Yes, but the job is shifting. Typing code is becoming cheaper; deciding what to build, judging whether it is correct, and owning it in production is not. Aim to be the engineer who directs and checks AI output, and build the judgement that makes that possible.
What people tell me
I am studying computer science or teaching myself to code, and every week I see a new demo of AI building an entire app from a prompt. People online say junior developers will not exist in a few years. I still enjoy building things, but I am starting to wonder whether I am spending years preparing for a job that will disappear before I get there. My family asks whether I should switch to something safer, and I do not have a good answer.
A composite of the messages behind this question, with personal details left out.
Key takeaways
- AI makes writing code cheaper. It does not make deciding, verifying and owning software cheaper, and that is where engineers earn their pay.
- The entry-level bar has risen. Plan to show judgement and finished work, not just syntax.
- Treat AI as a tool you must learn to use well, the same way earlier engineers learned compilers, frameworks and search engines.
- Judge the career by the problems companies still need solved, not by demos designed to go viral.
- If you enjoy building and fixing things, that interest is still the strongest predictor of doing well.
The honest answer first
Yes, I still think it is worth it, and I say that as someone who uses AI tools every working day. But I will not pretend nothing has changed. The part of the job that is getting cheaper is producing code. The part that is not getting cheaper is knowing what should be built, noticing when something is subtly wrong, and being accountable when it breaks at two in the morning.
If you picture software engineering as "a person who types code from a specification", then yes, that job is shrinking. If you picture it as "a person who turns messy problems into working, reliable systems", that job is growing, because there is more software to build, connect, secure and maintain than ever.
What the demos do not show you
A demo shows the first hour of a project. Real work is the next two years. In most of the systems I have worked on, the hard parts were never "write a login page". They were things like:
- Two services disagree about what a customer's status is, and nobody knows which one is right.
- A feature works for a thousand users and falls over at fifty thousand.
- The product manager asks for something that contradicts a promise made to another customer.
- A small change passes every test and still breaks billing for one country.
AI helps with each of these, but it does not own them. Someone has to understand the business, read the logs, ask the right people, and decide. That someone is an engineer.
What has genuinely changed
I want to be fair to your worry, because it is not irrational.
| What used to be enough | What is expected now |
|---|---|
| Knowing the syntax of one language | Reading and judging code you did not write, including AI output |
| Building a to-do app from a tutorial | Shipping something real, deployed, with a clear reason to exist |
| Asking a senior how to do a task | Trying first, using AI well, then asking a precise question |
| Completing assigned tickets | Understanding why the ticket exists and what could go wrong |
The entry bar has moved up. That is uncomfortable, but it is also clarifying: you now know exactly what to practise.
How to decide for yourself
Instead of reading predictions, test the question against your own situation. Ask yourself these four things:
- Do I enjoy the moment something finally works? That feeling survives every change in tooling.
- Am I willing to keep learning for the whole career? This was always true of software. AI just made it obvious.
- Can I tolerate being confused for a while? Most engineering is confusion followed by clarity.
- Would I still want this if it paid a little less than I hoped? If yes, you are choosing the work, not just the salary.
If you answer yes to most of these, the arrival of AI is a reason to adjust how you prepare, not a reason to leave.
How to prepare for the version of the job that exists
- Build fundamentals deliberately. Data structures, how the web works, databases, networking basics, how to debug. These are what let you judge whether AI output is right.
- Use AI, but make yourself explain everything it produces. If you cannot explain a line, you do not own it yet.
- Finish things. One deployed project with real users, even ten of them, teaches more than five half-built clones.
- Practise communication. Writing a clear pull request description or a short design note is becoming a bigger part of the job, not a smaller one.
- Stay close to a domain. Engineers who understand payments, healthcare, logistics or education are much harder to replace than engineers who only understand a framework.
What I tell families who ask
When parents ask me whether their child should still study software, I usually say this: the risk is not choosing software engineering. The risk is choosing it passively, collecting a degree and expecting the market to be the same as it was in 2019. A student who builds, ships, reads other people's code and learns to work with AI tools is in a strong position. A student who waits is not, and that would be true in any field.
Protect yourself from the noise
Most viral claims about AI replacing engineers come from people selling courses, tools or attention. Notice who benefits from the prediction. Then look at what companies actually hire for, what your seniors do all day, and what problems are still unsolved around you. Your decisions should come from evidence you can see, not from the loudest post in your feed.
If you are still stuck
Read How to Use AI as a Companion, Not a Crutch and The Fundamentals Worth Learning as a Software Engineer, then browse the rest of the career questions library. If you want someone to look at your specific situation and plan, join Sefism and, as a member, book a 1:1 session.
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- The Fundamentals Worth Learning as a Software EngineerFrameworks are the fastest moving and least valuable part of what you know. This is the layer underneath: how a computer runs your code, data structures as trade offs, complexity at conversational depth, storage, networks, concurrency, version control, testing as a design tool, and the two skills engineers skip that decide how far they get.
- How to Start Your Career as a Software EngineerA practical route into the first software engineering job: pick one language and stop shopping, build things too big for a tutorial, learn the five fundamentals you cannot skip, get your code in front of a human, and apply earlier than you feel ready. Includes a twelve month shape to work to and an honest read on what the current entry level market has changed.
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