A new and growing fear I have is that AI cuts off some of the most well-trodden pathways to intellectual growth. Every time the AI solves a problem that I would not have been able to efficiently solve alone, it has replaced an interaction that previously would have happened with a mentor/supervisor/code reviewer. This is faster for the person seeking the micro-assistance, but there is a flipside: fewer human-to-human acts of mentorship and shared problem solving. We lose a lively ingredient of team formation, expert formation, and a source of joy for all involved. As this scales up it feels plausible that we end up with a wider deagradation of intellectual standards. I like the Cognitive Comons framing.
If the LLMs work as advertised, we won't need experts.
Even today, with the current state of the art, we need far fewer experts. LLMs are already much better at debugging than humans are, for example. Math proofs are falling at a unprecedented rate. And so on.
I think this has already happened to some extent, with social media etc. crowding out reading books. Even higher quality stuff like HN pales in comparison with what a good book brings, and it's a daily struggle to keep reading.
At least we're (mostly) not scrolling feeds all day at work. AI is going to be like this for all our professional competencies.
>Every time the AI solves a problem that I would not have been able to efficiently solve alone, it has replaced an interaction that previously would have happened with a mentor/supervisor/code reviewer.
I suppose that Socrates would strongly agree, seeing how the same argument could be applied to learning from a book.
For anyone that does LLM supported work it's crystal clear that skill decline is real. Surprisingly, the leadership at my work doesn't give rats ass about it. They published AI engineering manifesto, pushing for loops, workflows, advocating for producing many times more code. All of this ignoring the elephant in the room.
I don't see it being "crystal clear" that skill decline is real. People are doing work they wouldn't have been able to do before. They are getting a more broad exposure to technologies than they have before.
Cynically they may be banking (however misguided it turns out to be) on model skill in aggregate growing faster than the skills of their staff decline at which point, bin the staff, use the model.
The hype from non-technical people about AI would make you think they already discovered AGI.
> Or are we quickly approaching an apex where the people running these companies realize AI cannot completely replace human developers?
The boosters are loud and the failures are made silent, until the failures become impossible to ignore. Tens to hundreds of millions of dollars spent on AI in enterprise software companies need to show RoI at some point, and the promises of executives leaning on AI's productivity improvements to make their accelerated release schedules need to actually make the releases. Its currently a very dangerous career move to publically apply critical thinking to LLM capabilities. I think the math sort of does itself.
I don't know, I find AI still misses the mark so much for stuff that's not happening inside a well constrained framework with very specific guardrails and boundary conditions. Every time I have agents build something half complex that I would be easily able to pull off myself (albeit not at their pace) I always ask if everything is correct and working and I get a resounding "yes!", then I test it and it's essentially a steaming pile of garbage which I then tell the agent and after which it goes "you're absolutely right to push back, this isn't good at all!" as if it was clear from the beginning. I don't know if we'll achieve real intelligence soon with these things but what we have now isn't there yet. It's incredibly useful but I don't think you can replace a good programmer with it, you can make a good programmer into an excellent one though!
Same goes for writing, anyone who has written long complex texts with LLMs knows that a ton of editing is required to make it half decent.
It doesn't seem clear to me at all. e.g. I've been having a fun time learning Godot this last week with codex laying down groundwork for logic, generating assets, etc. This gives me something concrete that I can work with and ground myself within instead of starting from a blank canvas. I don't much care for the actual scripting logic because it's all basically trivial to me with a few decades of programming experience, and I'm not going to be able to develop my artistic abilities to a useful capacity anytime within the next few years, so I can instead focus on learning architecture/organization patterns and game systems that I'm interested in while I read through docs.
So basically, if you use it to wave away things you've mastered or explicitly don't want to learn at the moment, you can stay focused on things you are figuring out. I don't see it as any different from writing "by induction" in a math proof without writing all the details because you and the reader know you could easily work them out. In this way I can learn things that I simply would not have the time to dig into before, increasing my skillset. As is was before AI, metacognition is the most widely useful skill to have.
I don’t think it’s as clear cut as you make it out to be.
Maintenance <> growth
Once you got a skill it’s quite hard to actually lose it. It’s like riding a bike as they say.
I find all the reading of those walls of code exercises my mind better than the writing did. I’m sure I lost memories of how to read a file by hand or balance a tree but who cares?
I am thinking about architecture and design a lot more these days and every decision I make has to be actually argued for even to myself. I can no longer lean back and say “that’s how we always do it” or “too expensive to change now” as I find many people in practice actually do. They were just coasting on premade architectural choices and their “skills” consisted of knowing arcane incantations and syntactic details completely unrelated to the (business) problem at hand.
I am not convinced many developers actually have the skills they think they have. They could wrestle syntax and mess around with tooling, but could they abstract properly? Define clear semantic boundaries? Have proper civil discussions about responsibilities and where they should lie on the right level of abstraction? Nothing has changed in that regard. If anything that part has been amplified. (“taste”)
I've got a friend whose firm has started using 'cognitive surrender' to describe the habit of delegating thinking to the LLM in the way that we used to skim code reviews and say 'LGTM' while missing something important.
I think as an existing expert in my field (robot devops) what I feel most strongly is not that I'm losing my skills but rather than I'm losing my nerve.
Like, problems that I would previously dig into on my own, poking at this and that log file to try to understand what happened to get a system into a particular state, now claude is mediating most of those kinds of interactions and it is the one doing the first pass surfacing of "okay I discovered X, Y, and Z things that are hinky, and I'm not totally sure yet what this all means, but let's look together."
Nowadays I feel almost naked looking at a terminal where I'm typing each character myself. A lot of the old instincts around tab completion and grepping through the --help output of every tool, all that stuff has atrophied somewhat. Maybe that is genuine skill decline?
I think this is a really important idea. Good problem solvers, in general, are people who are prepared to go in and poke around, to see what happens when you disable this or add that.
The only way I've found to make juniors develop that instinct is to make them do it until it comes naturally--to continually demand they verify their assumptions, test out theories while debugging. If you don't practice being comfortable with the unfamiliar, you lose the knack.
It's fine, of course, to lose the knack to AI, as long as you'll never need to solve a problem the AI can't (or can't access). I don't think that's a good assumption for everyone to make.
> Good problem solvers, in general, are people who are prepared to go in and poke around, to see what happens when you disable this or add that.
you have to do all this when dealing with AI too. The syntax is a conversation vs a strict grammar but you still have to do all the exploration, verification, and evaluation you always had to do as a developer. You have to know how to explain what you want and know when you have it, that's not always an easy skill to learn though.
i don't really buy the "loosing skills" line. I'm not old enough to remember but it seems like this would be similar to an expert slide rule operator lamenting the skill loss because of using a calculator. A new tool has come out that antiquates some skills but requires a new set of other skills to master.
> More people that are not devs, are using AI to create things, and are skilling up to a mediocre level?
What skills are being developed by non-devs prompting AI to churn out code they don't understand? Note that I'm not debating if the code is good or works or whatever, I'm asking if any real skills are being developed merely by prompting AI towards some goal.
my sister and two of her friends put together a small company. She asked me to work on a mobile app for them, like a small niche LMS. I was making progress but fell behind, i have a demanding job/family and she doesnt. She asked if she could try to take it over, i told her about claudecode and then gave her some magic words to get started with. Basically, some framework names and some other technologies involved and some prompt guidance. She completely took off with it and came up with a flow where she chats with ChatGPT and it tells her what to tell ClaudeCode, she copy/pastes into claudecode does it's thing.
How could it possibly work? Except she got through setting up her own Apple dev account, all the certificates, subscription entitlement, revenuecat integration, TestFlight, all the paperwork that goes along with a listing and all of that in about 3 weeks. She submitted her app to the AppStore for review last friday.
She didn't learn any coding but she sure as hell produced a pretty good mobile app with a subscription payment system all on her own with the help of ChatGPT and ClaudeCode. She's also producing different tools that run on her website which is hosted on squarespace. I have no idea how she's doing it but she has all these little questionairre and decision tree pages backed by google sheets. She's the second non-coder i've seen really blossom with AI and just run with it building stuff she's always wanted but never knew how.
If they are producing code, there is some knowledge being absorbed. Nobody is using AI to create an app is learning zero.
Its just what they are learning, is not as much as they think, and really random, not-structured. Like reading just a few random chapter of a book across multiple classes. They get a smattering of ad-hoc tidbits of knowledge.
I am really worried if using AI is reducing someone's already gained knowledge. Moving someone backwards. That has me a bit more scared.
> If they are producing code, there is some knowledge being absorbed. Nobody is using AI to create an app is learning zero.
I think the knowledge being absorbed is 'how to use AI to make apps', not necessarily anything about the code itself. I've seen lots of people who are very effective with AI suddenly poleaxed in interviews without it. Going from 'able to explain exactly what needs to happen' to 'staring at a blank page with no ideas'.
>I am really worried if using AI is reducing someone's already gained knowledge. Moving someone backwards. That has me a bit more scared.
It absolutely is. It removes a lot of friction from pathways that kept previous knowledge firm.
This isn't unique to AI. When calculators came around, people had a new tool that decreased the need to do arithmetic in your head. This has lead to a decline in the percentage of people who knew arithmetic, but can no longer do it adequately without the external tool. Another example would be assembly skills declining amongst software engineers after compiled languages become dominant.
What I would argue is unique to AI is two-fold. The first being that the scale of things being automated is enormous. Keeping with just software, AI isn't only a tool that decreases the need to write code, it's also a tool that decreases the need to do your own research, debugging, system design, version control, etc. The list of tasks a regular engineer is responsible for that have not been consumed by AI is small. To make matters more frightening, this is just a single industry. The same applies to multiple other knowledge heavy fields (e.g. mathematics).
The second thing I'd argue is unique about AI is the addictive nature of it. These LLMs are deliberately coerced into being sycophants that blow smoke up your ass unless you deliberately tell the model not too (and it may still do it anyways!). A calculator didn't compliment your insight for asking it to calculate the square root of -1.
Furthermore, many people have talked about the resemblance of LLMs to slot machines--there's a feeling that anything could be possible if you only prompt the model correctly. Your first prompt is wildly off the mark, but your next prompt is better though still not quite right, so you continue forward. You keep iterating your instructions, your word choices, your tone, all in search of the desired response. In this way, an LLM is a slot machine, your prompt the lever you pull, and the output is the jackpot.
All this taken together, we have a machine that decreases the need to engage with the critical faculties of your own brain, which has been trained to keep your attention through flattery, and whose probabilistic nature preys on our love for gambling.
AI is good enough to generate a first pass for an increasingly large number of projects. An engineer using AI can create an even larger amount.
But muscle memory and expertise are not fixed. They fade over time without use. If you're no longer writing code yourself, you'll get rusty on syntax in the short term. In the long term, you'll get rusty on code structure and layout.
But many engineers are no longer reviewing code either. Reviewing code written by an AI is now the bottleneck, so you're expected to allow AI to review it as well.
So we have a group of people who are no longer engaging in either the writing of the code, or the analysis of the code that's written.
Obviously, this would lead to skill loss.
Many would argue that they're not truly losing skills, because they're more engaged in the grander architecture of the code. To that I would say--your job title says engineer, not architect.
It is probably skill loss on one side and gain on another. In a few years it is going to matter even less if you can code manually. The hard part now is understanding what agents do. How can you gain a bit of confidence in their work? I think it relates to testing, you can trust the parts you test.
Similar to the argument made by John Blow several years ago. Though, Blow blamed frameworks/engines and layers of abstractions. I wonder if the authors came to this framing themselves, or listened him.
The pseudo-academic style of the article is awful. All abstractions and buzzwords, no examples. Also, the tragedy of the commons concept is far older than 1968. The term "common" refers to common grazing land upon which anyone could graze animals.
That's an English term. The American equivalent is "open range".
Getting past that, the author has a point. There's a loss of shared expertise when there aren't people around learning and doing something. In the US, we've seen this in manufacturing. The number of Americans who know how to set up a good production plant is much lower than it was in the 1980s. That's the consequence of the hollowing out of American manufacturing. The author talks about AI vs. white collar work, but fails to make the connection with outsourcing vs. blue collar work.
The loss of this expertise has recently been made very clear in the US as attempts are made to scale up weapons production for the US's various wars. Progress is very slow, as has been seen with both artillery ammo and air-defense missiles.
> Also, the tragedy of the commons concept is far older than 1968. The term "common" refers to common grazing land upon which anyone could graze animals. That's an English term. The American equivalent is "open range".
The term is not "common" or "commons" but "tragedy of the commons" which dates back to a book in 1968 by Garrett Hardin. Elinor Ostrom subsequently showed that most or all of Hardin's assumptions and claims were wrong, or at the very least, far from universal, and that the process he describes elides what actually drives the destructions of resources held in common: greed and power.
I think you might be thinking about Elinor Ostrom and she didn't show that his assumptions and claims were wrong exactly. She expanded on the work to clarify the difference between managed vs unmanaged commons. Showing that successful commons are managed in some way with established rules and limits around the usage. The term still has use especially if it is used to clarify the need for management of common resources to avoid the tragedy.
which is also basically the same point made by Hobbes about the state of nature
Hobbes, Rousseau were effectively both trying to solve the "tragedy of the commons" problem with political collaboration. Hobbes is widely misunderstood by (romantic, naive, libertarian) fools to have said that absent a powerful soverign, life is "nasty brutish and short". What he more accurately said is that, "an unmanaged commons is nasty bruitsh and short" -- to use that kind of language
Rousseau, to the same romantic strain is widely misunderstood to have said that 'the unmanaged commons' is a delight. Instead, he basically said politically community isnt that required anyway, and if you're in trouble, just run away.
In either case, the tragedy of the commons issue is well known in other guises throughout the history of philosophy
> In either case, the tragedy of the commons issue is well known in other guises throughout the history of philosophy
It really isn't. There are lots of anecdotes that appear to illustrate the tragedy, throughout history. But what they lack is what Ostrom added: the historical account of how a previously managed commons had its management structures dismantled by greed and power and only then was in a position to become a "tragedy". The result is that a very significant number of people believe that resources held in common always lead to "tragedy", when in fact the lesson is "the structures used to manage resources held in common are critically important, and severe action should and must be taken against those who try to dismantle them". We can hold resources in common, but must be ever vigilant and actionful against the selfish bastards who seek to manipulate them for their own advantage.
Right, and that's pretty much exactly Hobbes analysis of the state of nature which is a hypothetical stripping away of such systems of political management.
The application of this principle to physical resources is novel, sure -- but the observation that "enforced coordination solves the rational self-interest problem" isnt novel.
But Ostrom's novel observation wasn't that. It was "all those stories of how shared ownership of resources leads to tragedies? they're wrong, and part of a mostly uncoordinated attempt to get you to abandon the idea of shared ownership of resources. It is, as ever, greed and power that lead to those tragedies".
These are both ways of saying that the problem isn't the commons (shared ownership), it's an issue of accountability (where "abandon something broken and find another" is a way of exercising accountability).
My understanding of Ostrom's work was to show that basically almost all commons were managed, and that when they were not, that was the result of selfish actors deliberately seeking to dismantle the management structures to further their own individual interests.
Us manufacturing is larger than ever and still growing. It is mostly automated though so Tht number of people who work in manufacturing is shrinking. Still the expertise is still there and they do hire people all the time.
My approach is to write a bit of totally AI-free code every day. So after a day of Claude, I'll spend at least 30 mins wrestling with something. The gnarlier the better, e.g.leet code or Project Euler-type stuff. I think of it as like lifting weights for the mind. The more I struggle at the edge of my knowledge and skill the better.
The other thing is to give your agent a skill not to solve certain key problems unless explicitly prompted. Write the scaffolding sure, but leave the juicy parts alone. And if I get stuck, I have it enter into a dialogue with me, nudging me towards understanding.
I solved a Leetcode Medium on my first try in about 10 minutes the other day and it felt so amazing. Thinking through architecture, project composition, composability, etc when working with agents felt fine for a while but it made me feel disconnected from what I'm actually building.
Actually engaging my brain to solve the lower-level, on-the-ground code allows me to think of better ways to do things while I'm writing them. It's like writing anything. You start with something you want to convey, a thesis, and then it evolves and becomes better as you write it. An agent will just write it with no thought, as in it will reflect one of the LLM 'ghosts' as Andrej Karpathy puts it, doing something in the same way that someone in the training data has done it on a similar or different problem. This is why I get conniptions now when I am sent generated text or am expected to read it on a public forum. It's disrespectful of the time of every person expected to read it.
That said, I wouldn't like to go back to the before times without having the agentic option available. Ideally, businesses should not mandate how LLM's are to be used at their company, and just let the devs find their own flow. That is, if quality is even a factor that any company optimizes for anymore.
I thought the article was going to be about countless millions of minds trying to win the internet lottery and merely reinventing the wheel or failing.
I'm not worried about AI taking jobs. I'm worried that humanity has lost the ability to share at such a monumental level that basic sustenance and financial security are out of reach, even with AI.
After lifetimes of negative reinforcement, the only salvation seems to be the disruption of capitalism itself. Somewhat ironically, the wealthiest and most powerful people in the world seem to be investing trillions of dollars into AI to do just exactly that.
This paper misrepresent fundamental concepts from Ostrom’s work, which won a Nobel, and fails to contextualize Hardin’s theories. As a 'human resource development' paper it somehow pretends that capitalism doesn't exist?
Hardin coined "The Tragedy of the Commons" in 1968, elaborated on his theory in his 1974 paper "Lifeboat Ethics: the Case Against Helping the Poor". He was a eugenicist and specifically targeted refugees. He lobbied US Congress in opposition to international famine relief. His evidence-free theories must be contextualized within his overall white-nationalist project. https://www.splcenter.org/resources/extremist-files/garrett-...
The paper asserts that "expertise within a profession" is a commons. As an industry we've never been able to define the specific, task-level role boundaries between PM/designer/engineer across companies. So how is a profession defined? What are the units of expertise here? This paper fails to provide a defensible definition of their commons. Ostrom's commons need clear boundaries and resource units. https://en.wikipedia.org/wiki/Elinor_Ostrom
From the abstract: "*The paper reframes expertise development as collective stewardship*" which ignores capitalism.
Firms are incentivized to build proprietary expertise, eg Slang at Goldman. Individuals are incentivized to build proprietary expertise and use it as leverage for higher compensation. Even if we fudge commons into a more generic collective action problem, this paper doesn't reckon with the tension between market incentives and collective expertise. This tension comes up again and again, in open source, in academia, and corporate L&D programs.
The author goes on to use professional organizations as an example of governance for their Cognitive Commons. The examples of medicine, law, and engineering are particularly bad as these are credentialed, legally enforced enclosures of expertise, and not self-policing or democratic. They are the opposite of commons.
The author should have just written about expertise as a resource pool and avoided the commons. This was a frustrating read. The paper is a disservice to the commons literature.
Perhaps, but I find when I use AI (for fixing things), I learn a few things here and there as the AI-provided answers are often wrong. And when I work around these errors, the learning occurs.
for a lot of the article i was mentally replacing "ai" with "calculator" and going "yeah we will just need longer education times. more schooling etc."
but the phrasing of "who pays for the increased schooling times?" is a good one.
i think "debt" can be a decent way to conceptualize the cost and repayment of training someone. feels evil to say, but viewing people as firms you can invest in and expect returns upon. you know not all loans will be repayed, but hopefully they'll average to a profit. (risk management etc.)
student loans are. a decent example. the government/private enterprise gives money to pay for education, then this is repayed, providing a financial incentive for paying for someone else's longer schooling timelines. firms investing in training can be viewed as an extension of student loans. but then ah, there are countless stories of how debtor/creditor relationships can be exploited. indentured servitude etc. there are a lot of complications coming to mind. also "altruistic" people who give without expectation of repayment. or the divide between like, communal vs individualistic cultures. (individualism, i argue, encourages the formalization of debt, as opposed to a more communal culture where the expectation of repayment is informal.) you could do math on how many people pay vs how many people benefit, who is the biggest stakeholder, etc.
but i am on my lunch break and need to get back to my work. good article tho. good topic to bring up.
A new and growing fear I have is that AI cuts off some of the most well-trodden pathways to intellectual growth. Every time the AI solves a problem that I would not have been able to efficiently solve alone, it has replaced an interaction that previously would have happened with a mentor/supervisor/code reviewer. This is faster for the person seeking the micro-assistance, but there is a flipside: fewer human-to-human acts of mentorship and shared problem solving. We lose a lively ingredient of team formation, expert formation, and a source of joy for all involved. As this scales up it feels plausible that we end up with a wider deagradation of intellectual standards. I like the Cognitive Comons framing.
If the LLMs work as advertised, we won't need experts.
Even today, with the current state of the art, we need far fewer experts. LLMs are already much better at debugging than humans are, for example. Math proofs are falling at a unprecedented rate. And so on.
Let consumers consume.
I think this has already happened to some extent, with social media etc. crowding out reading books. Even higher quality stuff like HN pales in comparison with what a good book brings, and it's a daily struggle to keep reading.
At least we're (mostly) not scrolling feeds all day at work. AI is going to be like this for all our professional competencies.
>Every time the AI solves a problem that I would not have been able to efficiently solve alone, it has replaced an interaction that previously would have happened with a mentor/supervisor/code reviewer.
I suppose that Socrates would strongly agree, seeing how the same argument could be applied to learning from a book.
Maybe it just means more people are pushing some envelope (their own) faster and more often than before.
In the aggregate everyone maybe learns on average about as much new stuff in a year anyway.
There _are_ still humans out there talking to each other.
This already happened in many other areas. You don't ask locals for directions anymore. You just have Google Maps.
There used to be jokes about how men don't want to ask for directions...
Someone recently linked the "eating your seed corn" wiki page about AI and it feels like it sums it up.
Except there's mass directives to eat as much seed corn as you can
For anyone that does LLM supported work it's crystal clear that skill decline is real. Surprisingly, the leadership at my work doesn't give rats ass about it. They published AI engineering manifesto, pushing for loops, workflows, advocating for producing many times more code. All of this ignoring the elephant in the room.
I don't see it being "crystal clear" that skill decline is real. People are doing work they wouldn't have been able to do before. They are getting a more broad exposure to technologies than they have before.
Cynically they may be banking (however misguided it turns out to be) on model skill in aggregate growing faster than the skills of their staff decline at which point, bin the staff, use the model.
The hype from non-technical people about AI would make you think they already discovered AGI.
>> model skill in aggregate growing faster than the skills of their staff decline at which point, bin the staff, use the model.
How people in tech are aware they're training their replacements?
Or are we quickly approaching an apex where the people running these companies realize AI cannot completely replace human developers?
> Or are we quickly approaching an apex where the people running these companies realize AI cannot completely replace human developers?
The boosters are loud and the failures are made silent, until the failures become impossible to ignore. Tens to hundreds of millions of dollars spent on AI in enterprise software companies need to show RoI at some point, and the promises of executives leaning on AI's productivity improvements to make their accelerated release schedules need to actually make the releases. Its currently a very dangerous career move to publically apply critical thinking to LLM capabilities. I think the math sort of does itself.
I don't know, I find AI still misses the mark so much for stuff that's not happening inside a well constrained framework with very specific guardrails and boundary conditions. Every time I have agents build something half complex that I would be easily able to pull off myself (albeit not at their pace) I always ask if everything is correct and working and I get a resounding "yes!", then I test it and it's essentially a steaming pile of garbage which I then tell the agent and after which it goes "you're absolutely right to push back, this isn't good at all!" as if it was clear from the beginning. I don't know if we'll achieve real intelligence soon with these things but what we have now isn't there yet. It's incredibly useful but I don't think you can replace a good programmer with it, you can make a good programmer into an excellent one though!
Same goes for writing, anyone who has written long complex texts with LLMs knows that a ton of editing is required to make it half decent.
It doesn't seem clear to me at all. e.g. I've been having a fun time learning Godot this last week with codex laying down groundwork for logic, generating assets, etc. This gives me something concrete that I can work with and ground myself within instead of starting from a blank canvas. I don't much care for the actual scripting logic because it's all basically trivial to me with a few decades of programming experience, and I'm not going to be able to develop my artistic abilities to a useful capacity anytime within the next few years, so I can instead focus on learning architecture/organization patterns and game systems that I'm interested in while I read through docs.
So basically, if you use it to wave away things you've mastered or explicitly don't want to learn at the moment, you can stay focused on things you are figuring out. I don't see it as any different from writing "by induction" in a math proof without writing all the details because you and the reader know you could easily work them out. In this way I can learn things that I simply would not have the time to dig into before, increasing my skillset. As is was before AI, metacognition is the most widely useful skill to have.
I don’t think it’s as clear cut as you make it out to be.
Maintenance <> growth
Once you got a skill it’s quite hard to actually lose it. It’s like riding a bike as they say.
I find all the reading of those walls of code exercises my mind better than the writing did. I’m sure I lost memories of how to read a file by hand or balance a tree but who cares?
I am thinking about architecture and design a lot more these days and every decision I make has to be actually argued for even to myself. I can no longer lean back and say “that’s how we always do it” or “too expensive to change now” as I find many people in practice actually do. They were just coasting on premade architectural choices and their “skills” consisted of knowing arcane incantations and syntactic details completely unrelated to the (business) problem at hand.
I am not convinced many developers actually have the skills they think they have. They could wrestle syntax and mess around with tooling, but could they abstract properly? Define clear semantic boundaries? Have proper civil discussions about responsibilities and where they should lie on the right level of abstraction? Nothing has changed in that regard. If anything that part has been amplified. (“taste”)
" skill decline is real"
Is this -> More people that are not devs, are using AI to create things, and are skilling up to a mediocre level?
or
Is this -> Existing experts, are actually loosing there skills? Using AI is reducing someone already known skills.
Why not both?
I've got a friend whose firm has started using 'cognitive surrender' to describe the habit of delegating thinking to the LLM in the way that we used to skim code reviews and say 'LGTM' while missing something important.
I think as an existing expert in my field (robot devops) what I feel most strongly is not that I'm losing my skills but rather than I'm losing my nerve.
Like, problems that I would previously dig into on my own, poking at this and that log file to try to understand what happened to get a system into a particular state, now claude is mediating most of those kinds of interactions and it is the one doing the first pass surfacing of "okay I discovered X, Y, and Z things that are hinky, and I'm not totally sure yet what this all means, but let's look together."
Nowadays I feel almost naked looking at a terminal where I'm typing each character myself. A lot of the old instincts around tab completion and grepping through the --help output of every tool, all that stuff has atrophied somewhat. Maybe that is genuine skill decline?
I think this is a really important idea. Good problem solvers, in general, are people who are prepared to go in and poke around, to see what happens when you disable this or add that.
The only way I've found to make juniors develop that instinct is to make them do it until it comes naturally--to continually demand they verify their assumptions, test out theories while debugging. If you don't practice being comfortable with the unfamiliar, you lose the knack.
It's fine, of course, to lose the knack to AI, as long as you'll never need to solve a problem the AI can't (or can't access). I don't think that's a good assumption for everyone to make.
> Good problem solvers, in general, are people who are prepared to go in and poke around, to see what happens when you disable this or add that.
you have to do all this when dealing with AI too. The syntax is a conversation vs a strict grammar but you still have to do all the exploration, verification, and evaluation you always had to do as a developer. You have to know how to explain what you want and know when you have it, that's not always an easy skill to learn though.
i don't really buy the "loosing skills" line. I'm not old enough to remember but it seems like this would be similar to an expert slide rule operator lamenting the skill loss because of using a calculator. A new tool has come out that antiquates some skills but requires a new set of other skills to master.
> More people that are not devs, are using AI to create things, and are skilling up to a mediocre level?
What skills are being developed by non-devs prompting AI to churn out code they don't understand? Note that I'm not debating if the code is good or works or whatever, I'm asking if any real skills are being developed merely by prompting AI towards some goal.
my sister and two of her friends put together a small company. She asked me to work on a mobile app for them, like a small niche LMS. I was making progress but fell behind, i have a demanding job/family and she doesnt. She asked if she could try to take it over, i told her about claudecode and then gave her some magic words to get started with. Basically, some framework names and some other technologies involved and some prompt guidance. She completely took off with it and came up with a flow where she chats with ChatGPT and it tells her what to tell ClaudeCode, she copy/pastes into claudecode does it's thing.
How could it possibly work? Except she got through setting up her own Apple dev account, all the certificates, subscription entitlement, revenuecat integration, TestFlight, all the paperwork that goes along with a listing and all of that in about 3 weeks. She submitted her app to the AppStore for review last friday.
She didn't learn any coding but she sure as hell produced a pretty good mobile app with a subscription payment system all on her own with the help of ChatGPT and ClaudeCode. She's also producing different tools that run on her website which is hosted on squarespace. I have no idea how she's doing it but she has all these little questionairre and decision tree pages backed by google sheets. She's the second non-coder i've seen really blossom with AI and just run with it building stuff she's always wanted but never knew how.
I don't think it is zero.
If they are producing code, there is some knowledge being absorbed. Nobody is using AI to create an app is learning zero.
Its just what they are learning, is not as much as they think, and really random, not-structured. Like reading just a few random chapter of a book across multiple classes. They get a smattering of ad-hoc tidbits of knowledge.
I am really worried if using AI is reducing someone's already gained knowledge. Moving someone backwards. That has me a bit more scared.
> If they are producing code, there is some knowledge being absorbed. Nobody is using AI to create an app is learning zero.
I think the knowledge being absorbed is 'how to use AI to make apps', not necessarily anything about the code itself. I've seen lots of people who are very effective with AI suddenly poleaxed in interviews without it. Going from 'able to explain exactly what needs to happen' to 'staring at a blank page with no ideas'.
> If they are producing code, there is some knowledge being absorbed. Nobody is using AI to create an app is learning zero
Maybe not actually absolutely zero, but it's much closer to zero than what they would learn creating the app themselves
>I am really worried if using AI is reducing someone's already gained knowledge. Moving someone backwards. That has me a bit more scared.
It absolutely is. It removes a lot of friction from pathways that kept previous knowledge firm.
This isn't unique to AI. When calculators came around, people had a new tool that decreased the need to do arithmetic in your head. This has lead to a decline in the percentage of people who knew arithmetic, but can no longer do it adequately without the external tool. Another example would be assembly skills declining amongst software engineers after compiled languages become dominant.
What I would argue is unique to AI is two-fold. The first being that the scale of things being automated is enormous. Keeping with just software, AI isn't only a tool that decreases the need to write code, it's also a tool that decreases the need to do your own research, debugging, system design, version control, etc. The list of tasks a regular engineer is responsible for that have not been consumed by AI is small. To make matters more frightening, this is just a single industry. The same applies to multiple other knowledge heavy fields (e.g. mathematics).
The second thing I'd argue is unique about AI is the addictive nature of it. These LLMs are deliberately coerced into being sycophants that blow smoke up your ass unless you deliberately tell the model not too (and it may still do it anyways!). A calculator didn't compliment your insight for asking it to calculate the square root of -1.
Furthermore, many people have talked about the resemblance of LLMs to slot machines--there's a feeling that anything could be possible if you only prompt the model correctly. Your first prompt is wildly off the mark, but your next prompt is better though still not quite right, so you continue forward. You keep iterating your instructions, your word choices, your tone, all in search of the desired response. In this way, an LLM is a slot machine, your prompt the lever you pull, and the output is the jackpot.
All this taken together, we have a machine that decreases the need to engage with the critical faculties of your own brain, which has been trained to keep your attention through flattery, and whose probabilistic nature preys on our love for gambling.
Definitely the latter.
Both?
AI is good enough to generate a first pass for an increasingly large number of projects. An engineer using AI can create an even larger amount.
But muscle memory and expertise are not fixed. They fade over time without use. If you're no longer writing code yourself, you'll get rusty on syntax in the short term. In the long term, you'll get rusty on code structure and layout.
But many engineers are no longer reviewing code either. Reviewing code written by an AI is now the bottleneck, so you're expected to allow AI to review it as well.
So we have a group of people who are no longer engaging in either the writing of the code, or the analysis of the code that's written.
Obviously, this would lead to skill loss.
Many would argue that they're not truly losing skills, because they're more engaged in the grander architecture of the code. To that I would say--your job title says engineer, not architect.
It is probably skill loss on one side and gain on another. In a few years it is going to matter even less if you can code manually. The hard part now is understanding what agents do. How can you gain a bit of confidence in their work? I think it relates to testing, you can trust the parts you test.
>The hard part now is understanding what agents do. How can you gain a bit of confidence in their work?
How can you understand them when everything changes again 6 months from now?
Why surprised? The elephant is in someone else's room.
Similar to the argument made by John Blow several years ago. Though, Blow blamed frameworks/engines and layers of abstractions. I wonder if the authors came to this framing themselves, or listened him.
https://www.youtube.com/watch?v=q3OCFfDStgM
Maybe we have to subsidize zoomers until we can figure this out.
The pseudo-academic style of the article is awful. All abstractions and buzzwords, no examples. Also, the tragedy of the commons concept is far older than 1968. The term "common" refers to common grazing land upon which anyone could graze animals. That's an English term. The American equivalent is "open range".
Getting past that, the author has a point. There's a loss of shared expertise when there aren't people around learning and doing something. In the US, we've seen this in manufacturing. The number of Americans who know how to set up a good production plant is much lower than it was in the 1980s. That's the consequence of the hollowing out of American manufacturing. The author talks about AI vs. white collar work, but fails to make the connection with outsourcing vs. blue collar work.
The loss of this expertise has recently been made very clear in the US as attempts are made to scale up weapons production for the US's various wars. Progress is very slow, as has been seen with both artillery ammo and air-defense missiles.
> Also, the tragedy of the commons concept is far older than 1968. The term "common" refers to common grazing land upon which anyone could graze animals. That's an English term. The American equivalent is "open range".
The term is not "common" or "commons" but "tragedy of the commons" which dates back to a book in 1968 by Garrett Hardin. Elinor Ostrom subsequently showed that most or all of Hardin's assumptions and claims were wrong, or at the very least, far from universal, and that the process he describes elides what actually drives the destructions of resources held in common: greed and power.
I think you might be thinking about Elinor Ostrom and she didn't show that his assumptions and claims were wrong exactly. She expanded on the work to clarify the difference between managed vs unmanaged commons. Showing that successful commons are managed in some way with established rules and limits around the usage. The term still has use especially if it is used to clarify the need for management of common resources to avoid the tragedy.
which is also basically the same point made by Hobbes about the state of nature
Hobbes, Rousseau were effectively both trying to solve the "tragedy of the commons" problem with political collaboration. Hobbes is widely misunderstood by (romantic, naive, libertarian) fools to have said that absent a powerful soverign, life is "nasty brutish and short". What he more accurately said is that, "an unmanaged commons is nasty bruitsh and short" -- to use that kind of language
Rousseau, to the same romantic strain is widely misunderstood to have said that 'the unmanaged commons' is a delight. Instead, he basically said politically community isnt that required anyway, and if you're in trouble, just run away.
In either case, the tragedy of the commons issue is well known in other guises throughout the history of philosophy
> In either case, the tragedy of the commons issue is well known in other guises throughout the history of philosophy
It really isn't. There are lots of anecdotes that appear to illustrate the tragedy, throughout history. But what they lack is what Ostrom added: the historical account of how a previously managed commons had its management structures dismantled by greed and power and only then was in a position to become a "tragedy". The result is that a very significant number of people believe that resources held in common always lead to "tragedy", when in fact the lesson is "the structures used to manage resources held in common are critically important, and severe action should and must be taken against those who try to dismantle them". We can hold resources in common, but must be ever vigilant and actionful against the selfish bastards who seek to manipulate them for their own advantage.
Right, and that's pretty much exactly Hobbes analysis of the state of nature which is a hypothetical stripping away of such systems of political management.
The application of this principle to physical resources is novel, sure -- but the observation that "enforced coordination solves the rational self-interest problem" isnt novel.
Agreed that it's not novel.
But Ostrom's novel observation wasn't that. It was "all those stories of how shared ownership of resources leads to tragedies? they're wrong, and part of a mostly uncoordinated attempt to get you to abandon the idea of shared ownership of resources. It is, as ever, greed and power that lead to those tragedies".
These are both ways of saying that the problem isn't the commons (shared ownership), it's an issue of accountability (where "abandon something broken and find another" is a way of exercising accountability).
My understanding of Ostrom's work was to show that basically almost all commons were managed, and that when they were not, that was the result of selfish actors deliberately seeking to dismantle the management structures to further their own individual interests.
.. presumably hyper-inequality also erodes the use of common resources, or resources available to commoners ?
eg. large companies ( 'hyperscalars' ) pre-ordering RAM and GPUs driving up the prices to the point where PC gamers and AI enthusiasts cant upgrade.
Us manufacturing is larger than ever and still growing. It is mostly automated though so Tht number of people who work in manufacturing is shrinking. Still the expertise is still there and they do hire people all the time.
My approach is to write a bit of totally AI-free code every day. So after a day of Claude, I'll spend at least 30 mins wrestling with something. The gnarlier the better, e.g.leet code or Project Euler-type stuff. I think of it as like lifting weights for the mind. The more I struggle at the edge of my knowledge and skill the better.
The other thing is to give your agent a skill not to solve certain key problems unless explicitly prompted. Write the scaffolding sure, but leave the juicy parts alone. And if I get stuck, I have it enter into a dialogue with me, nudging me towards understanding.
I solved a Leetcode Medium on my first try in about 10 minutes the other day and it felt so amazing. Thinking through architecture, project composition, composability, etc when working with agents felt fine for a while but it made me feel disconnected from what I'm actually building.
Actually engaging my brain to solve the lower-level, on-the-ground code allows me to think of better ways to do things while I'm writing them. It's like writing anything. You start with something you want to convey, a thesis, and then it evolves and becomes better as you write it. An agent will just write it with no thought, as in it will reflect one of the LLM 'ghosts' as Andrej Karpathy puts it, doing something in the same way that someone in the training data has done it on a similar or different problem. This is why I get conniptions now when I am sent generated text or am expected to read it on a public forum. It's disrespectful of the time of every person expected to read it.
That said, I wouldn't like to go back to the before times without having the agentic option available. Ideally, businesses should not mandate how LLM's are to be used at their company, and just let the devs find their own flow. That is, if quality is even a factor that any company optimizes for anymore.
I thought the article was going to be about countless millions of minds trying to win the internet lottery and merely reinventing the wheel or failing.
I'm not worried about AI taking jobs. I'm worried that humanity has lost the ability to share at such a monumental level that basic sustenance and financial security are out of reach, even with AI.
After lifetimes of negative reinforcement, the only salvation seems to be the disruption of capitalism itself. Somewhat ironically, the wealthiest and most powerful people in the world seem to be investing trillions of dollars into AI to do just exactly that.
It's so wild to have a whole paper about this... that's also LLM written.
What are we even doing here?
This paper misrepresent fundamental concepts from Ostrom’s work, which won a Nobel, and fails to contextualize Hardin’s theories. As a 'human resource development' paper it somehow pretends that capitalism doesn't exist?
Hardin coined "The Tragedy of the Commons" in 1968, elaborated on his theory in his 1974 paper "Lifeboat Ethics: the Case Against Helping the Poor". He was a eugenicist and specifically targeted refugees. He lobbied US Congress in opposition to international famine relief. His evidence-free theories must be contextualized within his overall white-nationalist project. https://www.splcenter.org/resources/extremist-files/garrett-...
The paper asserts that "expertise within a profession" is a commons. As an industry we've never been able to define the specific, task-level role boundaries between PM/designer/engineer across companies. So how is a profession defined? What are the units of expertise here? This paper fails to provide a defensible definition of their commons. Ostrom's commons need clear boundaries and resource units. https://en.wikipedia.org/wiki/Elinor_Ostrom
From the abstract: "*The paper reframes expertise development as collective stewardship*" which ignores capitalism.
Firms are incentivized to build proprietary expertise, eg Slang at Goldman. Individuals are incentivized to build proprietary expertise and use it as leverage for higher compensation. Even if we fudge commons into a more generic collective action problem, this paper doesn't reckon with the tension between market incentives and collective expertise. This tension comes up again and again, in open source, in academia, and corporate L&D programs.
The author goes on to use professional organizations as an example of governance for their Cognitive Commons. The examples of medicine, law, and engineering are particularly bad as these are credentialed, legally enforced enclosures of expertise, and not self-policing or democratic. They are the opposite of commons.
The author should have just written about expertise as a resource pool and avoided the commons. This was a frustrating read. The paper is a disservice to the commons literature.
Using AI to summarize this.
Perhaps, but I find when I use AI (for fixing things), I learn a few things here and there as the AI-provided answers are often wrong. And when I work around these errors, the learning occurs.
for a lot of the article i was mentally replacing "ai" with "calculator" and going "yeah we will just need longer education times. more schooling etc."
but the phrasing of "who pays for the increased schooling times?" is a good one. i think "debt" can be a decent way to conceptualize the cost and repayment of training someone. feels evil to say, but viewing people as firms you can invest in and expect returns upon. you know not all loans will be repayed, but hopefully they'll average to a profit. (risk management etc.)
student loans are. a decent example. the government/private enterprise gives money to pay for education, then this is repayed, providing a financial incentive for paying for someone else's longer schooling timelines. firms investing in training can be viewed as an extension of student loans. but then ah, there are countless stories of how debtor/creditor relationships can be exploited. indentured servitude etc. there are a lot of complications coming to mind. also "altruistic" people who give without expectation of repayment. or the divide between like, communal vs individualistic cultures. (individualism, i argue, encourages the formalization of debt, as opposed to a more communal culture where the expectation of repayment is informal.) you could do math on how many people pay vs how many people benefit, who is the biggest stakeholder, etc.
but i am on my lunch break and need to get back to my work. good article tho. good topic to bring up.