I am not in a CS program myself, but I guest lecture for CS students at CMU about 2x/year, and I'm in a regular happy hour that includes CS professors from other high-tier CS schools.

Two points of anecdata from that experience:

- The students believe that the path to a role in big tech has evaporated. They do not see Google, Meta, Amazon, etc, recruiting on campus. Jane Street and Two Sigma are sucking up all the talent.

- The professors do not know how to adapt their capstone / project-level courses. Core CS is obviously still the same, but for courses where the goal is to build a 'complex system', no one knows what qualifies as 'complex' anymore. The professors use AI themselves and expect their students to use it, but do not have a gauge for what kinds of problems make for an appropriately difficult assignment in the modern era. The capabilities are also advancing so quickly that any answer they arrive at today could be stale in a month.

FWIW.

"The professors use AI themselves and expect their students to use it, but do not have a gauge for what kinds of problems make for an appropriately difficult assignment in the modern era."

I'm a prof, recently retired but still teaching part-time. This is exactly the problem. AI is here, people use it, so it would be stupid (plus impossible) not to let students use it. However, you want your students to still learn something about CS, not just about how to prompt an AI.

The problem we are heading towards as an industry is obvious: AI is perfectly capable of doing most of the work of junior-level developers. However, we still need senior-level developers. Where are those senior devs going to come from, if we don't have roles for junior devs?

currently in cs masters program at ivy: i think it's like thinking that pure math study evaporated when we made the calculator, or that we suddenly shouldn't have bothered with Riemann sums because of the FTC. ai to coding is much the same in the sense of moving to a layer of higher abstraction. i don't think cs curriculums have to change drastically to accommodate this; however, the onus on not getting it wrong increases since ai produces probabilistic output. finally, you can have a chat bot do all the work for you to your own detriment i suppose...

I’m studying for an MSc in Architectural Computation at the Bartlett, UCL – essentially computer science for architects, with a focus on geometry, simulation and computer graphics. I’m very grateful for this question, because it gives me a chance to synthesise the ideas I’ve had since I started the programme.

Even though our professors are getting worried, the institution itself hasn’t changed dramatically yet when it comes to generative AI. There is an openness from our professor to discuss the matter, but change is slow.

What does work in the current programme —and in my oppinion exactly what we need for next generations— is that we are exposed to an astonishing number of techniques and are given the freedom to interpret and implement them. The only drawback is that some students simply paste LLM outputs as their scripts, while others spend time digging deeper into the methods to gain finer control over the models. This inevitably creates a large discrepancy in skill levels later on and can damage the institution’s reputation by producing a highly non‑homogeneous cohort.

I think the way forward is to develop a solid understanding of the architecture behind each technique, be able to write clear pseudocode, and prototype quickly. Being able to anticipate what goes in and what comes out has never been more important. Writing modular, well‑segmented code is also crucial for maintainability. In my view, “vibe‑coding” is only a phase; eventually students will hit a wall and will need to dig into the fundamentals. The question is can we make them hit the wall during the studies or will that happen later in their career.

In my opinion, and the way I would love to be taught, would be to start with a complex piece of code and try to reverse‑engineer it: trace the data flow, map out the algorithm on paper, and then rebuild it step by step. This forces you to understand both the theory and the implementation, rather than relying on copy‑and‑paste shortcuts.

Hope that is of any use out there, and again, I think there is no time less exciting (and easy!) than this one to climb on the shoulders of giants.

I've been doing programming and sys admin as a hobby for a long time and only recently started my bachelors in compsci, and I'm sad to have waited so long as almost everything has been infested with ai to some degree.

Large well-regarded CS schools still have 'systems' and other traditional CS specializations. I would encourage looking at those programs.

Experience is still needed too. You can't just blindly trust AI outputs. So, my advice is to get experience in an old-fashioned CS program and by writing you own side projects, contributing to open source projects, etc.

The curriculum in my university mostly didn't change. Most CS topics didn't change through ML research.

The main change was in testing/exams. There was a big effort towards regular testing assisted by online tools (to replace the system with one exam at the end in favor of multiple smaller tests). This effort is slowly being winded down as students blatantly submit ChatGPT/Claude outputs for many tasks. This is now being moved back to a single exam (oral/written), passing rates are down by 10-20% iirc.

Going into CS as a career will be interesting but the university studies/degree are still likely worth it (partly spoken from a perspective where uni fees are less than 500€ per semester). Having a CS degree also does not mean you become a programmer etc. but can be the springboard for many other careers afterwards.

Having a degree and going through the effort of learning the various fundamentals is valuable, regardless of everything being directly applicable. There is also the social aspects that can be very valuable for personal development.

Get them to learn the fundamentals and understand them deeply just like they should/might have in the past.

They can do so at an accelerated rate using AI on verifiable subject matter. Use something like SRS + copilot + nano (related: https://srs.voxos.ai) to really internalize concepts.

Go deep on a project while using AI. To what extreme can they take a program before AI can't offer a working solution? Professors should explore and guide their students to this boundary.

Obligatory reference to "The illustrated guide to a Ph.D." - https://matt.might.net/articles/phd-school-in-pictures/

You probably should ask about a particular program because there are as many answers to your question as there are programs. Even in a single school there are often several tracks. Some are very theory and math heavy, others are more practical.

The part that hasn’t changed is being in a cohort of people like yourself and living in a community centered around a school (and again this varies from school-to-school). I had a lot of fun and met many interesting people who inspired and motivated me. It’s the fastest way to jumpstart your professional network.

I had moved from a small, boring town to a city and the semi-structured life of a student living on-campus made that transition easy and provided an instant social life.

My regret is that I didn’t take advantage of all the things I could have with respect to my electives. I wish I had taken art history or intro to film or visual arts 101 or modern literature or just about any other humanities course that was available to me.

If you want somebody to tell you to skip school, you’ll probably get that advice here too. If all you are after is the piece if paper at the end you probably should skip school or do it remotely. It’s cheaper and more concentrated but you miss the most valuable part of university life.

If entrepreneurship is your thing, you might be better off in a business program.

I’m also interested in what CS curriculums are right now and furthermore what students actually think of it. I suspect nothing has changed in terms of curriculum other than being more rigorous about “academic dishonesty” like detecting if someone used ChatGPT generated answers.

What I hope will change is less people going into the CS field because of the promise of having a high-paying career. That sentiment alone has produced an army of crud monkeys who will overtime be eaten by AI.

CS is not a fulfilling career choice if you don’t enjoy it, it’s not even that high-paying of a career unless you’re beyond average at it. None of that has changed with AI.

I think the right way to frame career advice is to encourage people to discover what they’re actually curious in and interested by, skills that can be turned into a passion, not just a 9 to 5.

Many universities used to have basic skills without the rigorous academic culture of top universities. They're being completely decimated by AI: professors downskilling themselves by openly using it in the course, often even responding to questions with suggestion to prompt it yourself. Some will prognosticate themselves about how everything outside the tiniest subset of their subject will be replaced soon enough. Students themselves seem to either understand AI as academically dishonest or believed the propaganda, thinking they HAVE to "learn" it to have a chance at a career, even at the expense of actual subjects. If you remotely suspect that, don't rely on prior evidence, run.

Meanwhile other unis are still majority high class faculty members holding the bar, but are suffering a decline in the quality of new students. You can absolutely learn in those places, but you're likely to to find many capable peers.

I don't have the data what's going on at global top CS programs, presumably much better than this. I do predict we're gonna suffer a multi-generational loss of skilled talent, with three generations of mediocre programmers converted to AI zombies incapable of performing their job, with or without it.

I am a teen who is hopefully going to go to college (Preferably CS), My reason is and was that I really love tinkering with computers and code related automation/scripts (more importantly thinking about scripts)

And to be honest, my intention with going to college is hopefully to rip off any use of AI that I do or have a more learning experience because right now I am bounded severely with time but my curiosity still exists, so I just build things to "prove" that its possible. But within college, I would be able to give time to the thinking process and actually learn and I do feel like I have this curiosity which I am grateful for.

So to me, its like it gives me 4 years of doing something where I would still learn some immense concepts and meet people interested (hopefully) in the same things and one of the ideas I have within college is to actually open up a mini consultancy in the sense of helping people/businesses migrate over from proprietory solutions to open source self-hosted solutions on servers.

My opinion, is that people need a guy who they can talk to if any solution they use for their personal projects for example go wrong, you wouldn't want to talk to AI if for example you use self-hosted matrix/revolt/zulip (slack alternatives) and I think that these propreitory solutions are so rent-seeking/expensive that even if I have a modest fees in all of this, I wish to hopefully still charge less than what they might be paying to others and host it all on servers with better predictability of pricing.

Solopreneurship is never this easy yet never this hard because its hard to stand out. There was a relevant thread on Hackernews about it yesterday that I read about it, and the consensus there from what I read was that marketing might-work but producthunt/these directories are over-saturated.

Your best options are to stay within the community that you wish to help/your product helps and taking that as feedback.

That's my opinion, at least, being honest, I am not worried about what happens within Uni right now but rather the sheer competition within my country to reach a decent CS uni college as people treat it as heaven or just this race seeing what other people are doing and I feel like I am pissed between these two spots at the moment because to get into CS Uni, you have to study non CS subjects (CS doesn't even matter) but my interest within CS gets so encapsulating that its hard to focus on the other subjects. Can't say if that's good or bad but I really have to talk myself into studying to remind what I am studying for (even after which I can still slip up as I get too interested but that's another matter)

Good reminder for me to study chemistry now... wish me luck :)

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lots of chatgpt i assume