10 Brutally Honest Truths About Getting Into Tech
The things nobody tells you, and what to do about them.
Everyone wants to work in tech. The high salaries, the freedom to work from anywhere, the sense that you’re building something that actually matters. It sounds almost too good to be true.
Here’s the thing: it’s not too good to be true. But the path to get there is messier, harder, and more surprising than most people expect. Having worked with hundreds of career-changers and ambitious learners, we’ve seen where people stumble — and where they eventually break through.
So here are 10 brutally honest truths about getting into tech. Read them carefully. Because if you understand them upfront, you’re already ahead of the crowd.
1. A Degree Alone Won’t Get You Hired
Let’s start with the big one. Many people assume that if they just get the certificate or the diploma, the job offers will follow. They won’t — not automatically.
Employers want to see what you can do. They want evidence that you can take technical specifications, turn them into real architecture, and ship working code. That’s why at AmsterdamTech, the curriculum is built around real-world projects from day one: rebuilding databases, coding C libraries, completing a final industry project that mirrors actual working conditions. The credential matters, but only because of what it proves you’ve done.
The takeaway: Your portfolio is your real degree. Build it intentionally, from the very beginning.
2. You Need More Skills Than You Think
Most aspiring tech professionals focus on one language or one tool. But look at what employers actually want from an AI and ML engineer: advanced algorithms, advanced data structures, C++, Python, network programming, Git, cloud tools, and more. That’s a wide stack.
The good news is that a structured programme builds this depth progressively. You start with fundamentals — variables, functions, basic data structures — and by the time you’re done, you’re working with TensorFlow, Keras, PyTorch, and Jupyter on complex, real-world datasets. The breadth isn’t meant to overwhelm you; it’s what makes you genuinely valuable.
The takeaway: Don’t just learn one thing. Learn how things connect. That’s what makes a real engineer.
3. Technical Skills Are Only Half the Battle
Here’s something the tutorials won’t tell you: the engineers who advance fastest aren’t always the best coders. They’re the best communicators, collaborators, and problem-solvers.
Tech employers consistently rank soft skills — structured problem-solving, written and verbal communication, teamwork, the “get-it-done” attitude — right alongside technical capabilities. This is why a serious programme doesn’t just teach you how to write code; it teaches you how to think, how to lead, and how to consult. Because the engineers who make real impact in organisations are the ones who can translate complex technical ideas into clear human ones.
The takeaway: Invest in your communication and leadership skills as seriously as your coding skills.
4. You Can’t Learn This Passively
Watching lecture videos and reading textbooks will take you only so far. The leap from understanding to doing is enormous in tech — and it only happens through practice.
The most effective learning model is experiential: you face a real problem, you attempt to solve it, you get feedback, and you try again. Weekly assignments, module projects, peer collaboration, and a final industry project that spans months — that’s what actually builds the skills that employers are looking for. Passive consumption is comfortable. Active building is what changes your career.
The takeaway: If your learning programme doesn’t make you uncomfortable on a regular basis, it probably isn’t working.
5. The Foundations Are Harder Than You Expect (and More Important)
Many people want to jump straight to AI and machine learning. They want to train neural networks and build intelligent systems. And they will — but not before they understand what’s underneath.
Pointers. Memory management. Algorithms. Data structures. Assembly language. Hash tables. These are not glamorous topics. But they are the difference between an engineer who can only use tools and an engineer who can build them. The first year in a rigorous AI/ML programme is deliberately deep in C and back-end fundamentals, because everything that comes later — Python, PyTorch, TensorFlow — makes far more sense, and can be applied far more powerfully, when you understand the foundations.
The takeaway: Don’t skip the fundamentals. Embrace them. They will pay dividends for your entire career.
6. Your Background Doesn’t Disqualify You
One of the most persistent myths about getting into tech is that it’s for a certain type of person: young, already technical, probably with a computer science degree. This simply isn’t true.
Some of the most effective tech professionals come from completely different backgrounds — healthcare, finance, logistics, the arts. What they bring is domain expertise that makes their technical skills genuinely rare. Whether you’re a fresh high-school graduate, a working professional looking to pivot, or someone who started a degree and had to pause — if you have the drive and meet the entry requirements, the door is open.
The takeaway: Your previous experience isn’t a liability. It’s context that can make your technical work uniquely powerful.
7. The Interview Process Is Its Own Skill Set
Passing 40 technical interviews is a real milestone in a serious AI/ML programme — and there’s a reason for it. The tech hiring process is notoriously rigorous, and being good at the job is not the same as being good at interviewing for it.
Technical interviews test you on algorithms, data structures, problem-solving under pressure, and your ability to communicate your thinking in real time. This is a skill that needs to be practised repeatedly, not crammed the week before. Programmes that weave interview preparation throughout the curriculum — not as an afterthought — prepare graduates who are ready to perform when it counts.
The takeaway: Start preparing for interviews on day one. Treat it as a core competency, not an add-on.
8. The Freelance Path Is Real, But You Have to Be Ready for It
One of the genuine freedoms that tech offers is the ability to work independently. AI and ML engineers are in high enough demand, with a broad enough skill set, that freelancing is a viable and often lucrative path. But it takes more than technical ability to succeed on your own.
You need to know how to position yourself, pitch your services, build a track record, and manage client relationships. A programme that teaches you both the technical craft and the business of being a tech professional gives you options that most graduates simply don’t have. You can work for a top company, or you can work for yourself. Or both.
The takeaway: Build your freelance skills alongside your technical ones. Independence is worth preparing for.
9. Community Matters More Than You Think
Learning in isolation is one of the most common reasons people quit. The material gets hard, the feedback loop disappears, and motivation evaporates. This is why the environment you learn in is as important as the curriculum itself.
Peer-to-peer learning isn’t just a nice addition to a programme — it’s a core learning mechanism. Working through problems with others, reviewing each other’s code, explaining concepts out loud, and celebrating shared progress: these aren’t soft extras. They’re what makes difficult technical learning sustainable. Weekly workshops, mentoring from industry practitioners, and a genuine cohort community all make the difference between people who finish and people who don’t.
The takeaway: Find your people. The engineers around you will shape how far and how fast you go.
10. The Time to Start Is Now, Not When You Feel “Ready”
There will always be a reason to wait. Another course to finish first, another skill to develop, another moment when the timing feels better. The truth is that the demand for AI and ML engineering talent is not going to pause while you get ready for it.
The learning curve is real, but so is the opportunity. A part-time, online programme that fits around your existing life removes the biggest excuse: that you can’t afford to step back from your career to invest in your future one. You can study 14-20 hours a week, keep working, and emerge on the other side with a degree, a portfolio, and the skills that the market actually wants.
The takeaway: The best time to start was yesterday. The second best time is today.
The Bottom Line
Getting into tech — especially into a high-demand field like AI and ML engineering — is genuinely hard. It takes time, sustained effort, a willingness to struggle through difficult material, and the self-leadership to keep going when the going gets tough.
But it is also one of the most accessible high-value careers in the world right now. The tools exist. The programmes exist. The demand exists. What it takes is the decision to begin, and the discipline to follow through.
If you’re serious about making the move, AmsterdamTech’s Bachelor of Science in AI and ML Engineering was built for exactly this moment, for people who are ready to do the work, on their terms, without putting the rest of their life on hold.
The next start date is closer than you think. Are you in?