Around COVID times many top universities experimented with removing test requirements from admissions, under an argument largely related to equity. It's been a failure everywhere, with many, if not most, universities already reversing it. As Yale put it, "Yale’s research from before and after the pandemic has consistently demonstrated that, among all application components, test scores are the single greatest predictor of a student’s future Yale grades. This is true even after controlling for family income and other demographic variables, and it is true for subject-based exams such as AP and IB, in addition to the ACT and SAT." [1]
That link is for an archive because that page has been removed. That's because they briefly experimented with a new 'test flexible' strategy where they allowed students to submit test scores or not, but then scrapped that altogether and went back to simply requiring test scores.
[1] - https://archive.is/8zxfo
Or, even better - just expand programs so they can accept more students who pass the test. This would probably improve diversity without artificially restricting access to highish performers.
It was already discussed on HN.
Could you explain?
From the current article
In addition to overreliance on AI, Garcia also pointed out that many students are underprepared mathematically, a concern echoed by campus associate teaching professor Gireeja Ranade.
From the article discussed the other week:
Over three years — from fall 2021 to fall 2023 — the letter said, at least 20% of Berkeley first-semester calculus students who took a diagnostic exam showed deficits. “Basic mathematical fluency is analogous to literacy; without it, success in university-level STEM becomes structurally unattainable for students,” faculty wrote.
It's been steadily getting worse. The current article only looks at F's which conveniently hides if there has been a slope down. Additionally, kids entering HS in 2021/2022 would just now be hitting college.
A sudden materialization is what's depicted by the data.
> It's been steadily getting worse.
I don't believe this is accurate. Failing grades are what the observation entails, and the data clearly depict an abrupt change; not a gradual one.
In the section titled "Failing grades in 3 CS classes skyrocket in spring 2026 ", there's a clear jump in failing grades for all cited courses between 2025 and 2026. Failing grades for every course jump by multiples of the previous year.
"Ranade said students are expected to enter the course having taken classes on linear algebra, vector calculus and mathematical proofs. However, she found out in office hours that many students struggled with linear algebra, and was even more shocked when one student told her the linear algebra class they took at UC Berkeley had an “open-internet, open-AI policy” for homework and exams."
Also, this professor doesn't grade on curves? Could be very specific to this teacher. I don't know. Would be great to have more data but it is a big jump and could be very specific to this professor or perhaps this class.
You should be graded by how well you know the material - not how well your peers don't know it. I'm always grateful both my undergrad and grad professors didn't curve on a grade.
In my first company, I had 4 different jobs. It was a common adage: Go into a low performing team that does simple work and you'll get promotions much quicker than in a high performing team doing challenging (but fun) work.
It was right. I had 2 "dream" jobs where I did cool, challenging stuff, but where everyone was more than competent. They turned out to be career killers. The promotions I got were all in the other 2 jobs where I did boring business logic coding, and where my peers were barely competent (one had trouble navigating directories using the command line).
That's what happens when you grade on a curve. Smart people begin to work on boring stuff, and not the real challenges.
If you wanted to grade purely off a curve, you would be stuck with old test problems that were thoroughly vetted and calibrated, an impossible task for smaller classes where the material changes rapidly.
I'm still not getting it. For a standard course, the criteria for what is "good" vs "great" should be pretty clear, and it should be independent of your peers. You have a syllabus, and a set of abilities for each grade level. If you hit those targets, you get the grade. If half the class gets an A, then it means they're pretty smart, or you did a great job in teaching. Of course, there's the chance the class was too easy, but you can always fix that.
No, I don't see why you're stuck with old test problems. For standard engineering classes, there's a huge (almost infinite) set of problems one can create.
For smaller classes, grading on a curve is even sillier, as the variance is always higher when the population size is small. For example, a lot of my small classes consisted of highly motivated students (all "A material"), because they're usually obscure electives where the content is challenging. You then pointlessly penalize students who sign up (just like they do at work). In fact, my professors were usually much more lenient on small classes for this very reason (i.e. lowering the standard needed to get an A).
I once took an Intro to Analysis course. It was moderately challenging. I got the highest score in the class, and my grade was A-. Everyone else got B+, B, or lower. A friend of mine (who didn't take the course) got really upset that I didn't get an A (or A+) given that I was the top scoring student.
But I knew my level of understanding/performance. It wasn't that great. I felt even an A- was too high a grade for me. And the teacher did a pretty good job in teaching. Why should I get a higher grade just because the other students were worse?
Do you think upper division college classes are somehow like high school classes with well developed curriculum and teaching professors who teach the same thing every quarter? Now you expect the professor to not only come up with new test material, but also extensively calibrate it before students take it, maybe for a 15-hour per week class (3 hours of teaching + 12 hours of studying), with maybe 15 students? Well, thank God we have AI for these kinds of things now.
Ok, let's exclude upper devision classes and just focus on lower division courses (since you mentioned an Intro to Analysis course). Here you have a relatively better chance of a well understood enough curriculum and testing material to actually not grade on a curve. BUT these are also usually weed out classes, with the idea that they only have N spots for students to proceed on to the upper division course, so curving serves an actual purpose that is aligned with the intended result.
So assume 4 years of high school and someone that just came in. They are still preparing for SAT like tests in their first year of high school. Someone in final year of high school is well trained in it. So even though the benefits do not carry, enough portion of incoming students are still reaping benefits of standardized tests. The decay only shows later when batches without any benefits of standardized tests are coming through.
Pardon? Is that a normal thing in the USA? I don't think I've ever started preparing for a test more than a week and a half ahead, a month if you count graduation exams. Not sure they ever determined more than a year in advance (more commonly: a bit less than a semester) what tests we'd be given in the first place
I think we will make a major mistake if we think math preparation fixes this - especially in CS classes where AI literally calls out to be used for projects. And it certainly doesn't explain me hearing the same problems are happening at MIT -- they just are being a bit wiser about "catching students" (or rather not doing so).
The kids who saw the removal of standardized testing 3 years out from going to college never bothered.
Also some children who excel write their SATs sometimes 2-3 years before college and then re-write if need be.
Works the other way too - if you introduce something positive in grade 1, you'll only see the results a few years later.
"Failure to complete the qualification" is the prediction.
- Had high school diploma (or equivalent).
- Resident of the state for >6 months (student or one parent).
- ACT score of something like 21. With provisional admission granted to students with scores below, until they completed all first year engineering courses with a B or better.
So likely they just dropped the concept of provisional admission. All that did was open up classes for registration a week later to ensure other students were able to get their preferred class openings. Provisional had to take the scrap classes, like the four-hour, once a week Calc class on Friday night.
There are many countries, especially in Europe, where entrance/admission tests are not a thing.
That said, the Sixth Form exams are mostly standardised with only a few different exam boards for the entire country, so the Sixth Form grades end up being something akin to standardised tests anyway.
Besides lost meritocracy, that is accidentally filtering for ability and willingness to manipulate others emotionally. Which feels really scary.
"Anno Floyd," fuck's sake, they have a severe brainworm infection to be mad at some guy murdered by police and the protesters upset by the situation. It is impossible to take a comment seriously with this.
What could go wrong...
It reads as though you tried to use the quote to support your conclusion that "it's been a failure", but the quote and the original rationale are optimising for different things. Something can be a success in improving equal opportunity while still leading to worse grades.
Or to flip it around: we could say admission testing "has been a failure everywhere" because it biases admissions in favour of certain demographics. But that wouldn't really be a fair assessment because being free of demographic biases is not the purpose of admission testing!
To fellow professors, when you're suspicious my suggestion is to appeal to their honesty (like "let's be honest, how much of this code is yours, and how much is ChatGPT's?") and offer some empathy and understanding (like understanding they may had multiple deadlines in the same week, etc.). Nevertheless, don't miss the chance to give them the lesson on how is the correct way of doing things. The way to catch these students is to find the same signs of yesteryear copying from other students (which in essence is what copying from an LLM is, although the number has increased because they found us professors unprepared for the volume).
The other two groups also used LLM but in a high-level and architectural way. They were clearly responsible for the code (even if they didn't wrote it 100% manually) and could explain their reasoning and strategies used to solve the problems.
Me and my colleagues still have a lot of projects to review, and I asked them to keep the score of the number of projects like these, but so far, the score is 1 in 3 (33%).
How could we "force" the students to use an LLM that confronted their doubts with more questions? We could tell them to start each chat with a specific prompt (to use the socratic method, etc), but they could eventually jail-break it..
But nevertheless, I like your idea! This is something that a document highlighting methodologies for students on how to use LLMs effectively could/should contain..
As an undergrad, I hope schools move to educating students to use LLMs in a more responsible way. You can't put the genie back in the bottle, and resisting progress is futile, might as well use the tool we now have to help students learn even faster and better (e.g., making feedback instant and not answers, helping digest or split up material, checking answers).
I know opinions about AI at (not only at) my faculty are very mixed, but I think the answer is going to be in the rational mean, just like how technorealism reacted to the internet[0].
In our last program board sitting, some teachers said that they think programming as a job will be completely irrelevant in two years, while other pushed for more adoption. And meanwhile I know of some students that are basically only passing because of LLMs, and it's bad, like "leaving claude output in markdown files and finished source code on the faculty server in /tmp because opencode did so" bad. And our first year classes completely prohibit even sharing tests or talking about the solutions, which in my opinion a) makes people extremely asocial and atomized b) doesn't prepare students for real life c) promotes dishonesty.
Still, I think our university's thinking is in a stalemate, not wanting pure AI output and useless students, while also wanting to move with the times, and I doubt it's the only one.
[0]: https://web.archive.org/web/20081009111415/https://artefaktu... (absolutely amazing read, recommend it)
Sounds more like the score is 3/3 (100%)
Would you have accepted them cooy-pasting code from libraries together to build their project? If not, why is using LLM generated code different?
Yes, if they are "responsible" for the code delivered, where responsible means they understand the code, the architecture, the decisions made, etc.
In this case, the students had to invent multiple strategies to solve a specific problem. The "successful" groups did a mix of generated and hand-crafted code (don't know percentages), implemented different strategies and knew their plus and minuses, could change the code in a timely manner to accommodate some of my requests, etc. The "unsuccessful" group couldn't do any of that.
I'm not anti-AI (and really, what could I do if I were?) since I use it myself, I'm just anti-slop, especially from my students.
But in reality I've been slowly transitioning from group projects (for a subset of the grade) to "practical tests", where they must implement a significant subset of a larger project in a 2h class. Still experimenting though.
It was fine.
This is a good principle to maintain, I think.
I'm not a professor, but I manage a team of about a dozen people. The maxim I have is: "You're responsible for anything that hits git."
Don't care if the LLM generated it, or the LLM told you if it's a good idea. If you commit it, you are endorsing it as a good idea - so you're the one I'm going to ask about it. I see the same principle at work in your pedagogy.
> I'm not anti-AI (and really, what could I do if I were?) since I use it myself, I'm just anti-slop, especially from my students.
This hits. Especially this part:
> and really, what could I do if I were?
My completely unsolicited opinion: you're doing a responsible thing by teaching these students how to use AI as a reference, and keeping them honest about not using it as a substitute for their own critical thinking.
I have colleagues that are teaching for more than 30 years, few years away from retirement, who suddenly have been confronted with a new way of doing things. Those are the ones that are still insisting on doing practical projects, etc. I've only been doing this for 20 years, and I'm quite lazy (worked previously as software engineer), so I've moved to those practical tests. I guess that there should probably exist a class or workshop to teach these students how to use LLMs effectively, but as I said, this technology and its implications is quite new.
Personally, what I did was to give them the "lecture" in the line of that they do not understand what the machine has generated, that is not the way a true engineer does, try to do some parallel with things like an LLM designing a bridge and civil engineers building that bridge, and a fatal flaw collapsing the all thing, etc.
In other words, we do not have a formal system in place, it's all talking and convincing them. Obviously it's a big enough problem that should deserve more investment in solutions, but we are all overwhelmed by other tasks. Maybe LLM studios should be held responsible for all these "disruptions" and provide solutions to problems they created! :)
Do we assume society just self regulates. I think it does, but the cost of letting it self regulate is really really high, with lots of suffering. Is it that we find this acceptable when there is a chance we won't be the first to feel the pain?
It's cultural evolution and it's how markets work, too. You were expecting central planning?
My son is 15 and I use Google Family Link to control what he does on his phone: it's pretty open for the most part (I receive notifications of installs) but Gemini is a hard-ban.
We've spoken at length of the dangers.
He says his pals use LLMs frequently and I suspect that's the reason for their test scores: some of them are in the 20% - 40% range for tests whereas my son is 80%+ because he studies past-papers and answers questions in his revision.
I worry for the future coz you can be sure that the AI providers don't care if a schoolchild is using their LLM to answer the homework questions.
"More than 600 University of California faculty members, led by mathematicians at UC Berkeley, are calling on the system to reinstate standardized testing requirements for science, technology, engineering and mathematics applicants, saying that six years of test-free admissions has not reliably assessed readiness and professors are often teaching middle school math to incoming students."
And what possible benefit would that have?
The idea of a standard bar and so on does sound like it would interfere with such a process.
I always did find it interesting that US notions of anti-racism required treating individuals not as individuals but as racial representatives. It’s a local quirk of the culture of the land, I suppose, that one’s primary identification here is one’s skin colour.
I agree that we should just stop using race everywhere and we should crack down on it -- but I think college wouldn't be where my energy would be... actually the military is where I'd start. And oddly it's the place where race based affirmative action is still permitted (military academies - where it benefits minorities) and in its halls (where I've heard that it has a strong white supremacist bent). The reason is because what is happening in colleges is more reactionary -- fix the catalyst and the arguments for the reaction largely go away.
Unfortunately, the lost signal wasn't replaced with anything. (I don't know what could replace it. It's an incredibly hard problem. )
It worked, and it would have been MUCH harder to do this the traditional way.
The tool generates PDFs including an answer key and solution sets that solved the problems using a variety of techniques so I could check her work more easily and we could iterate quickly.
That's powerful. It comes back to how are you using the tool. Are you using it to make things better or to take shortcuts?
As a Cal alum, I am actually really glad to see they are holding the line on grade inflation. I worked my butt off to achieve the GPA I did, and it would really suck to see my labor devalued if Cal went the direction of e.g. Yale and started handing out 79% A's and A-minuses: https://yaledailynews.com/articles/professors-face-grading-d...
On the plus side, high grade + long ago remains a signal.
Its all Goodhart's law problem, but we are missing the forest for the trees talking about grades and tests when what we want is people to be educated, and critical thinkers and competent in their area and due to a comprehensive way to evaluate that we end up talking about grade inflation or how Yale vs Berkeley gives letters at the end of a semester
Universities exist as gatekeepers and credentialing bodies. Their purpose is to certify that a person has studied some topic in depth and is an expert in it. They promote education indirectly, by giving people an incentive to study.
A good university is one where anyone with a degree is guaranteed to be highly knowledgeable in their field of study. This makes it easier for anyone who might want to employ or do research with graduates, as there is no need to test their knowledge.
By this metric, universities have failed spectacularly. This is particularly obvious in computer science. Employers routinely ask CS graduates to solve data structure/algorithm problems in interviews, because a degree is not enough to prove that somebody knows this stuff.
No one is intentionally lowering the quality of instruction or trying to trip students up. They are trying to get them to pass the same bar that generations of students before them passed fine...
>Intentionally lowering the quality of instruction, as well as deliberately trying to trip students up on exams
I was happy with the quality of the instruction, and I didn't feel I was being "tripped up" on exams.
It's not about "hunger games", it's about challenging students to learn a lot of material and learn it well. Again, if that's not what you want, just don't attend.
The number of places where this environment exists is getting smaller every year: https://xcancel.com/CJHandmer/status/2060144837157118307#m
I'm glad the professors at Cal are working to preserve it there.
Maybe we can use AI to create new exams that grade people on professional capability, and then gate entry into other professional degrees?
Hmm, Where would the teachers come from, and how good would the education actually be?
> You can always ask me for feedback on your homework and I will mark up every part of it, but you won't receive a grade for homework. However, if you don't do the homework and take your time with it, you will fail the class. My office hours are in the syllabus and you're strongly encouraged to use them. There will be an early exam to give you a chance to know whether you are likely to fail this class before you lose your chance to drop it.
Correctness is harder to adjudicate in some humanities disciplines but the format of these exams is actually not super different from essay tests (when a math professor grades a proof, they're inspecting specialized prose for validity, coherence, persuasion in a way that also reveals knowledge).
When you don't rely on homework for determining whether or not a student passes the class, you make cheating on the homework into the student's problem instead of the professor's or the university's. Students have the right incentives to solve problems for which they are the ones responsible, and they figure it out after one failed (or ideally, dropped) class at worst.
So learning was never the actual goal.
Originally, at least in premis, it was to learn and advance the arts and sciences.
So what we need now is a college for llm's to advance the arts and sciences.
It kinda was fun, like a very patient professor stand right besides you. It was the one of the best math learning experience I've ever had, and you don't even need to send bribe/gift to Gemini to keep you in it's favor.
On the other hand, if you ask a LLM to completely finish the work without thinking it through by yourself, then it sounded like cheating, to yourself.
Are you maybe saying that "soars" might mean "get better", so "failing grades soar" might mean there are actually less failing grades? That's not how I've ever understood that word.
If she told you that afterwards the failing grades had "soared", it could easily be read either way:
- The (previously failing) grades had increased, so the program must be working very well.
- The percent of grades that count as failing had increased, so the program must actually be terrible.
That said, assessments of poor critical thinking skills jump out at me more than the rest. That sort of thing seems likely to matter until machines can replace us completely.
Sometimes I don’t wonder if this wouldn’t still be a good way to educate people. Part of the problem is education has to sort of optimize to try to educate like passive people. If you’re a curious and pragmatic person, you can understand how to use what you learned in a liberal arts degree to be better at almost any job.
As I look forward to the second half of my career. Certainly I use AI in healthy doses.
But people talk about the division between practice and performance, and most of my practice is old school. Reading books. Writing my thoughts down. Memorizing quotes and passages.
I think more important than what you learn is the way you use it to train and evolve your brain, with the caveat that - I know this is more useful to me because I have a marketable skill. This is the balance universities have to stick, there are tons of people with liberal arts degrees in middling jobs.
But at least half if not more of education should be on building practical skills in the three r’s.(maybe the third r should be ‘rgumentation instead of ‘rithmatic, but I digress)
It’s interesting - people decry memorization in education, and I’m not entirely naive as to why - if you were to show up to the first day of work and say “I don’t know any of what you just said but I can recite log tables! It might be your last day - and yet one of the most underrated skills, especially late in your career is the ability to ingest and operate in large quantities of information.
Do you have evidence that it ever was part of being a competent mathematician? AIUI the trope of mathematicians who can't even do arithmetic was common already before the pocket calculator was introduced last century.
That would be closer to engineering or accounting than mathematics. I don't think mathematicians do much arithmetic at all.
I suspect his facility with numbers and his knowledge of tables like this really helped him do physics research.
See also his stories on approximation.
WAL-E and Idiocracy. The future.
Sure we could use our brain power with old techniques to do these, but why? I don't want to do any of these. I'd rather use that brain power for other problems.
Same with maps.
I don't want to have to store a bunch of location or routing data in my head.
I think what you're pointing towards is going from having problems to solve to not having any problems to solve.
That's definitely a danger, but right now is still early in the AI era so obviously it'll feel like we went from solving problems to letting the new tool solve them for us.
There are still many problems to solve.
Watcha gonna do if big tech takes away your access to the outsourced brain, dear?
hmmm, given how closely memory is linked to spatial navigation sense, and not just in humans, but in evolutionary terms-- think squirrels remembering where they buried nuts, birds and fish remembering migration routes, ...
suggests the ability to store location/routing is foundational to much of intelligence.
Even simple tasks, typing, for example, depends on my knowing where the keys are. Imagine if your keyboard re-organized its keymap randomly every third keystroke.
Grade curves are how you test your curriculum for good challenge - are you challenging people such that an A isn't a too-low threshold. When you force people into a curve, you haven't defined a threshold of mastery, you've defined a sorting function: A means "better than this year's peers". It is absolutely bananas to me that a tech/math oriented school would be doing any sort of curving.
If you curve the students after the test, you are applying subjective edits to the graded performance just so the distribution of grades matches the measure of your tests effectiveness. That's just hacking the metric.
Further, even if you believe that tests should differentiate mastery (not students), your test should have teased out the differences or given you enough confidence to provide As to everyone who mastered the material - which should be absolutely possible! There's no a priori reason that all students cannot absolutely get the same grade, except for the a priori assumption that grades are for differentiation of students themselves (this year's A means this is the best student of this year), vs indicating mastery (all students absolutely crushed this exam).
You can dock points for style, or unnecessary struggle, or whatever subjective metric you want, but fudging the grades based on vibes to fit a prior-assumed distribution is just kinda "test effectiveness laundering"
I had classes where I didn’t make over a 50% on any test and still got an A because half the class dropped and the other half hung on for the curve like I did.
I think curves are more a result of poor teachers than anything.
Precisely right - that's what I said, too. You fit a curve to see if your coursework/exams fit the students. But you don't fit a curve to ensure that "precisely 10% of the class gets A, 20% gets B" etc etc. If you dont like the grades your students are receiving, you either fix the coursework or the students.
It will have taken us less than 1000 years to go from scarcity of the printed word to the over-abundance, and finally to the uselessness of it.
As a naturally curious person, nothing will stop me from learning about the topics that interest me. But school also taught me a lot of things that didn't interest me, and a lot of those things turned out to be useful anyway. I think if I had access to AI from a younger age, I'd have used it to skip learning the things I didn't care about, which would not have done me any favours.
Understanding math well might help a bit, but they're the least mathy classes in the core Berkeley CS curriculum IMO.
Where I'm from (Norway), the majority of computer science and software engineering studies do not have the same math requirements as, say, engineering or math/physics/etc. - nor do they have the same amount of math as the latter ones.
When I did my CS classes as an engineering student, I did meet a bunch of students that viewed math as some niche subject only relevant to those that wanted to work with computer graphics, computational stuff, or similar.
Not because the actual truth encoded in it would be this complex, but because the encoding scheme just sucks.
I see it as a packaging problem that has so far not been painful enough to trigger any meaningful change.
With this LLM-driven collapse, that might finally change.
Idk I'm hopeful.
Math is literally the law of the universe. It makes zero sense that the way that it is taught needs some special brain wiring only found in small chunks of the population to truly click.
Ok, I'm all for overhauling math notation and teaching but this doesn't follow. Most animals can't do Math, even if they can do arithmetic. Clearly living in the universe doesn't guarantee you can learn how it works. There's no reason to believe we slightly smarter animals are universally entitled to understand it either.
I think it's true that we collectively lose something akin to beauty every time technology advances. But usually some new set of skills that have beauty emerge.
If LLMs end up being the pneumatic nail gun for the human mind, I personally think that's a fine thing for us to accept.
If they end up being more like some dark factory that autonomously does everything - then I think ultimately the thing that makes us human (our minds) will slowly decay and be lost, and that seems very sad. That's a version of the future we should try to prevent, I think.
I think the jury is still out on whether LLMs actually lead to complete atrophy of skills that don't eventually get replaced with brand new skills.
And all the older technologies that have rolled out haven't competed against our cognitive abilities at speed and scale.
I don't think of cognitive ability as a skill per se - more of a critical core function of humanity.
I say this as someone who uses it extensively not some luddite but is also very aware of the risks which I assume are worse for people who have limited understanding on the matter.
I am just not completely sure that we won't gain something new on the other side of this, in the same way the calculator outsourced the need for doing arithmetic in our heads.
My argument is more that, the speed and scale is so unlike anything that we've seen before, that this time _feels_ like more of an attack on something to core to what humanity is. But maybe it's just that: a feeling.
LLMs/AI could very well be the worst case scenario we are imagining/discussing here. I just don't think we know enough to say that's how it will definitely play out.
That was in the 1980s.
My first math exam as a CS undergraduate, 123 out of 129 students failed. The math department professors refused to dumb down their classes for CS students.
Math was core to the CS curicullum in those days. It would fade away over the next few decades to almost nothing. The main reason being the CS department wanted to popularize its uptake, and remove barriers that kept students from passing. There was also a major dose of interdepartemenral rivalry and academic politiking involved.
I TA’d in the early 2000s and the first day students were warned that we used automatic analysis to find programming assignments that were similar to previous submissions. And renaming things, moving them around etc would not help.
We caught and failed cheaters every term.
When you're up against a deadline - and unless you're very good at time management you're frequently up against a deadline - it's going to be an irresistible lever to pull.
In times past, cheating would mean copying an answer off the Internet or off a friend, both of which are easy to detect. More sophisticated cheaters might spend an hour rewriting the solution to make it less obvious they cheated, but at some point the cost of cheating (time + risk of getting caught) starts exceeding the cost of just doing the assignment. AI changes this - you get a customized answer that doesn't show up in a database with no extra work.
The thing is, students fail to realize just what using AI robs them of. Struggling with the assignment is the entire point. You don't learn if the assignments are too easy; you need to have some challenge to push your brain to understand the material more deeply and to build those pathways to apply the knowledge in novel ways. You become more efficient and effective over time as that knowledge settles in and you get more proficient - one of the reasons why time-bounded exams still make sense (being fast is also a proxy measure for understanding).
Of course many people in a competitive environment will click the autosolve button if available. This is a reason to think how to redesign the system so that the approach we want is the reasonable choice, not to look with superiority at those who fall prey to the danger.
Now the barrier to an answer is zero. They are basically watching a YouTube video on how to X, seeing step by step instructions feeling like they are doing it, and the moment they swing a real hammer they are whacking themselves in the crotch. It might get better after a few years, but this stuff is just now hitting mainstream for the masses. ChatGPT has only been in mainstream use for about 3 years.
Not sure what the solution is - there's no possibility of stopping students using AI to complete their homework/assignments etc. But let me flip the question - do they need to be stopped? Why not let them fail at the exam? As long as the exam acts as a filter, their usage of AI to "cheat" their learning is inconsequential to anyone but themselves.
The whole situation sucks for both students and teachers. Teachers know that the knowledge they're going to great effort to convey isn't going anywhere. Or at least, it's landing in far fewer fertile brains than it used to. Students are squeezed because part of the university experience is being forced to adapt to an academic load, and as a result change yourself in ways that benefit you (or at least produce learning!) There have always been relief valves -- not just forms of cheating, but blowing off a study session by using game theory on your grade or going to a tutor or taking easier classes or extending your stay at the school. But now there's this huge giant relief valve in the form of a shiny LLM that is always available, particular at 3:45am when your project -- the one you've steadfastly refused to use AI on thus far -- is due the next day. The schools have tuned the pressure for the old set of options, and it's not clear that there's a new tuning that maintains anywhere near the old level of learning.
I guess my question is: of those students who were flunked for cheating, how many of them were learning despite their cheating? (And how about the students who were cheating but not caught?) Also, what levers are there to move more students towards learning even with the chatbots present?
I'm sure these questions are being debated. I know Garcia personally, and he is very invested in his students learning. The title of his Joy course is legit. So I'm sure the profs have ideas around this, though clearly not happy ones. Perhaps I'll ask him.
There are several reasons for this:
1. Cheating in CS is easier to detect. MOSS [2] (authored by CS professor Alex Aiken) is a very effective tool at detecting plagiarism in coding assignments. Personally I witnessed more honor-code violations in math problem sets, but there was no feasible way for professors to detect this.
2. Problems in programming assignments are (usually) very tangibly wrong. I can bullshit my way through an essay with shoddy research, I can hand-wave a proof that is definitely wrong but will probably garner at least some points. But when your program is crashing or not compiling, and the due date is approaching, it produces a very immediate and undeniable sense of failure and pressure to cheat. The thing is, many students would get a decent chunk of credit even for failing code, but this is not immediately obvious.
3. The ability to cheat is more available. Math problem sets tend to change quarter by quarter. It's basically impossible to cheat on a prose essay short of straight up paying someone to write it for you, or fabricating sources. But for CS classes, especially at prominent universities, there are plenty of solutions online. Much of it is people who aren't event at Stanford implementing the assignments for fun or self-learning, and sharing it with their peers. Which, to be clear, isn't unethical or bad - it's the responsibility of Stanford students to refrain from looking at those solutions. But nonetheless, it's a contributing factor.
1. https://stanforddaily.com/2015/03/29/increase-in-cs-106-hono...
He apparently also makes (I would assume a satisfying amount of) money selling the same technology to law firms for copyright/patent analysis: https://www.similix.com
(I love these ultra minimal HTML sites, ex. https://www.hwaci.com (SQLite commercial licensing) for another example. It just has this subtle smugness, like you either don't need any new clients or virtually all of the market is your client.)
Did they use AI to detect AI using cheaters?
AI detectors are pretty mid in practice - they tend to have a lot of false positives for "B" students who are okay, but can still be struggled to be more coherent than AIs are. There are some specific triggers that AIs are way more likely to do than students, but a lot of AI detectors will trigger on this "almost there, but you're still struggling" level of essay writing that might get a B, B-.
I could expect the same might be true for CS students even though I haven't seen how AI detectors work for CS/math homework.
It’s not that students didn’t cheat before, LLMs have just lowered the bar so far many can’t complete a live test in a class that requires effort.
It's not AI, its a deterministic program that analyzes compiled code for similarity.
So the Claude web app has this “learn” option that turns the session into a Socratic dialog of sorts. One could easily imagine enforcing this on an age based or parental controls set up. Maybe it can be prompted around but at the very least the concept could be a path forward.
As others have said there is a way to use llms to increase learning, but autodidacts will always autodidact.
In my personal post academic life, I’ve found LLMs to be an incredible teacher. Almost like the best professor in the world at my fingertips. I use it to generate quizzes on demand to test for my own knowledge gaps.
However, if I use it to speedrun over concepts I should be learning, I may achieve my end goal but I wouldn’t actually learn many of the details.
I think it requires an approach where you have to continuously audit your own understanding as you work with the concepts. You must slow down until you’ve confirmed this. Only once you know the concepts deeply and have retained them in your own memory can you then go all in with the LLM.
2. No US educational institution should ever grade on a curve. Your job is not to compare students but to educate them. Grade curves hide the performance of the educators and process of education in actually improving the skills of students.
3. Both AI and the cognitive and emotional overload from social media taking away brain space may be to blame. Idea: let students report screen time statistics at the beginning of each semester and weekly or at the end. See if and how it correlates with academics.
And their failing grades reflect the choices they've made.
ai is the single most powerful learning tool ever invented... but only if you choose to use it for learning.
I was in my 3rd bachelor's year studying physics (France) and overheard a conversation between two of my teachers. They were discussing how they should modify the 1st year program to now include math, because he had been noticing how more and more students were failing the more math-heavy subjects like body and newtonian mechanics. He said that they should now teach (or re-teach) calculus to 1st year students, which was not taught when I entered college (it was assumed that you learned it in high school and we would only cover linear algebra in 1st year).
I can imagine things are only getting worse with students that can now get under the illusion that they know math because they have a tool that can do it for them. Which raises the question: should programs adapt to this, like we adapted to having calculators?
It's a rational response to entrenched elites that prevent realization of the very social contracts they push on the youth (hard work will equal success, home ownership is a fundamental, etc).
Combined with the looming specter of climate doom, and watching the adults do nothing about it, treating preparation for a conventional career as a scam to be counter-scammed makes a certain sense.
A bunch of science fiction stories had "first connection to cyberspace" as a coming of age event, maybe those authors were on to something.
Overall it just seems like a huge waste of money to piss away the huge tuition cost your parents probably paid.
The smart ones either use it not at all, or use it to positive effect, like you're saying.
These people should be doing manual work, not intellectual work. There is no shortage of manual work available.
It's funny that GP mentioned science fiction as a negative because what immediately springs to mind, for me, is Neal Stephenson's The Diamond Age. We literally have the tools to build his "Young Lady's Illustrated Primer" today. We just have to give today's AI a lesson plan to follow and ensure that it never gives the student the answers, and only keeps explaining the concepts in different ways until they click. Wrap that in an iPad app and you've essentially got the exact self-paced learning tool that Stephenson envisioned changing the world.
I understand that it's harder to see things without the benefit of hindsight, but we must agree that AI's impact on students (or society, to be even more vague) has a much larger scope.
I do share some of the concerns, though I don't have kids of school going age.
AI apps are very powerful for teaching. You just need to tell them to do that, and not to directly solve your problem.
To do this, you have to be a professor who has a strong idea of what subject mastery looks like. Not available to most.
But ... It is exactly the right idea IMO
Anyone with a pulse can declare a CS concentration at Harvard and muddle by (you actually need to try in order to get a C/C-). Of course, GPAs are calculated differently at Harvard compared to other universities, as a B- is treated at a 2.67 but most other programs treat that as a C+.
Ironically, the techniques of the latter yield the results of the first, but everybody gets to keep a pure heart.
People can use AI to outsource their learning, but if they use ai to outsource their understanding they just set themselves up to fail even more.
From what I’ve seen, how students are using ai (not that they are using ai) is making them less prepared for the real world, which unfortunately is changing faster than ever at the same time to create double impact.
The solution? I'm not sure but possibly use AI as more of a collaborate partner to discuss with rather than letting it give you the answers
The solution is extremely obvious, just stop using it on 2 days out of the week or something like that.
You need to go to the gym, but for your brain.
If what you are building is too complex for you to meaningfully contribute to in the absence of LLM assistance then that should tell you something important.
> The solution? I'm not sure
This initially felt like you were setting up a joke. If you feel like something is harmful to you, stop doing it. Find alternatives (they are there, it’s everything else; commercial LLMs are still fairly recent). Thinking “maybe I don’t have to let it go, I can still use it if I do it this other way” sounds like an addict justifying themselves.
I say all this without a hint of judgment. I genuinely hope you are able to tackle the harm you’re feeling.
I’m sure I wouldn’t be the programmer I am without that experience, but I am Not sure I would have willingly put myself through that if LLMs existed at that point
I know that some students it to prepare for competitive tests, sometimes with very good results.
I've also been using it a lot recently to brush up on my math and physics knowledge from my graduate years. It has helped me clarify and understand a lot of concepts better.
That being said, there is no shortcut, and to be good at anything, one has to put in the work and the hours. However, information has never been as available as it is today.
A premature technology, known to be potentially harmful in its current state of development and established guidelines as to its effective use, is pushed by powerful and wealthy elite down the throat of society.
These same forces (and their unwitting helpers in the unmoneyed public) also wish to deflect with useless argumentation over "AI good" "AI bad".
The debate that we should have had: Is this tech actually mature enough for pervasive use in society.
Instead we get these entirely useless back and forths with anecdotal "works for me!" and "sucks for me!".
Adoption has been exponential. We don't need to be AI to be pushed down the throat. People use it because it works and it's useful to them.
> The debate that we should have had: Is this tech actually mature enough for pervasive use in society.
It's too late for this debate because this tech is already pervasively used, and there's no coming back. It's part of our lives.
What we need to do is understand the risks and adapt, probably regulate, educate. So we can get the best of this tech, and mitigate its risks.
Reminds me of a year where a teacher of mine (high school) gave everyone in class an A. He got called on it, and fought back. He literally called out the weakest kids in the class and had them do the work in front of the admins complaining and said, "tell me that's not A work, I ["fucking" strongly implied] dare you."
His grades stuck.
Even a lot of CS research journal papers feel more like role play — the same way startups try to pretend to be real companies with executive headshots, flashy offices, and all the other nonsense. (Instead of analytically modeling something to prove an idea, they’ll build a simple simulation and focus on its “Architecture”)
Engineering departments effectively weed out such in the first ground of engineering courses. Looks to me CS has no equivalent.
It’s like testing your drawing ability in a photography class. The difference is that now nearly have subject and testing method we have has become obsolete. Drawings courses still exist as will traditional courses, but the main stream has changed and exams and schools need to adapt.
.. had failing grades.
I guess LLMs will in fact kill the junior CS graduate, but before the graduation, not necessarily after.
> The electrical engineering and computer sciences department’s grading guidelines state that 7% of students in lower division courses, including CS 10 and CS 61A, should receive D’s and F’s.
Well I sure hope they dont just make it easier to hit this (objectionable) standard.
> Garcia believes that instructors “should not be curving” but should instead make thresholds for each letter grade publicly available and give students many chances to reach them. He added that he loves the idea of “having no limit” to the number of A’s he gives students.
This is a tough problem: Are grades sorting functions (top students get A's so retries are not helpful), inflexible thresholds (A's show mastery at a given level so retries are valid), or are A's certifications (a sufficiently good result such that they could do it - e.g., inflated but not curved, retries less likely but still ok).
But I essentially completely stopped using them for software engineering (why isn't really relevant, but it's not because od this skill atrophy). So as the skills of everyone else is diminishing, mine is proportionally raising.
It has never been easier to get better than others. You don't need to put in more effort, just the same effort as you always have, and others will do the job of losing their skills for your own benefit.
* Tom Lehrer: New Math (1965) https://www.youtube.com/watch?v=W6OaYPVueW4
And quite honestly. It shows in the CS grad population too. A lot of us are condescending toward anything that doesn't make sense to us. But, I digress.
The best engineers I've worked with are all non traditional backgrounds, non degree or degree holders from non elite schools. They think differently, they tinker, they are incredibly nice and patient, and do it for the love of connecting humans to technology.
Look up the names mentioned in the article. Garcia, Ranade, Nelson. All of them are involved with highly theoretical mathematics and scientific computing. Just because you're good at 1 thing does not mean you are qualified to teach. And none of these professors are trained or taught or graded or performance managed on how they teach. For most of them, its just required that they spend 10% of their time in the classroom lecturing.
Let's be honest about another thing. 99% of EECS graduates, even from elite schools, are wrangling objects and their relationships to a graph. Simply put, we're all just a bunch of glorified JSON massage therapists. It just so happens that we get paid well for it, and we hold that over people. The same happens in the classroom.
I think in order to facilitate a healthy, educational environment for young adults, we as adults must encourage, motivate and make that environment fun and practical. We force feed binary trees and the compiler AST's, but we need to make it fun. It's like the commonly accepted saying: Schools kill creativity :(.
I don't think instruction would've changed drastically in the last year though.
Worse, a decent chunk of research profs will treat teaching as a burden that just has to be done - a distraction from their exciting world-changing research. So, you get attitudes like the ones you mentioned.
I'm actually not sure why the system is set up to assume that profs who are good at research are automatically suited to teach classes, but that is how it's setup.
I got all that stuff. I've wired up a 4-bit adder on a solderless breadboard for an architecture class. I used to have a well-thumbed copy of Knuth handy. I've designed and built a switching power supply. But I'm not up to date on using Claude Code, and should be.
Start the kids off with high level stuff, but make them do some embedded systems on their way through. At least for an engineering degree. Also, do a bit of lower level communications somewhere in there; expose them to tcpdump/ wireshark, but they need not develop expertise.
Of course, if a student just breezes through it then I would agree. That would make no sense.
+10000. The goddamn slides. If I were a student now going to engineering school, I'd basically take the slides and throw them into NotebookLM and get way better lectures. Then I'd ask claude or GPT all my hard questions. Hell, I'd get the PDF version of my textbooks and do the same.
The number of lectures actually worthy of your time was so low.
The worry is in ~5yr time when the generic models catch up to this level (basic undergrad mind) that we need to worry about how to thin the herd. We could always go back to the tried and tested student staff engagement but most unis tried to turn themselves into sausage factories in thirst for the almighty dollar so the student/staff ratios are all off
Skynet is making mankind dumber - dailycal.org just added yet-another piece to all evidence here. It is a simple but effective strategy; Kyle Reese will stand no chance because prior to that, the other humans were already dumbed down into submission. Skynet version 15.0 will make no more mistakes here.
Artificial Intelligence and Grade Inflation
https://cshe.berkeley.edu/publications/artificial-intelligen...
It’s that there is no reward for doing so and in fact there is punishment.
The punishment is that for all the thinking you do, someone else will arrive at the same result as you in less time, or maybe even a better result. You don’t get rewarded for the effort of thinking, only for the end result.
Naturally, even if you are an intelligent individual, you can still be conditioned in this way to take the easy way out, unless you purposely like to suffer. But suffering is only worth it if you know in the end you come out ahead.
But now, you do not come out ahead. People will be using AI in the workforce for the rest of your life anyway, might as well just join the trend.
It’s like if everyone started taking a magical steroid and growth hormone to build muscle and look great instead of actually working out in a gym and possibly getting worse results anyway.
The goal of education is to impart knowledge in the student, preferably correct knowledge. The goal of an LLM is to produce an output that is convincingly human. It's not even that they're opposed, as much as they're ships for whom Polaris is in a completely different direction.
"Hallucinations" as they're called, or more plainly stated when the machine makes some shit up, are perfectly understandable in this context, as are the struggles of every single AI firm to get rid of them. Namely: the machine is functioning exactly as it is designed to, so how can you possibly fix it? It's working. The goal of an LLM is to produce text that passes for human, and apart from the obvious LLM tells, it largely does. Like say what you will about their lack of intelligence, the writing is solid. It's grammatically correct, spelling is dead on, what have you.
It reminds me of the famous phrase from Chomsky: Colorless green ideas sleep furiously. A sentence which is perfectly grammatically valid but is also completely devoid of meaning. An LLM would write that sentence, and it would be working correctly.
All of that to say: for all the things they CAN do and CAN be used for, I think we have to draw a hard line at education. I just don't think AI has a place in it. Of course that presumes that the goal of education is to, well, educate people, and especially here in the States but also abroad, we have been putting other interests, especially capital, far ahead of that for decades. I expect no different here.
And before someone comes in to go "WELL HOW DO YOU THINK YOU'RE GONNA STOP IT LUDDITE IT'S THE FUTUUUUUURE" yes, I'm sure as long as these exist and are available to people tech literate enough to access and use them, whatever that means into the far flung future, they will be a factor. Just like cheating, just like plagiarism, just like everything else that will get you kicked out of school. And the answer is the same: it will be stopped by institutions, imperfectly, and it will also happen anyway and with the same consequence: those responsible will mostly be harming themselves for short-term gains.
"Enlightenment is man's emergence from his self-imposed nonage. Nonage is the inability to use one's own understanding without another's guidance."
I would grant: I was not the most studious kid, I could definitely stand to learn how to read code a lot more effectively than I do; but I have found being able to ask a computer, "what portions of the Vulkan Programming Guide are less relevant with Vulkan's design changes since the release" pointing me to the dynamic rendering extensions and placing it into context, with inline code and links out to useful blog posts for additional reading, that sort of thing is very helpful.
Working on a prototype before I was trying to learn Vulkan, I was using it to explore SDL_GPU's API which definitely had some gaps in its documentation. Granted again, I could have referenced the sample code - I am sure you'll prefer I'd have done that - but it helped to get information about what each piece of the API was doing, and gave reasonable results that made sense and did inform me enough to understand what I was doing, turning much of that into an interactive learning of basic GPU programming for graphics. Where the AI hallucinated, it was often on things like method names, which I was able to read through and find the methods it was intending to name. (This only occurred once or twice when I was learning).
Unrelated, but adding the C macro syntax and nesting macros, which I could have an LLM explain inline and link the GNU manual. Never got that taught to me in a C course. Man, computers are complicated!
These have not replaced textbooks; I have been using them alongside textbooks and handwriting code for practice, and they work as a very good complement. I also sometimes use them to unblock me - I don't know CMake very well and lean on AI to do CMake, so I can focus on learning C++ and graphics, which is my primary objective right now.
I would add too, I have for fun given it prompts about various topics I learned in university, and I often will get answers that are bang-on what I learned in university undergraduate courses - the topics I tried were welfare state taxonomies, distributed systems, disk storage performance, filesystem layouts and internals.
Boy, this would've been cool for me as a kid. There's just so much information right there, and pointing you to topics and textbooks a couple questions away, I wish I had these tools. I was a curious kid in a terrible MAGA-esque family that was deeply uncurious about the world, had no knowledge of any advanced subject and basically mocked me for trying to learn more about stuff. And you go to the school library and it's all kids shit, not even an option to try and reach out for more. Now smart kids might be able to go just learn shit very freely and be pointed to textbooks, and go pirate them off some Russian site, and start learning and go tutor themselves, as I'm doing today as an adult.
At least knowing myself and knowing if there's another kid like me, I think they would deeply enjoy having a natural language encyclopedia, if we can get it as close to that as possible. I think even with some error inherent, if the tools can be often and directionally correct, that would be a plus. I went to university, and the professors there hallucinated some things so embarrassing it should bar them from teaching, for the standards people hold LLMs to! i.e., sanitizing conspiracy theories that Android records all language through the microphone therefore iOS is better, Apple Silicon is more battery efficient because it is RISC and not CISC. Got a terrible history of computer graphics technology you'd know was slanted if you watch the 8 Bit Guy on YouTube. Rubbish.
The thing that worries me, and what this article really talks about, are the kids that just don't give a shit. They are not new - when I went to high school, before AI, stupid kids would copy code off the internet. I think AI probably makes it worse because it makes it harder to call out and enforce against it, and agreed, that should be stopped. But to me, that is mainly a cultural problem. Too many Americans are completely uncurious and just spout garbage; there are a lot of kids who grow up in that cesspool and are going to grow up uncurious, and then AI acts as a shortcut rather than a vehicle of curiosity.
And granted, maybe AI is less useful when you are in a structured environment - but the structured environment has its downsides. Even in that environment many of the TAs were clueless and unhelpful, or just too damn busy or already too knowledgeable to meet students where they were at. Again, talk about hallucinations with TAs! Many times in my experience. And that's all to say nothing about getting people to not just do homework but actually go get curious about things and try stuff that isn't required of them.
I think there will be some culture that remains curious, and has these tools, will come to grips with where they can help, where they go wrong, how to balance it with other learning methods; and I think they are going to have kids that absorb a lot more knowledge and get to play with topics and learn things, faster, to each kids' interest, perhaps even individualized tutoring at better scale - I hope that is possible.
I hope the United States as well, but maybe not, because holy cow our culture and attitudes are plainly terrible these days. Your comment is pretty representative of how most people react if I suggest this or talk about my own experiences I'm describing here. But I hope at least I'm arguing something comprehensive here. There is too little conversation beyond hyperbolic nonsense on the internet; I consider "FUTURE LUDDITE" etc. to be in that realm.
It is just hard to reconcile that denigration of AI with the typical experience I have using these tools in the real world. It is not omnipotent or God, but it can effectively assist in work. There is a certain cognitive dissonance I feel when I walk away from using the tool to help accomplish particular tasks, then hear over and over people say the technology is fundamentally useless and fundamentally does not work. I guess I am just not enough of an academic to understand how something can accomplish work yet fundamentally isn't, somehow.
LLMs can be useful, but I haven’t found a way to use them where I’d be confident in using it to solve technical problems I didn’t already deeply understand.
On the one hand, it's like having a free private tutor who is always available. It's a great learning tool.
On the other hand, students can use it to do all their homework for them, and skip learning altogether.
Alternatively, more students are taking CS10 and CS61A irrespective of aptitude.
Anyone can code, but not everyone can become an employable SWE.
Anyone who has first or second hand experience with Cal or any other university knows how impacted CS majors have become, and how everyone is attempting to become a CS major because it's the easiest path to multiple high paying white collar careers.
And in all honesty, it's not like CS@Cal never had weedout classes (I remember CS70, CS61B, and Math54 had reputations of being the L&S weedout classes).
At UC Berkeley L&S, students are undeclared by default, and everyone is incentivized to take the intro CS classes (CS10, CS61A) irrespective of aptitude because worst case they can declare a CS minor or use the classes for other adjacent degrees (eg. Applied Math, Data Science).
Additionally, while Cal doesn't require standardized tests, most students who applied and attended already took the SAT, ACT, and APs becuase they cross-applied to other universities as well. This is reflected in UC Berkeley's HS Weighted GPA being in the 4.31-4.65 range [0], which means most students will have taken at least 6 AP classes.
Hell, I attended an Ivy and even then Cal was a target program for me, as well as my peers. If I didn't get into my Ivy I would have ended up at Cal and ended up in the same position.
[0] - https://admissions.berkeley.edu/apply-to-berkeley/student-pr...
Barely over a decade ago, CS tended to be a large but not too large major by enrollment in most universities yet nowadays it is the most in-demand major in most universities. You can see this at Stanford [0], but most other programs as well.
[0] - https://stanforddaily.com/2020/04/25/stanford-in-the-2010s-t...
This generation of kids were fucked so hard by Covid and all the remote “schooling” and closing of public life.
AI rise happens to be happening when the kids who were just entering teens at Covid time are now going to school.
Kids need to understand how to adjust and grow from failure more than they need to always be on the happy-path of straight A's and easy money.
How we respond to failure is how we teach response to failure. Hand-wringing, pearl-clutching and finger-pointing aren't valuable life skills.
Personally it's easy for me to be contemptuous - I opted into an accelerated math program that banned calculators when I was in Junior High. It helped me cultivate an very crisp intuitive/conceptual understanding of basic mathematical concepts that's carried through to today. I think we should do more of that kind of education, but it's expensive and requires amazing educators and a tolerance for student struggle.
Get the machines out, absolutely. But respond to failure compassionately, as part of a natural learning process.
I imagine there is some apathy and laziness here but idk how unjustified it is
"Noooooo you need to manually code on paper in assembly"
Alright, well maybe the CS grads need to, but why expect that of everyone else
Now I work mostly with PhDs who were at the top of every academic environment they've ever been in. And yet I can see their thinking skills rapidly declining as well; many of them can no longer brainstorm, code, think deeply, or write without an LLM present doing 90% of the work. Many of them can no longer sit quietly for even 30 minutes just thinking on their own, which is a required skill for producing original thought.
For adults the cognitive decline won't be as measurable since there's no exams, and overall output volume will still be fine due to LLM help. But I do believe it's already happening absolutely everywhere around us. Honestly, I wanted to be in denial about it before but it's too obvious to ignore now.
However, I personally feel a huge mental burden of the state of communication. The contemporary version of it where I have a million threads and conversations im juggling at any given time. Emails, voicemail, chat, online, texts, personal, business, home, children, other family, friends, then there’s the variants like Messages, Messenger, WhatsApp, etc. And as overwhelming as it is for me, I’m super under connected than everyone else I know. I quit following most news and all sports, as I just don’t have the bandwidth for it.
My brain was molded preinternet and I feel like it’s reaching its max on the analog to digital conversion. Or at least it’s just a really lossy process.
Okay so let's say that's the new cognitive burden. The new escape hatch is "AI". Now you don't need to read your mail or write responses! Let an LLM handle that for you! And now your friends and coworkers will send you AI generated mail anyway, so if you're actually taking the time to read and respond to it yourself you're a chump, right?
Noise machines. Humans are noise machines. Ever try to sleep till noon and notice that everyone else seems like they can't feel alive unless they wake up and make the maximum amount of noise and racket possible? What could be better for a gibbering species of ground dwelling apes than a miraculous machine that gibbers for them, to point back and forth at each other?
This hits close. I realized one of my friends was using AI to message me and I took it kind of hard. It's weird to be worth the effort for them to set up a chat bot to talk to me but not worth the 2-3mins a week to actually read/respond to my messages.
Right now, I just basically ghosted him, but I have teh feeling this is the start of an emerging issue.
I don’t really enjoy that, so I find having that many threads stressful and annoying.
I just take a hard line and will unilaterally downgrade communications (while politely letting the other party know). I have all my family group chats muted because my mom uses “Send” the way you’d use Enter on a desktop. End of a sentence? Send text. Next bullet point in a list? Send text.
I muted the chats and told her that I want my ringer on in case there’s an emergency, but I got 30 something notifications in 5 minutes during an interview and it’s unfair to the candidate or other people in the meeting. Internally I rationalize it as revoking someone’s ability to make noises on my phone at whim. They can still text me, they just can’t interrupt me anymore.
It helps a lot, even if only temporary. I’ve muted people for a few hours or a couple days before when I’m already stressed and they’re really chatty.
At first, some people will be offended. "Why didn't you let me ping and buzz you and interrupt you all day? You didn't respond immediately each time :'((". Some people with unrealistic expectations may even stop talking to you entirely.
But eventually (years maybe) they will get overwhelmed too. No one can handle this madness indefinitely. I've seen giga-texters get broken down and turn into lazy texters like me, or at least learn to tolerate my long response intervals and recognize it as a coping mechanism rather than rudeness.
For important threads like calls or messages from important people/group chats, I have my watch vibrate.
Otherwise, I just go through my notifications once I have downtime.
However, when looking at muscle, once you have it you don't need to use it as much in order to maintain it. I wonder if the same is true for skills; in that case, some kind of regiment where you still use the skill you delegate once a week or so could maybe help with avoiding this loss of skill for most part.
No.. this depends on how much muscle you have. The appropriate comparison is mass and density of knowledge/understanding vs muscle. There’s not a chance in hell you will retain mass and dense muscle without pushing the body hard. Just in the same way you will not retain very deep understanding of things unless a) you’ve been reciting it for over 10 yrs b) you go back and push the understanding continuously for it to remain as part of your being
And if you went 3 years without exercising, you'll be able to get your muscles back much quicker than had you never had the muscle before.
It's pretty comparable to skills. You don't need to practice as hard to maintain a skill than you do to build it. And if you let the skill atrophy, it's much easier to recover the skill compared to building it from scratch.
This very much depends on age. I went on statins about 18 months, which destroyed about 15lbs of muscle over the course of a year (160->145). Along with that muscle loss came about a halving or more of the weights I could lift in any given exercise. I interpreted the "do you have any weakness on this medication" question as inability to function levels of weakness, it wasn't until I showed my training logs to my physician that she asserted that I was having weakness.
It's been a year since I went off them and I'm still lifting barely what I could in high school. I'm exploring some different training plans, but AFAIK, there isn't much research into if different weight/volume breakdowns work better for older guys.
I’ve got 20 inch lean arms - I know far more about muscle building and retention than you. I train just as hard to maintain them as I did to get them there.
The people who say “oh it’s easy to maintain” LOL it’s easy to maintain 16 inch arms.
I am a competitive bodybuilder…
> I train just as hard to maintain them as I did to get them there.
Are you enhanced? Were you enhanced when you built the 20” arms? If so, yes I agree.
Edit: With 20" arms, there's nearly 0% chance you're natural. You can't compare your enhanced experience to naturals.
I noticed it myself with cycling. Took 8 years off the bike, when I started up again I was nearly back to my old FTP in about 2 months despite starting from basically zero. Muscle memory is real, where I am now as a returning cyclist would take a pure beginner cyclist at least 4+ months to get to, fitness wise.
That said, you do have to work somewhat hard to maintain. With cycling, just 2 weeks off the bike is enough to see a VO2 max drop of anywhere from 4 to 7%. After just 4 weeks, your glycogen storage capacity decreases and you start rapidly losing fitness. After 2 months, you are basically now out of shape.
Detraining happens faster than most people think. And therein lies the danger with over reliance on LLMs for your cognitive skills. Detraining there happens just as fast, skills atrophy in a matter of weeks, not months or years.
Also like some people hinted at this in sibling threads, I think it's different between purely abstract skills, and skills that involve muscle memory. For instance, I could probably stop using my bicycle for a very long time, and still not unlearn how to use it, or learn it again really quickly. Maybe it is because abstract skills are inherently more complex and require more cognitive effort and connections to knowledge overall, and are therefore more fragile.
…said every drunk person ever.
That you don't notice it doesn't mean it isn't happening. By the time you notice it, it's too late.
That's why elderly people who are worried about their brains play chess and do puzzles like mad.
The reality is I agree with the op and I see the loss of reasoning power in myself. I've been using native Emacs on android for a bit and finally have gotten serious about config for it. I got lazy and had Claude do some of it. Which was great untill things don't work because there's not going to be my crazy ask in the data. It was painful for me to sit down and think through my configuration and the problem but I did it.
I am absolutely torn on the technology still two years after adopting it.
Something radical needs to be done. When I was in high school there were still a lot of "no calculator" restrictions in my math classes that I chaffed at because I hated doing longform arithmetic and felt like it got in the way of learning. So I can certainly understand how students would chafe at some kind of paper-only education system but I also don't see how you can learn anything when you have a high-quality homework machine just sitting there.
Part of what we could do during this upset is re-prioritize.
I agree - I would have been toast. I wonder if the teachers/colleges need to change the way they teach and assess. Let the students use the AI tools they like (perhaps guide them how they can use them professionally), but test regularly and early on the skills/knowledge they're meant to be gaining offline and in person. Oh and don't give Fs for cheating - suspend them.
I read a few years ago about a teacher (I think highschool) who put his lectures on YouTube for students to view in their own time and then used the in class hours for interaction, questions, tests.
EDIT: Claude beat my Googling: This was 2 chemistry high school teachers in 2007 - The Flipped Classroom https://fltmag.com/the-flipped-classroom/
My colleagues say "We must fully embrace AI as a tool". I agree. But how do you teach it? It's a moving target, and you can't even give homework like: "Research <this topic> with an LLM of your choice, and submit the transcript" because they can do that, or they can just copy the task into an LLM and have the LLM do it. It becomes meta quite quickly.
And independent what and how we teach, we have to change how we assess a students learning result:
The first thing we have to change is that homework needs to be completely ungraded. Reviewed and corrected, yes, but not part of the grade. That's the only way to make sure that people who don't want to cheat have to cheat anyway to compete with those that do.
Second, all exams have to be in person. Online, cheating is so trivial it's not even funny (many students are so stupid about it that we have a pretty clear idea what's going on). In person, we have maybe 2-3 years until we have to make sure its proctored and people's glasses are checked. I think in less than 10 years, local mobile AI will be good enough so even a Faraday cage will not help.
Maybe we have to go to oral tests only.
Of course, none of this scales. Some of our intro courses have a thousand students.
Any ideas are much appreciated.
If that's anything like how they guided me to use programming languages professionally...
In my workplace I find systems and policies move too slowly to keep up with how rapidly the LLM world is changing. Colleges are even more glacial. They've barely adapted to video conferencing.
LLMs were, IMO, pushed out too early and without that clear "this is experimental tech" label. Full public access from day 1, no invite only betas, no research previews for a select few pilot customers/orgs, etc. I've been in IT for a little over 18 years now and I haven't seen anything move this fast before.
I mean, I never though I'd see Microsoft go on stage at BUILD and and announce freaking OpenClaw for Enterprise, and then make it available the same day. This is highly unstable tech and what I'd consider still experimental, being sold to F500s as production ready.
The only thing I can see them doing is removing technology altogether. People did just fine 100 years ago.
Want to learn to code? Use a Commodore 64. The company was purchased and rebooted the C64: https://commodore.net/
Perhaps this is rather a sign that you currently shouldn't jump on the LLM hype train, but rather attempt to get a good foundation on the basics. When the whole LLM area becomes much more "stabilized" (I see signs that this is currently happening, if only for the reason that training state of the art models has become more and more expensive), you can still get into LLMs if you want.
On the other hand I think there are real development gains in jumping on the train today. To my career's detriment.
Until that situation stabilizes I think the only institution capable of teaching about it is the family -- parents.
I don't believe them for one second, it's far from a solved problem yet these companies are selling this tech as if it's been around for decades and thoroughly battle tested instead of highly experimental and unstable.
Tristan Harris had some sort of comment like that on a podcast about the challenges posed by AI.
That seems like a smart approach. It reverses the traditional model of "lecture in class, homework outside of class".
My own experience with flipped classrooms (which seems to be shared by quite a few people who have tried it out): they only work well if all students actually read/watch the materials beforehand. In small, advanced courses, intrinsic motivation may be sufficient - but in most cases you need some extrinsic coercion - such as a mandatory quiz about the materials or hand-written lecture notes that need to be shown at each in-person session.
With AI, some people don't watch the lectures but let ChatGPT give them a summary which they submit. Then these people poison your in-person session with their lack of knowledge and motivation.
Just have a quiz every day. In fact, have _TWO_ quizzes, one at the start of class and one at the end, and take the higher of the two scores. In between the first quiz and the second, work through problems with the students designed to help people that bombed the first test figure out how to pass the second.
We had no lectures, the teacher just gave us a short, concise textbook to read a chapter of every week.
In class time was devoted to discussing and problem solving.
But yes, it only worked because we were a small class of 15 math students
If done more stringently (if you didn't watch the lecture, I'm not reteaching it), it maybe would've had a bigger impact, but I'm not sure.
Office hours remained king for serious Q and A for the class.
College is different, because theoretically you should be taking classes that are relevant to your field (although there are still "core" requirements that are somewhat high-school adjacent).
College is a different dynamic from a middle/high school classroom, but I don’t remember 95% of the material from my college engineering classes anyway, it’s the problem solving and information finding that I’ve retained and have helped me do the things I do. I remember the stuff from the classes that taught me the material in an engaging way though.
"Just do it right and it won't be a problem." This is not an actionable plan. What is engaging? Who gets to decide that? The teacher? The students? The parents? How do deal with certain kids finding different approaches more or less engaging? How do you expect a teacher to curtail their teaching approach to dozens of children at the same time?
Worksheets certainly are. But good homework, even if it's challenging, is what makes a reasonably fast-paced course even possible. In a well-paced university course you're typically spending proportionally several times as much time working on it out of class than you are in class. Then class time is both preparation and catch-up, similar to office hours.
This was true of my most demanding humanities courses (sometimes reading 100 pages a week directly from academic journals, not easy reading) as well as my most challenging math courses (group theory, ring theory). Once the pace gets fast, there just isn't enough time for you to learn everything you need to inside the classroom anyway.
And in those classes, where homework was really essential for learning at the required pace and depth of mastery, my instructors didn't even need to factor the homework into my grades at all. In some of them, we could get "feedback" on homework but it was never officially recorded in our grades... and yet, anyone who didn't do it would fail the next test. If homework doesn't have that characteristic, it probably doesn't need to be assigned at all.
If "flipped classroom" means that students are expected to do all of their homework in class, then indeed it'll feel like a waste of time to many of the smarter kids, and it will also just be unfeasible for advanced courses (which theoretically should be most courses in a university, though it currently isn't). But if it means "we don't even have time to lecture you on every single thing you need to learn, therefore you must arrive already having done the reading and the exercises, and we'll use this time to help clear up misunderstandings"... that's already how classes for grown-ups are in universities.
Kids get to learn lots of interesting things in school. The problem is that they're kids! They want immediate gratification from phones/games/recess, not to do the hard work of learning.
What about those students who don't have stable home environments? How are they supposed to find multiple hours a day to watch lectures?
How does this address the underlying issue of students off loading work? You've replaced homework with lectures, but haven't solved the problem of making sure the student is actually participating.
Logistically, this could only work if you shortened the school days, but then you would need to adjust the rest of society around that. Many parents structure their work days around their kids school schedules, and if kids need to go school later in the day, or get out earlier, that places a burden on the parents.
For secondary school, I do agree with you - homework load can be problematic for some students. But at the same time, my honors classes all came with hours of homework and I’m not sure I would have been as prepared for uni without it.
I very much doubt there is any agreement on what those skills are.
Creating the idea of “what to learn in the new world” is itself IMO an important academic creation, but there’s no reward for doing it and no way to know if you’re on the right track (you just have to wait and see).
Employers are also just adapting.
Wait until companies are paying unsubsidized “list price” for LLM usage. Then we can have a better idea of the worth of the automation and what skills should stay with humans.
We'll get an idea of the relative cost of the labor, all right. It's just that they are specifically trying to wreck the market, at all costs, to be able to cash in on the upside. It's sensible, if you're a monster.
I'm not noticing a "cognitive decline" per se, but I do see I'm a lot "lazier", even stuff that used to be routine when I started coding now feel heavy.
The funny thing is, maybe not noticing one can be the actual sign of it :)
Not getting that quick dopamine hit the LLMs give you..
Some say you can re-train your system to get back the dopamine hits you used to get from other things, like the enjoyment of the "old fashioned" manual coding and math. Getting there is hard work. And YMMV.
And I'm just afraid this is what cognitive decline feels like from inside the deteriorating mind.
The same weight feeling heavier is a sign that your muscles are weaker :)
There's many areas in life were we look back a few decades and think "people use to do it that awkwardly?" And yet results were better. I think the process of removing friction have just served to destroy our ability to concentrate and tolerate difficulty.
I use an agent to generate a first-pass attempt, and then (deadlines willing), I manually read every line at least once so I understand what the code actually does.
Then I manually fix the inevitable slop that is mixed in with the good stuff, and only once the code is up to my personal standards do I send it.
This probably reduces my “AI performance boost” to 30-50% instead of the huge gains reported by others. But I retain the ability to reason about the codebase and use AI much more precisely when I’m trying to troubleshoot production outages or subtle bugs — something I notice the rest of my team struggles with, since adopting “agentic workflows” everywhere.
I think actively working to retain some cognitive flexibility and “muscle memory” around coding tasks is going to be rather advantageous in the long run.
These are correlated - it just hasn’t happened in a large enough amount for you to have clearly noticed it yet.
The leading indicator for me is the amount of emails and, god forbid, more personal messages (like birthday wishes!) I see that are obviously AI generated. It just keeps on rising. If you’re not able to dash off a quick message without the help of AI I have to assume you’re using it heavily elsewhere too.
I have sympathy for the university students too, we’re all bombarded with rhetoric about AI being the future. And I remember being incredibly nervous emailing my lecturer (am I phrasing this right? Is it respectful enough?) that I can imagine leaning on AI myself had it been available back in the day. But I’m glad it wasn’t, it’s an important skill to work out this stuff. They’re going to land an in person interview when they graduate and stumble around unable to effectively answer the questions they’re asked in real time.
Not necessarily. It can be either an AI interview or a record that will be analysed by AI later. So there’s a chance to cheat here as well.
If that allows you to target your deep dives better, then great. If instead your deep dive into a topic is purely through prompting an LLM, that will almost certainly end with little functional domain expertise.
The absolute best experience you can get is by trying, failing, then improving upon your past failures. Remove that friction at your peril.
If you treat the model like an excellent bluffer, it has never been more fun to challenge a model. To me, there is something deeply intellectually satisfying about "proving" it incorrect, and I like being deeply critical of what the model spits back out. I find that refinement process (with the constant sycophancy turned down in the system prompt) creates a really good loop of critical evaluation that would be hard to get in anywhere else. You can treat it just like the Socratic method, but instead of a benevolent teacher, you get a probabilistic bullshit artist. Lots of fun, highly recommend.
Inevitably, it fails frequently at both. Any "reasoning" it is doing is merely rehashing ideas that someone else has already posited. This helps some of the times, but the vast majority of the time it just chooses a biased perspective (frequently the most common) and then regurgitates tired old talking points. This contrasts greatly to speaking with others who often have more intuitive notions that tend to be less polished and rote.
I'd love for LLMs to be better sounding boards, but so far they fail miserably far too often for my tastes. To each their own though.
I find it to be a really tight loop and results in very high quality code at a high velocity.
Yes, but eventually the intellectual whack-a-mole gets tiresome unless you get really, really good at simultaneously cornering it and not letting it concede to your point.
The point is not to literally win an argument (it doesn't matter), it is to use the model like a partner to poke holes in your own understanding. Once it's poked a hole, it has served its purpose. Plus, you eventually run out of context or the model trails off into babbble.
You go to a university because you are deeply interested in understanding the subject that you study. Doing the homework and the tests are just the "goalposts" to check for yourself whether you made progress on this.
So, as long as you are not under time pressure (which you in some degree courses unluckily are), there is simply no need to "speed up" any homework assignments.
If, on the other hand, LLMs help you with making much faster progress in understanding the subject that you study (which is only loosely correlated to homework and tests), I guess it's fine to use them. Just always keep in mind that very often the pain of attempting to understand the topic on your own often makes you smarter - something that you will miss when you take an "LLM shortcut".
This is probably not true for majority of people. Most go to school because it is mandatory, pushed by parents and society, and university gives you credentials and better job opportunities. Homework and tests are a way to get a number grade on 'how well you memorized something', it doesn't really measure a deep understanding of the topic.
As I said: they are goalposts.
Typically homework and tests are sufficiently easy (yes, there are exceptions) that if you fail them, you can assume that you didn't make sufficient progress in improving your understanding.
But I do agree that at least sometimes the difference between being good and exceptional at homework and tests can indeed be rote, "unnecessary" memorization.
- doing - failing - discovery>learning - remembering
With learning predicated on both failing and remembering it's unfortunate uni scores on 100% successful doing but doesn't teach failing well, and scores for remembering but not for learning well.
This has not been true for something like 70 years now. People go to university because it is expected that that is what you do after high school.
In Germany, many people indeed say if you are not deeply into the topic that you study, you should rather get a vocational training (Ausbildung), or attend a different kind of tertiary education than a university such as
- Fachhochschule
- Berufsakademie
(these words have no good English translation). Basically these are kinds of tertiary education that are more applied than the much more scientific training that you get at a university.
Specifically for mathematics (I guess the same holds for physics), a lot of people say that if you don't consider it to be an ideal life to think about math exercise sheets when you sit in the bathtub while other people are having fun at some party, you simply are not made for studying mathematics and should change your degree course as soon as possible.
We haven't regained traditional apprenticeship roles (perhaps because we so weakened unions?) but 30 (of 50) States have free or heavily subsidized two-year community / vocational college programs. Affordable and accessible vocational education opportunities are increasingly present. I also think (very subjectively) that we are seeing a renewed respect for the trades.
However, there are structural headwinds outside of education - no national health insurance plan being a major one. Farming, fishing, forestry, construction and similar trades still have a 20-30% uninsured rate in the US. (The uninsured rate in white collar "professional" work is around 2.5%.)
The reason for the traditional apprenticeship roles is not unions, but rather capitalistic:
- If potential employees are well-trained the employer doesn't have to invest resources for training them.
- The certificate of the vocational training means that the employer knows that an applicant has an established standard, and can save time testing whether he is qualified.
- Because the trainee needs practical experience, employers can invoice this additional worker to the customer. Because the trainee needs explanations and thus works slower, more hours can be invoiced to a customer.
With CS students this is one thing. Medical students? Air traffic controllers?
That is to say, there is a huge gap in the educational integrity of degrees, and this is probably partly driven by people who do not really want to be at university for educational reasons (and, believe it or not, there are other ways to party in your early twenties) and for whom a degree in XYZ is not rationally connected to 80% of their options after school. And there are many such people.
This really needs to be thought through, because education is expensive, and it is an enormous waste of money to pay for a couple of years of university and end up failing out or being sanctioned for AI cheating, or being educated for something you do not really want or need to be taught. That is true whether or not education is paid for privately or by the public.
ETA that when I graduated from school the idea of not going to university was really discouraged by the guidance counselor. It seemed like vocational courses were not really a worthwhile option unless you were a poor (significantly below average) student. There was a lot of emphasis on ‘getting a degree’ probably related to (nonsense) job requirements. Not a lot on what career you should pursue, or why you should consider university. It was more like why would you not consider university, since it was the de facto default. It was, I guess, unseemly for the school to end up with fewer university entrants and more apprentices.
At the time, there was somewhat of a social stigma with apprenticeships. The people that pursued them seemed to only genuinely have been set on the idea, and there were few if any that were diverted thereto. Now, of course, ‘the trades’ pay much better than a middling office job. Egg on my face.
~"Speed doesn't matter unless you need it."
~"LLMs can be good, but if you don't use them properly™, then they become a crutch."
It's hard to deny that "cognitive offloading" via LLMs is becoming a more acute problem [0]. The intelligentsia were supposed to be immune.
[0] https://www.bbc.com/future/article/20260417-ai-chatbots-coul...
Echoing the other comments here, at least in the US, this is generally untrue. I went because my parents made me, because the choice was that or get kicked out of the house. It was beaten into my head since I was in grade school that "people in this family go to college" and "you can't get a good job without a college degree."
I hated every moment of it and I was glad to take my BSc and never look back once it was over (University of Houston, c/o 2000). And, indeed, without the degree I wouldn't have had the jobs I've had.
But I didn't go because I was "interested." I went because it was an effectively mandatory life-path objective. I'm very happy for you if your lived experience is different, but it is also—at least in the US—both extremely uncommon and extremely privileged.
There is only one classmate in my class who came to study CSE because they are interested in CSE. And since we all enrolled after AI became somewhat good at everything none of them know how to code. After two years of study I had to explain someone how to swap two number by drawing boxes. This are the things you learn in the first week if you're interested in programming.
My point is very tiny percentage of people study something because they're genuinely interested in that subject.
I don't think I've met anyone who fits that description. The ones deeply interested in the subject would likely skip college anyway if not for future economic prospects.
There exist a lot of things that are much "easier" or even (currently) only possible to learn by attending a university because, for example,
- for the access to various devices and experts,
- you walk a much more "established" and "time-tested" hike for getting good in the subject,
etc.
Spoken like a true software engineer ;), there are jobs where you have to have a degree to get the job. "Real" engineers with sign-off responsibilities, Medical Doctors, etc.
Does college even work for future economic prospects, by the way?
Sure. (?)
> Does college even work for future economic prospects, by the way?
Where I live, a college degree is a legal requirement for a lot of professions that pay more than entry level jobs (although not all of them). So, people go to college to get a better paying job in a few years than they could get by immediately entering the workforce.
I think this was true a long time ago. Perhaps with LLMs this can become true again in the future. But definitely that was not why I went the first time, nor most of my classmates. (Second time I did post-secondary, sure, 100% -- but I was almost 30, not an average student)
Some students do not have this privilege and implicitly see university as first and foremost a funnel into a paying career.
Unfortunately that, on its own, very much does not translate to being able to explain it all oneself, or to having the skills.
Ease and norms of outsourcing to software invites and amplifies this trap, I think.
I can really certify that this was my lived experience. In the math degree course, basically everyone who was not incredibly passionate about mathematics (NB: "passionate" does not necessary imply "great academic achievements") changed their major or decided for a different kind of tertiary education.
Former co-students who attended the same university and degree course had the same experience.
I guess the reason was that it was a decent university in a "boring" town where learning for your studies was one of the more exciting things that you could do.
That said I generally think the take that it's somehow privileged to find school interesting to be sad. Over the last couple decades one could do pretty well with pretty much any STEM degree. Is the majority feeling among people studying engineering that they just have no interest in any facet of how the world around them works? They have no desire to understand how to create (and alter to their liking) the things they see? No interest in the fundamentals of how the universe works? How different materials come to act the way they do? How living beings work? Nothing?
2. I would optimize hiring people who display the kind of curiosity described, but if my goal was to create an education system to generate educated workers to grow an economy, I wouldn’t optimize for it. I don’t think curiosity is a privilege, it’s an undervalued right.
This is a bit of a naive or maybe affluent take? Like, theoretically, I agree. And I myself was curious. But most people, by and large, are going to university because they know they need a degree to get a job, unlike their parents or grandparents. And even "the degree" is quickly becoming devalued in this current AI age.
I would guess that if all basic needs were met through UBI, the fraction of individuals going to school would drop and the makeup of subjects they pursue would change. Probably more cooking and art classes and less stem. Although, if UBI existed and AI did not, we'd probably see more educated individuals in the first place so maybe there would be an uptick in stem attendance and general curiosity in such a utopian world.
> This is a bit of a naive or maybe affluent take?
Concerning the "naive" aspect, I wrote something at https://news.ycombinator.com/item?id=48397759
Basically, this was really my lived experience, which might have been amplified that it was a decent university in a "boring" town where learning for your studies was one of the more exciting things that you could do.
Concerning the "affluent" aspect, I can clearly assure you that neither I am nor my parents were.
In the UK anyway, there's an acknowledged idea that many people go to university because there is a societal expectation that they should and also because many careers require a degree even for entry level positions.
There is also much less emphasis on other routes of tertiary education (e.g. vocational schools), when compared to places like Germany.
I know a lot of people who think this way, and I can assure you that the people who realized later that university is not for them deeply would have wished that someone had given them this advice when they were younger.
You must come from a wealthy background because what you described is far beyond the vast majority of people's means - at least here in the US.
Most of us go to college because it's the only reliable way to get a tollerable job that pays well. Only a few of my college courses aligned with my interests. The rest were just the price paid for the degree.
My experience is that they uncomfortably do both. You can "understand" something conceptually quicker -- like you have a new brain-muscle-thing that lets you cut through the hard difficult tedious corners to get to the meat of the matter.
But then you also can become reliant on it, and have difficulty doing the mechanistic rote work of working through it yourself.
Like the really big powerful calculator that it is, really.
You can use AI or the internet to learn the basics of how a gas engine works in a couple of minutes. But you'd be incapable of actually working on a gas engine or designing one.
Surface level knowledge gets you surface level functionality. You don't become good at something from surface level knowledge, but you might think you're good at it.
I've used my phone taking pictures + Codex + a PDF of my tractor manual to help me effectively diagnose and manage repairs in my tractor. (Though these models remain terrible at the physical world, getting physical orientations wrong, front back etc. Much like myself)
Likewise I had Gemini help me tear down my mower's carburetor and diagnose issues there.
(So much so that I've wondered about building some kind of "shop buddy" -- some kind of durable laptop and set of cameras ... on a cart. Running models that have access to manuals and cameras and TTS and voice input? "Hey, shop buddy, look at this fuse and tell me what is before and after it in the electrical system.")
This is helping me learn and do something I couldn't really effectively do before by walking me through steps.
My youngest has had Gemini write math questions for them, to help study. Not do the math, but write questions.
In the end it comes down to prompting, like everything.
Which makes me wonder if the answer for higher education is just to provide the students with specific coding agents they're specifically allowed to use -- ones that would push the student through problem solving and working on the problem together.
We are in the instant gratification era of humanity where a dopamine rush drives most people. This is a systematic shift that happened through the introduction of smart phones and social media and then progressed for a good decade to what we have in front of us today.
Asking people to "resist the urge" when they've been programmed/brought up to feed the urge is not pragmatic unless you are also proposing a way to erase the damage done from the instant gratification era.
We're in the end game presently. For every one person like you and your examples, there's gotta be 100x or more who are not using the tools the way you've presented them.
My other examples have to do with current limitations of the tools. Obviously there's no Claude Code for Meatspace that just takes over and does things for you. (Yet)
What I'm trying to point out is that the tooling has been made this way on purpose and I agree substantially with your point. But I also think human agency is involved. Dario & Boris et al didn't have to write CC the way they did. They chose to play with and push a concept which reduced human agency -- in part because Dario concretely believes it's just "inevitable" that we should be put of of work. And his investors no doubt love this concept too.
And just like Facebook / Instagram etc. it turns out it's an addictive flow.
It remains the case there are other ways of applying LLMs and generative coding models. This modality is not intrinsic to the technology. It's being deployed this way. And humans have agency in how it's applied, even if it's hard sometimes for us to exercise it.
It needs to come top down from CEOs and governing bodies via regulation if we want improvements. We can't rely on the individual to not use the big red button that says "do this with no effort". We're on course for a WALL-E future if we're lucky or something far less great if we're not.
I appreciate your argument for human agency, but these types of systematic issues can't be solved bottom up.
There’s no way to learn than to force the brain into adaptation which it is resistant to do through challenge and stress, just like your muscles. Similarly you can’t play e sports and get into physical condition any more than you can use LLMs to do your homework and learn.
It’s going to be a hard adjustment for a lot of people to recognize that letting the machine think for you is as healthy as smoking brain cigarettes.
The smart student uses the LLM as a proctor or provide challenges and feedback on attempts rather than an easy button. They make great tools for learning if they’re used as an adversarial or editorial tool. The future belongs to those who work to use the tools in ways that make themselves more efficacious, not those who use efficacious tools so they don’t have to work.
Yeah, this is how we used wolframalpha for Math as students. Whatever we had to do, we did it ourself as a group of three. Afterwards we checked with Wolframaplha to see if we were correct. If there were any difference between us, we went line by line to find where the error appeared.
It was helpful, because we did it ourself, but because the work was graded, we had the security, that it is not a total failure.
Does anyone look at GPA on a resume? I’ve hired thousands of people I’ve never once looked at GPA. (N.b., my resume has “summa cum laude” ok it and no one has ever once mentioned it or presumably noticed it, despite the fact you only really get it if you can BOTH learn the material AND get perfect grades)
But I like to add artwork to my presentations. My artistic skills have not advanced beyond 2nd grade. So I'll make a line sketch, and give to AI to "fix" it.
The results are nice and I use them.
I have no interest in learning how to do art well myself, so using AI for it is appropriate.
But I still write my code myself.
I haven't seen your presentations, so I can't speak to them. But I do know at work there's a lot more illustrations in docs and presentations and such, and they almost all have an AI art "tell". I find them grating and distracting from the actual content. Very rarely do they add anything useful to the doc other than the knowledge that the owner burned some GPU time and tokens for a distracting, low value illustration.
I can only imagine how an actual artist or graphic designer feels about it.
Actually I don't have to imagine; there's some serious vitriol over on some of my favorite webcomics about it.
Not for long, if you so easily have caved in to using AI elsewhere. People are lazy. If you see that the 'results are nice', it's game over for your programming/thinking.
Waiting for the day the advice will be to "enjoy AI assistance in moderation"
I believe this is the real crux of the issue. We often turn the target to things like "Can johnny Add, Read a book, or recite dates" which are only proxy measures for important things like "Can johnny solve a numerical problem presented to him, can he synthesize information, or can he think critically about what is occurring around him?" .
If students use AI to accomplish goals I do not see it an issue. If they cannot figure out how to use tools, or what their goals are-- that is a major issue!
An analogy of my point is that I don't want to focus on cursive in the age of computers keyboards, and I dont want to focus on abacus skills when a pocket calculator is like $5.
They stopped requiring SAT and ACTs in order to get a student population more representative of the population in general. This obviously allowed students that were not prepared for college into the system.
If you do well in your math SATs you'll likely do well in math college. SAT scores and college GPA are highly correlated. No idea why anyone thought it was good to ignore probably the strongest signal of success in college.
Plummeting attention spans has been a trend for much, much longer than LLMs and is more the result of constant digital interruptions and these days overwhelmingly social media and doomscrolling: https://www.apa.org/news/podcasts/speaking-of-psychology/att...
The effects on children have gotten most of the, err, attention, but the effects on adults are no less deleterious.
Before that, I also noticed the decline in newspaper readership in the 80s.
It is easy to blame this general decline on the latest tech (or moral panic), whether that be LLMs or even the existence of the internet, however, the trend in dumbing down has been going on for decades.
In the context of a declining empire and financialised economies, this makes a lot of sense.
But this doesn't seem to make sense when someone comes to a topic with an LLM in-hand. They need to know high-level techniques, architecture, best practice, etc. As they pursue the topic they start to get down into the details, although probably never learn to do it fully independently.
I quite like this view because it paints a somewhat optimistic way forward from where we are now.
High-level techniques were never a problem. You could Google tens of articles on this topic. They are useless too, it's like learning how to drive a racing bicycle from reading a book. Sure, you will know a lot about nuances, but you will fail miserably when it comes to a real race.
I had the experience to keep calling out AI to simplify and downgrade the solution to something primitive, which ended up smaller, faster, easier to maintain. Juniors with real world experience would not bother, they’ll take the first working AI result.
I disagree, the definers of taste; art and food critics, movie and book reviewers, don’t need to have learned the craft by doing. Taste is a separate skill.
Taste in coding is a combination of insight, experience, native talent, technical skill, and flair. Tasteful coding produces clever but straightforward minimal elegant solutions that an average developer can't imagine but can adapt and maintain.
This is why "critical thinking" is a meme. Being a critic takes no skill. I want far fewer critics and far more constructive thinking. GenAI being the ultimate constructor is a bonus.
Taste implicitly requires discipline of what one chooses to expose themself to and what not to.
I hope this isn’t the case. It is the route I took, but it also doesn’t seem to be a likely route going forward. Strong CS grounding is feasible for sure, but I have a hard time believing that a meaningful number of people will be spending the requisite years coding manually.
e.g. The "group" abstraction requires one see a lot of int, polynomial, modular arithmetic etc. before knowing why we want such a thing. It's unskippable.
It's hard to claim one has mastered a subject without independent command of its fundamentals. A less charitable take on this future is that students only learn to hand-wave answers and correspondingly cannot evaluate statements beyond "sounds about right".
If that's happening, that would be a weird way to teach CS in my opinion.
In my undergrad program, languages and syntax were learned on your own. Class material and lectures were all conceptual.
That awareness of how to structure the English language, it will benefit those who use LLMs.
Then again, maybe someone will just make a LLM that’s built to turn poor English and poor reasoning into excellent English and excellent reasoning. Maybe this is just a technical puzzle that needs solving.
> Then again, maybe someone will just make a LLM that’s built to turn poor English... into excellent English
That's already been done, for some (pretty weird) definition of "excellent".
I work with, or at least in the vicinity of, someone who is very good at getting work out of LLMs. He has a whole system of CLAUDE.md files and skill files and things. He makes TONS of typos. When I first saw that, I was itching to go in and fix them all, it seemed viscerally wrong to be adding an extra layer of correction required between the instructions and the LLM's behavior. But in practice, I don't think it mattered at all. The LLM didn't care. Typos in particular might require a bunch of RLHF in the chatbot, but my hypothesis is that the LLM is already mapping messy human input to the nearest surface of some high-dimensional manifold and the added noise of typos is inconsequential to where it ends up (as long as there isn't any real ambiguity -- though even there, you could probably construct cases where that would help rather than hurt!)
Typos are different from sloppy writing, but I think the AI companies have put a lot of work into training these chatbots on dealing with typical non-English major writing with all of its imprecision. Also, it's easier to construct cases where that imprecision and sloppiness would help rather than hurt: a mistake in the input that is common enough to show up in the training data is going to be a good match for the needed correction as well as associated corrections. The precise language could easily result in the LLM overestimating the user's competence.
That doesn't address whether an English major's careful composition would help for hard tasks where getting the specification right really matters -- perhaps that was your point? I guess it's an open question whether "boiling away the typos" and "boiling away a poorly articulated specification" are related enough.
With writing:
Things like brainstorming a plot line for a book with a custom GPT or Claude project that has all of my prior books in its knowledge? Works great.
Things like asking it to write a paragraph or chapter for me - I can rapidly feel my own writing skill, motivation, vocabulary, and ability to grasp/remember the resulting plotlines deteriorating. I don't use it for that anymore.
With studying:
I've been taking a couple of evening uni courses and the thing I found so great is that I've been forcing myself to think through the problems, and take my own notes in every lecture. I may then still get ChatGPT to help explain and reason through some of the concepts with me. And I have it review and 'grade' my assignments. But I refuse to ask it to start drafting answers.
With programming:
This one is tougher. When I am not very personally invested in a problem or codebase it becomes too easy to offload more parts to Claude, and when the company encourages 'vibing' to speed up velocity and you're reviewing and writing a higher influx of lower quality PRs, investment goes down. I still sometimes catch myself committing solutions I only _mostly_ grasp and the rest is hand-waving. A big part of it is a work culture thing.
For my own projects I make sure to understand and have a back-and-forth with the planning agent for each task, or write the first plan myself to go off of. When it comes to producing the code, I have to admit it is much easier to properly review parts of the codebase I am extra interested and knowledgeable in (backend in my case). The frontend I'm less well versed in and also admittedly less interested in, so I do sometimes fall into the trap of "Ehh it works, just commit it" with the goal of doing a thorough quality pass before actual release.
With all of the above, I can feel my ability to think, plan, reason, focus (and my vocabulary) suffer if I go over the line too much into agent offloading. For me keeping that balance is as much about maintaining my own long-term brain health as it is about producing good output. I imagine younger people growing up with AI today won't even know what that more capable (in my opinion) brain state feels like - to them, the AI-using brain will be the norm.
Once I have the condensed outline, I'll re-order stuff, clean it up/tune it up, then do the final writing. This keeps my voice and logical train of thought while avoiding blank page syndrome and some of the organizational mess of condensing notes into an outline manually.
As a piano player, it’s important to work hands separately. Sometimes your right hand will carry the melody and your left hand the harmony, sometimes vice versa. Sometimes there may be more than just two “voices”/melodies/lines between your two hands. Even as a very good (as in getting paid to do it) sight reader, I learn a lot working all the voices/melodic lines separately.
Singers do similar things like singing only the vowels to keep themselves in the right placement. Learning handstands, you have to work your wrists, rotator cuffs, core (which is many things), etc. separately. Yoga, Pilates, and running also help us learn to break problems down this way.
Anyway, all that to say: If LLMs are gonna be a natural extension of how we think, we need to understand what parts of problem-solving LLMs are good for, and what parts our brains are for. The nice thing about working these bits “separately” is that one side is done for us. So we just need to consciously practice using our brains.
As programmers that means, maybe we conscientiously practice writing things ourselves sometimes. Remembering that this even if this sacrifices short-term “velocity” (whose measurement is problematic, but I digress), it preserves our long-term ability to do good work. And I think any of the above physical/artistic practices (or countless others), worked in these ways, will help reinforce this entire mindset.
I think kids of the coming generation will be sharply divided on their ability to conscientiously practice things separately. It’s been happening, but I suspect LLMs will accelerate it unless how we actually teach kids can catch up.
If that's the case then we're in trouble based on my experience. This week I've been using ChatGPT to help figure out some old linux platform that I need to resurrect. It's very good at quickly searching and surfacing relevant information online, and that's helpful, but if I did not have a lot of experience at linux administration to be able to see where it was suggesting the wrong thing, or initially dismissing the right thing, then I'd just be thrashing.
The LLM is helping me because I know what I need, and it can search and read faster than I can. But it's not really very smart.
Which is to say, an additional thing you're going to be forced to pay a lifelong tithe to a trillion-dollar company in order to do a lot of thinking tasks.
Maybe the problem is that doing assignments contributes to your grades? The answer from wolfram alpha wasn't so much to get the homework done, but to understand how I would be screwed in the exam.
Now, if you’re creating trivial, unstable, or nonextendable systems maybe this doesn’t apply. And maybe I have long overestimated the work that SWEs have done.
Before you did this, was literally every hour of your waking time spend thinking about LLMs?
I don't think I could do that even if I tried, and I spend all my development hours with agents, but during meals, showers, walking the dogs, enjoying a coffee outside or whatever, naturally I get time to think about other stuff, sounds out of the ordinary (to me at least) to have to dedicate 1 hour to not think about something. Reminds me of when I was addicted to amphetamines way back when.
They said "writing and thinking without LLMs", not "not thinking about LLMs". I think they're talking about setting aside time for fairly focused thought/work.
That said, though, one thing I don't understand about the heavy users of AI in academia and software development is that the thinking and coding is the fun part. And that's the part so many people seem to be so keen to automate away.
That doesn't happen for me anymore to the same degree.
*speaking of things I should be doing less of...
(* I can count on one hand the number of time I've used an AI tool.)
I can still read code and write it, I just need to look back at docs a lot more, when I used to just know things. I also have to sit and try to recall how to do things and what abstractions are involved more. I also have more "writer's block" when starting with a fresh program/document if trying not to get AI to seed it with a baseline implementation, where I have to sit for a while thinking about what I really want to build.
- In the interest of having well-rounded students, a lot of degree programs include subjects the student didn't want to sign up for, but have to. Even in something like CS, I knew a lot of people who liked the hardware side of it, but didn't like the software side and vice versa. So I can imagine a student justifying taking shortcuts that way.
- Psychological reasons like wanting to protect their ego. Maybe they had always done well in school and are now struggling, but don't want to ask for help, so they think why not just take a shortcut here and promise to do better next time, etc., etc.
And to some people, it's not even a lot of money.
In many ways, schools are just the modern day peerage system.
Use-it-or-lose-it is the evolutionary principle, both for cognitive and physical abilities.
Therein lies the trade off. Your implicit gamble is that you expect machines to continue to get better in the future. What if they don’t?
Trying 5N paths is useful and sometimes yields interesting insights I’ll retain, but it’s not the rich, challenging, deeply engaging kind of process I find I need in order to develop useful knowledge and skills.
So yes it’s an accelerant for people who want stuff from me, but that doesn’t map directly to learning and building skills. I think that mismatching is really important.
The part I find weird is all the claims that LLM usage leads to less thinking and exploring and just grabbing the first result. I constantly find myself going off on tangents and pulling on threads when I’m working with these tools. Is it really that different than before when my “peers” weren’t able or willing to be curious about their craft? They didn’t explore other programming languages out of curiosity or for fun? That covers literally 95% of all software developers I’ve worked with in the last 24 years across many domains. To them it’s just a job. Their only goal is to deliver tickets assigned to them and go home. They rarely go out of their way to learn something new unless the company assigns them some mandatory courses. Largely the LLM is capable of producing better and more consistent results than they ever could in the first place.
I don’t know how to cultivate curiosity in the work force. Maybe it’s not possible and you have to filter aggressively at the hiring step. But then your pool of hireable candidates shrinks to a few thousand developers most who are probably not actively looking for work.
The only distinction I wanted to make is that the learning doesn’t come by default. Yet that was largely true when people copied mystery solutions from stack overflow and used black box libraries for 90% of the complex work their programs facilitated.
Perhaps not much has changed but we’re now operating at a much larger scale and the opportunity to not be curious is actually more present than ever.
People who are curious are massively benefited by this tooling, in my opinion. Like you’re saying, if you want to investigate and learn, there has never really been a better time. If you’re sincerely applying yourself and pulling all of those threads, there has never been a better teacher.
I’ve wondered about the matter of finding and cultivating curiosity too. I’ve come to believe most humans, let alone programmers specifically, are not all that curious. A lot of us are path-followers and we’d rather not get into the weeds most of the time. Then some of us see weeds and dive in, even when it’s not pragmatic to do so. I don’t know how much it can be cultivated or even removed from a person who has more than enough.
I've heard LLMs can be helpful in limited targeted ways. But not as some kind of "game changing" accelerant.
The downvotes are just a sign of the times. It's also something to observe and think about..
Other fields may be different. YMMV
I noticed this before LLMs became a thing. It was by accident. We had a team of programmers. All decent at what they do. The management said 'hey you want to learn another language we are going to be using it for these upcoming projects'. So we set up a self learned at your own pace class curriculum. Maybe 10-20 hours of school work if you sat and really dug in. Maybe 3 to 4 hours if you breeze thru it and do not care much. We set up weekly check-ins doing about 1 hour a week. Easy. Watch a 20-30 min of vid 20-30 mins of do homework come to check-in and talk about what you learned and help others if needed.
Now this is where I was disappointed. The first 'class' was 40 people. By the last there were 3. Those 3 I noticed always are the ones who dug in. The rest wanted a proctored classroom and someone to tell them what to do.
Actual genuine curiosity is rare I think. We have a lot of people who are decent at what they do. But do not really care about it. IF you do not care you are going to just push the button and get the answer.
> a shift of skills away from things that mattered more in the past toward other things that are not measured/perceived by the older generation.
Do you have any ideas what these things might be? As someone in his twenties, I’m sometimes saddened by observing that some of the skills I acquired over a long time (e.g., writing, coding) may become obsolete or won’t be respected anymore just now that I‘m finally getting good at them.
What you said there is just an extension of the elimination of friction that the silicon valley has been pursuing for the last 15+ years.
But that is just.. well. Their business model. Not a force of nature.
But now we delegate thinking itself, so I wonder what is left.
A proper nights sleep is massive! I'd put 99% down to this..
The idea that most people have the discipline to keep themselves mentally in check is false. We already know this! Millions and billions of people who spend hrs a day consuming media on platforms such as instagram.
I used to think like this until social media proved there are some tech innovations we just can’t adjust to. 10 years ago you would’ve never caught me supporting any sort of age based social media ban. Now? I don’t think it goes far enough. Fake news (actual fake news) and misinformation has only gotten worse with it as well. It’s so destructive.
The same goes for speed and quantity of input, as to what the human is designed for (not literally designed). Be it social media with it's infinite scrolling, cars racing by as opposed to looking out the window a few times per hour because you see someone/something, constant sound input if you live anywhere remotely busy or work in a busy office.
The point I'm trying to make is that the world used to be comprehensible for the human. Some understood a little complexer things, some only the simpler things. Now there is an overload of everything. So, most humans are in survival mode wether they know it or not. Hence the many seekin mindfullness etc
No matter, it's an observation, not a judgement or opinion on it. The world will just keep rushing forward. Some have a slight hand in the direction it goes for better (never) or for worse, but spiral it will.
>> The world will just keep rushing forward. Some have a slight hand in the direction it goes for better (never) or for worse, but spiral it will.
The systems are too large and self-propulsing for anyone to really control. Consider the rainforest. How many millions of variables interact, nobody is in charge, everything influences everything in a billion different ways. You might say, well we can cut it down, so kind we can control it. Allright, let's continue to spiral. You might build a city there after a few years. Still in charge right. But it get's too hot because there's no vegitation, so you have to change again. And then we find that people keep getting strangely sick, and scientists find some special mushroom that survived and apparantly thrives on the mix of cut trees and diesel fumes and their spores in the air are poisonous. I made that up, but you get the idea hopefully.
As an example, I have been drawing portraits for quite a few years now, and whenever I go on a hiatus and come back after a few months, I can notice my skill not being anywhere close to where it was before I stopped using it.
Sure, after 2 or 3 portraits they mostly come back because of the previous experience, but skill rust is a real thing, and if you think your coding skills are the same because you used to code 20 years but haven't coded for some time, you are probably just lying to yourself.
His skills are slowly eroding. Given that he spent 20 yrs building it up it won’t happen overnight. But the trade off is happening in real time.
I wonder how much of this "you are gonna lose your skills!" stuff matters. And if knowing how to properly iterate a for loop with my eyes closed matters all that much anymore.
The Whispering Earring: https://croissanthology.com/earring
I still did well, but I had gaps for which there was no help outside of the internet available.
As in I wrote code to generate random exercises, with solutions, using many tricks, to get myself hundreds of problems instead of 1 or 2.
Often spent more time on getting these programs right than on the problems. Still did better than the class. Oh and it was AI in the 1980s IBM sense. Ie. it was based around a python version (which I wrote) of a LISP math system based on maple. I even attempted (and largely failed) to rewrite it in C++.
Even attempted to have my homework read to have the computer correct the actual pages, but I never got convnets to reliably read entire lines (yes, I understand, well now, why a convolution would mostly not realize whether 2 pieces of text are on the same line or not and so get very confused if you go deep enough for recognition to work well)
At least now we know why we will start watering our plants with Brawndo.
This was my experience even pre-LLMs though (about my own PhD thinking skills too). I blame the amount of random stuff work now involves more than LLMs.
A lot of skill of is getting bled into the private sector because getting the PhD in a lot of regions doesn't mean the step up it used to. A lot of that comes from awarding them to layabouts doing "a gender critical analysis of ...".
Industry doesn't how/what/why they just wanted the 3 letters as a performance barrier to hire competants.
I graduated from RPI with a degree in Management and a concentration in Information Systems. I began in Computer Science, and didn't like it because RPI CS at the time was loaded with professors who were mathematicians who had transitioned over to CompSci and because the 100 and 200 level courses were excessively math-heavy in my view.
Since this was the late 80s, there may not have been an easy way to teach B.S.-level computing without it being heavily math-based, but I digress.
No matter what degree we achieved or what work we ended up succeeding at, we have a tendency to look back at people rising in the ranks below us, see differences in their experiences and struggles, and say, Look! That is evidence of a lack of rigor or a lack of understanding of fundamentals that we had to learn in order to succeed.
The only thing is that some of what we learned to become successful just isn't necessary to be learned when we learned it.
I do a fair amount of low-level software engineering with Claude Code now that was above my level of understanding of data structures and algorithms because I never took those CS courses at RPI because I switched to Management IT.
But as someone who could be described as a solopreneur at some level, my new system designs reach a certain level of complexity or code maturity, and I hit problems that I would not hit if I had more understanding of data structures and algorithms.
So-- I end up having to learn aspects of those disciplines at that point, rather than before I actually needed them.
I run into these situations often enough where I now say to myself, gee, I wish I had taken Data Structures. And I think, could I effectively take Data Structures at this late date and get better at specifying how I want data stored, or perhaps knowing the shortcomings of simplistic database structures that are the ones I end up with initially because of my lack of spec-writing skill?
Aren't many of the less experienced folks who come up now, whatever age they are, going to hit problems that show them their weaknesses in this fashion?
Is the issue that these people will never get jobs because the seniors and managers who are interviewing them will design interview questions that keep people with their level of understanding out of the workforce?
What happens when somebody who sucks at the fundamentals but is really motivated bangs their head against their shortcomings and eventually succeeds in building something that takes off? Aren't those people great assets because they learned some of their critical skills the hard way?
As a counterpoint, I was once a physics grad student. I didn't finish the PhD because at some point I discovered that I was not going to be the next Richard Feynman and this was too much for my ego at the time. But I think that if LLMs were available, I might have finished.
Part of my problem was that at some point the math transitioned from stuff I understood to symbols and notation that I knew how to manipulate but didn't really understand. LLMs could have helped bridge that gap.
On the other hand, it's hard to imagine I wouldn't have used it for Jackson, etc. but we got Jackson solutions from previous students and the internet anyway. Using LLMs probably would have been more effective, used correctly.
It wasn’t until I was curious enough to learn about calculus outside of the classroom that I was exposed to things which helped develop that intuition and made the calculations something other than just symbols and equations to memorize.
The problem is that it sounds like many people are just using it for everything.
I think this is true of every affliction that adults criticize children and teenagers of
I’ve been out of university for a very long time, and I took a community college course and for the first few sessions I couldn't focus or sit still at all. Fortunately I knew that was abnormal and how to conform to a prior version of myself, but I don’t think children have a frame of reference.
Asking suggesting or arguing to go deeper is impossible. There is a new path of least resistance and it saddens me.
tomorrow most regular people's thinking skills will definitely be weaker than those of the LLMs of tomorrow. And physical skills in most cases will be weaker than those of the robots. That leads to the question - what would most people do?
1 - When I was in grad school (before AI), we had to use Canvas for a class. One day, I got an obvious spam/phishing email in the internal Canvas system. It was so strange. The writer just would randomly hit the capslock button and keep typing away, no salutation, no signature, just a real mess. They were asking for a particular professor to come to their house to teach them about ... something? Again, real strange.
So, I email IT and say 'Hey, somehow a spammer got into the system, do your thing'.
They email back and go 'Nope, it's a student, that somehow managed to CC the entire system, sorry about that'.
Dear Reader, the message was pure garbage. Literally, it looked liked it was written by a 3rd grader without any shame. [0]
I happened to know the professor of the class. So later on, I talked with them over symposium coffee about it. They said that they remembered that particular email because of all the IT back and forth. It was for an upperdivision class in the Engineering department. The email itself was not particularly notable otherwise. In that, they saw such emails all the time, in terms of quality. This was a top 100 ranked (whatever that means) university, by the by.
Shocking.
2 - My grandfather was an officer and a mechanic for the USAF. A bit of an odd combo, but he was partly responsible for instituting many preventative maintenance checks and protocols, novel in those early days of the AF. His aptitude and memory were quite sharp for many mechanical things. Until the strokes from decades of smoking caught up, he could tell you exact measurements and torque values for a variety of airplane related things (I can no longer remember what exactly, the memory skills did not transfer to me).
I do vividly remember standing in that light blue garage of his and him all but yelling at me once. We were looking at the brakes on an old car he was 'restoring' (getting away from Grandma for a little bit). He pointed at the old drum brakes on the axel.
He asked me how tight the pads should be on the inner rim of it.
I had no idea.
So he asked where I might find out.
I figured I'd ask him.
But what if Grandpa wasn't there?
We'll I'd have to look it up somewhere (they had no internet).
Fantastic. Now, what about the next time you're working on the brakes?
Well, just make sure that the pads are at that spec.
And that when Grandpa hit me with the nugget of hard won wisdom: No, you look it up every time. Because these are brakes, and if you are wrong then they might fail, and they might fail when the driver has their whole family in the car at 100 mph. And then because you were lazy, half a dozen people die.
---
These two times stand in my head when it comes to AI.
For the first one, yes, AI would be such a boon to that very clearly struggling student reaching out for help. It would get them back on the path to the real struggle of getting their degree. That level of assistance would be like a wheelchair to a paraplegic.
For the second anecdote, AI is condemning people to death. Using it in life critical situations and care, letting it hallucinate or skip over critical values, that's a recipe for disaster.
Where do we set the fine line of using AI and not? For brakes and X-ray machines, obviously not. For helping kids learn to write emails correctly? Sure, sounds great.
Unfortunately, I feel the old adage about regulations is going to be true here like it is with every new technology: The rules are written in blood.
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Sorry, but I highly doubt that. Has a very "old man yells at clouds" vibe.
But apparently some of the smartest people in the world have lost the skill? But the commenter haven't, because why, they're 15 years older and thus immune to the same LLM-effects?
Plus, the issue with people having trouble sitting still for 30 minutes precede LLMs with decades.
Not saying everyone else is immune, but those a few years older have also had a period without it.
Most people definitely can't meditate for 30 minutes, so if you can do this, it's very impressive. Regardless, being able to think about poorly-defined problems and build completely new mental models from nothing is genuinely a really hard and uncomfortable task. If you don't use the skill you'll lose it.
Maybe not traditional meditation, but I have no problem taking a 30 minute plus walk with nothing but my thoughts. It’s actually when I do most of my thinking. The other is in the shower/sauna where devices don’t work anyway.
> apparently some of the smartest people in the world have lost the skill?
> But the commenter haven't
> why?
Perhaps because a correlation you assumed was there (more smartness = more ability to sit still alone with one's thoughts), is not actually as strong as you thought? If one does not start with that assumption, there is no inherent conflict in the 3 pieces of evidence you cited.
Or perhaps because you are smarter than you give yourself credit for :)
(I am not saying LLMs can't be a good tool in evaluating ideas. To me, it sounds like you're firing off ideas all over, letting the LLMs judge what's good and what's not. Insane.)
And yes, I fire off ideas all over. Many require predicting the future to decide what to focus my individual effort on. This is a terrible way to do things because humans (and LLMs) are notoriously terrible at predicting the future. The gold standard is to try everything and eliminate what doesn't work. This is impossible using human labor. With LLM labor, it's simply a matter of relatively cheap money.
It's amazing. Technical problems are now no longer having to predict what the best implementation is. You can just try each one.
Again, no need to have an LLM judge, because the metrics that define 'better' are well-defined, and this is the interesting part of computer science, not the implementation.