I’m not noticing the decline in my own abilities any more than I had before using them. I finished undergrad 20 years ago and my once sharp math skills had been severely diminished within only 5-10 years. Just simple arithmetic and percentages that I could rapidly do in my head became dependent on calculators/spreadsheets. For all other trivia type knowledge, my brain has offloaded it to the internet RAM in my pocket. It’s a familiar feeling of when some question comes up and I think “oh, I used to know that, let me look it up”. Maybe I just already hit my personal floor of stupidity before LLMs.

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.

I'm dumb as a rock and I don't have a PhD, but since ~1 year ago I started forcing myself to do small bits of coding and math manually.

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.

This likely varies person by person or the way people adapted AI. For me AI replaced the boring part of writing code, but has not replaced the fun part of thinking about code and problem solving.

I'd argue that this is an adjustment period that society has to go through. The way we are using electronic devices today, in some years it will probably be looked at like smoking cigarettes. And I'd argue that a lot of the "decline" is due to 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.

LLMS didn’t invent cheating just made it easier. When you cheat you’re the one who cheats yourself because the point of an education is to learn, not complete the assignments and get high marks on tests alone. No one benefits and no one other than you is materially hurt by cheating, but you are absolutely the one who is hurt.

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.

I have observed this in myself when I began to over-leverage AI in my workflows. I've since become more deliberate with what kinds of tasks I will use it for, although I still slip up.

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.

I've been wondering if there would be a benefit to inverting how we teach subjects now. Previously we would teach from the bottom, and build up. Semi-colon goes here, curly brace goes there, and then build up to architecture, systems, etc.

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.

At this rate humans will become avatars remotely controlled by LLMs. Ironic conclusion of the consciousness debate.

I think it varies tremendously from one role to the next. I'm a senior software engineer and LLMs, the way I'm using them, improve almost everything I do. I use them to write most of my code now, but first I spent twenty years writing code before LLMs came into existence and second writing code is like 5% of my job. Most of my job is research, investigation, and architecture. I treat LLMs just like a junior engineer. I give them clearly defined jobs that I could do on my own just fine, that I already spent years doing. The problem here is that students are using LLMs to automate everything BEFORE they become proficient at it themselves. Letting college students use LLMs for homework is like letting kindergarteners use calculators instead of counting on their fingers.

I recently switched back from a Tesla to an older car without permanently having a map visible. Suddenly my brain has to think about routes again and it definitively feels like my brain has to put in more effort again to handle it.

On the contrary, with the amount of times I went to ask for help and was failed pedagogically, plus not being able to afford tutoring like my peers had, I think access to an LLM would have genuinely boosted my grades.

I still did well, but I had gaps for which there was no help outside of the internet available.

Do any of said PhDs gain anything positive from LLM usage? Or does it only lead to declining thinking skills in your view?

Yeah, it's a scary thought. I feel the pull of it every time I'm stuck on a code problem that I don't want to search solutions for and hand-code... and I also feel myself wanting to reach for the crutch of an LLM when I just have something boilerplate and easy to do. It's incredibly tempting to just ask the question and have the "thinking" done for you. Until you have actual skin in the game and realize that it doesn't reason, and its "thinking" is utter shit. Then it's like: you got addicted to cigarettes and now you have to quit, because this habit is poisonous. It really does lead very quickly to cognitive decline if you rely on them, or even think about asking them while you're writing code.

I see this too. There is this theme where people are more and more going only as far as the ai does.

Asking suggesting or arguing to go deeper is impossible. There is a new path of least resistance and it saddens me.

> Many of them can no longer sit quietly for even 30 minutes just thinking on their own

Sorry, but I highly doubt that. Has a very "old man yells at clouds" vibe.

>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;

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?

I feel the opposite. My brainstorming has increased rapidly. I can now just throw ideas at an LLM to rapidly validate.

If the removal of standardized testing in 2021 was the real reason, then why is there a sudden spike of failure rates happening right now?

Berkeley chancellor told students to vote for 2020 California Proposition 16, which would've repeated 1996 Proposition 209 that banned race-based admission in public universities. Prop 16 failed. Subsequently, Cal started ignoring SAT/ACT scores. I have to think this was their alternative way of taking fewer Asian students, who average highest on that. Soon after I got an email from the same chancellor praising the change for bringing more racial diversity. The email included before and after numbers where % Asian decreased and all others increased.

I'm not American so maybe I am missing some context. But how did admissions work without test scores?

Unpopular opinion: turning public universities into an academic hunger games is diametrically opposed to their purpose for existing, which is to create an educated populace. Intentionally lowering the quality of instruction, as well as deliberately trying to trip students up on exams, is not improving educational outcomes for anyone. People who complain about "grade inflation" have completely lost sight of why public education exists in the first place.

Back in the university, I took both math and CS courses and a significant percentage of students seemed interested in neither math nor programming but rather in the jobs they would get afterwards. I didn't notice the same thing with math majors.

Failing news headlines soar after AI takes over reporters jobs.

I suspect the ambiguity might be part of making it "clickbaity", as it naturally causes you to wonder which meaning it's about and become more interested in reading.

Terrible comparison. You don't need to be good at drawing to be good at photography.

You do need to be good at math to do e.g. physics (or math itself!), nomatter the tools at your disposal.

In my uni, rates of honor code violations in introductory CS classes were high even before AI. I was a section-leader for the CS106 series at Stanford, and the honor code violations were common. In 2015, ~20% of one intro class was suspected of an honor code violation [1]. Often, the CS department comprised the majority of honor code violations in a given quarter.

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...

2. https://theory.stanford.edu/~aiken/moss/

Anybody know how many students take CS 10 in a typical spring semester?

It's too damn tempting to not use. You have a magical machine that, on command, will spit out the answer to your question in 10 seconds, whereas you'd need to spend hours to do the assignment the Good Old Fashioned Way. Even students who aren't just there for the prestigious degree are falling victim to this.

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).

These people would’ve failed with or without AI.

I can't agree. That's similar to past arguments for banning books and the internet.

Plagiarism isn't new, and those things enabled it too.

ban has always been the failing option

It’s like the greatest teacher. Plus it’s not toxic like social media. Banning it would be a shame.

I'm confused by Garcia's statement as well because CS@Cal traditionally uses a bell curve which is even stricter than Harvard's changes, because Harvard doesn't have the same stringent GPA requirements to declare a concentration unlike declaring an impacted major at L&S Cal.

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+.

In a broad sense, this distinction between Harvard and Cal is the distinction between an old money Ivy and a flagship state school. One exists to propagate a social hierarchy, and the other aims to allow all entrants to succeed.

Ironically, the techniques of the latter yield the results of the first, but everybody gets to keep a pure heart.

Grades only matter as much as being able to transfer just to the real world.

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.

New Math finally causes havoc 61+ years after introduction? Oh my.

* Tom Lehrer: New Math (1965) https://www.youtube.com/watch?v=W6OaYPVueW4

So is dead and has been for some time. 1500 questions per month. https://data.stackexchange.com/stackoverflow/query/1926661

University education is weird. Research profs (who make up a large fraction of all profs in a typical R1 institution), are hired for research ability and are only minimally evaluated on teaching ability. Furthermore, few research profs actually receive any kind of mandatory training on how to teach; a typical research prof might be assigned a course to teach and then just let loose to do so on the first day of the semester. If a prof actually cares they may attend some optional teaching training - but I stress that these are optional at many of the institutions I know of. (I suppose if someone gets really bad teaching evals they may be advised to attend said trainings - but for a tenured prof, that's just advice).

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 really wonder if it's important to learn all that low-level stuff at this point. Most programmers today will never write a binary tree or a hash table. Modern high-performance ones are generic components you get from libraries. Even MIT gave up on teaching from Structure and Interpretation of Computer Programs.

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.

> According to Berkeleytime, 35.3% of CS 10 students and 10.6% of CS 61A students received F’s in spring 2026. In spring 2025 and spring 2024, the percentage of F’s did not exceed 10% for either class.

I don't think instruction would've changed drastically in the last year though.

The fact that you are talking about Dan Garcia, a huge figure in computing education research and an excellent teacher, and the Beauty and Joy of Computing curriculum makes this hilarious. You should look up some details about both.

> I felt frustrated that the professors didn't ever teach. They had slides. They read off slides, verbatim. They explained things sometimes if you asked them, but most often in a very elitist and condescending tone

+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.

Berkeley CS does teach for real

Garcia and Ranade are Teaching Professors. Their primary responsibility is to teach, develop curriculum, and do pedagogical research. This job posting explains: https://saberbio.wildapricot.org/Job-board/12919068

The question comes sooner than the students being tested on the job market. Another possibility is that dropping standardized testing was a net bad idea.

Spring 2026 saw a marked shift in student performance. We saw it in intro physics courses on the East coast too. I bet anyone who cared to look saw it.

Some people need Jesus, but y'all need Kant ;)

"Enlightenment is man's emergence from his self-imposed nonage. Nonage is the inability to use one's own understanding without another's guidance."

https://www.columbia.edu/acis/ets/CCREAD/etscc/kant.html

Respectfully, I disagree. I think there's absolutely a case for AI being encouraged in younger people, and there's room for these tools. I've been leaning on LLMs for side learning in side projects, and it has concretely helped me with conceptual questions about math and Vulkan as I've been trying to learn some graphics basics with side projects.

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.