I do agree with this perspective ... But I do understand the anxiety tho.
The advice I give you is the same I'm trying to believe in myself: just chill dude... AI isn't human and while can't be one, you are "safe"...
We are still figuring it out while it happen to find another way to "feel valuable" after code-writing is no longer the source of our self-value.
Hold onto the roller coaster hand rail, as we become the agrarian society we were all along.
Energy farmers in the desert.
Every farmer fixing his own tractor.
Thats where we come from.
The "everyone is special, and every product is specialized" period is ending.
Everyone is an expert on exactly the things they decided they wanted to be an expert on now.
Thats where we're headed.
If you listen carefully, you can hear the sound of humans being plugged into the matrix, and downloading their fill of cartridges, in ever increasing numbers.
I never fully grasped when others would say that religion is meant to bring structure to peoples lives. As a world with AI starts to unfold I finally understand what they meant. Humans are incapable of functioning normally let alone progressing in theirs lives without rigid structure.
I have been doing this professionally for over twenty years, and I still learn something every day. Over the past couple years, with AI augmentation, I learn even more, move faster and go deeper into the systems that I work on.
A CS degree was never going to provide you with all of the tools that you need for a successful career. It provided you with the basics to get your foot in the door.
AI is not replacing you or an education. AI is raising the bar of what you can comprehend and retain. From someone who is technically minded (and self-taught), all I have ever wanted is to keep learning.
> A CS degree was never going to provide you with all of the tools that you need for a successful career. It provided you with the basics to get your foot in the door.
Yes and no.
I concur that it was never about the tools, although I've seen plenty of people advocating it had a more "practical" approach, using the same tools as companies use, and in the same way.
On the other hand it's not only about the basics.
In there you can go at depth in aspects that you will have to fight to use, like proper engineering practices, and might only be vital on the later stages of your career. And I feel that those learnings, plus the possibility of acquiring a breadth of knowledge that is hardly possible otherwise, is what gives CS graduates a leg up to other practitioners of the art of software development.
I wonder how you'd do without your AI augmentation if you find yourself not being able to use it for whatever reason. I wonder what you retained when the AI is not working... or with a very expensive augmentation that wants more cut from your pay...
> I wonder how you'd do without your AI augmentation if you find yourself not being able to use it for whatever reason. I wonder what you retained when the AI is not working... or with a very expensive augmentation that wants more cut from your pay...
Realistically, "AI is not working" is probably not a realistic scenario. He'll just stop working, just like if the network goes down.
The real risk of AI augmentation is to your value as an employee. Maybe the augmentation makes you feel and perform like Iron Man, but if the suit is doing the heavy lifting, and some cheaper guy could wear it and do pretty well, how are you going to keep that fat paycheck? It's not like any of us have tenure.
Someone could use being able to work without AI as a performance metric if they are looking for people to drop though. Because lots of people can AI code, but the best coders are likely highly correlated with being able to code without AI. It will possibly lose that connotation over time, but certainly not within just a few years.
Harsh opinion: but if you just want to pay the bills this is not for you. Software engineering is a career, not a job.
Far too many people focus on passing an interview and then let legal protections keep their position, all while draining resources that could have been better allocated to someone who is actually passionate. They saw my generation do reasonably well and came for the paycheck. I got into this because there were very few people who could rise to the occasion of problem solving that the industry needed. I did not walk into the room with all of the skills or an idealized salary, I walked into the room with a mentality.
Perhaps the "chaos" being introduced is simply a long overdue market correction.
I'll let the downvotes tally how pervasive this is.
While it's a nice-to-have to marry the idea of income and meaning, it's not universal - nor can it be. It's okay to exchange your labor time for money and healthcare, because money and healthcare come first, before "passion."
Passion in tech is for 25 year-olds, recruiters, co-founders, and imbeciles.
> AI is not replacing you or an education. AI is raising the bar of what you can comprehend and retain. From someone who is technically minded (and self-taught), all I have ever wanted is to keep learning.
AI is, in my opinion, absolutely murdering the junior dev market. It's now much more efficient to pay for a senior or staff engineer and set them up with a subscription to claude or codex. I think things are going to remain fine for senior+ engineers, but I genuinely don't know what advice I'd give to graduating devs at this time.
> It's now much more efficient to pay for a senior or staff engineer and set them up with a subscription to claude or codex.
this has been happening for years though. just think of cloud computing and things like kubernetes: a single person or a small team can handle the amount of computer that twenty years ago required a full team of sysadmins, a facility (either on premise or datacenter space), huge (upfront!) capital expenditure and ongoing costs (connectivity and power).
same goes for web frameworks (think rails and similar)
Ok, but all of these technologies have bounded impact, and they present new abstractions that need to be learned by engineers.
Coding agents are different in that they are unbounded in the tasks they can do (although reliability is an issue), and you can work with them the same way you work with other engineers (e.g. via natural language).
Also, coding agent capabilities are advancing much faster than people can sit down and learn their limitations. All the tricks we learned for Opus 5 became obsolete in a couple months with Opus 5.5.
> Stanford researchers find the employment trends they identified for entry-level workers last year are persisting and expanding. Specifically, employment levels for workers ages 22 to 25 in the most “AI-exposed” occupations are now 19 percent below those of their peers in fields less exposed to AI disruption.
I do learn every day thanks to AI, I agree with that part. But I'm increasingly convinced that AI is likely to replace either me or some of my teammates in the near future.
In the last year, half of my team has been laid off (or left) already and won't be replaced, and we don't see any drop in productivity and the tools are getting better by the day. We need experts to drive all this, but not as many people as before.
Companies that let SW engineers go because of productivity improvements are being stupid. These people will go and build new products, and will compete with your company.
If your company is a big corporation, I don't think they need to worry about the file and rank SWEs. I know that I'm going to compete with Google, Meta or Microsoft. But stupid or not, that's what they're doing at the moment.
Yeah they don't need to worry about them, they will just probably join some startup that the bigco can eventually buy, instead of innovating internally, it's just bad management as usual. Bigcos might not see smaller companies as a competition, but they do compete with them for SW engineers.
>I have been doing this professionally for over twenty years, and I still learn something every day. Over the past couple years, with AI augmentation, I learn even more, move faster and go deeper into the systems that I work on.
Too bad a big percentage wont get to do that professionally anymore due to AI.
>AI is not replacing you or an education. AI is raising the bar of what you can comprehend and retain.
No, it also makes the job market hell and cuts down on positions, on top of an already squeezed pre-AI environment.
And that's if one is still OK with the fact that coding work (something many of us enjoyed in itself) turned into "project manager for AIs".
AI has been a godsend to my learning especially in regards with CS and Mathematics. It does not get tired of explaining me not only the 'what' but also 'why' and 'how'. Plus lots of learning rabbit holes.
I was never a good student (not academically inclined at all) and I have learnt more with AI in past 6 months than the engineering degree taught me.
> A CS degree was never going to provide you with all of the tools that you need for a successful career. It provided you with the basics to get your foot in the door.
+1
Aside from teaching me the _basics_ of programming and discrete mathematics, it did not help my career. I, in-fact (due to poor GPA), did not even have a SWE role out of school and had to find my own way here into a successful career. (I struggled even with LeetCode easys coming out of college) I attribute it to creating opportunities for luck (networking) and an appetite for life-long learning (which came well after college).
Many people in the tech world seem to espouse a great love for "always learning", but the way in which it is said always feels funny to me. Learn about what? When people say this, do they mean that they want to always be learning the next programming language, the next web framework, the next thick-client frontend web framework, the next python packaging CLI tool (pypi, virtualenv, uv ...), the next Linux distro? I'm tired of the churn. Most of these tools are not different enough from each other and don't have different enough use cases to be worth learning, but that's what a job in tech often seems to boil down to. You learn to do a basic set of tasks with one toolset, then once you get proficient, you swap all those tools out for another set of different tools that work completely differently but accomplish the same goal in a way that is not appreciably better or worse than the last set of tools. With AI, this will only get worse, as the cost for making new tools goes down. There seems to be a lot of churn just for the sake of churn.
There's two ways to grow the space of things to learn: breadth and depth.
You're only thinking about new stuff adding "breadth". I think most people would agree with you that new tools rarely have new use cases, so not truly adding "breadth", but that's where we get into "depth".
When looking at long-term learning, there's a lot of "depth" and subtlety in the way the existing tools work. There also comes a point where you have a firm grasp of all implementation details, but are unhappy with the ergonomics of the existing tools to do a particular job. What was an odd use case can become more common with time. This is how new tools and terminologies are born (adding "breadth"), but you have to remember there are always less experienced devs you need to communicate with. Their understanding will be based on these newer tools. Similarly, you spend the rest of your career "catching up" with the writings of the devs more experienced than you.
Why does any of this matter? Why don't we standardize and end this cycle? Well, that would prematurely kill off innovation. AI training may be allowed to become stale, but human knowledge cannot. The goal is always to improve the existing code and the new code.
I'll give you a recent example: I've been offloading sparse experts to the CPU on a dual socket Zen system. These processors have multiple CPU dies, which connect through shared CCDs, which contain the L3 cache and the bus lines into DRAM and PCIe. (Additionally in the dual socket architecture there is a fabric which links the sockets.) And so by characterizing the physical architecture of the CPU and placing jobs accordingly, I can extract the maximum amount of performance from the system I have on hand.
Dell and some of the other hyperscalers have characterized these systems over the years in terms of maximum throughput via both the cache and the DRAM bus (and I have met or exceeded their numbers). But, nobody has actually characterized (and/or documented) the way that L3 cache eviction and retention works. Using a combination of fuzzing, patent research, and performance analysis, I have been able to characterize the operation of the system and tailor my stack accordingly.
Is this churn? Nah, this hardware is many years obsolete for mainstream deployment. Will this benefit me in my profession? Time will tell. Regardless, I had fun because I enjoy learning.
> Many people in the tech world seem to espouse a great love for "always learning", but the way in which it is said always feels funny to me. Learn about what? When people say this, do they mean that they want to always be learning the next programming language, the next web framework, the next thick-client frontend web framework, the next python packaging CLI tool (pypi, virtualenv, uv ...), the next Linux distro
For me it’s been: Learn more about unix through OpenBSD as a daily driver, learn how the usb and pci stack works in the kernel, refresh my knowledge about operating system, learn a simpler processor (avr) and interfacing, learn digital logic and computer organization, reignite my interest in electronics, refresh my knowledge about control systems, learn how to model dynamic systems, quick dive into robotics…
This is just basic curiosity over the last year. When I was younger and had more time, I explored 3D modeling, making games, pen-testing and reverse engineering, learning various software and platforms,…
It's not that it's forbidden fruit, but it's akin to asking your mom for every answer on your 3rd grade homework. You get the correct answer and learn nothing.
Sure, in the real world outside of school, you'll be able to get help from many external tools. But when the entire point is to learn something, any tools that prohibit you from learning should be avoided.
But the act of teaching is a double standard between the teacher and the students. I remember courses , exercises and exams when I was in highschool and university where calculators were forbidden because the goal wasn't to get the numerical answer, that was just the icing on the cake
Being able to write a coherent critique that stays practical and doesn't stray into bombastic moralisation or interpersonal frustration is a rare skill that speaks well to your communication skill and taste. I think you will do just fine in a world of AI in engineering.
I used to have a lot of anxiety about Ai wiping out all the Software specific jobs, software development/testing/project management, etc. I actually do think that's still likely to be the case going forward. Pick an industry and try and become an expert in it. Someone will still have to prompt and dream up new features. There are still things that you can't prompt your way to, but they're on the relationship side, not the technical side. I'm not sure what I would say to someone currently doing a CS degree, Math degree, Physics degree, or wanting to do one. Most likely I would say if you want to have a stable income, get good at sales.
And you can sell to who, exactly?
The whole thing is so violently deflationary I think the world economy will collapse. I just look at the Audio Technology space and whats happening right now - why would anyone release a product in this space today? What about SaaS? Anyone can vibecode pretty much anything. So why would anyone pay for it?
Answers so far are assuming that this is complaining that their traditional CS course is not that useful for getting a job anymore.
That’s not what it’s about. It’s actually from someone who genuinely wants to learn for the sake of learning who’s talking about how AI is creating havoc in her studies.
This is a blog post where it’s easy to see which commenters did not read the blog before commenting.
The real villain in this story is the teaching staff. They need to get their act together and stop using generative AI for courseloads.
In other words, they need to do their job. The parts about AI mandates changing every 3 months reeks of an overly bureaucratic, admin-heavy structure where the students’ learning is a lower priority being performative about AI for your boss.
Neither do you, but either way that should become their students' problem.
People have to do their jobs. Having AI generate slop and then sending it off to students or coworkers is not acceptable. It's not the students' problem to deal with things that may or may not be going on in the administration.
They're paying a lot of money to get an education and the university, as a whole, needs to do their job.
Saying that the teaching staff is under pressure excuses nothing. Students are paying for an education and it needs to be delivered at acceptable quality.
I would argue the job thing is more at risk and opportunity for learning is at an all time high. You've got 24/7 access to a very skilled personal tutor.
AI can teach you too, you just have to put the effort in to tell it to teach you. In some contexts, I might want it to fire off a utility bash script and I have no interest in learning bash so I just run it and don't worry too much about the implementation. But if I'm getting it to help me write Java code for work, I review the output and ask it about parts that I don't understand. And it teaches me way better than the old way of reading dry/bad documentation.
While AI is writing much much more of my code these days, I still need to be able to speak intelligently about what I am building, why I chose a given design and the tradeoffs between alternatives. When fixing things, it's still important to understand what broke and why. I don't think that will ever change in any organization that operates in high compliance environments or cares about delivering high quality products.
I don't think programming becoming 'easier' has make understanding programs any easier or less important. Even if all of the above goes away, I think computer science has taught me a lot about how to think about problems.
Yes! It has actually made me even stronger at thinking how to imagine the solution since I know if I explain it well the agent will execute well. Being in CS this was always a skill we learned but shrinking problems and defining them is now how the world must operate exceptionally now.
Writing the code was always partly a proxy for understanding it. When the proxy gets free, the request and test has to harden.
Also it requires upfront decision making (that use to be easy to slip past when talking to humans) now will go off on a tangent that immediately affects your competitive speed and cost.
So, machines forcing us to be better is the moral of the story.
I decided to finally get a CS degree at juuuust the right time (well, for my personal reasons of "love of the game" at least - individual interests may vary), having wrapped up ~2 years ago.
It was interesting to see things change towards the end. Some was the chaos from the lack of consistency the article highlights just starting to emerge. Mostly though, it was just the rampant surge in cheating and the classes not being prepared for it.
First off, congrats on spending time doing side projects in OpenProcessing. Keep working on projects that keep you motivated and keep you learning.
We've been there, lost in our learning paths. Even before AI there were times when we were overwhelmed with our CS journey. You'll overcome, don't you worry about that.
The CS degree was never the end of the learning journey. It was always _what you put into it_. You'll learn what you practice, and uni just happens to be a good place to practice things. Whether that's optimizing AI harnesses, understanding how CPU caches work, crunching pixels on a GPU or the principles of distributed computing... it will all make you a better thinker and good at what you do one day.
Why are more people (bots?) using bearblog. The landing page claims it's fast and privacy focused, but it offers so little... I could recreate it myself in a day so why aren't people running their own blogs, especially in CS where you could learn something doing it.
I’m hearing this more and more from both students and teachers. It seems like everyone in education is frustrated and can’t figure out how to use AI for education productively yet.
TBH I’m also thinking about how to use AI at work for learning. (My work environment is 100% all-in on AI.) Everyone can see and admit that AI coding is making us lazier and that it produces too much code to do old-style line by line reviews. Some people are using agent teams to churn out millions of lines of code where there’s zero hope of reviewing it. We don’t yet know how to keep quality high and complexity low while doing agent coding, we’re just having them write lots of tests and hoping that when things break they’re easy to fix. Agents should get better at broad architecture in the future, but they’re definitely not there yet.
> The amount of work involved has also tripled
This has to be one of the big ironies of using AI. I’ve heard this from other students too. The problem is that old-style assignments where the level of difficulty was hand-crafted is way way too easy for an LLM, therefore teachers need to crank the level of difficulty so the student has to actually spend some time on the assignment. But LLMs are getting better every month and the difficulty is so high that no students can possibly do the assignment by hand and now small errors in assignment design can lead to large errors in effort and time required.
> It seems like everyone in education is frustrated and can’t figure out how to use AI for education productively yet.
Oh, it's terrible. I'm a computer science instructor at a university (I have 20 years of industry experience prior) and it was already bad enough before trying to predict where the market was going to be 4 years in the future. But a year ago, it was tough to predict where the market was going to be in one month.
So yeah, we can teach all kinds of stuff. But is it the right stuff? With one month of visibility, who can tell?
But now I feel like it's settled down a little bit. And now I feel like I have more like 7 to 8 months of visibility. One thing that's helped is that we see the hiring differential between seniors and juniors. So now we can answer the question, what does the market want? It wants the expertise between junior and senior. And I'm confident it'll want that for at least the next 7-8 months.
Good luck cramming that into 4 years, though! I figure we have to try to bridge the gap as best we can. Eventually, seniors will become rare enough that the expense isn't worth it. It'll become better to hire cheaper most-qualified juniors.
There's still some seriously open-ended questions though. How much does coding matter, for example? If you ask senior devs who don't do any coding how much they rely on their coding expertise to make decisions, you'll get a zillion answers across the spectrum. This becomes even more true for senior devs who use AI for coding. Heck, ask 100 devs if it's important that you can even read code or not and see what comes out!
And then there's the question of how do you teach those systems level thinking skills? Historically, universities haven't taught those skills. You came out of the University with general problem solving skills in technology, and then at your job you were exposed to a small corner of the codebase to fix some bug. Gradually, over the years, you learn more and more of the codebase and become more and more senior. We have to get that into our curriculum. I'm working on that as we speak and will be working on that for the next several years, it looks like; even if my position vanishes, I'll keep attacking it—this is a tough problem that's fun to try to crack.
Anyway, I hope someday my forward visibility gets back up to 18 months. Won't that be amazing. I'd also like to buy some RAM. ;)
In that, 500 person frosh lectures are dead. 32 students per class, 6 classes a day are dead.
What remains? The Oxbridge model that has been going on for nearly a millennia.
Briefly: 4-10 students and a proctor. You do the reading/work, you meet a few times a week. The proctor facilitates discussion and guides students. Many schools do this, but not many students get to see it.
Why? Because it is very expensive. The proctor has to be at least a graduate in the same subject, likely having a graduate degree in it. These are small classes by design. And don;t even get started on the training that the proctors have to do to get ready and not be a lawsuit generation machine.
I feel this is really the only way forward. Which is almost maximally unfortunate.
Yes, 'Cheap' Big State University will still exist, but it's program will be so degraded by AI that it's graduates will be seen as worthless. While 'Very Expensive' Small Liberal Arts College with the Oxbridge model will guarantee that the students are at least minimally educated to bullshit well enough. Meaning that the rich and poor gap grows and grows.
This sounds awful, but the culprit is the terrible faculty at this institution:
> Whereas before, assignments were designed by a person who thought them through at least once, then approved by other people in the department, now I doubt they're getting read more than once (if that). Errors abound, often with corrections coming a week or two before the deadline.
This is entirely unacceptable. Professors who are using AI to generate assignments and course loads need to be discipline and removed if they can’t start doing their job.
From the title I thought I was going to be reading an article wondering how AI would devalue a CS degree, but this is actually a story about a CS program falling apart because the instructors are flailing about without AI.
I think it's worth asking what a CS degree is for. Is it
to teach you how to code and make you obviously hire-able
by industry? Maybe it's to teach you how to think about
data and code? If you think it's the former, then AI is
probably going to be a problem for you. If the latter,
it's neutral to somewhat good.
I've been hiring CS grads for 4 decades (yikes!) and
there's definitely been a shift. Not all existing
programs are bad. Not all young software devs are bad.
But... the percentage of recent grads that seem to
understand what data processing systems are about has
definitely fallen.
Us old farts in industry must share some of the blame
here. Our industry seems to be pretty boom or bust.
Someone comes in with a stack of money and tells people to
build { the web | blockchain | AI | virtual worlds | etc }
and suddenly there's a rush. At the start of the gold
rush, we're not sure how to build everything that's going
to be the next google [[1]] so we fill the room with young
kids who have seen some recent tooling and will work
cheap. We don't develop them. We want them to be
compliant drones dancing to a tune played by a CTO and
VP/Eng who got their jobs because they're the children of
the fund investors who gave the startup a bunch of money.
And then we go back to Universities and tell them "OMG!
Your not bar-raising your grads by teaching them __fill in
the blank__!" [[2]]
So... I don't doubt it looks like AI is destroying the
degree. But maybe it's just making it hard to ignore
previous problems with the uni to industry pipeline.
And I didn't mention the Mythical Man Month once in this
comment. I'm getting better.
1. I'm not sure too many people want to be Google at the
moment, but over the last 25 years, they've certainly
done well.
2. I'm intentionally misspelling "you're" because in my
time in industry, I've found only about 15% of people
would pass a 7th grade spelling test.
I'm one of those younger people. Having graduated from a CS program recently, I think that the main point of that degree was to teach me a wide range of relevant concepts and ideas in math and computer science that should allow me to reason about lots of different topics in the field.
I don't think the degree should be optimized toward hireability, but to me its value has still greatly diminished. My assumption was that the CS degree wouldn't follow every whim of the industry, but will still give me enough skills and knowledge to be able to pick up anything CS-related without too much extra training. There's no promise of a job, but there was an expectation that you were learning something useful and versatile that you could then turn into a tech job with some added work. Now, the value of all that has mostly vanished.
Instead of just one tech niche being automated away, leaving the others relatively intact, AI has plugged every opening that people like me could get into at the start of their careers, and this effect is the same across almost the entire industry. And for the people who got in, their employers are already valuing those fundamentals less and less as workers are encouraged to paper over any gaps in knowledge with AI. The relevance of all those learned skills has fallen. I think the only people this doesn't negatively impact are ones who are dead set on going into academia and becoming a researcher in a CS field. And maybe not even then, as AI seems to be taking the spotlight from most other research and is getting shoehorned into every new idea. Thinking the degree is a job training exercise and expecting your core skills to be relevant after graduation at all outside academia are different positions.
Can I ask about the uniform width formatting? Is this some convention from the old days, like formatting text to perfectly fit on a terminal screen?
This is a good article. AI just introduces new bureaucracy, workloads, useless discussions and is a hindrance.
No one needs AI in universities or software engineering. It only benefits the middle managers and mediocre people who jump on any bandwagon in order to help their careers.
No software has gotten any better since AI, except that some forgotten bugs from the 1990s were found, just as 20000+ bugs have been found by the Luddite OssFuzz system before AI.
The uni I recently graduated is also very confused regarding what to do... Things have converged towards less hands on assignments and more theoretical exams, something I know for sure I would've hated during my studies.
It's awkward, you're very unlikely to stop students from using AI for certain assignments (i.e: first programming class) for the good of their fundamentals, but there's also no other teaching tool being used to get them equivalent experience to solving assignments without AI.
That seems like a normal academia dysfunction, AI is just another thing they are failing to adapt to. I graduated 10 years ago and on the job I had to unlearn most of what I learned from the Java course, for example.
I haven't seen a better example of "those who can't do, teach" than CS professors.
Wesley Crusher was kidnapped away to a planet with no children. Their bodies were decaying but their technology was superior to the Federation's so they needed to steal other people's kids.
They gave Wesley a laser scalpel, to teach him to chisel wood, with whatever his mind could picture.
Wesley complained, he said, "dood, this isn't art! i'm not even chiseling!"
And the mentor-kidnapper said, "No, Wesley, you are doing art. The art is in your mind. The tool is just doing the work."
And so then Wesley threw the perfect wooden dolphin at the wall, infuriated that all the pleasure had been removed from the art. But he lived the rest of his life on that planet, making perfect wooden laser sculpted dolphins, cause that was his job and his boss told him too bad.
For a little while Wesley made popsicle stick art during his free time at night to try and feel the sensation of choice and control, but eventually just gave up and played mariokart until he was an old man.
It’s probably time students shift further away from studying computer science and more toward software engineering. It provides real skills that are still relevant in the AI era. Less memorizing algorithms, more SDLC, team management, economics, and working with real customers on their requirements.
Isn't having something that can solve general problems based on prior art without the requisite effort in learning the prior art completely antithetical to education in general? Not saying they aren't useful tools, but asking a an AI to write bubble sort isn't learning bubble sort? The point was never to write bubbles sort it was to learn how and why.
if you only do CS for the job or the money you where wrong from the beginning. I do this for 45 years now and every year ther was a horror story how now tech will change everything. You have to empbrace the change and have fun in adapting. CS is nothing to sleep on. If you only do it for the money do something else.
AI clearly means you need to change how you teach and evaluate students. We are only a few years in though, and it's still extremely unclear what effective teaching with these tools looks like.
The worst part is that the evaluation loop is measured in multiple years! If you start trying some new education mechanism today you won't get a robust idea of how well it works until your current cohort graduate in 3-6 years time.
Students effectively all become guinea pigs for untested new ways of learning and teaching.
I think we have a solution for the evaluation part: the overwhelming majority of a student's grade is computed from in-person assessments only. Some instructors allow repeats on exams as well.
The teaching part is as hard, but it has always been hard. The students you get one year are completely different the next year, for various reasons. As long as you have competent administrators, all is good--this is what we sign up for.
The solution is closed notes assessment, without any electronics used.
If tests evaluate understanding is up for debate. But that is what we switched to in the sciences.
Universities are going to have a reckoning moment, as most degrees 1) are not economically viable and 2) don't make sense to be taught in the current mode of instruction.
> it's still extremely unclear what effective teaching with these tools looks like.
We can't say what effective teaching is if we don't first decide/rediscover what teaching/education is for. Job training? Citizen building? Compliance development?
Yes it's tough; but, Eric at Stanford is killing it. CS146S: The Modern Software Developer
Stanford University • Fall 2026 https://themodernsoftware.dev/
I use AI extensively at work. I don't vibe code - I'm extremely intentional about what I have it do.
I keep getting excited because I can ask it questions and learn so much from it, but then I realize that a lot of what I'm learning is specific technical knowledge that I don't really need anymore.
That said, the important stuff in building systems has never been the coding. It's the wisdom to organize the system in such a way that it works well and is changeable. AI is still really bad at this. But it's super good at the for loops and if statements, which is actually the easy stuff.
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[ 0.23 ms ] story [ 92.3 ms ] thread"all the worlds expertise has just been handed to me in an adundance that I cannot fully comprehend yet"
carry on student, carry on.
its just the ghost of your ego.
Also, its their degree. AI doesn't need one. You have AI.
From product to producer in 24 hours. Go!
The advice I give you is the same I'm trying to believe in myself: just chill dude... AI isn't human and while can't be one, you are "safe"... We are still figuring it out while it happen to find another way to "feel valuable" after code-writing is no longer the source of our self-value.
Hold onto the roller coaster hand rail, as we become the agrarian society we were all along.
Energy farmers in the desert.
Every farmer fixing his own tractor.
Thats where we come from.
The "everyone is special, and every product is specialized" period is ending.
Everyone is an expert on exactly the things they decided they wanted to be an expert on now.
Thats where we're headed.
If you listen carefully, you can hear the sound of humans being plugged into the matrix, and downloading their fill of cartridges, in ever increasing numbers.
Talk is cheap again.
See you at the replicator parts store. :}
I have been doing this professionally for over twenty years, and I still learn something every day. Over the past couple years, with AI augmentation, I learn even more, move faster and go deeper into the systems that I work on.
A CS degree was never going to provide you with all of the tools that you need for a successful career. It provided you with the basics to get your foot in the door.
AI is not replacing you or an education. AI is raising the bar of what you can comprehend and retain. From someone who is technically minded (and self-taught), all I have ever wanted is to keep learning.
Yes and no.
I concur that it was never about the tools, although I've seen plenty of people advocating it had a more "practical" approach, using the same tools as companies use, and in the same way.
On the other hand it's not only about the basics.
In there you can go at depth in aspects that you will have to fight to use, like proper engineering practices, and might only be vital on the later stages of your career. And I feel that those learnings, plus the possibility of acquiring a breadth of knowledge that is hardly possible otherwise, is what gives CS graduates a leg up to other practitioners of the art of software development.
YMMV and a leg up can be easily overtaken, yes.
Realistically, "AI is not working" is probably not a realistic scenario. He'll just stop working, just like if the network goes down.
The real risk of AI augmentation is to your value as an employee. Maybe the augmentation makes you feel and perform like Iron Man, but if the suit is doing the heavy lifting, and some cheaper guy could wear it and do pretty well, how are you going to keep that fat paycheck? It's not like any of us have tenure.
Good for you! Many people just want to be able to pay their bills.
Your post is entirely too dismissive of the chaos this new technology is introducing.
Far too many people focus on passing an interview and then let legal protections keep their position, all while draining resources that could have been better allocated to someone who is actually passionate. They saw my generation do reasonably well and came for the paycheck. I got into this because there were very few people who could rise to the occasion of problem solving that the industry needed. I did not walk into the room with all of the skills or an idealized salary, I walked into the room with a mentality.
Perhaps the "chaos" being introduced is simply a long overdue market correction.
I'll let the downvotes tally how pervasive this is.
That's fine, but don't attempt to gatekeep your profession. It makes you look like an inconsiderate asshole.
Passion in tech is for 25 year-olds, recruiters, co-founders, and imbeciles.
AI is, in my opinion, absolutely murdering the junior dev market. It's now much more efficient to pay for a senior or staff engineer and set them up with a subscription to claude or codex. I think things are going to remain fine for senior+ engineers, but I genuinely don't know what advice I'd give to graduating devs at this time.
this has been happening for years though. just think of cloud computing and things like kubernetes: a single person or a small team can handle the amount of computer that twenty years ago required a full team of sysadmins, a facility (either on premise or datacenter space), huge (upfront!) capital expenditure and ongoing costs (connectivity and power).
same goes for web frameworks (think rails and similar)
Coding agents are different in that they are unbounded in the tasks they can do (although reliability is an issue), and you can work with them the same way you work with other engineers (e.g. via natural language).
Also, coding agent capabilities are advancing much faster than people can sit down and learn their limitations. All the tricks we learned for Opus 5 became obsolete in a couple months with Opus 5.5.
> Stanford researchers find the employment trends they identified for entry-level workers last year are persisting and expanding. Specifically, employment levels for workers ages 22 to 25 in the most “AI-exposed” occupations are now 19 percent below those of their peers in fields less exposed to AI disruption.
I do learn every day thanks to AI, I agree with that part. But I'm increasingly convinced that AI is likely to replace either me or some of my teammates in the near future.
In the last year, half of my team has been laid off (or left) already and won't be replaced, and we don't see any drop in productivity and the tools are getting better by the day. We need experts to drive all this, but not as many people as before.
AI is the tractor in The Grapes of Wrath, and you’re the one driving it.
Too bad a big percentage wont get to do that professionally anymore due to AI.
>AI is not replacing you or an education. AI is raising the bar of what you can comprehend and retain.
No, it also makes the job market hell and cuts down on positions, on top of an already squeezed pre-AI environment.
And that's if one is still OK with the fact that coding work (something many of us enjoyed in itself) turned into "project manager for AIs".
I was never a good student (not academically inclined at all) and I have learnt more with AI in past 6 months than the engineering degree taught me.
+1
Aside from teaching me the _basics_ of programming and discrete mathematics, it did not help my career. I, in-fact (due to poor GPA), did not even have a SWE role out of school and had to find my own way here into a successful career. (I struggled even with LeetCode easys coming out of college) I attribute it to creating opportunities for luck (networking) and an appetite for life-long learning (which came well after college).
You're only thinking about new stuff adding "breadth". I think most people would agree with you that new tools rarely have new use cases, so not truly adding "breadth", but that's where we get into "depth".
When looking at long-term learning, there's a lot of "depth" and subtlety in the way the existing tools work. There also comes a point where you have a firm grasp of all implementation details, but are unhappy with the ergonomics of the existing tools to do a particular job. What was an odd use case can become more common with time. This is how new tools and terminologies are born (adding "breadth"), but you have to remember there are always less experienced devs you need to communicate with. Their understanding will be based on these newer tools. Similarly, you spend the rest of your career "catching up" with the writings of the devs more experienced than you.
Why does any of this matter? Why don't we standardize and end this cycle? Well, that would prematurely kill off innovation. AI training may be allowed to become stale, but human knowledge cannot. The goal is always to improve the existing code and the new code.
Dell and some of the other hyperscalers have characterized these systems over the years in terms of maximum throughput via both the cache and the DRAM bus (and I have met or exceeded their numbers). But, nobody has actually characterized (and/or documented) the way that L3 cache eviction and retention works. Using a combination of fuzzing, patent research, and performance analysis, I have been able to characterize the operation of the system and tailor my stack accordingly.
Is this churn? Nah, this hardware is many years obsolete for mainstream deployment. Will this benefit me in my profession? Time will tell. Regardless, I had fun because I enjoy learning.
For me it’s been: Learn more about unix through OpenBSD as a daily driver, learn how the usb and pci stack works in the kernel, refresh my knowledge about operating system, learn a simpler processor (avr) and interfacing, learn digital logic and computer organization, reignite my interest in electronics, refresh my knowledge about control systems, learn how to model dynamic systems, quick dive into robotics…
This is just basic curiosity over the last year. When I was younger and had more time, I explored 3D modeling, making games, pen-testing and reverse engineering, learning various software and platforms,…
If you're highly in demand or sitting on millions of equity, more power to you, you can work on projects outside at home by the pool thanks to AI.
Sure, in the real world outside of school, you'll be able to get help from many external tools. But when the entire point is to learn something, any tools that prohibit you from learning should be avoided.
That’s not what it’s about. It’s actually from someone who genuinely wants to learn for the sake of learning who’s talking about how AI is creating havoc in her studies.
The real villain in this story is the teaching staff. They need to get their act together and stop using generative AI for courseloads.
In other words, they need to do their job. The parts about AI mandates changing every 3 months reeks of an overly bureaucratic, admin-heavy structure where the students’ learning is a lower priority being performative about AI for your boss.
People have to do their jobs. Having AI generate slop and then sending it off to students or coworkers is not acceptable. It's not the students' problem to deal with things that may or may not be going on in the administration.
They're paying a lot of money to get an education and the university, as a whole, needs to do their job.
Saying that the teaching staff is under pressure excuses nothing. Students are paying for an education and it needs to be delivered at acceptable quality.
AI can teach you too, you just have to put the effort in to tell it to teach you. In some contexts, I might want it to fire off a utility bash script and I have no interest in learning bash so I just run it and don't worry too much about the implementation. But if I'm getting it to help me write Java code for work, I review the output and ask it about parts that I don't understand. And it teaches me way better than the old way of reading dry/bad documentation.
It's what you make of it.
I don't think programming becoming 'easier' has make understanding programs any easier or less important. Even if all of the above goes away, I think computer science has taught me a lot about how to think about problems.
Writing the code was always partly a proxy for understanding it. When the proxy gets free, the request and test has to harden.
Also it requires upfront decision making (that use to be easy to slip past when talking to humans) now will go off on a tangent that immediately affects your competitive speed and cost.
So, machines forcing us to be better is the moral of the story.
It was interesting to see things change towards the end. Some was the chaos from the lack of consistency the article highlights just starting to emerge. Mostly though, it was just the rampant surge in cheating and the classes not being prepared for it.
First off, congrats on spending time doing side projects in OpenProcessing. Keep working on projects that keep you motivated and keep you learning.
We've been there, lost in our learning paths. Even before AI there were times when we were overwhelmed with our CS journey. You'll overcome, don't you worry about that.
The CS degree was never the end of the learning journey. It was always _what you put into it_. You'll learn what you practice, and uni just happens to be a good place to practice things. Whether that's optimizing AI harnesses, understanding how CPU caches work, crunching pixels on a GPU or the principles of distributed computing... it will all make you a better thinker and good at what you do one day.
Stay curious.
Provide examples.
It's hard for readers to gauge whether you're complaints are legitimate or overreactions.
> The amount of work involved has also tripled
are hard to evaluate if I don't know what the work was before. Nor can I compare with my education to see if the workload is reasonable.
> I am here to learn, god damn it, not barely scratch the surface of the 15 deliverables I have for a 3,000 word assignment.
What type of CS courses require a 3000 word assignment?
TBH I’m also thinking about how to use AI at work for learning. (My work environment is 100% all-in on AI.) Everyone can see and admit that AI coding is making us lazier and that it produces too much code to do old-style line by line reviews. Some people are using agent teams to churn out millions of lines of code where there’s zero hope of reviewing it. We don’t yet know how to keep quality high and complexity low while doing agent coding, we’re just having them write lots of tests and hoping that when things break they’re easy to fix. Agents should get better at broad architecture in the future, but they’re definitely not there yet.
> The amount of work involved has also tripled
This has to be one of the big ironies of using AI. I’ve heard this from other students too. The problem is that old-style assignments where the level of difficulty was hand-crafted is way way too easy for an LLM, therefore teachers need to crank the level of difficulty so the student has to actually spend some time on the assignment. But LLMs are getting better every month and the difficulty is so high that no students can possibly do the assignment by hand and now small errors in assignment design can lead to large errors in effort and time required.
Oh, it's terrible. I'm a computer science instructor at a university (I have 20 years of industry experience prior) and it was already bad enough before trying to predict where the market was going to be 4 years in the future. But a year ago, it was tough to predict where the market was going to be in one month.
So yeah, we can teach all kinds of stuff. But is it the right stuff? With one month of visibility, who can tell?
But now I feel like it's settled down a little bit. And now I feel like I have more like 7 to 8 months of visibility. One thing that's helped is that we see the hiring differential between seniors and juniors. So now we can answer the question, what does the market want? It wants the expertise between junior and senior. And I'm confident it'll want that for at least the next 7-8 months.
Good luck cramming that into 4 years, though! I figure we have to try to bridge the gap as best we can. Eventually, seniors will become rare enough that the expense isn't worth it. It'll become better to hire cheaper most-qualified juniors.
There's still some seriously open-ended questions though. How much does coding matter, for example? If you ask senior devs who don't do any coding how much they rely on their coding expertise to make decisions, you'll get a zillion answers across the spectrum. This becomes even more true for senior devs who use AI for coding. Heck, ask 100 devs if it's important that you can even read code or not and see what comes out!
And then there's the question of how do you teach those systems level thinking skills? Historically, universities haven't taught those skills. You came out of the University with general problem solving skills in technology, and then at your job you were exposed to a small corner of the codebase to fix some bug. Gradually, over the years, you learn more and more of the codebase and become more and more senior. We have to get that into our curriculum. I'm working on that as we speak and will be working on that for the next several years, it looks like; even if my position vanishes, I'll keep attacking it—this is a tough problem that's fun to try to crack.
Anyway, I hope someday my forward visibility gets back up to 18 months. Won't that be amazing. I'd also like to buy some RAM. ;)
In that, 500 person frosh lectures are dead. 32 students per class, 6 classes a day are dead.
What remains? The Oxbridge model that has been going on for nearly a millennia.
Briefly: 4-10 students and a proctor. You do the reading/work, you meet a few times a week. The proctor facilitates discussion and guides students. Many schools do this, but not many students get to see it.
Why? Because it is very expensive. The proctor has to be at least a graduate in the same subject, likely having a graduate degree in it. These are small classes by design. And don;t even get started on the training that the proctors have to do to get ready and not be a lawsuit generation machine.
I feel this is really the only way forward. Which is almost maximally unfortunate.
Yes, 'Cheap' Big State University will still exist, but it's program will be so degraded by AI that it's graduates will be seen as worthless. While 'Very Expensive' Small Liberal Arts College with the Oxbridge model will guarantee that the students are at least minimally educated to bullshit well enough. Meaning that the rich and poor gap grows and grows.
> Whereas before, assignments were designed by a person who thought them through at least once, then approved by other people in the department, now I doubt they're getting read more than once (if that). Errors abound, often with corrections coming a week or two before the deadline.
This is entirely unacceptable. Professors who are using AI to generate assignments and course loads need to be discipline and removed if they can’t start doing their job.
From the title I thought I was going to be reading an article wondering how AI would devalue a CS degree, but this is actually a story about a CS program falling apart because the instructors are flailing about without AI.
I don't think the degree should be optimized toward hireability, but to me its value has still greatly diminished. My assumption was that the CS degree wouldn't follow every whim of the industry, but will still give me enough skills and knowledge to be able to pick up anything CS-related without too much extra training. There's no promise of a job, but there was an expectation that you were learning something useful and versatile that you could then turn into a tech job with some added work. Now, the value of all that has mostly vanished.
Instead of just one tech niche being automated away, leaving the others relatively intact, AI has plugged every opening that people like me could get into at the start of their careers, and this effect is the same across almost the entire industry. And for the people who got in, their employers are already valuing those fundamentals less and less as workers are encouraged to paper over any gaps in knowledge with AI. The relevance of all those learned skills has fallen. I think the only people this doesn't negatively impact are ones who are dead set on going into academia and becoming a researcher in a CS field. And maybe not even then, as AI seems to be taking the spotlight from most other research and is getting shoehorned into every new idea. Thinking the degree is a job training exercise and expecting your core skills to be relevant after graduation at all outside academia are different positions.
Can I ask about the uniform width formatting? Is this some convention from the old days, like formatting text to perfectly fit on a terminal screen?
No one needs AI in universities or software engineering. It only benefits the middle managers and mediocre people who jump on any bandwagon in order to help their careers.
No software has gotten any better since AI, except that some forgotten bugs from the 1990s were found, just as 20000+ bugs have been found by the Luddite OssFuzz system before AI.
It's awkward, you're very unlikely to stop students from using AI for certain assignments (i.e: first programming class) for the good of their fundamentals, but there's also no other teaching tool being used to get them equivalent experience to solving assignments without AI.
I haven't seen a better example of "those who can't do, teach" than CS professors.
They gave Wesley a laser scalpel, to teach him to chisel wood, with whatever his mind could picture.
Wesley complained, he said, "dood, this isn't art! i'm not even chiseling!"
And the mentor-kidnapper said, "No, Wesley, you are doing art. The art is in your mind. The tool is just doing the work."
And so then Wesley threw the perfect wooden dolphin at the wall, infuriated that all the pleasure had been removed from the art. But he lived the rest of his life on that planet, making perfect wooden laser sculpted dolphins, cause that was his job and his boss told him too bad.
For a little while Wesley made popsicle stick art during his free time at night to try and feel the sensation of choice and control, but eventually just gave up and played mariokart until he was an old man.
AI clearly means you need to change how you teach and evaluate students. We are only a few years in though, and it's still extremely unclear what effective teaching with these tools looks like.
The worst part is that the evaluation loop is measured in multiple years! If you start trying some new education mechanism today you won't get a robust idea of how well it works until your current cohort graduate in 3-6 years time.
Students effectively all become guinea pigs for untested new ways of learning and teaching.
I think we have a solution for the evaluation part: the overwhelming majority of a student's grade is computed from in-person assessments only. Some instructors allow repeats on exams as well.
The teaching part is as hard, but it has always been hard. The students you get one year are completely different the next year, for various reasons. As long as you have competent administrators, all is good--this is what we sign up for.
If tests evaluate understanding is up for debate. But that is what we switched to in the sciences.
Universities are going to have a reckoning moment, as most degrees 1) are not economically viable and 2) don't make sense to be taught in the current mode of instruction.
We can't say what effective teaching is if we don't first decide/rediscover what teaching/education is for. Job training? Citizen building? Compliance development?
I keep getting excited because I can ask it questions and learn so much from it, but then I realize that a lot of what I'm learning is specific technical knowledge that I don't really need anymore.
That said, the important stuff in building systems has never been the coding. It's the wisdom to organize the system in such a way that it works well and is changeable. AI is still really bad at this. But it's super good at the for loops and if statements, which is actually the easy stuff.