The future of computer science

I'm graduating with a computer science degree this spring and I've watched the field change drastically right in front of my own eyes these past four years.

So, I want to lay out where I think it's headed to hopefully help some of you. I

think AI slop will disappear or at least start to get filtered out because we pretty much saw this huge trend of companies adding AI into everything like AI video generators, AI images. There's

an AI chatbot in every website now. But

I think companies are going to realize that no one actually uses these things.

And I think most people are tired of getting AI pushed down their throats.

So, this means the companies pushing the AI slop are either going to get wiped out or get forced to pivot to making things that actually impact people. So,

I can see the useless parts of the AI bubble start to pop as the years go by.

But what does that mean for a CS student or a developer? Well, I think you're going to be expected to show a higher level of proof as the years go on. I

think companies are going to want you to use AI, but to make something actually meaningful. You're going to be expected

meaningful. You're going to be expected to be able to leverage AI to its fullest in order to learn and ship real things.

So essentially, the era of building projects or learning something just for the sake of it is over. And obviously,

if you're just starting out, you're going to have to make some projects just for the sake of it. And I definitely encourage that. But as you progress, you

encourage that. But as you progress, you really need to have some sort of intent to show competency. Like if you can make a project or even just write code that someone else had to approve, like a pull request that got reviewed and merged, or

a project where users depend on it, even if it's just 10 people from your school or five people from a Discord server.

Basically, if you can make anything that shows you didn't just vibe code some AI slop, that's the best new signal for competency as a student. Not how big your project is or what tech stack it

uses. It's can your work hold up under

uses. It's can your work hold up under someone else reviewing it or when people actually try to use it because that's the new best indicator that you actually know what you're doing and you didn't just push some random slot. And so, I

think that's what's going to matter more than ever.

The generalist developer, I'd say, is the most at risk role in software right now because AI is simply better than you at breath. And probably not just

at breath. And probably not just slightly better, but way better. It can

do a little bit of everything pretty good and very fast. However, where I think it really struggles is simply just going in depth, going deep enough in a specific domain to actually understand what's really happening. Because think

about it, when you want to do something really good, you really can't rely on AI. For example, if you want a really

AI. For example, if you want a really good-look website, you can't rely on AI to make the design because it really only makes generic UIs. Or if you want to write low latency code, you also can't rely on AI because you know you

have to benchmark or triple check your code. And like let's just say you're

code. And like let's just say you're doing something like embedded. Do you

really think AI can replace a specialist embedded programmer? Or what I'm trying

embedded programmer? Or what I'm trying to say is do you really think AI can replace any specialist? And I think if you say yes to that question, then you don't really know how deep a lot of these things go. So as a student or any

developer, that's where you can come in because the only real moat left is specialization. I think in the world of

specialization. I think in the world of AI, you need to pick a lane within computer science and go deep enough that you can answer any questions without help, know how to debug without guessing, and know how to look at AI

generated code and just feel when something's wrong. And I think a good

something's wrong. And I think a good sign of going deep in a domain is being able to make things that test your fundamentals just at the top of your head without any help. So, if you're front end, you should be able to build

something like Wordle in React or whatever framework. If you're backend,

whatever framework. If you're backend, you should be able to build a basic API server from scratch. If you're into systems, you should know how to build like a simple shell or a file explorer, understanding how programs actually run.

And I think if you can get to that point where you've specialized so good that you have a good enough baseline competence to write simple things in your field, then you'll stand a great chance with getting a job. But here's

the problem with computer science right now. it's that schools are still

now. it's that schools are still producing general list. They'll

essentially give you an intro course or maybe if you're lucky, you'll get like two courses on your specific field throughout your entire four years of college and that simply isn't enough because you're going to end up with

broad exposure to everything but really shallow depth and then you graduate and you're supposed to somehow compete with people who have years of experience and it's almost like you're just not even going to have a chance. And so I think

there's a huge widening gap between what school teaches you and what actually makes you valuable. And I really believe it's going to get wider until the curriculum starts getting better. So in

my honest opinion as a computer science student, I would say to use AI to get through all of the irrelevant stuff as fast as you can and spend that same time going deep on something and trying to get any experience that you can get. In

the upcoming years, interviews are going to get a lot harder. But what I think people are getting wrong is that they think that the data structure and algorithms interviews are going to go away because AI can solve leak code

problems. But this style of interview was never really about finding the solution anyways. The interviewer

solution anyways. The interviewer already knew the solution. All of these problems have been answered online for the entirety of leak code. The whole

point was always to see how you think and communicate under pressure. And I

just can't really see that changing that much. What I do think is changing is

much. What I do think is changing is that smaller companies and more mid-level companies are going to start adding more practical interviews and they're going to become a lot more common as the years go on. I think more

companies are going to want to watch you actually build something alongside an engineer. And so that's a completely

engineer. And so that's a completely different skill than solving leak code questions. So I think you need to start

questions. So I think you need to start doing both. Keep doing leak code. Work

doing both. Keep doing leak code. Work

through neat code 150 as early as you can. Get to the point where you can

can. Get to the point where you can really think of at least some sort of approach to any medium problem that you see. But you should also try and

see. But you should also try and practice building. And I don't want that

practice building. And I don't want that to scare you. Like it really shouldn't be you building anything crazy. You can

just ask AI to give you something to make, something that just shows baseline competency for whatever role you're going for. And then just close

going for. And then just close everything. Don't get any outside help.

everything. Don't get any outside help.

Just use your code editor. And if you do that once or twice a week, you will find your gaps faster than anything else. And

also on the topic of interviews, just make sure you have at least two things to talk about for every bullet point on your resume. Practice the STAR method

your resume. Practice the STAR method even when you don't have anything coming up because the worst thing that can happen is you finally get a call and then you fumble what should be the easiest part of the whole process because you couldn't remember what you

actually did.

Okay, this one is probably the most optimistic prediction that I have for the future of computer science. I think

the whole computer science grind is about to get a lot more fun, which I know a lot of people are going to disagree, but in my view, as AI takes over the boring parts, like doing random

prerec homework or helping you learn the syntax of a new language, what's left is really the fun stuff. And the only way I can argue this is really that you need to have like-minded friends. Because

when you're building things that you care about with people you like, it really stops feeling like a grind. Like

getting no sleep at a hackathon with your friends, working on something cool.

In my opinion, that's pretty fun. Or

joining a club and working on a genuinely cool project with people who actually care. That's fun, too. Or

actually care. That's fun, too. Or

studying at the library with your friends is also pretty fun for like a final or a midterm coming up. And I

don't know, maybe I just have a warped view of fun. But in my opinion, I really think the whole grind culture can be fun if you just find the right people. So

this is honestly my biggest piece of advice for computer science moving forward and in the future. Find your

people. people who are curious and who want to get better and who actually care. Because if you have that, then you

care. Because if you have that, then you really don't even have to force consistency. It just kind of happens.

consistency. It just kind of happens.

And if you're struggling to find those kind of people in person, there is a subreddit called r/programming buddies where people post what they're working on and look for people to build with.

But definitely try finding friends like in person at your university first. Join

a club or go to a hackathon or talk to the people in your classes. And also on the topic of fun, I would say try and look at all this AI stuff not as a threat, but just as endless opportunity because the barrier to building

something real has really never been lower. You can ship something with

lower. You can ship something with actual users faster than any generation of computer science students before you.

And I think the students who genuinely get excited about that and run with it, those are the ones that are going to have a really exciting few years ahead.

And in order to ensure that you're part of that future, you actually need to have the fundamentals to back it up. And

that's why I think Brilliant is worth talking about because it's genuinely one of the best ways to build those fundamentals in a way that actually sticks. Brilliant, today's sponsor, is

sticks. Brilliant, today's sponsor, is an interactive learning platform that teaches you math, CS, and programming through actually solving problems, not just watching videos. And I think that distinction really matters a lot because

there's a big difference between watching someone explain an algorithm and actually working through it yourself. Brilliant makes you do the

yourself. Brilliant makes you do the latter, which is why concepts actually stick instead of just feeling kind of familiar. For CS students specifically,

familiar. For CS students specifically, their algorithmic thinking course is definitely the one that I would point you to, especially if you're trying to understand the concepts behind what you're studying and not just memorize

them for an exam or a quiz. And I've

been trying to really master Python. And

so, if you're like me, they specifically have a thinking in Python course, which is another great one that I definitely point you to as well. And the courses are built by people from MIT, Harvard, and Stanford. And it works for pretty

and Stanford. And it works for pretty much anyone, whether you're just starting out or trying to fill gaps in your existing knowledge. So, if you want to try it out, Brilliant is giving you a full 30 days for free at brilliant.org/lattis.

brilliant.org/lattis.

And if you decide you want to keep going, they're also giving my viewers 20% off an annual premium subscription.

Link is in the description. So, thank

you so much to Brilliant for sponsoring this video.