> The elite research layer of higher education (Stanford, Berkeley, MIT) is still an AI accelerant. But across the country, as Scott Latham told me, “the vast majority of institutions are in a state of AI paralysis.”
Tbf, the vast majority of institutions also suck.
State flagships and some of their second rung, a couple ivies and ivy tier privates, and military academies provide the bulk of talent that you need. And these universities have dedicated pipelines for people who start off at community colleges.
These universities are also much larger - UC Berkeley's and UCLA's 2025 undergrad population is almost double it's 1960 population [0].
And those who had skill or educational deficiencies can remediate them via non-trad or working professionals masters like GT, UT Austin, and CU Boulder's OMSCS for less than $15k.
And for the good reason. I have yet to see 1 (one) reason how giving infinite cheat machine to students is of any benefit to them.
The secret to mastery remained unchanged for a thousand years: books, lectures and homework. All of this can be done with pen and paper level of technology.
I'm not sure what to make of this? Is any healthy skepticism about AI now treated as motivated luddism?
Academics has its many issues but universities are hardly the only sources of criticism about AI, and for all its benefits AI is poorly understood and often applied in relatively half-assed ways, even when it's well-intended. I would worry if thoughtful individuals weren't skeptical at all about AI.
It also seems reasonable to, say, want to evaluate students on their own merits free of the influence of a "homework machine" (to borrow a phrase from Shel Silverstein).
I'm reluctant to make predictions, and wouldn't describe myself as "anti-AI" but this sort of piece reminds me uncannily of the dot-com bubble.
The primary use case of universities are to produce a generation that can contribute to the society in a meaningful and positive manner. Nowadays in-order to do that, AI literacy is a must for graduates just like it was 15 years ago when it was a must to have computer literacy.
AI is a tool like internet was and it's the responsibility of the curriculum to teach proper usage of that tool rather than fighting against it.
It is causing far more pain than benefit in the majority of institutions.
Particularly the cheating. Many students are flagrantly using it + the detection tools either aren't configured to check for it, or, if they do have something like TurnItIn, it doesn't work, and flags legitimately created content sans AI.
For people who teach through writing, there are great tools out there like MATCHA: https://matchawriter.com. It's a secure writing environment that verifies authorship by recording the full writing process and, when needed, can restrict copy-paste, automation, and access to other applications.
In my opinion, most of Academia's pains are wholly self-inflicted.
This author has a ton of axes to grind and a poor grasp of computing history or how industry works. Pretending like it’s strange or even bad that industrial datacenter builders with tens of billions of leveraged capital can outbuild the GPU capacity of chronically underfunded grant dependent universities whose mission is not to create industrial scale compute resources is like complaining local technical school auto-mechanic program isn’t out-producing Ford’s auto manufacturing numbers.
But then as soon as the author rolled out complaints about DEI and ethics, you knew what was going on. Okay, please leave higher ed and work for the industry you love. As you say, they are doing just fine.
Lots of people talk about cheating here. That is not the framing I put in my mind. Or rather, if the students are cheating anyone it's themselves.
LLMs exist and are extremely useful in academia. I am currently riding a productivity wave, where I can spit out formalised proofs – not quite at the pace at which I can think of the theorems to prove, but almost. The thing braking my progress is that I have to build all the existing theory too, not just my own new stuff.
But for students, who do not yet know anything, it is a danger. From observing my students, it seems to take a certain amount of willpower to do the exercises without letting ChatGPT give them (or "explain them") the answer. And reading an explanation is not the same thing as working it out yourself. You just don't learn doing it, by not doing it!
Some students have this will power, some students don't. And it is also situational. Some students have stuff going (work or other factors) which makes it really hard too.
Yes, they can of course keep asking ChatGPT later in life, but they will not have the fundamental understanding which is required to even use LLMs for the advanced stuff. ChatGPT cannot explain you advanced concepts if you don't understand the fundamentals!
So, to test the students' understanding, we are back to having on-site exams, with the old-school supervision. And more students will fail, because there is now a way to solve exercises without learning.
AI is totally going to replace the university system. This is like the coal and oil guys sabotaging EVs and renewable energy. Good for them, bad for us.
The issue is more one of current passive, abstract forms of assessment. This is laziness on the part of instructors. They will simply have to move to more complex, project-based assessments and accompanying interviews, which is more time consuming but will be far more accurate than the more traditional system of assessment was anyway. On sum it will be better for everyone.
The author completely ignores the cognitive debt of using AI in my understanding. there is no high level work if you don't understand the basics, so the basics must be taught not glimpsed on
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[ 0.24 ms ] story [ 6.2 ms ] threadTbf, the vast majority of institutions also suck.
State flagships and some of their second rung, a couple ivies and ivy tier privates, and military academies provide the bulk of talent that you need. And these universities have dedicated pipelines for people who start off at community colleges.
These universities are also much larger - UC Berkeley's and UCLA's 2025 undergrad population is almost double it's 1960 population [0].
And those who had skill or educational deficiencies can remediate them via non-trad or working professionals masters like GT, UT Austin, and CU Boulder's OMSCS for less than $15k.
[0] - https://www.universityofcalifornia.edu/about-us/information-...
The secret to mastery remained unchanged for a thousand years: books, lectures and homework. All of this can be done with pen and paper level of technology.
Uh in what universe? Obviously physical books are helpful but are not most of the new ones being written with the assistance of an LLM?
Academics has its many issues but universities are hardly the only sources of criticism about AI, and for all its benefits AI is poorly understood and often applied in relatively half-assed ways, even when it's well-intended. I would worry if thoughtful individuals weren't skeptical at all about AI.
It also seems reasonable to, say, want to evaluate students on their own merits free of the influence of a "homework machine" (to borrow a phrase from Shel Silverstein).
I'm reluctant to make predictions, and wouldn't describe myself as "anti-AI" but this sort of piece reminds me uncannily of the dot-com bubble.
AI is a tool like internet was and it's the responsibility of the curriculum to teach proper usage of that tool rather than fighting against it.
Particularly the cheating. Many students are flagrantly using it + the detection tools either aren't configured to check for it, or, if they do have something like TurnItIn, it doesn't work, and flags legitimately created content sans AI.
In my opinion, most of Academia's pains are wholly self-inflicted.
But then as soon as the author rolled out complaints about DEI and ethics, you knew what was going on. Okay, please leave higher ed and work for the industry you love. As you say, they are doing just fine.
LLMs exist and are extremely useful in academia. I am currently riding a productivity wave, where I can spit out formalised proofs – not quite at the pace at which I can think of the theorems to prove, but almost. The thing braking my progress is that I have to build all the existing theory too, not just my own new stuff.
But for students, who do not yet know anything, it is a danger. From observing my students, it seems to take a certain amount of willpower to do the exercises without letting ChatGPT give them (or "explain them") the answer. And reading an explanation is not the same thing as working it out yourself. You just don't learn doing it, by not doing it!
Some students have this will power, some students don't. And it is also situational. Some students have stuff going (work or other factors) which makes it really hard too.
Yes, they can of course keep asking ChatGPT later in life, but they will not have the fundamental understanding which is required to even use LLMs for the advanced stuff. ChatGPT cannot explain you advanced concepts if you don't understand the fundamentals!
So, to test the students' understanding, we are back to having on-site exams, with the old-school supervision. And more students will fail, because there is now a way to solve exercises without learning.
- Students want a credential because business and society demands a credential
- Universities want income to support their missions of research, education and other "motives"
- Professors want to do research, and they don't really want to teach (in general)
- AI is a resource for learning, assessment and project development
- Society needs educated people
What ideas do you have to make everyone satisfied and maximally functional?
you then make sure it behaves and doesn't let them cheat too much. guide them to answers, don't just spit out code like Claude is want to do