You could say COBOL has had this "problem" for 40 years also. That's why we need to constantly be inventing new ways of making things. The old ways are always forgotten over time.
If you REALLY need something long-forgotten, then you have lazy-load it back into being at significant cost. That's the price of constant progress.
> I run engineering teams in Ukraine. My people lived the other side of this equation. Not the factory floor. The receiving end.
With all due respect, but many european taxpayers help pay for Ukraine. I am not disagreeing on the premise of the West killing itself via systematic recessions - Trump invading Iran leading to inflation as an example - so a lot of things are going on that show a ton of incompetency both in the USA and the EU, but at the same time I also get question marks in my eyes when this criticism comes from a country that receives money from others. That money could instead go to make EU countries more competitive, for instance. I am not saying this should necessarily be the case, mind you; I fully understand the nature of Putin's imperialism. But we need to really consider all factors when it comes to strategic mistakes with regards to production - and that includes taking up debts all the time. There are always a few who benefit in war, just as they benefit from subsidies from taxpayers (inside and outside as well).
>I read the Fogbank story and recognized it immediately. Not the nuclear material. The pattern. Build capability over decades. Find a cheaper substitute. Let the human pipeline atrophy. Enjoy the savings. Then watch it all collapse when a crisis demands what you optimized away.
>In defense, the substitute was the peace dividend. In software, it’s AI.
Before it was AI, the cheaper alternative was remote contract dev teams in Eastern Europe, right?
>The combination of technical skill and the judgment to know when the AI is wrong barely exists in the market anymore.
I see a talent pipeline collapse in next 5 years. "Software engineering is over coding is a solved problem" as being chanted by semi literate media and the AI grifter's marketing departments would further scare away the allocation of human capital to software engineering easily commanding 3x rise in salaries due to resource shortage.
This will end with the way of COBOL with a few people that still have the expert-level understanding of refactoring old code without causing outages or service disruption.
We’ll see, but right now I now see developers 24/7 hooked onto their agents and in the future we will experience a de-skilling problem which clean code, best practices, security and avoiding NIH syndrome will be all flushed down the toilet.
The problem is a management pattern:
removing people and organizational slack because they don’t generate immediate profit,
and then expecting the knowledge to still be there when it’s needed.
Short-term cost cutting leads to less junior hiring,
and removes the slack that experienced engineers need in order to teach.
As a result, tacit knowledge stops being transferred.
What remains is documentation and automation.
But documentation is not the same as field experience.
Automation is not the same as judgment.
Without people who have actually worked with the system,
you end up with a loss of tacit knowledge—and eventually, declining productivity.
AI is following the same pattern.
What AI is being sold as right now is not really productivity.
In many domains, productivity is already sufficient.
What’s being sold is workforce reduction.
The West has seen this before, especially in the case of General Electric.
GE pursued aggressive short-term financial optimization,
cutting costs, focusing on quarterly results, and maximizing shareholder returns.
In the process, it hollowed out its own long-term capabilities.
It effectively traded its future for short-term gains.
The same mindset is visible today.
The core problem is that decision-makers—often far removed from actual engineering work—
believe that tacit knowledge can be replaced with documentation, tools, and processes.ti cannot.
Tacit knowledge comes from direct experience with real systems over time.
If you remove the people and the learning pipeline,
that knowledge does not stay in the organization. It disappears.
The way the system is supposed to work is that companies that make bad decisions fail, and provide room for companies that do not make bad decisions to appear or grow bigger. Which works as long as you have an environment with fair competition where people are free to start and grow companies without running into entrenched interests or undue hardship.
It's much more than a management problem, experienced software engineers are actively opting into apathy and atrophy of their craft.
I see many peers getting worse in their abilities. It's especially disheartening to see people I admired for their problem solving devolve into someone who delegates more and more of their reasoning to LLMs. It really negatively affects working with them. If you have a concern or criticism of "their" approach to a problem they either dismiss it off hand as invalid, or they go discuss it with their LLM of choice making themselves a bottleneck to collaboration.
As the article suggests I suspect we're in for a real dark age of software as companies struggle to know who to keep, if they can even trust that those who have vital knowledge and skill today will retain it going forward.
Companies can grow organically or through strategy and adding new verticals to a point. Eventually they're too large for that. They own the whole market, they can't get regulatory approval for acquisitions and so on. At this point they only really grow at the same rate the industry or the economy does.
At this point (or often long before), the only way to increase profits is to raise prices and/or reduce costs. Profits tend to decline over time so there is constant pressure to reduce costs to statisfy the insatiable need for increasing profits.
This is the real product AI is selling: cutting wages. It's a combination of displacing workers (which, thus far, hasn't been all that successful). Where it is successful is to have the threat of layoffs hanging over your workers, getting them to do extra unpaid work for the same wages and making sure they can't ask for raises.
That's what's paying for all this AI investment.
So I agree with you: the real problem isn't AI. It's capitalism.
There's economic / capitalist pressure to reduce cost / increase revenue and optimize for short-term profits; that's on the corporate side, anyway.
But applying the military hardware stuff to software is IMO a bit of a leap; I get the similarities, but where demand for software hasn't slowed down at all, demand for military hardware and ammunition just wasn't there.
The alternative would have been to keep all the factories alive, maintained, staff employed (or training staff ready to onboard rapidly hired staff when capacity had to go up), supplied stockpiled (and rotated), etc. And who would be willing to pay for that?
In times of peace the voter wouldn't want the government to spend billions on the military if it wasn't necessary... except for the US which still spends billions a year on the military even in peacetime. But not on their production facilities it seems.
I think the problem is even more general than that, and has existed since before LLMs. All of the decision makers are incentivized to chase short term gains and ignore everything else. Many tech companies already had huge gaps in knowledge around their own codebases simply because such knowledge and expertise is basically treated as a liability/expense rather than an asset.
I'm actually very optimistic about LLMs/AI for basically the opposite reason tech leadership/MBAs are - I think it will allow us to overcome organizational/business/marketing ing hurdles that tech companies rely on short-sighted MBA-style 'leadership' for in the first place. And not because I believe in OpenAI and Anthropic - I think the future is self-hosted or community -hosted open models, and open collaboration among willing peers, building open software to solve real problems in honest ways, rather than hierarchical top-down corporate hellholes pumping out pre-enshitified crapware full of ads, tracking and dark patterns.
On the other hand, we have government operations that spend staggering amounts of money, and accomplish nothing at all. This one even has lost $8 million and nobody knows what happened to the money:
AI is almost a distraction from the older pattern: management discovers a way to make the spreadsheet look better this quarter and the hidden cost only shows up years later
It's interesting because i find when I'm less busy/stressed at work is when I spend more time motivated, doing better work, and fixing issues that otherwise would get left behind.
Agree. There is so much focus on "let's do the same thing we are doing now with fewer people". It is very boring and uninspired. How about "let's do something that we couldn't do before", instead?
> The core problem is that decision-makers—often far removed from actual engineering work— believe that tacit knowledge can be replaced with documentation, tools, and processes.ti cannot.
You need some experienced people around, but companies that rely on institutional knowledge to get everything done have always been doomed to fail.
Even before AI, turnover was a real thing. People churn jobs a lot in tech even when the pay is good. They get bored and jump companies, leave to join their friends' startup, or move to another city.
Every company I've worked for that operated on a belief that institutional knowledge was king and documentation and processes couldn't replace it eventually had to face the music when key employees left. Ironically this problem was at its worst at a company that compensated very well, because those key employees would often realize they had enough money to retire early or go take some risky startup job instead of sticking around to be the insitutional knowledge base.
The real problem is that the west is fractured and sees itself only as individuals, corporations and nations.
China sees itself as a civilization. Russia does too, ish (look up Dugin's Eurasian Empire).
The West has a hard time believing anyone wants to destroy it, so doesn't take the threat seriously. Meanwhile other civilizations are working to both destroy the west and ensure their own place in the future.
So we were OK outsourcing our production and knowledge for a quick buck and it's coming back to haunt us.
Even now, we still don't see ourselves as a civilization, actively work to undermine ourselves and help our enemies who openly want to destroy us, and are barely doing anything to defend ourselves. We seem to have also given up on the idea of a "democratic world", which was in vogue when I was growing up (Bush 2 years).
As for the thesis of this article, the positive is that code and knowledge, because of the fact preserving it is basically free, is still there. AI hasn't been good enough to displace it. And our technological advantage is still pretty wide and our military industrial complex is, for better or worse, coming back.
What you describe had been happened already when programming task became using search engines, passing data between libraries, and delegating coding to off-shore workers.
I'm seeing that in Big Tech now - there's no room anymore and it's super short term thinking (couched in the language of long term thinking). It's a really dangerous game for an incumbent to fritter away the ability to innovate because there is only enough capacity to focus on the here and now.
> The problem is a management pattern: removing people and organizational slack because they don’t generate immediate profit, and then expecting the knowledge to still be there when it’s needed.
Exactly. In direct contrast to this would be how Xerox and Bell funded laboratories just to pursue knowledge, without demands of profit. They ended up creating incredibly profitable things when driven my knowledge, and not profit.
I also read a book about math where the author argued that while the Greeks were driven to pursue truth for truth's sake, they ended up being far more productive and innovative. The Romans who were more driven to work for solutions to immediate practical needs, ended up being not so productive and innovative. He used this as a defense for efforts in pure math that seems to have no immediate application but ends up being massively, surprisingly powerful and productive for practical applications down the road. I think the same could be said for software development focussed on truth and correctness, rather than immediate productivity.
No, the problem is much more far-reaching than being limited to just corporations - it's a societal problem.
The article says the west is forgetting how to code, but actually the west is forgetting how to do math, how to draw and edit images, how to make music, how to write, how to read and even how to think.
And have you interacted with any kids recently? They're doing ALL their homework using ChatGPT. Forget kids, adults are even worse. At least kids can be supervised, but who's supervising the adults? How many of us have enough self-control to not reach out to the convenient AI app in our phones for every little thing?
This has massive repurcarions for our society as a whole. The bleak future depicted in Idocracy is becoming more and more of a surefire reality.
I think this is an important distinction. Documentation and automation can preserve artifacts, but not the actual capability.
A runbook can tell you what usually works, but it cannot tell you when the situation is no longer “usual.” That kind of judgment mostly comes from seeing real systems fail in messy ways over time.
Tools are still valuable, of course. But they work best when they help experienced people transfer knowledge, not when they are used as a reason to remove the people who understand the system.
Rather bad premise in the article.
1.) Germany, Italy and Eastern Europe are very industrial regions. The author forgets defence is not only the industry.
2.) The author doesn't show any source that Chinese developers don't use AI
We have both forgotten how to make things and also decided we can make more profit letting someone else make everything for every market. We have moved to a generation fixated on maximizing profit. However there is logic there as the cost to access the ability to make things is prohibitively expensive. As someone who makes open hardware with a nod to the environment and reusability, you can not justify or even find more locally sourced options than China.
Coding is different though, coding doesn't have a cost barrier, it has a ability barrier. I think we will loose a lot of people who never were passionate about programming and perhaps go back to a happy equilibrium. AI is only production ready if you have someone who understands software development. AI will improve speed to market if you have the right team, it doesn't remove the need for some to learn to code. You will of course end up with startups using exclusively AI but they will be those who end up with major security breaches or simply cannot scale as the AI goes in the wrong direction for the future. Tbh that's probably a positive as it weeds out the start ups that are focused on buzzwords for funding and not product.
There was a time when companies had terrible development practices and could forget how to build, test, and deploy software, but is anyone seeing that now? We have much better development practices nowadays.
It doesn’t seem much like defense industry problems.
> After spending an additional $69 million and years of reverse engineering, they finally produced viable Fogbank. Then discovered the new batch was too pure. The original had contained an unintentional impurity that was critical to its function.
Same thing that happened to the unfortunate Dr. Jekyll!
Excellent post. Two stand-out points are deskilling through abolition of apprenticeship (or equivalent progression through the rank and responsibility), and loss of institutional knowledge, especially tacit knowledge stored in individual people. These are people problems more than they are technology problems. Without continuity of process and practice stuff gets lost. Sometimes change really is progress, for example software safety and security practices have progressed over the past 50 years, but other times change is just churn, or choices driven by misaligned incentives which will bite later, as the article describes.
I can't not write the tired comment of how ridiculous it is to criticize AI and then use AI to write your article. It's tired, but so is this writing style.
For the actual problem, I fear this can't be solved by warning people, the pain will need to be felt. The system we live in, basically free market capitalism, cannot do anything else except local optimization. Maybe it's for the best, I don't know. The alternative of top down planning wouldn't have this problem, but it would have other problems. I work for a mid size somewhat luxury brand, and the major goal right now is cost cutting and AI for efficiency everywhere instead of using it to create better products or better ways to reach out customers. When I think about who will buy our luxury products if all jobs were optimized out of existence, I don't have an answer, but again I think the pain will need to be felt to change course.
Isn't that is the point of technological civilization development? People for example forgot how to weave on the handloom, or all the parts production and the maintenance for the watermills. And wooden sailships - top mastery of handling and engineering developed for millennia, gone.
As it was said - the future is here, it just distributed non-uniformly, so somebody is still and will be for some time sailing, manufacturing things and writing code.
> Leadership qualities. Our last hiring round tells you how rare that is: 2,253 candidates, 2,069 disqualified, 4 hired. A 0.18% conversion rate.
It's minor but this is just wrong. If you're going to hire 4 candidates, there could be 2,253 perfectly qualified candidates even if only 0.18% get hired. The conversion rate is meaningless; it just tells us how many jobs were on offer. There is no way that the skills this fellow wanted were so rare and difficult that only 1/500 candidates could possibly handle the job. Humans even in the 1/20 mark are pretty competent if you're willing to train them and legitimate geniuses crop up at around 1/200.
There's a certain irony in that the article itself is quite clearly assisted by AI. Not a criticism per se as I don't have a problem with AI assistance, but food for thought given the material being commented on.
I disagree with the premise - interesting but I interpret the same fact pattern differently.
The history of technology is the replacement of manual processes with automated ones.
Consider a very basic process: checkout of a restaurant.
Writing the price of each item on a sheet of paper, manually adding them and writing the total was replaced with typing in the prices and eventually with just pushing the button for the item. Paper still exists for jotting down your order but within seconds of leaving the table it’s transitioned to computer.
This has enabled lots of desirable advances- speed, accuracy, new payment rails, and increasingly, elimination of the server in checkout- you tap a credit card on a tabletop device.
Did we “forget” how to do checkout? No. We purposely changed it.
But if the internet connection goes down or the backend server powering the cash register app goes down, there is an atrophied and not-regularly exercised skill set (maybe not even trained, IDK) that has to be implemented on-the-fly and it’s slow and frustrating for everyone.
Businesses don’t exercise (or perhaps even train) this process because it’s just not needed enough to warrant the cost.
Military procurement of weapons systems is hardly the place to point to as a technological tradition. There are lots of cases where no one pays the money to keep a production process in place; the reasons are all related to shortsighted “cost savings” or failing to anticipate changing needs.
With coding today, we are seeing the same kind of shift in priorities as my restaurant example. Having humans write code in the 2020 (pre-GPT) tradition was extremely inefficient in terms of time-from-idea-to-implementation.
We’ve found a new way to do the mundane part of that task (the mechanics of translating spec to implementation).
We are figuring out how to do that while preserving quality (and a lot of it is learning how to specify appropriately).
Will we “forget” how to “build” code?
No, but the skills to generate source code by hand will atrophy just as the skills to draw blueprints by hand atrophied with the advent of CAD.
Will we find examples where someone prematurely optimized away knowledge of a skill or process, incorrectly thinking it was no longer needed? Of course.
But the productivity gains we get will be so great on average that no one will go back to doing things the old way.
There will be old-timers and hobbyists who will preserve some of that knowledge; for most it will just be a curiosity.
Speak for yourself. I now dare to code much harder problems and learning is bliss. No more having to sit down to dig needle-in-haystack through horrible documentation or random Stack Overflow posts.
LLMs are a magnificent tool if you use them correctly. They enable deep work like nothing before.
The problem is the education system focused on passivity (obeyance), memorization, and standardized testing. And worst of all, aiming for the lowest common denominator. So most people are mentally lazy and go for the easy win, almost cheating. You get school and interview cheating and vivecoders.
But it's not the only way to use LLMs.
Similarly, in Wikipedia you can spend hours reading banal pop-slop content or instead spend that time reading amazing articles about history, literature, arts, and science.
Perhaps the approach to, and leverage from, using AI is different for someone who's been active on HN for two decades, and junior devs who've been brought up on iPhones in the flawed school system you're describing?
As TFA says, the problem is that accumulating knowledge takes time and effort, and the AI hype and expectations on LLM-assisted coding helps with rationalizing ever more short-sighted decisions that squander or hinder that process.
180 comments
[ 3.5 ms ] story [ 90.0 ms ] threadIf you REALLY need something long-forgotten, then you have lazy-load it back into being at significant cost. That's the price of constant progress.
I'm going to steal that one and add it to Stross': "Efficiency is the reciprocal of resilience."
With all due respect, but many european taxpayers help pay for Ukraine. I am not disagreeing on the premise of the West killing itself via systematic recessions - Trump invading Iran leading to inflation as an example - so a lot of things are going on that show a ton of incompetency both in the USA and the EU, but at the same time I also get question marks in my eyes when this criticism comes from a country that receives money from others. That money could instead go to make EU countries more competitive, for instance. I am not saying this should necessarily be the case, mind you; I fully understand the nature of Putin's imperialism. But we need to really consider all factors when it comes to strategic mistakes with regards to production - and that includes taking up debts all the time. There are always a few who benefit in war, just as they benefit from subsidies from taxpayers (inside and outside as well).
>In defense, the substitute was the peace dividend. In software, it’s AI.
Before it was AI, the cheaper alternative was remote contract dev teams in Eastern Europe, right?
I see a talent pipeline collapse in next 5 years. "Software engineering is over coding is a solved problem" as being chanted by semi literate media and the AI grifter's marketing departments would further scare away the allocation of human capital to software engineering easily commanding 3x rise in salaries due to resource shortage.
We’ll see, but right now I now see developers 24/7 hooked onto their agents and in the future we will experience a de-skilling problem which clean code, best practices, security and avoiding NIH syndrome will be all flushed down the toilet.
AI code generators are trolls. They confidently plausible content which is partly wrong. Then humans try to find their errors.
This is not fun. It has no flow.
People come and go at rates that would not be sustainable in any manufacturing business.
The problem is a management pattern: removing people and organizational slack because they don’t generate immediate profit, and then expecting the knowledge to still be there when it’s needed.
Short-term cost cutting leads to less junior hiring, and removes the slack that experienced engineers need in order to teach. As a result, tacit knowledge stops being transferred.
What remains is documentation and automation.
But documentation is not the same as field experience. Automation is not the same as judgment. Without people who have actually worked with the system, you end up with a loss of tacit knowledge—and eventually, declining productivity.
AI is following the same pattern.
What AI is being sold as right now is not really productivity. In many domains, productivity is already sufficient. What’s being sold is workforce reduction.
The West has seen this before, especially in the case of General Electric.
GE pursued aggressive short-term financial optimization, cutting costs, focusing on quarterly results, and maximizing shareholder returns. In the process, it hollowed out its own long-term capabilities. It effectively traded its future for short-term gains.
The same mindset is visible today.
The core problem is that decision-makers—often far removed from actual engineering work— believe that tacit knowledge can be replaced with documentation, tools, and processes.ti cannot.
Tacit knowledge comes from direct experience with real systems over time. If you remove the people and the learning pipeline, that knowledge does not stay in the organization. It disappears.
Also modern managers: You were in the bathroom for six minutes. I'm docking your pay.
I see many peers getting worse in their abilities. It's especially disheartening to see people I admired for their problem solving devolve into someone who delegates more and more of their reasoning to LLMs. It really negatively affects working with them. If you have a concern or criticism of "their" approach to a problem they either dismiss it off hand as invalid, or they go discuss it with their LLM of choice making themselves a bottleneck to collaboration.
As the article suggests I suspect we're in for a real dark age of software as companies struggle to know who to keep, if they can even trust that those who have vital knowledge and skill today will retain it going forward.
Companies can grow organically or through strategy and adding new verticals to a point. Eventually they're too large for that. They own the whole market, they can't get regulatory approval for acquisitions and so on. At this point they only really grow at the same rate the industry or the economy does.
At this point (or often long before), the only way to increase profits is to raise prices and/or reduce costs. Profits tend to decline over time so there is constant pressure to reduce costs to statisfy the insatiable need for increasing profits.
This is the real product AI is selling: cutting wages. It's a combination of displacing workers (which, thus far, hasn't been all that successful). Where it is successful is to have the threat of layoffs hanging over your workers, getting them to do extra unpaid work for the same wages and making sure they can't ask for raises.
That's what's paying for all this AI investment.
So I agree with you: the real problem isn't AI. It's capitalism.
But applying the military hardware stuff to software is IMO a bit of a leap; I get the similarities, but where demand for software hasn't slowed down at all, demand for military hardware and ammunition just wasn't there.
The alternative would have been to keep all the factories alive, maintained, staff employed (or training staff ready to onboard rapidly hired staff when capacity had to go up), supplied stockpiled (and rotated), etc. And who would be willing to pay for that?
In times of peace the voter wouldn't want the government to spend billions on the military if it wasn't necessary... except for the US which still spends billions a year on the military even in peacetime. But not on their production facilities it seems.
I'm actually very optimistic about LLMs/AI for basically the opposite reason tech leadership/MBAs are - I think it will allow us to overcome organizational/business/marketing ing hurdles that tech companies rely on short-sighted MBA-style 'leadership' for in the first place. And not because I believe in OpenAI and Anthropic - I think the future is self-hosted or community -hosted open models, and open collaboration among willing peers, building open software to solve real problems in honest ways, rather than hierarchical top-down corporate hellholes pumping out pre-enshitified crapware full of ads, tracking and dark patterns.
https://www.seattletimes.com/seattle-news/politics/does-noth...
You need some experienced people around, but companies that rely on institutional knowledge to get everything done have always been doomed to fail.
Even before AI, turnover was a real thing. People churn jobs a lot in tech even when the pay is good. They get bored and jump companies, leave to join their friends' startup, or move to another city.
Every company I've worked for that operated on a belief that institutional knowledge was king and documentation and processes couldn't replace it eventually had to face the music when key employees left. Ironically this problem was at its worst at a company that compensated very well, because those key employees would often realize they had enough money to retire early or go take some risky startup job instead of sticking around to be the insitutional knowledge base.
China sees itself as a civilization. Russia does too, ish (look up Dugin's Eurasian Empire).
The West has a hard time believing anyone wants to destroy it, so doesn't take the threat seriously. Meanwhile other civilizations are working to both destroy the west and ensure their own place in the future.
So we were OK outsourcing our production and knowledge for a quick buck and it's coming back to haunt us.
Even now, we still don't see ourselves as a civilization, actively work to undermine ourselves and help our enemies who openly want to destroy us, and are barely doing anything to defend ourselves. We seem to have also given up on the idea of a "democratic world", which was in vogue when I was growing up (Bush 2 years).
As for the thesis of this article, the positive is that code and knowledge, because of the fact preserving it is basically free, is still there. AI hasn't been good enough to displace it. And our technological advantage is still pretty wide and our military industrial complex is, for better or worse, coming back.
Exactly. In direct contrast to this would be how Xerox and Bell funded laboratories just to pursue knowledge, without demands of profit. They ended up creating incredibly profitable things when driven my knowledge, and not profit.
I also read a book about math where the author argued that while the Greeks were driven to pursue truth for truth's sake, they ended up being far more productive and innovative. The Romans who were more driven to work for solutions to immediate practical needs, ended up being not so productive and innovative. He used this as a defense for efforts in pure math that seems to have no immediate application but ends up being massively, surprisingly powerful and productive for practical applications down the road. I think the same could be said for software development focussed on truth and correctness, rather than immediate productivity.
No, the problem is much more far-reaching than being limited to just corporations - it's a societal problem.
The article says the west is forgetting how to code, but actually the west is forgetting how to do math, how to draw and edit images, how to make music, how to write, how to read and even how to think.
And have you interacted with any kids recently? They're doing ALL their homework using ChatGPT. Forget kids, adults are even worse. At least kids can be supervised, but who's supervising the adults? How many of us have enough self-control to not reach out to the convenient AI app in our phones for every little thing?
This has massive repurcarions for our society as a whole. The bleak future depicted in Idocracy is becoming more and more of a surefire reality.
A runbook can tell you what usually works, but it cannot tell you when the situation is no longer “usual.” That kind of judgment mostly comes from seeing real systems fail in messy ways over time.
Tools are still valuable, of course. But they work best when they help experienced people transfer knowledge, not when they are used as a reason to remove the people who understand the system.
In my experience, documentation is unwanted because it creates work and no revenue. Which makes the tacit knowledge all the more valuable.
Coding is different though, coding doesn't have a cost barrier, it has a ability barrier. I think we will loose a lot of people who never were passionate about programming and perhaps go back to a happy equilibrium. AI is only production ready if you have someone who understands software development. AI will improve speed to market if you have the right team, it doesn't remove the need for some to learn to code. You will of course end up with startups using exclusively AI but they will be those who end up with major security breaches or simply cannot scale as the AI goes in the wrong direction for the future. Tbh that's probably a positive as it weeds out the start ups that are focused on buzzwords for funding and not product.
It doesn’t seem much like defense industry problems.
Same thing that happened to the unfortunate Dr. Jekyll!
For the actual problem, I fear this can't be solved by warning people, the pain will need to be felt. The system we live in, basically free market capitalism, cannot do anything else except local optimization. Maybe it's for the best, I don't know. The alternative of top down planning wouldn't have this problem, but it would have other problems. I work for a mid size somewhat luxury brand, and the major goal right now is cost cutting and AI for efficiency everywhere instead of using it to create better products or better ways to reach out customers. When I think about who will buy our luxury products if all jobs were optimized out of existence, I don't have an answer, but again I think the pain will need to be felt to change course.
As it was said - the future is here, it just distributed non-uniformly, so somebody is still and will be for some time sailing, manufacturing things and writing code.
It's minor but this is just wrong. If you're going to hire 4 candidates, there could be 2,253 perfectly qualified candidates even if only 0.18% get hired. The conversion rate is meaningless; it just tells us how many jobs were on offer. There is no way that the skills this fellow wanted were so rare and difficult that only 1/500 candidates could possibly handle the job. Humans even in the 1/20 mark are pretty competent if you're willing to train them and legitimate geniuses crop up at around 1/200.
You mean the world?
Deepseek was being glazed here, Im sure chinese programmers use it like CC
The history of technology is the replacement of manual processes with automated ones.
Consider a very basic process: checkout of a restaurant.
Writing the price of each item on a sheet of paper, manually adding them and writing the total was replaced with typing in the prices and eventually with just pushing the button for the item. Paper still exists for jotting down your order but within seconds of leaving the table it’s transitioned to computer.
This has enabled lots of desirable advances- speed, accuracy, new payment rails, and increasingly, elimination of the server in checkout- you tap a credit card on a tabletop device.
Did we “forget” how to do checkout? No. We purposely changed it.
But if the internet connection goes down or the backend server powering the cash register app goes down, there is an atrophied and not-regularly exercised skill set (maybe not even trained, IDK) that has to be implemented on-the-fly and it’s slow and frustrating for everyone.
Businesses don’t exercise (or perhaps even train) this process because it’s just not needed enough to warrant the cost.
Military procurement of weapons systems is hardly the place to point to as a technological tradition. There are lots of cases where no one pays the money to keep a production process in place; the reasons are all related to shortsighted “cost savings” or failing to anticipate changing needs.
With coding today, we are seeing the same kind of shift in priorities as my restaurant example. Having humans write code in the 2020 (pre-GPT) tradition was extremely inefficient in terms of time-from-idea-to-implementation.
We’ve found a new way to do the mundane part of that task (the mechanics of translating spec to implementation).
We are figuring out how to do that while preserving quality (and a lot of it is learning how to specify appropriately).
Will we “forget” how to “build” code?
No, but the skills to generate source code by hand will atrophy just as the skills to draw blueprints by hand atrophied with the advent of CAD.
Will we find examples where someone prematurely optimized away knowledge of a skill or process, incorrectly thinking it was no longer needed? Of course.
But the productivity gains we get will be so great on average that no one will go back to doing things the old way.
There will be old-timers and hobbyists who will preserve some of that knowledge; for most it will just be a curiosity.
LLMs are a magnificent tool if you use them correctly. They enable deep work like nothing before.
The problem is the education system focused on passivity (obeyance), memorization, and standardized testing. And worst of all, aiming for the lowest common denominator. So most people are mentally lazy and go for the easy win, almost cheating. You get school and interview cheating and vivecoders.
But it's not the only way to use LLMs.
Similarly, in Wikipedia you can spend hours reading banal pop-slop content or instead spend that time reading amazing articles about history, literature, arts, and science.
As TFA says, the problem is that accumulating knowledge takes time and effort, and the AI hype and expectations on LLM-assisted coding helps with rationalizing ever more short-sighted decisions that squander or hinder that process.