In 2022, a Georgia Tech computer science graduate headed to San Francisco to start their $220,000 big tech software engineering job, believing they had made it. After years of studying and preparation, they felt successful and ready to take on the world. However, the real world was about to humble them in ways they never anticipated.
This journey from confident new graduate to seasoned engineer offers valuable insights for aspiring software engineers about the realities of working in big tech, the importance of continuous learning, and how to navigate the rapidly changing landscape of software development.
Always Be the Dumbest Person in the Room
The first piece of advice might sound counterintuitive: always strive to be the dumbest person in the room. When this engineer started their big tech job, they were put on a team where they were given a leadership-type role, despite being fresh out of college. They had previously interned there, so the company had trust in their abilities.
On this team, they were in charge of leading feature development for a major feature that ended up getting pushed to production. Things were going well—they liked their teammates, got positive feedback, and received great code review approvals with maybe one or two suggestions for improvement. They were also the scrum master, leading daily stand-up meetings, and felt confident they were on track for a quick promotion.
However, a major restructuring changed everything. They were shipped off to a different team where their peers were seasoned veterans—senior or principal software engineers with 10-plus years of experience. Some had been software engineers longer than this person had been alive. The standards on this team were really high, and suddenly, every code review came with 10 to 15 points of criticism.
Instead of leading features, they were now part of a subfeature working with other people. Their confidence took a big hit, and instead of thinking about promotion, they were praying they wouldn’t get fired. They started waking up early to code, attending meetings, coding until nighttime, and repeating the cycle. It got so intense they started dreaming in JavaScript.
The Growth That Comes from Struggle
As much as this engineer hated this difficult time, they now recognize it as one of the best periods of their life. Once they got out of the vicious cycle of coding and criticism, they became a stronger engineer. They learned better coding practices to get less negative feedback, contributed more to the team, and got more involved. They learned so much throughout the process.
The key insight is that if they had stayed on the previous team, they would have forever been the best engineer there. But that’s like saying you were really good at third-grade math and got an A+, but never bothered to try fourth-grade math. Their growth would have been stunted, and while they might have gotten promoted and earned money quickly, holistically as an engineer, they would have plateaued.
Always strive to be the dumbest person in the room because, especially as a software engineer in a world that’s changing so much, if you’re the dumbest person in the room, that means by definition you have the best opportunity to learn the most. This connects to the concept of 10,000 hours—it takes 10,000 hours of work in any subject to become a master. Instead of spending 10,000 hours coding, testing, and debugging yourself, you can “steal” those 10,000 hours from senior software engineers who have already put the work in.
Avoiding Shiny Object Syndrome
Many software engineers face the issue of shiny object syndrome. Just like a baby’s eyes light up when they see a shiny spoon, software engineers’ eyes light up when they see AI features or machine learning projects. This can be expanded to many different technologies in our buzzword era, where everyone claims to be creating AI startups, using machine learning, neural networks, and solving cybersecurity vulnerabilities with quantum computers.
This is problematic for two main reasons: intimidation learning and shallow knowledge. Intimidation learning occurs when you try to learn everything at once because you see other people who know multiple technologies. This leads to overwhelming yourself with different technologies, learning nothing in depth, and just acquiring shallow knowledge to throw on your resume.
The solution is to change your mentality. Instead of saying you want to learn AI, specify where in AI you want to specialize. Are you interested in the theoretical side, practical applications, research, specific frameworks, or fine-tuning machine learning models for certain problems? Find a technology you’re actually passionate about, dedicate three to four months to it, maybe do an internship or project, and go extremely deep into it.
Stop Chasing Every Trend — Build Depth That Pays
The fastest way to stand out in tech isn’t by learning everything — it’s by mastering one thing deeply. Pick a focus, commit for 3–4 months, and turn your skill into results that employers actually value.
Find Your Next Real-World Project →The Critical Importance of Communication
Communication is extremely important but often overlooked by many people in the tech industry. There are so many extremely bright people in tech who can competitively program and write code effortlessly, but because they can’t articulate themselves and communicate well, they’re not good software engineers. They don’t even get offers because they can’t pass behavioral interviews.
It’s like owning a Ferrari but being unable to drive it. If you fear you’re in this boat, start by putting yourself out there online. The easiest way to get employed nowadays is by posting projects you work on on the internet through LinkedIn, YouTube, or other platforms. Just talk about your technical work, and it will catch the eyes of people you want to work with.
This engineer regularly gets offered roles, whether software engineering roles at tech companies or CTO roles at startups, because of their online presence. Because of their communication skills, they’ve had the opportunity to network with C-level executives such as the CEO of Microsoft or the CEO/CPO of GitHub.
A Three-Step Communication Framework
Here’s a three-step framework to enhance your communication skills. First, take out your phone and record a five-minute video of yourself. It has to be improvised—no script, just you talking to the camera. The next day, review this video in three different ways.
First, turn up the audio and turn off the video. Put your phone face down while playing back the video. Listen to how your voice sounds. Are you talking with clarity? Are you enunciating properly? Are you speaking with the right pitch? Don’t just think about what you’re saying—think about how you’re saying it.
Second, look at the video with the audio shut off. Look at your hand gestures, facial expressions, and whether you’re expressing the message properly through your visual components, or if you look like a chaotic, insecure zombie.
Third, transcribe the audio and check if you’re using filler words like “um,” “but,” “so,” “like”—all terrible for your clarity. Through this process, you’ll see how clear your speech is and learn where to improve. Do this continuously, iteratively, every single day, and you will become a better communicator.
The AI Revolution: Death of Modern Software Engineering
AI will be the death of modern-day software engineering as we know it. Right now, software engineers get assigned tasks, write code in languages like JavaScript, Python, Ruby, or Java, and try to code out projects. There are tools like ChatGPT that can generate code and Cursor, an IDE that can finish tasks for you.
Companies have been adopting this, and at least in big tech companies, about 30% of their code is AI-generated at companies like Google and Microsoft. As AI continues to develop, we’ll see many more changes. It will reach the point where software engineers are no longer engineers but rather managers over teams of AI engineers.
Instead of being the person who writes code, we’ll have agents under us, and those AI bots will take in tasks, write out code, and spit out results while we manage all of them. Instead of being basketball players, we will become coaches, and all our players will be AI agents.
Preparing for the AI Future
The standard advice of just learning to code and getting a software engineering job no longer really applies. Instead, learn how to problem-solve and use AI. Can you design and architect solutions for AI agents to implement? Can you use AI agents to solve the hottest problems? Instead of just prompt engineering, can you use context engineering where you give as much information as possible so AI agents can get things correctly on the first try?
With AI, you need to be prepared. If you’re doing a coding project and not using AI, please start using it. If you’re not doing coding projects at all, start doing coding projects using AI and try to develop very quickly and efficiently. By getting this head start while most of the world is slow to implement AI, you will be way more successful as a software engineer.
FAQ Section
Q: How important is it to work with more experienced engineers?
A: Extremely important. Being the “dumbest person in the room” gives you the best opportunity to learn the most and accelerate your growth as an engineer.
Q: What’s the biggest mistake new software engineers make?
A: Shiny object syndrome—trying to learn everything at once instead of going deep into one technology they’re passionate about.
Q: How can I improve my communication skills for tech interviews?
A: Use the three-step framework: record yourself speaking, review audio-only, video-only, and transcribed text to identify areas for improvement.
Q: How should I prepare for AI’s impact on software engineering?
A: Start using AI tools now, learn context engineering, and focus on problem-solving and architecture rather than just coding.
The Path Forward: Embracing Change
The software engineering landscape is rapidly changing, and success requires more than just technical skills. It demands continuous learning, effective communication, and the ability to adapt to AI-driven changes. By being the dumbest person in the room, avoiding shiny object syndrome, improving communication skills, and preparing for AI’s impact, aspiring software engineers can navigate this evolving field successfully.
The key is to stay curious, keep learning, and always be prepared for the next challenge that will help you grow as both an engineer and a professional.
