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.
>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.
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.
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 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?
> 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.
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.
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--you're job title says engineer, not architect.
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.
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 though, 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.
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.
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
>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.
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?
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.
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?
> 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.
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
Maybe not actually absolutely zero, but it's much closer to zero than what they would learn creating the app themselves
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--you're job title says engineer, not architect.
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
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 though, 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.
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.
Using AI to summarize this.