how self-absorbed this is.
I was listening to some obscure band you wouldn't know with a few chicks I met roller blading last week, and I can't help but read your comment while imagining the first few notes of the second song playing. After that we watched The Simpsons and my advice is to not eat a cow, man.
But on second consideration, I also want to apologize to you. It's not that your comment in a vacuum is so terribly, incredibly self-absorbed. It's the cumulative effect of many little such comments over time, which I pay way too much attention to, and you could say your comment "triggered" me and I let all that frustration out in one solid chunk of snark. "Gobsmacked" is really the wrong word for that. I sometimes am, but your comment isn't the one.
The truth is, I still don't know how to put it more nicely, but please have the generosity to take my lampooning of your comment as the time honored tradition of whatever you call it when you point out the flaw in something by driving it to an absurd extreme. It does have a place, but my eye-rolling "being gobsmacked" was a bit too much indignation. tbh when I wrote that I thought I ought to get flak for that, but fuck it, I'm annoyed, I'll say it anyway. But I didn't get flak so now I feel the need to calibrate it myself a little.
In short, I think you're wrong, that comment was bleh, but so was mine. Have a great weekend and much fun showing your kid stuff you both enjoy.
> ...this shift advantages people who can build fast with AI tools and disadvantages people who can't. The bias to action is genuine, but it isn't neutral. The engineers who've adopted these tools effectively get heard more often, get their proposals taken seriously more often, and shape direction more than those who haven't.
If only this was limited to engineers.
I'm seeing this applied to every role across organizations. The designer that gets heard is the one who can vibe code the best. Same with strategists, writers (seriously), and other people for whom "coding" is not remotely their expertise or function. This exponentially accelerates the "cost of aligning organisationally" when you have not only several eng groups but every single person in your team regardless of role proposing their own solutions, that as an engineer it is now your responsibility to babysit (or as we are terming it, "harden").
Meanwhile, the quality of creative work suffers because instead of working in a surface that's designed for their trade and process (e.g. Figma) they jump straight to Claude or Cursor or v0 or whatever, where they're bounded by (and graded on) their ability to manipulate a bot, rather than their actual skillset.
>> I'm seeing this applied to every role across organizations. The designer that gets heard is the one who can vibe code the best. Same with strategists, writers (seriously), and other people for whom "coding" is not remotely their expertise or function.
It's not about vibe coding the best, it's about delivering value the fastest. And the value in this case is bringing clarity to the conversation when there are a lot of unknowns. As the article notes, prototypes are great for that. It's much easier to poke and prod at a prototype and use it to explore the problem space than to sit in on a presentation where the slides are just heavily distilled versions of an engineering design doc that is dozens of pages long (and most people will just skim anyway).
I know a few guys here who were doing sysadmin, devops, frontend jobs for a few years in India and now they are driving a taxi in India. AI took their job. There have been mass layoffs by foreign companies in India; fewer outsourcing contracts are flowing to India.
As a result, many service companies are moving to product businesses.
will it also happen to developers in other countries? I've no idea.
When I tell this to others, they go in disbelief, but people don't care till their own job is gone.
Did AI replace their job, or did the cost of having AI skyrocket so the companies did layoffs to make budget? There’s a difference.
There are plenty of use cases were you can easily replace a person with AI today.
AI is very good in creating a working website. We do not have the issue of internet explorer 6 anymore, frontend doesn't host security relevant code and it shouuld also not have the business logic. So its easy to let ai create a website which talks to a secure api endpoint.
Plenty of basic android apps are just the same thing: Some API Endpoint calling Frontend.
I know plenty of people i worked with, which I would replace tomorrow if i got the choice of more tokens vs. having to work with them, putting in a lot of effort in Code Review and not seeing real benefits.
If the industry thinks we don't want to grow anymore, than the market gets saturated -> issue 1. Issue 2 -> if the market is saturated and tokens costs a lot of money budget wise, you will have to reduce your team size to compensate for the budget -> further reduction of market capacity.
We know that AI is replacing jobs
No, it isn't sustainable. There is a paper called "The AI Layoff Trap"[1], it says that it is a prisoner's dilemma and this is why this dude feels like he's in an arms race.
On the other front, people are saying that NVidia can't deliver stable drivers for like 15 months and they don't want to take software updates at all, they are more happy with last year's drivers.
I think this is a black swan event in the industry. A lot of people already suffered and more people will suffer still. Industry is going to change for sure, but probably not in a way that you would expect. Black swan simply doesn't work that way, it doesn't change industry in a good way, hence black swan.
[1]: https://www.researchgate.net/publication/402969772_The_AI_La...
black swan is not a reference to the badness of blackness but the unlikelihood of something contradicting volumes of previous data.
Thus it can change industry in a good way or a bad way, because the black swan is unprecedented and unpredicted, its consequences and their nature is unsettled.
Traditionally in economics black swan is an unpredictable negative event.
The only thing that is unsettled here still is how many more people will lose their jobs and how much cumulative loss prisoner's dilemma will generate.
I saw random people on Internet suggesting to piggyback this disaster and dip into the crazy money that it is "generating", but in a zero-sum game somebody has to lose.
As a general rule in human history unpredictable events seem negative when they happen, the greater the unpredictability the greater the negativeness seems to people when it happens because unpredictability tends to destabilize systems set up to deal with predictable things, this however does not mean that the later consequences will remain negative for ever and ever.
All that said however, probably the greatest black swan in terms of unpredictability in human history was that there were two freaking big continents in between Europe and Asia when you try to circumnavigate the globe.
on edit: probably should be "the greater the unpredictability and the more things the unpredictable event connects to the greater the negativeness seems to be", because obviously the platypus while completely unexpected affected basically nothing at all.
As I understand it, it's something unpredictable and high-consequence, not necessarily negative. The origins are in Hume's problem of induction. https://en.wikipedia.org/wiki/Problem_of_induction
Unpredictable positive events usually just don't get attention. Something good just happened, okay, that's good. People just don't pay attention.
Technically black swan might not be negative, but for all practical reasons, "black swan event" is what people call impactful unpredictable negative events and they expect those events to be negative. In other words, it is a synonym for "a disaster" of sorts, only "economical".
This is how an economist would call a disaster.
they do get attention, but generally people don't dwell on how unpredictable they were. Operation Warp Speed got covid vaccines out within ~1 year. Initial predictions were more in the >= 5 years time range. Fast covid vaccines were very much a "white swan" event, but reporting around it wasn't "this is so unlikely to have worked out" etc.
The description I remember is the idea of holding the belief “all swans are white” until one encounters a black swan, and having to update their beliefs accordingly. What does this mean about swans?
But maybe that’s not the intended meaning either? It’s an interesting expression.
Yes. And ultimately the colour of swans in Western Australia impacts… almost nothing at all.
.. save decisions about cycling w/out lights around Perry Lakes at night.
I agree with the content, but... "1-2-1"? From context, this seems to be "1 to 1" or "1:1", but the numeral 2 would never make sense. Am I missing something?
> DORA was built for a previous era.
I’m curious where you’re running into limits there. Intuitively, it feels like stuff like change lead time and MTTR is rooted enough in “what is the customer experiencing” that it shouldn’t have been made outdated by AI. But what are you finding?
It makes me wonder if large engineering organizations are going to splinter. The coordination costs are getting, proportionally, much larger than they used to.
When I left my corporate engineering job wayyyyy back in March, there were engineers and engineering leaders going off and getting a lot done, individually or in small teams. But project management and QA couldn't keep up with it. Managers resorted to turning their tokens loose on Jira just to try to make sense of it all (which, ironically made them the first to hit their token goals on the dashboard every week, and brought Jira to it's knees).
And, even worse, the junior engineers had no idea what was going on or how to get involved in anything.
The result was an increasingly chaotic mess.
My take on this, which is almost entirely pulled out of my rear end because I last worked in a large company before the rise of agents, is that we’ll see a move from vertical teams of specialists who get pulled into projects to build a mobile app or handle infrastructure. Instead there’ll be a much stronger focus on teams of generalists, or combined teams of specialists from different fields, working on a feature or product end to end.
Coordination has in my experience always been the big bottleneck in getting anything done, it’s just not hurt so much because everyone expected a feature that could have been done in a fortnight to take months.
> Instead there’ll be a much stronger focus on teams of generalists, or combined teams of specialists from different fields, working on a feature or product end to end.
> Coordination has in my experience always been the big bottleneck in getting anything done
I work at a large enterprise you've heard of. They're currently re-organizing the product area to remove currently-static two pizza teams into an amorphous blob of feature-oriented teams. Once the feature is complete, the team is dissolved and the engineers re-enter the pool, tasked with new features.
All that to say, I think you're right on the money with your assessment.
Where does the feature go for long term ownership? That throws you build it you own it out the window. We are going to get more time for documentation and handover right, right? Engineers are famous for generating good documentation.
There’s going to be lots of documentation. It will be AI generated and no human will ever read it. But there will be a lot of it.
Some places I've worked in the past had a rotating support team who were tasked with dealing with bugs/small feature requests/incidents for a period. I'm not sure that's the right answer because nobody wants to be on the team that's just doing scutwork even if its just for a month or two but it is an option.
All fantastic questions I wish we had an answer for
Claude with Jira is the first time I've applied AI and felt like it was truly saving me time. The UI and search tools are so clunky, it feels much better to say "Find jira tickets like xyz" and read through their titles/summaries in the command prompt.
I am not sure if that's a good thing for Claude, or an indictment of Jira.
I've always been frustrated by charlatans who do a great job advancing their own goals at the cost of those foolish enough to actually care about doing a good job. AI is a very empowering tool for these kinds as it is super effective at creating facades of productivity and thoughtfulness.
I think AI has a lot of value when used with thoughtful care. I think a lot of the conversations around it are really about frustrations with charlatans.
I read it but I don't really understand the writing.
Same here - there's no real insight, no key takeaways, nothing useful. It's just a bunch of anecdotes and drivel hidden in self-promotion.
Sometimes these AI posts and same-y takeaway points are like murmurations of birds flocking.
Murmurations is a beautiful way of describing these
My favorite, guy comes back from 6 month parental leave to force upon people in the trenches his epiphany on "GenAI".
He returned from paternity leave in early 2024, so over two years ago.
In the past bullshitting would only get you so far, but now bullshitting is both pervasive and revered. Contributors who specialize in taking things all the way, getting not just features but entire projects complete and fully functional aren't even in the conversation. What matters is the appearance of speed, not proof of completeness.
Sustainability will be most stretched where the bottom line actually matters. Medical devices and data processing, commercial aviation operations, precision manufacturing, and such. Bullshitting only takes you so far until actually doing the thing and showing that with high confidence becomes the only criterion.
AI exerts pressure to flatten roles; everybody feels the false confidence to do everything, so companies need to ensure expert oversight, and that shipping means assuming responsibility. If the latter can't be realistically accomplished, shipping should not be allowed. I think a healthy approach is to encourage everybody to create their own POCs with the understanding that only domain experts ship production code.
I still believe in ensuring that contributors understand every line of shipped code. This creates a natural ceiling on the width of their domain; though AI can help them learn more quickly, everyone can't know everything about engineering.
AI does not increase co-ordination costs per se; it increases productivity. You can achieve the same result with fewer people using AI, so it should decrease co-ordination costs. Only when you add AI while keeping the organization constant should co-ordination costs increase.
You could have just told us you're a slop slinger and save everyone some time.
Do not bother reading this.
The author could have written a rather incisive 800 words on this if he'd really tried.
But I will not read 2500 words of redundant, repetitive slop. It's really bad writing.
There is no pacing or conclusion to speak of. It's sort of just a loose list alternating between upsides and downsides, punctuated by the usual bullshit list-y ad-copy summations:
It's really telling.
> the thinking time disappeared
It did not. The author decided against spending time thinking. Nobody is following him around whacking him on the head if he blocks off tomorrow morning to go heads down on something. At least, I don't think they are?
A tiny bit of project management discipline can go a long way.
Thinking that memorizing insane code rules is being skilled in making software is like thinking that memorizing all the generals' birth days is being skilled in warfare.
Before AI, trying to program even a simple thing was an exercise in frustration from rules that had only been put in place by programmers to protect their own jobs and make it as difficult as possible for a normal person to develop. Oh! You mixed tabs and spaces, now your code will not compile and you're stuck another day. Oh! You forgot a semicolon, now the code won't run, even though the software points out your missed semicolon and thus knows how to fix it.
AI takes care of all that bagage and now I and others can make fully functional software that solves real world problem for real people.
I'm afraid you never understood the job if you think it's just "memorizing insane code rules".
> memorizing insane code rules is being skilled in making software
That’s on the level of complaining about having to learn music theory to play the piano, or to learn grammar to write a report. Or having to learn the road rules to drive a car on the street.
Have you ever seen a police report? They are the people who write most reports of any profession, and usually they are full of grammar and spelling mistakes.
But I understand that the majority of hackers here think that spelling mistakes in a report means that somebody is bad at their job as a police officer. And I know that a good portion of hackers here think that a suspect should have all charges dismissed if a police officer has mixed tabs and spaces when typing his final report, or forgot a semicolon. Or used ' where he should have used ".
Nope. Doing a syntax error in a programming language is the equivalent of filing the wrong form or filing the form incorrectly. Like if the question was “Suspect Height” and you put “green”.
Indentation rule are about scoping, You need a delimiter to mark end of a statements, and quoting often have to do with value type and interpolation. They’re not merely visual markers. Messing them up is the equivalent of answering “400 miles” when asked “what’s the color of the sky?”.
ADDENDUM: Yep writing code is filing a form. And just like any form, it’s easy to validate basic errors like syntax and type of values. The hard thing is to validate what happens after the form is processed. i.e, the intent of filing that specific form.
I've never had a problem mixing tabs or spaces in any language that supported this, which is basically most languages.
I've spent the last three weeks working out a spec and didn't even start the development process yet.
The idea that the syntax of the language would ever be a bottleneck sounds ridiculous to me.
The gate keeping allegations are also incomprehensible. The vast majority of developers are working on making their jobs easier. There wouldn't be an endless stream of new programming languages, libraries and frameworks, if there was a software guild that you needed approval from to work on software. Even if such a guild existed, it would get obsoleted by the competition.
There are cases of people maximizing their own job security by writing terrible and incomprehensible code, but most experienced developers have gotten bitten by their own cleverness and try to make their code as easy to understand and as accessible as possible.
Things like Java Server Faces and Java Enterprise Edition died out a long time ago. The XML craze is over. Roy Fielding style REST/HATEOAS is dead and everything is an HTTP API with OpenAPI docs nowadays. People understand by now that micro service architectures only make sense for organisational purposes but not for technical reasons. NoSQL also waned and everyone is basically putting their JSON into PostgreSQL if they need to store complex hierarchical data.
Why do you even care about irrelevant things like semicolons? Like, any reasonable editor gives you squiggly lines so you can't miss them, meanwhile in practice having a line delimiter helps disambiguate hairy expressions and produce more readable error messages. For me they are an imperceptible cost that I couldn't care less about.
If you talked about null pointers, which are basically a landmine in every line you've ever written, waiting for a chance to explode, maybe you'd have a point but even nullable pointers are an idea that is being relegated to the history books.
> The idea that the syntax of the language would ever be a bottleneck sounds ridiculous to me.
Great! Then you can pick any man of the street and show him some code, and he will understand the syntax intuitively and start coding? Dollar signs, semicolons, brackets and === and the difference between "" and ''. It's all self explanatory.
Driving a motorized vehicle was a highly specialized task in the beginning. You had to prime fuel, adjust carb needles, maybe tighten a chain after a day of driving. Manufacturers did all they could to make vehicles as easy as possible for the users, so that they can focus on actually driving, and not fighting against the machine. Look at where cars are today - anybody can drive without needing any skills relating to the machine. AI is doing the same for programming, which is great.
Now a common man can make software without learning thousands of different arbitrary rules.
Can you pick any man of the street and show him some text in a foreign language and get him to translate it? Especially with a foreign script? Can you write in japanese? or Persian? You had to go to school to learn how to write, you were not born with that knowledge.
A programming language is way easier than learning a foreign natural language. I believe the issue you struggle with is formal logic, not the syntax. Not everyone is trained to think formally (and some may find it arduous).
> The engineers who've adopted these tools effectively get heard more often, get their proposals taken seriously more often, and shape direction more than those who haven't.
I want to point out if the organizational model or your team's engineers are resistant to change, it doesn't matter how good of an engineer you are, or how good at proposal writing you are. With or without AI.