> 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.
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.
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).
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.
> 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.
> ...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).
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.
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.
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 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?
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.
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.
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.
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.
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.
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.
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.
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 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.
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.
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.
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).
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.
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.
> 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.
> 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.
> ...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).
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.
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.
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".
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
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 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?
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.
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.
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.
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.
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.
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:
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.
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 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.