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.
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.
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.
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.
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.
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.
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.
We can view the bar association as a self-policing group to some extent. But the unauthorized practice of law is a crime in most jurisdictions, so it's policed by the State. In the context of Ostrom's commons this means a hypothetical 'lawyer-commmons' exists at the whim of the State and is unlikely to survive. In practice, a fake lawyer serving jail time can't get out of jail by negotiating with the bar association for access to lawyering. It's similar for medicine and engineering. I take your overall point that professional organizations have internal rules, decorum, membership criteria, etc. If you're at all interested, Ostrom's 8 rules for managing the commons are worth a read.
We will look back on this era as one of transition. AI is in its "look monkey can do tricks how cute" era - but it won't be long. For instance the era of "whoa" is happening right now in mathematics (Anthropic researchers are pushing math's frontier with little more than the prompt "you can do it" - ie. brute force), and in coding agents we're crossing over - look into leading-edge benchmarks like SlopCodeBench that are pushing labs to RL for long term codebase health and not just problem-solving. The reason I say coding agents will go in this direction is labs are competing to have the most appealing models and so seeking out new unsaturated benchmarks they can hill-climb and show flashy results from, demonstrate to customers they're the best and deserve spend.
What these cognitive tools do is make the mundane work we're lamenting the loss of redundant. Who cares if junior devs can't code? When this stuff really gets going they'll be using their answer boxes to both code AND to have have design discussions with a form of intelligence that has every PhD ever obtained and infinite patience.
Consider: long division used to be in school curricula; our forebears had to know it to be considered "educated"; and yet I, in my 40s, was never taught long division.
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[ 0.19 ms ] story [ 36.4 ms ] threadbut 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.
https://www.youtube.com/watch?v=q3OCFfDStgM
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.
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.
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.
What are we even doing here?
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.
What these cognitive tools do is make the mundane work we're lamenting the loss of redundant. Who cares if junior devs can't code? When this stuff really gets going they'll be using their answer boxes to both code AND to have have design discussions with a form of intelligence that has every PhD ever obtained and infinite patience.
Consider: long division used to be in school curricula; our forebears had to know it to be considered "educated"; and yet I, in my 40s, was never taught long division.
Where did it go?