A wake up call for computer science students

Key Takeaways

Computer science students, please listen up. You are in one of the hardest majors entering one of the hardest job markets during one of the hardest resets tech has [music] ever seen cuz of AI. And as much as I like to say, "Hey, don't worry. Everything's going to be fine." I need to be real with you. And I need you to wake up and lock in. Far too many students are still playing the old game. They think if they just pass their classes, do projects, get their degree, that they're automatically going to land a job. But the reality of the situation is far from that. And as a person who went through a bachelor's and master's from Georgia Tech in computer science, I'm going to give you four brutally honest wake-up calls every computer science student needs right now. First, your computer science degree is not enough. Because computer science is not the same thing as software engineering. And this was a huge mistake I made and it really messed with me early on. You see, my whole idea was that I would go to Georgia Tech, get a computer science degree, and then graduate and get a $250,000 software engineering job. So, I worked really hard to become a good computer science student. I learned Java, Python, C++, I studied for my classes, discrete math, intro to computing. And I was learning a lot. Matter of fact, I even graduated with a 4.0 GPA. But then, when I actually started applying for software engineering jobs, I had a very rude awakening. No one cared. Not a single recruiter asked me about discrete math proofs. No interviewer cared that I even knew five coding languages. No company actually cared about my computer science degree knowledge. All they cared about was, "Can you build a full-stack application? Can you design an API? Can you work with Git?" And that's when it hit me. I had spent so much time trying to become a great computer science student, but the job market was testing whether I could become a software engineer. And while those things are related, at their core, they are fundamentally different. Computer science teaches you how computers work. It gives you theory, the math, the algorithms, and the foundation behind computing. Software engineering is different. Software engineering is building products in the real world. It's working inside messy code bases, taking vague requirements from a customer, and building a web application that millions of people will use. And if anything breaks while those millions of people use it, you need to be able to get your hands dirty and go ahead and fix it. Think of it like learning how to drive. You could spend 10 years studying cars, learning how the engines work, how the brakes work, but until you have actually sat behind the wheel, put your foot on the gas, you are not a driver. That is the difference between computer science students that spend years just studying the theory versus software engineers that go day in day out actually doing the work. And so computer science students, we need to lock in. Stop spending all your time just focused on classes and GPA. You're just setting yourself up for failure. Instead, focus on these two areas. First, you need experience. And experience doesn't mean you need to have a Google internship. While that is amazing, projects are a great starting point. Build a real full-stack application. Find a club on campus and solve a problem they have. Maybe the debate club on campus has an issue with event registration, and you can use your tech background to create a landing page with a database to help them out. Then you take this project, put it on your resume. Trust me, it will look amazing because you actually applied your tech knowledge into software people use, which is exactly what software engineers do on a day-to-day basis. Second, you need skills. And while I can talk about so many different technical skills like APIs, full-stack development, system design, the number one skill you need to have is communication. Because that will be the differentiator between you and pretty much everyone else. A lot of computer science students can code, but they cannot clearly explain what they built. And software engineering is a team sport. And the best engineers write code, plus they are good at turning messy ideas into clear decisions that other people can understand. So, when you learn how to communicate, you become better to work with, which ultimately makes you a much stronger software engineer and way more likely to actually get hired. The second wake-up call for computer science students is stop wandering. A lot of students say, "I want to work in tech." But what does that actually mean? That's like going to an airport and saying, "I want to travel." Okay, do you want to go to New York, Tokyo, Dubai, Atlanta? You need a destination. So, we need to lock in and find your domain. Tech is not one thing. There's front-end, back-end, full-stack, cloud, cybersecurity, machine learning, data engineering, robotics, infrastructure, and the list goes on. Even in software engineering, there's so many different fields. So, if you're 18, 19, 20-year-old student with no experience, how on earth are you supposed to find out which lane is best for you? You can't just do them all. That's where you need to learn the concept of stealing the 10,000. Really quick, if you want to see the full power of AI, this is it right here. For me personally, there are so many skills I want to learn, like video editing, AI automation, graphic design. But, the hard part is not finding more information, it's knowing what to learn first, how to practice, and how to actually track my progress. So, I'm building a real skill learning dashboard in Lovable. Lovable is built for non-technical builders, so I can describe the product I want in plain English and turn the idea into a working app with front-end, back-end logic, data management, and hosting without needing to hire a full dev team. For this app, I type in a skill, like learn video editing, and it creates a full learning path with beginner, intermediate, and advanced lessons. It also gives me key concepts, recommended tools, common beginner mistakes, practice assignments, quizzes, projects, checklists, notes, and review materials, so I'm not just consuming content, I'm actually applying what I learn. Then, I can track every skill through each stage: not started, currently learning, lesson completed, project completed, and mastered. And with Lovable skills, I can save the way I want lessons, quizzes, and project structured, then reuse that playbook every time I add a new skill. That is what makes this more than a prototype. Lovable helps me build, run it, keep improving it from one place. This started as an idea in my notes, and now it's becoming a real product I would actually use long-term. If you're interested in building something that actually improves [clears throat] your life, check out Lovable, link in description. And now, back to the video. There's an idea that it takes roughly 10,000 hours worth of experience to master a skill. For example, Gordon Ramsay, the best chef in the world, has at least 10,000 hours of experience cooking and baking dishes. Stephen Curry, the best basketball player of all time, probably has 10,000 hours of experience shooting, dribbling, and passing a basketball. That's how these people became professionals in what they do. But, here's the issue. You can't just spend 10,000 hours in every tech domain. You can't spend 10,000 hours in front end, 10,000 hours in back end. By the time you finish trying to learn everything, you'll be 87 years old trying to get your first internship. So, instead of earning every hour yourself, you need to steal experience from people who already paid the price. What I mean by this is you need to network with people and steal from them. Find people who are three, five, or 10 years ahead of you. Engineers, alumni, founders, people working in areas you're curious about. Ask them what their day actually looks like. Ask them what skills matter. Ask them what parts of the job are exciting and what parts secretly suck. Through those conversations, you can see if you could picture yourself in that role. If a product manager tells you, "Yeah, most of my day goes in meetings." And that sounds miserable to you, good. You just saved yourself months of chasing the wrong path. But, if someone in back end engineering says, "I spend most of my days working with databases." [music] And that sounds exciting to you, then maybe back end is a lane worth exploring. That is the point of stealing the 10,000 hours. You're using someone else's experience to make better decisions faster. [music] You still have to develop your own skills, but you don't have to wander blindly. Because the students who win are not always the ones who work the hardest. They're the ones who figure out where they're going before they start running. Okay, so obviously computer science is very, very tough. So, that means you have to be very intentional about who you surround yourself with. The wrong people will drag you down, while the right people will push you forward. That's why the third wake-up call is choose builders, not performers. Performers are people who care more about looking smart than actually being smart. And yes, in every single computer science class, there are always going to be those performers. The people who love flexing. Yeah, I know Python, Java, C++, AI, machine learning. And I built my first app when I was 6 years old. And you'll be standing there with your imposter syndrome. And you're like, "Bro, I just learned about four loops yesterday. Am I cooked?" And I went through this my freshman year of college with these guys who tried to make me feel inferior because I only knew how to code in Java. But, let me tell you something very, very clearly. No one who is actually smart is obsessed with convincing everyone else that they're smart. Performers want to feel good by making others think they're good, not by actually achieving anything themselves. Often times, they actually know very, very little except maybe they know how to string a few buzzwords together to make themselves [music] sound impressive. So, no matter what you do, do not become friends or work with these people. These people are like those fake display laptops at electronic stores. From far away, they look real and valuable, but the moment you actually try to use it, you realize it's a plastic shell. That's what performers are. They're performative. But, builders are different. Builders may not always be the loudest person in the room, but they are actually doing the work. They build projects with you, share resources, they send you opportunities. They care more about improving holistically and together than looking superior. A rising tide lifts all boats. Long-term builders are usually the people who do well. They land internships, build startups, work at FAANG-level companies, and actually succeed because they spend their time becoming valuable, not pretending to be valuable. So, please, let's lock in, identify people in your class that are performers, avoid them, and identify builders, and buddy them. The fourth wake-up call every computer science student needs is to realize that AI didn't kill the market, it split it. The tech job market sucks, and everyone is blaming AI. Every single day you see companies with another headline of AI-related layoffs. And trust me, I get it. It's scary. You see tools like Cursor, Copilot that can generate code within a blink of an eye, and you wonder why you're spending hundreds of thousands of dollars to study computer science. And the scary part of this is tech companies are not slowing down. As I'm sitting right here today, this is the worst AI will ever be. AI models are only getting better. But, there's a part of this market that actually is super weird. In the news, [clears throat] there are headlines that because of AI, tech companies don't need to hire any more software engineers, and AI is causing the layoffs. But in reality, tech companies are quietly re-hiring software engineers. In early 2026, there were 67,000 active software engineering job postings, which is the highest level in 3 years. And postings were up about 30% in Q1. And so, the case of AI replacing developers is not as straightforward as you think. It's more like AI split software engineering into two different markets. And this is what computer science students must understand. The old market was such that anyone who could write basic code, and if they had a pulse, they were automatically hired as an engineer. They just had to write basic front-end code, a few test cases, and boom, you were safe. But now, in the new market, because of AI, companies are not looking for coders. They want owners. AI can generate code, but it does not know if that code would make sense in the real world. It can build you a login page, but it does not know if the login is secure. It can create a full-stack app, but it doesn't know if the database is designed properly. That is where real engineers still matter. Not because they can type every line of code by hand, but because they know what to build, how to check it, and how to take responsibility when it breaks. And so, right now, the biggest opportunity is to become a developer who can orchestrate AI. Because with the power of the tools, you can use AI to do the work that previously took 10 engineers to do. You can spin up a project with multiple different sub-agents. One agent in charge of the front-end, one for back-end, one for testing. At the center of all this is you, the human who's orchestrating these agents, understanding what they're doing, and assessing their work as if you're their manager. This is the new type of software engineering that's super, super hot, and that companies are fighting for. And so, it is not that the market is ruined because of AI, but rather, it's just restructured to a different skill set because of AI. And it is your job as a computer science student to awaken to this reality. Well, that's about all I have in this video. I really hope that you guys enjoyed it. And if you did, make sure to hit the like button. Subscribe if you haven't already. If you're interested in my absolutely free tech news letter, link for that down below in the description. And if you want to know what I wish I knew before I majored in computer science, you might want to watch this video right here.