The short answer: it has taken a real bite out of junior work and barely touched senior judgement. But the definition of "developer" is shifting underneath both, and that is the part worth paying attention to.
What AI is genuinely good at
- Boilerplate — forms, CRUD screens, basic UI
- Bug fixes where the error message says what is wrong
- Writing documentation, which nobody enjoyed anyway
- Generating test cases
- Translating code between languages
- Regular expressions and SQL, reliably better than most people write from memory
- Explaining an unfamiliar algorithm
Look at that list honestly. For a lot of people that was most of the working week.
What still needs a person
- System design — deciding how the pieces fit, and which pieces should not exist
- Trade-offs — speed against cost against how painful this is to maintain in two years
- Domain knowledge — the rules of banking or healthcare that nobody wrote in the ticket
- Strange bugs — the ones where the cause is three systems away from the symptom
- Talking to people — clients, product managers, and the person who asked for the wrong thing
- Review judgement — deciding what is good enough to ship today
- Security thinking — assuming someone is trying to break it
- Performance work at scale — where the answer depends on your actual traffic
The pattern is consistent: models are strong where the context fits in the prompt, and weak where it lives in the business, the history and the constraints nobody documented.
What the market is doing
Some of this is measurable and some is impression, so here is which is which.
- Measurable: large tech layoffs have run into the hundreds of thousands across 2024–25, weighted heavily towards junior and support roles.
- Measurable: studies on AI coding assistants consistently show meaningful time savings on well-defined tasks — the figures vary by study and task, but the direction is not in dispute.
- Impression, from hiring conversations: AI and ML specialists at the top of the Indian market are commanding multiples of what the same experience earned four years ago.
- Impression: a strong senior engineer with good tooling now produces what a small team used to.
Three ways this could go
These are judgements, not forecasts, and the percentages are how confident I am rather than anything computed.
Most jobs disappear — unlikely
Agents become genuinely autonomous, a small number of very senior engineers supervise everything, and the rest of the profession contracts sharply. Possible, but it requires reliability that current systems do not have.
Nothing much changes — also unlikely
AI settles in as another tool, like an IDE, and hiring returns to the old shape. This underrates how much has already changed at the entry level.
Augmentation — most likely by a distance
Every developer works alongside these tools. Junior roles shrink substantially, senior roles grow, and the total headcount stays broadly similar while its composition changes. This is what we are already watching happen.
Where you sit
- Zero to two years: the exposed position. The fix is not panic, it is speed — get past the boilerplate tier quickly and get fluent with the tools.
- Three to five years: arguably the best place to be. You have enough context to supervise AI output usefully, and enough time to reach senior before the squeeze reaches you.
- Five years plus: in demand, provided you actually adopt the tools rather than waiting them out.
Skills that hold value
- System design
- Deep knowledge of one domain — fintech, health-tech, security
- AI integration, done properly — retrieval, evaluation, agent design
- Security
- Distributed systems
- Communication
- Product thinking — knowing which feature not to build
Skills losing value
- Pure CRUD development
- Manual testing
- First-line support
- Front-end work that is only styling tweaks
- Simple scripting and data entry
If you are a student reading this at 2am
The thought is usually some version of: I spent four years on this degree and a chatbot writes better code than I do. That feeling is understandable and mostly wrong, because it compares your ability to write a function against a model's — which was never the valuable part.
Four things worth doing instead:
- Get the fundamentals solid. Data structures and system design decide interviews, and they decide them more now that everything above that layer is cheap.
- Pick one vertical and go deep. Breadth is what got commoditised.
- Use the tools daily. Not knowing them is no longer a defensible position.
- Build things in public. A repository with real commits beats a certificate in every conversation I have ever had.
The bottom line
AI is eating the boring code. The interesting problems are still ours. The practical strategy is to make sure the work you do is the interesting kind — and to start that migration before it becomes urgent.
If you are building something and want it done the modern way, talk to us. We use these tools daily and a person still reads every line before it ships.
FAQs
Is learning to program still worth it?
Yes, but the emphasis has moved. Memorising syntax was never the skill and is now clearly worthless. Problem solving, system design and fluency with AI tools is what the job actually is.
Which language should I learn first?
Python, because it is where AI, automation and data work happen. Add JavaScript for anything web-facing, and eventually one systems language like Go or Rust so you understand what is happening underneath.
Are junior developer jobs really disappearing?
They are shrinking, not vanishing. What has changed is that a junior is now expected to arrive able to supervise AI output rather than produce boilerplate — which is a higher bar than it used to be.
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